Gender Performance Scale as a Quantitative insight into Intracategorical Intersectionality

Scott Oatley (s2265605@ed.ac.uk)

Abstract

The measurement of identity categories within survey questionnaires and other instruments is an integral concept of social stratification quantitative analysis. This paper adopts an intracategorical approach in a quantitative context for the measurement of identity. The intracategorical approach emphasises the intrinsic heterogeneity of identity concepts. This paper applies this lens of intersectionality to the quantitative realm by deconstructing gender in its typically binary designation through social surveys and produces a measure of gender performance that provides a metric operationalisation of gender expression. This measure facilitates analysis of those at the gender periphery – individuals whose unique gender performance would be otherwise swallowed up by the overall variance of a binary gender measure. Using wave 10 of the UKHLS, this paper constructs via a factor analysis a measure of gender performance. This measure provides a metric scale documenting the masculinising or feminising role an individual performs within society. This measure is ‘gender blind’ and the masculinising or feminising performance an individual can take is not bound by their gender designation. The scale distribution is mapped onto gender measures to explore the bimodal nature of gender performance across generations. Furthermore, this paper uses this performance measure alongside gender and other identity categorisations to assess its utility in statistical models assessing income inequality. Findings indicate that using solely a gender measure erases the unique and often powerful impact that performance has upon income inequality. This paper provides evidence that a performance measure, even after controlling for gender itself, provides utility in the social scientific investigation of income disparity, and that often, the substantive impact of gender performance is inaccurately captured by a gender measure.

Key Words

Gender, Scale, Gender Roles, Masculinity, Femininity, Intersectionality, intracategorical

Justification for Notebook

Providing a literate workflow of any scientific investigations should be a baseline requiste of empirical research. Publishing a Jupyter Notebook allows individuals to not only reproduce the empirical work held within, it also provides a robust justification for each step in the research process. This process promotes open-science practices and encourages others to do the same.

Encouraging others to openly look at ones work invites critique. These critiques are welcome. The hope of using Notebooks is to have a much more organic and engaging research process whereby the research does not simply end once a paper is published. Critical comments can and will be incorporated into this Notebook to further research practices.

By providing a literate workflow where research, theory, justification, and ‘footnote’ analysis are all recorded in one place. This notebook invites a widespread auideance, ranging from other academics, to interested stakeholders. Whilst the language used in this Notebook is one intended for an academic auidence, the workflow presented should be possible for anyone that reads the Notebook and takes a methodical step-by-step approach to its application.

Using Stata

As Jupyter is a language agnostic program, the use of language used for analysis is left up to the individual researcher. For this Notebook, Stata is employed for all statistical analysis. Stata is a proprietary software and researchers MUST have access to Stata in order to undertake data analyses within the Jupyter notebook. A primary goal of extending this analysis is to undertake it in a different non-properiatory software.

Using Stata requires enabling the use of the ‘stata kernel’ in Jupyter. The instructions for which have been outlined in meticulous details in Connelly and Gayle (2019). For the sake of promoting a consistent introduction base for Jupyter Notebooks, and in an attempt to avoid needless confusing rhetoric at the beginning of such Notebooks, their original instructions are pasted below. I thank them for their work in pioneering best practices in the use of Notebooks for social scientific analysis:

Using Stata via Magic Cells

The approach for this Notebook uses Stata via magic cells. This facility can be downloaded and installed from this github repository.

At the command prompt you need to type:

pip install ipystata

In a code cell before using Stata you must type:

import ipystata

and then run the cell.

Each cell will now be a Stata code cell as long as you start your syntax with:

%%stata

For example to get a summary of the variables in Stata the cell should include the following code:

%%stata summarize

further information on using Stata via magic is available here.

Table of Contents

• Introduction
• Background
• Data
• Methods
• Descriptive Statistics
• Models
• Results
• Conclusions
• Notes
• Supplementary Materials
• References

Introduction

Background

Data

Preparation Programs

Prior to any data analysis, Stata requires initialisation within the ipykernal. Following this, the dataset - the 10th wave of the UKHLS is cleaned and re-coded for appropriate analysis.

import stata_setup

stata_setup.config("/Applications/Stata", "se")

  ___  ____  ____  ____  ____ ®
 /__    /   ____/   /   ____/      18.0
___/   /   /___/   /   /___/       SE—Standard Edition

 Statistics and Data Science       Copyright 1985-2023 StataCorp LLC
                                   StataCorp
                                   4905 Lakeway Drive
                                   College Station, Texas 77845 USA
                                   800-STATA-PC        https://www.stata.com
                                   979-696-4600        stata@stata.com

Stata license: Unlimited-user network, expiring 14 Sep 2025
Serial number: 401809305318
  Licensed to: Scott oatley
               University of Edinburgh

Notes:
      1. Unicode is supported; see help unicode_advice.
      2. Maximum number of variables is set to 5,000 but can be increased;
          see help set_maxvar.

Prior to any simulation/analysis, some basic set up is required to save datasets and path folders. Three global paths are set and assigned to the raw datasets, the working datasets that have been manipulated and changed from their raw state, and finally any output in the form of tables and figures.

%%stata

global rawdata"/Users/scottoatley/Documents/Stata Data and Do/Data/UKHLSBHPS/stata/stata13_se/ukhls_w10"

global workingdata"/Users/scottoatley/Documents/Stata Data and Do/Working Data"

global outputs"/Users/scottoatley/Documents/Stata Data and Do/Output"

. 
. global rawdata"/Users/scottoatley/Documents/Stata Data and Do/Data/UKHLSBHPS/
> stata/stata13_se/ukhls_w10"

. 
. global workingdata"/Users/scottoatley/Documents/Stata Data and Do/Working Dat
> a"

. 
. global outputs"/Users/scottoatley/Documents/Stata Data and Do/Output"

. 

After setting global file paths, the next step is to open the raw dataset set and recode and clean it into the working dataset for analysis. The 10th wave of UKHLS is merged with the ‘xwave’ dataset. This is done to gain access to some cross-wave derived variables - in particular the race variable.

%%stata

cd "$rawdata"

use "j_indresp.dta"

merge 1:1 pidp using xwavedat.dta
    
drop _merge

. 
. cd "$rawdata"
/Users/scottoatley/Documents/Stata Data and Do/Data/UKHLSBHPS/stata/stata13_se/
> ukhls_w10

. 
. use "j_indresp.dta"
(Substantive data for responding adults (16+), incl. proxies)

. 
. merge 1:1 pidp using xwavedat.dta
(label pid already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                       116,945
        from master                         0  (_merge==1)
        from using                    116,945  (_merge==2)

    Matched                            34,319  (_merge==3)
    -----------------------------------------

.     
. drop _merge

. 

Methods

Descriptive Statistics

The dataset is now cleaned and recoded to include only variables required for the factor analysis as well as subsequent modeling.

%%stata 

* Variables required for the factor analysis:
* Each 'couple function' is recoded into an ordinal categorical variable that assesses along traditional gender role lines the masculine/feminine (or neither) function of the individual    

codebook j_huboss j_hubuys j_hudiy j_hufrys j_huiron j_humops, compact

* Household financial decisions    
gen cash=.
replace cash=0 if j_huboss==3
replace cash=-1 if j_huboss==2
replace cash=1 if j_huboss==1

* Who does the grocery shopping
gen shop=.
replace shop=0 if j_hubuys==3
replace shop=-1 if j_hubuys==1
replace shop=1 if j_hubuys==2

* Who does the DIY jobs
gen diy=.
replace diy=0 if j_hudiy==3
replace diy=-1 if j_hudiy==2
replace diy=1 if j_hudiy==1

* Who does the cooking
gen cook=.
replace cook=0 if j_hufrys==3
replace cook=-1 if j_hufrys==1
replace cook=1 if j_hufrys==2

* Who does the washing/ironing
gen wash=.
replace wash=0 if j_huiron==3
replace wash=-1 if j_huiron==1
replace wash=1 if j_huiron==2

* Who does the cleaning
gen clean=.
replace clean=0 if j_humops==3
replace clean=-1 if j_humops==1
replace clean=1 if j_humops==2    

. 
. * Variables required for the factor analysis:
. * Each 'couple function' is recoded into an ordinal categorical variable that
>  assesses along traditional gender role lines the masculine/feminine (or neit
> her) function of the individual    
. 
. codebook j_huboss j_hubuys j_hudiy j_hufrys j_huiron j_humops, compact

Variable     Obs Unique       Mean  Min  Max  Label
-------------------------------------------------------------------------------
j_huboss   34319      9  -1.188904   -9   97  Household financial decisions
j_hubuys   34319     10  -1.402518   -9   97  Who does the grocery shopping ...
j_hudiy    34319     10   .3465427   -9   97  Who does the DIY jobs (couples)
j_hufrys   34319     10  -1.538069   -9   97  Who does the cooking (couples)
j_huiron   34319     10  -1.423089   -9   97  Who does the washing/ironing (...
j_humops   34319     10  -1.202046   -9   97  Who does the cleaning (couples)
-------------------------------------------------------------------------------

. 
. * Household financial decisions    
. gen cash=.
(151,264 missing values generated)

. replace cash=0 if j_huboss==3
(16,224 real changes made)

. replace cash=-1 if j_huboss==2
(2,185 real changes made)

. replace cash=1 if j_huboss==1
(2,279 real changes made)

. 
. * Who does the grocery shopping
. gen shop=.
(151,264 missing values generated)

. replace shop=0 if j_hubuys==3
(7,969 real changes made)

. replace shop=-1 if j_hubuys==1
(7,048 real changes made)

. replace shop=1 if j_hubuys==2
(5,558 real changes made)

. 
. * Who does the DIY jobs
. gen diy=.
(151,264 missing values generated)

. replace diy=0 if j_hudiy==3
(3,446 real changes made)

. replace diy=-1 if j_hudiy==2
(7,260 real changes made)

. replace diy=1 if j_hudiy==1
(7,900 real changes made)

. 
. * Who does the cooking
. gen cook=.
(151,264 missing values generated)

. replace cook=0 if j_hufrys==3
(5,881 real changes made)

. replace cook=-1 if j_hufrys==1
(8,052 real changes made)

. replace cook=1 if j_hufrys==2
(6,661 real changes made)

. 
. * Who does the washing/ironing
. gen wash=.
(151,264 missing values generated)

. replace wash=0 if j_huiron==3
(5,249 real changes made)

. replace wash=-1 if j_huiron==1
(8,344 real changes made)

. replace wash=1 if j_huiron==2
(6,802 real changes made)

. 
. * Who does the cleaning
. gen clean=.
(151,264 missing values generated)

. replace clean=0 if j_humops==3
(6,906 real changes made)

. replace clean=-1 if j_humops==1
(7,266 real changes made)

. replace clean=1 if j_humops==2    
(5,558 real changes made)

. 

The next set of variables to be re-coded and cleaned relate to the subsequent modeling post-factor analysis.

%%stata

* Net Labour Individual Income (Log Transformed)

summarize j_fimnlabnet_dv, detail

gen labincome=j_fimnlabnet_dv

replace labincome=. if labincome<0

summarize labincome, detail

gen logincome = log(labincome)

lab var logincome "Log Net Labour Income"

* Ethnicity

tab racel_dv

gen ethnic=.
replace ethnic=1 if(racel_dv==1)
replace ethnic=1 if(racel_dv==2)
replace ethnic=1 if(racel_dv==3)
replace ethnic=1 if(racel_dv==4)
replace ethnic=2 if(racel_dv==5)
replace ethnic=2 if(racel_dv==6)
replace ethnic=2 if(racel_dv==7)
replace ethnic=2 if(racel_dv==8)
replace ethnic=3 if(racel_dv==9)
replace ethnic=4 if(racel_dv==10)
replace ethnic=4 if(racel_dv==11)
replace ethnic=5 if(racel_dv==12)
replace ethnic=6 if(racel_dv==13)
replace ethnic=7 if(racel_dv==14)
replace ethnic=7 if(racel_dv==15)
replace ethnic=7 if(racel_dv==16)
replace ethnic=8 if(racel_dv==17)
replace ethnic=8 if(racel_dv==18)

label define ethnic_lbl 1"White" 2"Mixed/Multiple Ethnic Groups" 3"Indian" 4"Pakistani and Bangladeshi" 5"Chinese" 6"Any other Asian Background" 7"Black/African/Caribbean/Black British" 8"Other" 
label values ethnic ethnic_lbl

lab var ethnic "Ethnicity"

tab ethnic


* Current Social Class (NS-SEC)

gen nssec=j_jbnssec8_dv
replace nssec=. if nssec<0

label define nssec 1"1.1 Large employers and higher managerial and administrative occupations" 2"1.2 Higher professional occupations" 3"2 Lower managerial, administrative and professional occupations" 4"3 Intermediate occupations" 5"4 Small employers and own account workers" 6"5 Lower supervisory and technical occupations" 7"6 Semi-routine occupations" 8"7 Routine occupations"
lab val nssec nssec

lab var nssec "Current Social Class (NS-SEC)"

tab nssec

* Generations and Cohort Variables

gen generations=.
replace generations=1 if j_birthy>=1995 & j_birthy<=1999
replace generations=2 if j_birthy>=1980 & j_birthy<=1994
replace generations=3 if j_birthy>=1965 & j_birthy<=1979
replace generations=4 if j_birthy>=1946 & j_birthy<=1964
replace generations=5 if j_birthy>=1925 & j_birthy<=1945

lab def generations 1"Gen Z" 2"Millennials" 3"Gen X" 4"Baby Boomer" 5"Silent"
lab val generations generations

lab var generations "Generation"

tab generations


gen cohort=. 
replace cohort=1 if j_birthy>=1927 & j_birthy<=1949
replace cohort=2 if j_birthy>=1950 & j_birthy<=1959
replace cohort=3 if j_birthy>=1960 & j_birthy<=1969
replace cohort=4 if j_birthy>=1970 & j_birthy<=1979
replace cohort=5 if j_birthy>=1980 & j_birthy<=1989
replace cohort=6 if j_birthy>=1990 & j_birthy<=1999

lab def cohort 1"1927/1949" 2"1950/1959" 3"1960/1969" 4"1970/1979" 5"1980/1989" 6"1990/1999"
lab val cohort cohort

lab var cohort "Cohort"

tab cohort

* Sex

drop sex 
lab drop sex 

gen sex=.
replace sex=0 if j_sex==2
replace sex=1 if j_sex==1

lab def sex 0"Female" 1"Male"
lab val sex sex 

lab var sex "Sex"


* Age and Age2
* Age is centred based upon the grand central mean of the sample, these centred age is then used to produce the squared function of Age in Age2

gen age= j_dvage
replace age=. if age<0

mean age

egen agemean = mean(age)

gen age_cgm = age - agemean

lab var age_cgm "Age Centered at Grand Mean"

gen age2_cgm= age_cgm*age_cgm

lab var age2_cgm "Age Squared Centered at Grand Mean"

* Housework

summarize j_howlng, detail 

gen househours=j_howlng

replace househours=. if househours<0

lab var househours "Hours per week Spent on Housework"

summarize househours, detail 

. 
. * Net Labour Individual Income (Log Transformed)
. 
. summarize j_fimnlabnet_dv, detail

        amount income component 1: net labour income
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0       -3333.33
 5%            0       -1802.61
10%            0       -1028.75       Obs              34,319
25%            0        -916.67       Sum of wgt.      34,319

50%       528.88                      Mean           920.0224
                        Largest       Std. dev.      1142.421
75%      1549.61          10200
90%         2400          10800       Variance        1305126
95%      3041.56          10800       Skewness       1.590743
99%         5200          16500       Kurtosis       7.087739

. 
. gen labincome=j_fimnlabnet_dv
(116,945 missing values generated)

. 
. replace labincome=. if labincome<0
(32 real changes made, 32 to missing)

. 
. summarize labincome, detail

                          labincome
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs              34,287
25%            0              0       Sum of wgt.      34,287

50%       530.88                      Mean           921.2793
                        Largest       Std. dev.      1142.041
75%         1550          10200
90%         2400          10800       Variance        1304257
95%         3042          10800       Skewness       1.593641
99%         5200          16500       Kurtosis        7.08871

. 
. gen logincome = log(labincome)
(131,735 missing values generated)

. 
. lab var logincome "Log Net Labour Income"

. 
. * Ethnicity
. 
. tab racel_dv

        Ethnic group incorp. all waves, |
                codings, modes and bhps |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                                missing |     48,229       31.88       31.88
british/english/scottish/welsh/northern |     78,474       51.88       83.76
                          irish (white) |      2,149        1.42       85.18
     any other white background (white) |      3,344        2.21       87.39
      white and black caribbean (mixed) |        691        0.46       87.85
        white and black african (mixed) |        284        0.19       88.04
                white and asian (mixed) |        467        0.31       88.35
     any other mixed background (mixed) |        392        0.26       88.61
        indian (asian or asian british) |      3,824        2.53       91.13
     pakistani (asian or asian british) |      3,469        2.29       93.43
   bangladeshi (asian or asian british) |      2,130        1.41       94.84
       chinese (asian or asian british) |        514        0.34       95.18
any other asian background (asian or as |      1,102        0.73       95.90
     caribbean (black or black british) |      1,997        1.32       97.22
        african (black or black britih) |      2,868        1.90       99.12
any other black background (black or bl |        204        0.13       99.26
              arab (other ethnic group) |        535        0.35       99.61
any other ethnic group (other ethnic gr |        591        0.39      100.00
----------------------------------------+-----------------------------------
                                  Total |    151,264      100.00

. 
. gen ethnic=.
(151,264 missing values generated)

. replace ethnic=1 if(racel_dv==1)
(78,474 real changes made)

. replace ethnic=1 if(racel_dv==2)
(2,149 real changes made)

. replace ethnic=1 if(racel_dv==3)
(0 real changes made)

. replace ethnic=1 if(racel_dv==4)
(3,344 real changes made)

. replace ethnic=2 if(racel_dv==5)
(691 real changes made)

. replace ethnic=2 if(racel_dv==6)
(284 real changes made)

. replace ethnic=2 if(racel_dv==7)
(467 real changes made)

. replace ethnic=2 if(racel_dv==8)
(392 real changes made)

. replace ethnic=3 if(racel_dv==9)
(3,824 real changes made)

. replace ethnic=4 if(racel_dv==10)
(3,469 real changes made)

. replace ethnic=4 if(racel_dv==11)
(2,130 real changes made)

. replace ethnic=5 if(racel_dv==12)
(514 real changes made)

. replace ethnic=6 if(racel_dv==13)
(1,102 real changes made)

. replace ethnic=7 if(racel_dv==14)
(1,997 real changes made)

. replace ethnic=7 if(racel_dv==15)
(2,868 real changes made)

. replace ethnic=7 if(racel_dv==16)
(204 real changes made)

. replace ethnic=8 if(racel_dv==17)
(535 real changes made)

. replace ethnic=8 if(racel_dv==18)
(0 real changes made)

. 
. label define ethnic_lbl 1"White" 2"Mixed/Multiple Ethnic Groups" 3"Indian" 4"
> Pakistani and Bangladeshi" 5"Chinese" 6"Any other Asian Background" 7"Black/A
> frican/Caribbean/Black British" 8"Other" 

. label values ethnic ethnic_lbl

. 
. lab var ethnic "Ethnicity"

. 
. tab ethnic

                            Ethnicity |      Freq.     Percent        Cum.
--------------------------------------+-----------------------------------
                                White |     83,967       81.96       81.96
         Mixed/Multiple Ethnic Groups |      1,834        1.79       83.75
                               Indian |      3,824        3.73       87.49
            Pakistani and Bangladeshi |      5,599        5.47       92.95
                              Chinese |        514        0.50       93.45
           Any other Asian Background |      1,102        1.08       94.53
Black/African/Caribbean/Black British |      5,069        4.95       99.48
                                Other |        535        0.52      100.00
--------------------------------------+-----------------------------------
                                Total |    102,444      100.00

. 
. 
. * Current Social Class (NS-SEC)
. 
. gen nssec=j_jbnssec8_dv
(116,945 missing values generated)

. replace nssec=. if nssec<0
(16,057 real changes made, 16,057 to missing)

. 
. label define nssec 1"1.1 Large employers and higher managerial and administra
> tive occupations" 2"1.2 Higher professional occupations" 3"2 Lower managerial
> , administrative and professional occupations" 4"3 Intermediate occupations" 
> 5"4 Small employers and own account workers" 6"5 Lower supervisory and techni
> cal occupations" 7"6 Semi-routine occupations" 8"7 Routine occupations"

. lab val nssec nssec

. 
. lab var nssec "Current Social Class (NS-SEC)"

. 
. tab nssec

          Current Social Class (NS-SEC) |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
1.1 Large employers and higher manageri |        788        4.31        4.31
    1.2 Higher professional occupations |      1,637        8.96       13.28
2 Lower managerial, administrative and  |      5,464       29.92       43.20
             3 Intermediate occupations |      2,498       13.68       56.88
4 Small employers and own account worke |      1,848       10.12       67.00
5 Lower supervisory and technical occup |      1,191        6.52       73.52
             6 Semi-routine occupations |      3,172       17.37       90.89
                  7 Routine occupations |      1,664        9.11      100.00
----------------------------------------+-----------------------------------
                                  Total |     18,262      100.00

. 
. * Generations and Cohort Variables
. 
. gen generations=.
(151,264 missing values generated)

. replace generations=1 if j_birthy>=1995 & j_birthy<=1999
(2,274 real changes made)

. replace generations=2 if j_birthy>=1980 & j_birthy<=1994
(6,632 real changes made)

. replace generations=3 if j_birthy>=1965 & j_birthy<=1979
(8,863 real changes made)

. replace generations=4 if j_birthy>=1946 & j_birthy<=1964
(10,688 real changes made)

. replace generations=5 if j_birthy>=1925 & j_birthy<=1945
(4,346 real changes made)

. 
. lab def generations 1"Gen Z" 2"Millennials" 3"Gen X" 4"Baby Boomer" 5"Silent"

. lab val generations generations

. 
. lab var generations "Generation"

. 
. tab generations

 Generation |      Freq.     Percent        Cum.
------------+-----------------------------------
      Gen Z |      2,274        6.93        6.93
Millennials |      6,632       20.22       27.15
      Gen X |      8,863       27.02       54.17
Baby Boomer |     10,688       32.58       86.75
     Silent |      4,346       13.25      100.00
------------+-----------------------------------
      Total |     32,803      100.00

. 
. 
. gen cohort=. 
(151,264 missing values generated)

. replace cohort=1 if j_birthy>=1927 & j_birthy<=1949
(6,514 real changes made)

. replace cohort=2 if j_birthy>=1950 & j_birthy<=1959
(5,361 real changes made)

. replace cohort=3 if j_birthy>=1960 & j_birthy<=1969
(6,321 real changes made)

. replace cohort=4 if j_birthy>=1970 & j_birthy<=1979
(5,639 real changes made)

. replace cohort=5 if j_birthy>=1980 & j_birthy<=1989
(4,513 real changes made)

. replace cohort=6 if j_birthy>=1990 & j_birthy<=1999
(4,393 real changes made)

. 
. lab def cohort 1"1927/1949" 2"1950/1959" 3"1960/1969" 4"1970/1979" 5"1980/198
> 9" 6"1990/1999"

. lab val cohort cohort

. 
. lab var cohort "Cohort"

. 
. tab cohort

     Cohort |      Freq.     Percent        Cum.
------------+-----------------------------------
  1927/1949 |      6,514       19.90       19.90
  1950/1959 |      5,361       16.37       36.27
  1960/1969 |      6,321       19.31       55.58
  1970/1979 |      5,639       17.22       72.80
  1980/1989 |      4,513       13.78       86.58
  1990/1999 |      4,393       13.42      100.00
------------+-----------------------------------
      Total |     32,741      100.00

. 
. * Sex
. 
. drop sex 

. lab drop sex 

. 
. gen sex=.
(151,264 missing values generated)

. replace sex=0 if j_sex==2
(18,933 real changes made)

. replace sex=1 if j_sex==1
(15,386 real changes made)

. 
. lab def sex 0"Female" 1"Male"

. lab val sex sex 

. 
. lab var sex "Sex"

. 
. 
. * Age and Age2
. * Age is centred based upon the grand central mean of the sample, these centr
> ed age is then used to produce the squared function of Age in Age2
. 
. gen age= j_dvage
(116,945 missing values generated)

. replace age=. if age<0
(12 real changes made, 12 to missing)

. 
. mean age

Mean estimation                         Number of obs = 34,307

--------------------------------------------------------------
             |       Mean   Std. err.     [95% conf. interval]
-------------+------------------------------------------------
         age |   49.84779    .101836      49.64818    50.04739
--------------------------------------------------------------

. 
. egen agemean = mean(age)

. 
. gen age_cgm = age - agemean
(116,957 missing values generated)

. 
. lab var age_cgm "Age Centered at Grand Mean"

. 
. gen age2_cgm= age_cgm*age_cgm
(116,957 missing values generated)

. 
. lab var age2_cgm "Age Squared Centered at Grand Mean"

. 
. * Housework
. 
. summarize j_howlng, detail 

                 Hours per week on housework
-------------------------------------------------------------
      Percentiles      Smallest
 1%           -7             -9
 5%           -1             -9
10%            0             -9       Obs              34,319
25%            3             -9       Sum of wgt.      34,319

50%            7                      Mean           9.013462
                        Largest       Std. dev.      9.596656
75%           14            150
90%           20            150       Variance        92.0958
95%           26            150       Skewness       2.541031
99%           40            168       Kurtosis       21.34871

. 
. gen househours=j_howlng
(116,945 missing values generated)

. 
. replace househours=. if househours<0
(2,036 real changes made, 2,036 to missing)

. 
. lab var househours "Hours per week Spent on Housework"

. 
. summarize househours, detail 

              Hours per week Spent on Housework
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            1              0       Obs              32,283
25%            3              0       Sum of wgt.      32,283

50%            7                      Mean           9.809342
                        Largest       Std. dev.       9.31167
75%           14            150
90%           20            150       Variance       86.70719
95%           28            150       Skewness       2.881971
99%           40            168       Kurtosis       24.29138

. 

Finally, the sample is restricted to only those variables deemed a requirement for either the factor analysis or subsequent modeling. The final n=10,442.

%%stata 

keep if !missing(cash, shop, diy, cook, wash, clean, logincome, sex, ethnic, nssec, generations, cohort, age_cgm, age2_cgm, househours)

keep cash shop diy cook wash clean sex ethnic nssec generations cohort age_cgm age2_cgm pidp strata psu j_indinui_xw logincome househours

count

. 
. keep if !missing(cash, shop, diy, cook, wash, clean, logincome, sex, ethnic, 
> nssec, generations, cohort, age_cgm, age2_cgm, househours)
(141,189 observations deleted)

. 
. keep cash shop diy cook wash clean sex ethnic nssec generations cohort age_cg
> m age2_cgm pidp strata psu j_indinui_xw logincome househours

. 
. count
  10,075

. 

Factor Analysis

%%stata

corr cash shop diy cook wash clean

alpha cash shop diy cook wash clean, item 

. 
. corr cash shop diy cook wash clean
(obs=10,075)

             |     cash     shop      diy     cook     wash    clean
-------------+------------------------------------------------------
        cash |   1.0000
        shop |   0.0164   1.0000
         diy |   0.1365   0.3165   1.0000
        cook |   0.0926   0.5372   0.3198   1.0000
        wash |   0.0882   0.4285   0.4783   0.4481   1.0000
       clean |   0.0993   0.3865   0.3992   0.4365   0.6343   1.0000


. 
. alpha cash shop diy cook wash clean, item 

Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     covariance      alpha
-------------+-----------------------------------------------------------------
cash         | 10075   +       0.2595        0.1185        .3021508      0.7945
shop         | 10075   +       0.6939        0.5245        .2072554      0.7208
diy          | 10075   +       0.6919        0.4942        .2027451      0.7312
cook         | 10075   +       0.7291        0.5635        .1958143      0.7096
wash         | 10075   +       0.7977        0.6613        .1766785      0.6800
clean        | 10075   +       0.7582        0.6136        .1907707      0.6959
-------------+-----------------------------------------------------------------
Test scale   |                                             .2125691      0.7614
-------------------------------------------------------------------------------

. 

%%stata

factor shop diy cook wash clean, pcf

rotate

. 
. factor shop diy cook wash clean, pcf
(obs=10,075)

Factor analysis/correlation                      Number of obs    =     10,075
    Method: principal-component factors          Retained factors =          1
    Rotation: (unrotated)                        Number of params =          5

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.76596      1.96611            0.5532       0.5532
        Factor2  |      0.79986      0.18025            0.1600       0.7132
        Factor3  |      0.61960      0.15959            0.1239       0.8371
        Factor4  |      0.46002      0.10546            0.0920       0.9291
        Factor5  |      0.35456            .            0.0709       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(10) = 1.5e+04 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
            shop |   0.7121 |      0.4930  
             diy |   0.6595 |      0.5651  
            cook |   0.7369 |      0.4570  
            wash |   0.8190 |      0.3293  
           clean |   0.7812 |      0.3897  
    ---------------------------------------

. 
. rotate

Factor analysis/correlation                      Number of obs    =     10,075
    Method: principal-component factors          Retained factors =          1
    Rotation: orthogonal varimax (Kaiser off)    Number of params =          5

    --------------------------------------------------------------------------
         Factor  |     Variance   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      2.76596            .            0.5532       0.5532
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(10) = 1.5e+04 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

    ---------------------------------------
        Variable |  Factor1 |   Uniqueness 
    -------------+----------+--------------
            shop |   0.7121 |      0.4930  
             diy |   0.6595 |      0.5651  
            cook |   0.7369 |      0.4570  
            wash |   0.8190 |      0.3293  
           clean |   0.7812 |      0.3897  
    ---------------------------------------

Factor rotation matrix

    -----------------------
                 | Factor1 
    -------------+---------
         Factor1 |  1.0000 
    -----------------------

. 

Original factor analysis included “cash” which did not load well. This was re-run and taken out of subsequent factor loadings.

%%stata

predict factor1

rename factor1 gender_performance

lab var gender_performance "Gender Performance"

. 
. predict factor1
(option regression assumed; regression scoring)

Scoring coefficients (method = regression; based on varimax rotated factors)

    ------------------------
        Variable |  Factor1 
    -------------+----------
            shop |  0.25744 
             diy |  0.23842 
            cook |  0.26642 
            wash |  0.29609 
           clean |  0.28244 
    ------------------------


. 
. rename factor1 gender_performance

. 
. lab var gender_performance "Gender Performance"

. 

%%stata

summarize gender_performance, detail

ttest gender_performance, by(sex)

corr gender_performance househours

. 
. summarize gender_performance, detail

                     Gender Performance
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -1.581812      -1.581812
 5%    -1.581812      -1.581812
10%    -1.314622      -1.581812       Obs              10,075
25%    -.9071414      -1.581812       Sum of wgt.      10,075

50%     .0119728                      Mean           5.27e-09
                        Largest       Std. dev.             1
75%     .9474893       1.652703
90%     1.332223       1.652703       Variance              1
95%     1.652703       1.652703       Skewness       .0334424
99%     1.652703       1.652703       Kurtosis       1.816378

. 
. ttest gender_performance, by(sex)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
  Female |   5,052   -.7608885    .0089854      .63866   -.7785038   -.7432732
    Male |   5,023    .7652815     .009226    .6538727    .7471946    .7833684
---------+--------------------------------------------------------------------
Combined |  10,075    5.27e-09    .0099627           1   -.0195289    .0195289
---------+--------------------------------------------------------------------
    diff |            -1.52617    .0128776               -1.551413   -1.500927
------------------------------------------------------------------------------
    diff = mean(Female) - mean(Male)                              t = -1.2e+02
H0: diff = 0                                     Degrees of freedom =    10073

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. corr gender_performance househours
(obs=10,075)

             | gender~e househ~s
-------------+------------------
gender_per~e |   1.0000
  househours |  -0.4947   1.0000


. 

%%stata

gen gender_performance_2 = gender_performance^2

lab var gender_performance_2 "Gender Performance Squared"

summarize gender_performance_2

cd"$workingdata"

save ukhlswave10_cra_so_v1, replace

. 
. gen gender_performance_2 = gender_performance^2

. 
. lab var gender_performance_2 "Gender Performance Squared"

. 
. summarize gender_performance_2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
gender_per~2 |     10,075    .9999008    .9034915   3.01e-06   2.731427

. 
. cd"$workingdata"
/Users/scottoatley/Documents/Stata Data and Do/Working Data

. 
. save ukhlswave10_cra_so_v1, replace
file ukhlswave10_cra_so_v1.dta saved

. 

%%stata

grstyle clear
set scheme white_tableau
grstyle init
grstyle set legend 2, nobox
grstyle set legend, inside

. 
. grstyle clear

. set scheme white_tableau

. grstyle init

. grstyle set legend 2, nobox

. grstyle set legend, inside

. 

%%stata

cd"$outputs"

twoway (kdensity gender_performance if sex == 0 & cohort==1, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==1, lcolor(red) lpattern(dash)), ///
       legend(off) ///
       title("1990/1999", size(vsmall)) ///
       ytitle("Kernal Density", size(vsmall)) ///
       xtitle("", size(vsmall)) ///    
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///       
       note("n=578", size(vsmall)) ///
       saving(gen1, replace)

twoway (kdensity gender_performance if sex == 0 & cohort==2, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==2, lcolor(red) lpattern(dash)), ///
       legend(off) ///
       title("1980/1989", size(vsmall)) ///
       ytitle("", size(vsmall)) ///    
       xtitle("", size(vsmall)) ///        
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///       
       note("n=2,074", size(vsmall)) ///       
       saving(gen2, replace)
       
twoway (kdensity gender_performance if sex == 0 & cohort==3, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==3, lcolor(red) lpattern(dash)), ///
       title("1970/1979", size(vsmall)) ///
       ytitle("", size(vsmall)) ///        
       xtitle("", size(vsmall)) ///        
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///       
       note("n=2,074", size(vsmall)) ///
       legend(label(1 "Female") label(2 "Male")) ///       
       saving(gen3, replace)
       
twoway (kdensity gender_performance if sex == 0 & cohort==4, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==4, lcolor(red) lpattern(dash)), ///
       legend(off) ///
       title("1960/1969", size(vsmall)) ///
       ytitle("Kernal Density", size(vsmall)) ///
       xtitle("Gender Performance", size(vsmall)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///       
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       note("n=3,008", size(vsmall)) ///           
       saving(gen4, replace)
       
       
twoway (kdensity gender_performance if sex == 0 & cohort==5, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==5, lcolor(red) lpattern(dash)), ///
       legend(off) ///
       title("1950/1959", size(vsmall)) ///
       ytitle("", size(vsmall)) ///        
       xtitle("Gender Performance", size(vsmall)) ///      
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///       
       note("n=1,313", size(vsmall)) ///           
       saving(gen5, replace)
       
twoway (kdensity gender_performance if sex == 0 & cohort==6, lcolor(blue) lpattern(solid)) ///
       (kdensity gender_performance if sex == 1 & cohort==6, lcolor(red) lpattern(dash)), ///
       legend(off) ///
       title("1927/1949", size(vsmall)) ///
       ytitle("", size(vsmall)) ///        
       xtitle("Gender Performance", size(vsmall)) ///      
       xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
       ylabel(0(0.2)0.8, grid labsize(tiny)) ///
       note("n=182", size(vsmall)) ///         
       saving(gen6, replace)    

. 
. cd"$outputs"
/Users/scottoatley/Documents/Stata Data and Do/Output

. 
. twoway (kdensity gender_performance if sex == 0 & cohort==1, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==1, lcolor(red) lpat
> tern(dash)), ///
>        legend(off) ///
>        title("1990/1999", size(vsmall)) ///
>            ytitle("Kernal Density", size(vsmall)) ///
>            xtitle("", size(vsmall)) ///    
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///           
>            note("n=578", size(vsmall)) ///
>            saving(gen1, replace)
file gen1.gph saved

. 
. twoway (kdensity gender_performance if sex == 0 & cohort==2, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==2, lcolor(red) lpat
> tern(dash)), ///
>        legend(off) ///
>        title("1980/1989", size(vsmall)) ///
>            ytitle("", size(vsmall)) ///    
>            xtitle("", size(vsmall)) ///            
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///           
>            note("n=2,074", size(vsmall)) ///       
>            saving(gen2, replace)
file gen2.gph saved

.            
. twoway (kdensity gender_performance if sex == 0 & cohort==3, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==3, lcolor(red) lpat
> tern(dash)), ///
>        title("1970/1979", size(vsmall)) ///
>            ytitle("", size(vsmall)) ///            
>            xtitle("", size(vsmall)) ///            
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///           
>            note("n=2,074", size(vsmall)) ///
>        legend(label(1 "Female") label(2 "Male")) ///       
>            saving(gen3, replace)
file gen3.gph saved

.            
. twoway (kdensity gender_performance if sex == 0 & cohort==4, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==4, lcolor(red) lpat
> tern(dash)), ///
>        legend(off) ///
>        title("1960/1969", size(vsmall)) ///
>            ytitle("Kernal Density", size(vsmall)) ///
>            xtitle("Gender Performance", size(vsmall)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///           
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>            note("n=3,008", size(vsmall)) ///               
>            saving(gen4, replace)
file gen4.gph saved

.            
.            
. twoway (kdensity gender_performance if sex == 0 & cohort==5, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==5, lcolor(red) lpat
> tern(dash)), ///
>        legend(off) ///
>        title("1950/1959", size(vsmall)) ///
>            ytitle("", size(vsmall)) ///            
>            xtitle("Gender Performance", size(vsmall)) ///          
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///           
>            note("n=1,313", size(vsmall)) ///               
>            saving(gen5, replace)
file gen5.gph saved

.            
. twoway (kdensity gender_performance if sex == 0 & cohort==6, lcolor(blue) lpa
> ttern(solid)) ///
>        (kdensity gender_performance if sex == 1 & cohort==6, lcolor(red) lpat
> tern(dash)), ///
>        legend(off) ///
>        title("1927/1949", size(vsmall)) ///
>            ytitle("", size(vsmall)) ///            
>            xtitle("Gender Performance", size(vsmall)) ///          
>        xlabel(-1.8(0.2)1.9, grid labsize(tiny)) ///
>        ylabel(0(0.2)0.8, grid labsize(tiny)) ///
>            note("n=182", size(vsmall)) ///                 
>            saving(gen6, replace)    
file gen6.gph saved

. 

%%stata

graph combine gen1.gph gen2.gph gen3.gph gen4.gph gen5.gph gen6.gph, ycommon ///
title("Distribution of Gender Performance Measure by Gender over Birth Cohorts", size(small)) ///
subtitle("Illustration of the Bimodal Expression of Gender Performance", size(vsmall)) ///
note("Data: UKHLS Wave 10, N=10,075", size(vsmall)) ///
caption("Positive values associated with increasing levels of masculinity, negative values assoicated with increasing levels of femininity", size(vsmall))

graph export "perfgen.png", width(2000) replace

. 
. graph combine gen1.gph gen2.gph gen3.gph gen4.gph gen5.gph gen6.gph, ycommon 
> ///
> title("Distribution of Gender Performance Measure by Gender over Birth Cohort
> s", size(small)) ///
> subtitle("Illustration of the Bimodal Expression of Gender Performance", size
> (vsmall)) ///
> note("Data: UKHLS Wave 10, N=10,075", size(vsmall)) ///
> caption("Positive values associated with increasing levels of masculinity, ne
> gative values assoicated with increasing levels of femininity", size(vsmall))

. 
. graph export "perfgen.png", width(2000) replace
file perfgen.png written in PNG format

. 

%%stata 

collect clear

table (var) (), statistic(fvfrequency sex ethnic nssec) ///
                    statistic(fvpercent sex ethnic nssec) ///
                    statistic(mean logincome age_cgm age2_cgm gender_performance gender_performance_2 househours) ///  
                    statistic(sd logincome age_cgm age2_cgm gender_performance gender_performance_2 househours)             
collect remap result[fvfrequency mean] = Col[1 1] 
    collect remap result[fvpercent sd] = Col[2 2]
collect get resname = "Mean", tag(Col[1] var[mylabel]) 
    collect get resname = "SD", tag(Col[2] var[mylabel])
collect get empty = "  ", tag(Col[1] var[empty]) 
    collect get empty = "  ", tag(Col[2] var[empty])
    
count
    collect get n = `r(N)', tag(Col[2] var[n])
    
collect layout (var[0.sex 1.sex ///
                    1.ethnic 2.ethnic 3.ethnic 4.ethnic 5.ethnic 6.ethnic 7.ethnic 8.ethnic ///
                    1.nssec 2.nssec 3.nssec 4.nssec 5.nssec 6.nssec 7.nssec 8.nssec ///
                    empty mylabel ///
                    logincome ///
                    age_cgm ///
                    age2_cgm ///
                    gender_performance /// 
                    gender_performance_2 ///
                    househours ///
                    empty n]) (Col[1 2])

collect label levels Col 1 "n" 2 "%"            
collect style header Col, title(hide)
collect style header var[empty mylabel], level(hide)
collect style row stack, nobinder
collect style cell var [sex ethnic nssec]#Col[1], nformat(%6.0fc) 
collect style cell var[sex ethnic nssec]#Col[2], nformat(%6.2f) sformat("%s%%")     
collect style cell var[logincome age_cgm age2_cgm gender_performance gender_performance_2 househours], nformat(%6.2f)
collect style cell border_block[item row-header], border(top, pattern(nil)) 
collect title "Table 1: Descriptive Statistics for Gender Performance Model"
collect note "Data Source: Wave 10 UKHLS"
collect preview

collect export "Table1.docx", replace

. 
. collect clear

. 
. table (var) (), statistic(fvfrequency sex ethnic nssec) ///
>                                         statistic(fvpercent sex ethnic nssec)
>  ///
>                                         statistic(mean logincome age_cgm age2
> _cgm gender_performance gender_performance_2 househours) ///  
>                                         statistic(sd logincome age_cgm age2_c
> gm gender_performance gender_performance_2 househours)                     

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                       |  Factor-variable frequency   Factor-variable percent        Mean   Standard deviation
-------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------
Sex=Female                                                                                             |                      5,052                     50.14                                 
Sex=Male                                                                                               |                      5,023                     49.86                                 
Ethnicity=White                                                                                        |                      8,739                     86.74                                 
Ethnicity=Mixed/Multiple Ethnic Groups                                                                 |                        148                      1.47                                 
Ethnicity=Indian                                                                                       |                        396                      3.93                                 
Ethnicity=Pakistani and Bangladeshi                                                                    |                        372                      3.69                                 
Ethnicity=Chinese                                                                                      |                         43                      0.43                                 
Ethnicity=Any other Asian Background                                                                   |                        101                      1.00                                 
Ethnicity=Black/African/Caribbean/Black British                                                        |                        247                      2.45                                 
Ethnicity=Other                                                                                        |                         29                      0.29                                 
Current Social Class (NS-SEC)=1.1 Large employers and higher managerial and administrative occupations |                        516                      5.12                                 
Current Social Class (NS-SEC)=1.2 Higher professional occupations                                      |                        996                      9.89                                 
Current Social Class (NS-SEC)=2 Lower managerial, administrative and professional occupations          |                      3,298                     32.73                                 
Current Social Class (NS-SEC)=3 Intermediate occupations                                               |                      1,309                     12.99                                 
Current Social Class (NS-SEC)=4 Small employers and own account workers                                |                      1,023                     10.15                                 
Current Social Class (NS-SEC)=5 Lower supervisory and technical occupations                            |                        696                      6.91                                 
Current Social Class (NS-SEC)=6 Semi-routine occupations                                               |                      1,459                     14.48                                 
Current Social Class (NS-SEC)=7 Routine occupations                                                    |                        778                      7.72                                 
Log Net Labour Income                                                                                  |                                                         7.250775               .78288
Age Centered at Grand Mean                                                                             |                                                        -3.375032             11.09118
Age Squared Centered at Grand Mean                                                                     |                                                         134.3928             164.5306
Gender Performance                                                                                     |                                                         5.27e-09                    1
Gender Performance Squared                                                                             |                                                         .9999008             .9034915
Hours per week Spent on Housework                                                                      |                                                         9.208437              7.45823
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

. collect remap result[fvfrequency mean] = Col[1 1] 
(24 items remapped in collection Table)

.         collect remap result[fvpercent sd] = Col[2 2]
(24 items remapped in collection Table)

. collect get resname = "Mean", tag(Col[1] var[mylabel]) 

.         collect get resname = "SD", tag(Col[2] var[mylabel])

. collect get empty = "  ", tag(Col[1] var[empty]) 

.         collect get empty = "  ", tag(Col[2] var[empty])

.     
. count
  10,075

.         collect get n = `r(N)', tag(Col[2] var[n])

.         
. collect layout (var[0.sex 1.sex ///
>                                         1.ethnic 2.ethnic 3.ethnic 4.ethnic 5
> .ethnic 6.ethnic 7.ethnic 8.ethnic ///
>                                         1.nssec 2.nssec 3.nssec 4.nssec 5.nss
> ec 6.nssec 7.nssec 8.nssec ///
>                                         empty mylabel ///
>                                         logincome ///
>                     age_cgm ///
>                     age2_cgm ///
>                     gender_performance /// 
>                     gender_performance_2 ///
>                     househours ///
>                                         empty n]) (Col[1 2])

Collection: Table
      Rows: var[0.sex 1.sex 1.ethnic 2.ethnic 3.ethnic 4.ethnic 5.ethnic
            6.ethnic 7.ethnic 8.ethnic 1.nssec 2.nssec 3.nssec 4.nssec
            5.nssec 6.nssec 7.nssec 8.nssec empty mylabel logincome age_cgm
            age2_cgm gender_performance gender_performance_2 househours empty
            n]
   Columns: Col[1 2]
   Table 1: 28 x 2

------------------------------------------------------------------------------------------------------------------------------
                                                                                                       |           Col        
                                                                                                       |          1          2
-------------------------------------------------------------------------------------------------------+----------------------
Sex=Female                                                                                             |       5052   50.14392
Sex=Male                                                                                               |       5023   49.85608
Ethnicity=White                                                                                        |       8739   86.73945
Ethnicity=Mixed/Multiple Ethnic Groups                                                                 |        148   1.468983
Ethnicity=Indian                                                                                       |        396   3.930521
Ethnicity=Pakistani and Bangladeshi                                                                    |        372   3.692308
Ethnicity=Chinese                                                                                      |         43    .426799
Ethnicity=Any other Asian Background                                                                   |        101   1.002481
Ethnicity=Black/African/Caribbean/Black British                                                        |        247   2.451613
Ethnicity=Other                                                                                        |         29   .2878412
Current Social Class (NS-SEC)=1.1 Large employers and higher managerial and administrative occupations |        516   5.121588
Current Social Class (NS-SEC)=1.2 Higher professional occupations                                      |        996   9.885856
Current Social Class (NS-SEC)=2 Lower managerial, administrative and professional occupations          |       3298   32.73449
Current Social Class (NS-SEC)=3 Intermediate occupations                                               |       1309   12.99256
Current Social Class (NS-SEC)=4 Small employers and own account workers                                |       1023   10.15385
Current Social Class (NS-SEC)=5 Lower supervisory and technical occupations                            |        696   6.908189
Current Social Class (NS-SEC)=6 Semi-routine occupations                                               |       1459   14.48139
Current Social Class (NS-SEC)=7 Routine occupations                                                    |        778   7.722084
empty                                                                                                  |                      
mylabel                                                                                                |       Mean         SD
Log Net Labour Income                                                                                  |   7.250775     .78288
Age Centered at Grand Mean                                                                             |  -3.375032   11.09118
Age Squared Centered at Grand Mean                                                                     |   134.3928   164.5306
Gender Performance                                                                                     |   5.27e-09          1
Gender Performance Squared                                                                             |   .9999008   .9034915
Hours per week Spent on Housework                                                                      |   9.208437    7.45823
empty                                                                                                  |                      
n                                                                                                      |                 10075
------------------------------------------------------------------------------------------------------------------------------

. 
. collect label levels Col 1 "n" 2 "%"                    

. collect style header Col, title(hide)

. collect style header var[empty mylabel], level(hide)

. collect style row stack, nobinder

. collect style cell var [sex ethnic nssec]#Col[1], nformat(%6.0fc) 

. collect style cell var[sex ethnic nssec]#Col[2], nformat(%6.2f) sformat("%s%%
> ")         

. collect style cell var[logincome age_cgm age2_cgm gender_performance gender_p
> erformance_2 househours], nformat(%6.2f)

. collect style cell border_block[item row-header], border(top, pattern(nil)) 

. collect title "Table 1: Descriptive Statistics for Gender Performance Model"

. collect note "Data Source: Wave 10 UKHLS"

. collect preview

Table 1: Descriptive Statistics for Gender Performance Model
---------------------------------------------------------------------------------------------
                                                                           |       n        %
Sex                                                                        |                 
  Female                                                                   |   5,052   50.14%
  Male                                                                     |   5,023   49.86%
Ethnicity                                                                  |                 
  White                                                                    |   8,739   86.74%
  Mixed/Multiple Ethnic Groups                                             |     148    1.47%
  Indian                                                                   |     396    3.93%
  Pakistani and Bangladeshi                                                |     372    3.69%
  Chinese                                                                  |      43    0.43%
  Any other Asian Background                                               |     101    1.00%
  Black/African/Caribbean/Black British                                    |     247    2.45%
  Other                                                                    |      29    0.29%
Current Social Class (NS-SEC)                                              |                 
  1.1 Large employers and higher managerial and administrative occupations |     516    5.12%
  1.2 Higher professional occupations                                      |     996    9.89%
  2 Lower managerial, administrative and professional occupations          |   3,298   32.73%
  3 Intermediate occupations                                               |   1,309   12.99%
  4 Small employers and own account workers                                |   1,023   10.15%
  5 Lower supervisory and technical occupations                            |     696    6.91%
  6 Semi-routine occupations                                               |   1,459   14.48%
  7 Routine occupations                                                    |     778    7.72%
                                                                           |                 
                                                                           |    Mean       SD
Log Net Labour Income                                                      |    7.25     0.78
Age Centered at Grand Mean                                                 |   -3.38    11.09
Age Squared Centered at Grand Mean                                         |  134.39   164.53
Gender Performance                                                         |    0.00     1.00
Gender Performance Squared                                                 |    1.00     0.90
Hours per week Spent on Housework                                          |    9.21     7.46
                                                                           |                 
n                                                                          |            10075
---------------------------------------------------------------------------------------------
Data Source: Wave 10 UKHLS

. 
. collect export "Table1.docx", replace
(collection Table exported to file Table1.docx)

. 

Models

Before any formal modeling can take place, the UKHLS is a complex social survey. This means appropriate adjustments for its complex survey design are required.

%%stata 

save "$workingdata/gender.dta", replace

svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cgm c.age2_cgm i.ethnic i.nssec 

. 
. save "$workingdata/gender.dta", replace
file /Users/scottoatley/Documents/Stata Data and Do/Working Data/gender.dta
    saved

. 
. svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

Sampling weights: j_indinui_xw
             VCE: linearized
     Single unit: scaled
        Strata 1: strata
 Sampling unit 1: psu
           FPC 1: <zero>

. 
. svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cg
> m c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,630                          Number of obs   =     10,060
Number of PSUs   = 3,409                          Population size = 9,295.5002
                                                  Design df       =      1,779
                                                  F(19, 1761)     =     156.96
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3167

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2702328   .0284395     9.50   0.000     .2144544    .3260111
gender_per~e |   .1026319   .0149324     6.87   0.000     .0733451    .1319188
gender_per~2 |  -.0189667   .0101517    -1.87   0.062    -.0388772    .0009438
     age_cgm |  -.0127472   .0011873   -10.74   0.000    -.0150759   -.0104184
    age2_cgm |  -.0009016   .0000769   -11.72   0.000    -.0010524   -.0007507
             |
      ethnic |
Mixed/Mul..  |   .0510384    .064667     0.79   0.430    -.0757929    .1778698
     Indian  |   .0218302   .0393125     0.56   0.579    -.0552732    .0989337
Pakistani..  |  -.1956906   .0595791    -3.28   0.001    -.3125429   -.0788383
    Chinese  |   .1751179   .0984699     1.78   0.076    -.0180109    .3682467
Any other..  |  -.0646881    .062071    -1.04   0.297    -.1864279    .0570518
Black/Afr..  |  -.0287417   .0626855    -0.46   0.647    -.1516866    .0942032
      Other  |   .2733452   .1570865     1.74   0.082    -.0347484    .5814387
             |
       nssec |
1.2 Highe..  |  -.1917456   .0371966    -5.15   0.000    -.2646993    -.118792
2 Lower m..  |   -.365915   .0293951   -12.45   0.000    -.4235676   -.3082625
3 Interme..  |  -.6313358    .034718   -18.18   0.000    -.6994281   -.5632435
4 Small e..  |  -1.202829   .0611222   -19.68   0.000    -1.322708    -1.08295
5 Lower s..  |  -.5574836   .0334521   -16.67   0.000    -.6230931    -.491874
6 Semi-ro..  |   -.844068   .0300317   -28.11   0.000    -.9029691   -.7851669
7 Routine..  |  -.9473985   .0386734   -24.50   0.000    -1.023249   -.8715484
             |
       _cons |   7.795486   .0318613   244.67   0.000     7.732997    7.857976
------------------------------------------------------------------------------
Note: 13 strata omitted because they contain no population members.
Note: Variance scaled to handle strata with a single sampling unit.

. 

%%stata

save "$workingdata/TEMPFILE.dta", replace


* Need to install fre ado file first.   
    bysort strata psu: keep if _n==1
    bysort strata: generate n_psu = _N
    fre n_psu
    list pidp strata psu if n_psu==1
    
    * Advice from Longhi and Nandi is to merge the strata with the adjacent ones.
    
    bysort strata: keep if _n==1
    sort strata
    generate newstrata =strata[_n+1] if n_psu==1
    fre newstrata
    list pidp strata psu newstrata if n_psu==1
    keep if n_psu==1
    keep strata newstrata n_psu
    merge 1:m strata using "$workingdata/gender.dta"
    fre n_psu
    replace strata = newstrata if n_psu==1
    drop _merge
    
svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)
svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cgm c.age2_cgm i.ethnic i.nssec 

. 
. save "$workingdata/TEMPFILE.dta", replace
file /Users/scottoatley/Documents/Stata Data and Do/Working Data/TEMPFILE.dta
    saved

. 
. 
. * Need to install fre ado file first.   
.         bysort strata psu: keep if _n==1
(6,652 observations deleted)

.         bysort strata: generate n_psu = _N

.         fre n_psu

n_psu
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   1     |        599      17.50      17.50      17.50
        2     |       1812      52.94      52.94      70.44
        3     |        282       8.24       8.24      78.67
        4     |         96       2.80       2.80      81.48
        5     |         40       1.17       1.17      82.65
        7     |         28       0.82       0.82      83.46
        8     |         16       0.47       0.47      83.93
        17    |         17       0.50       0.50      84.43
        29    |         29       0.85       0.85      85.28
        34    |         34       0.99       0.99      86.27
        85    |         85       2.48       2.48      88.75
        186   |        186       5.43       5.43      94.19
        199   |        199       5.81       5.81     100.00
        Total |       3423     100.00     100.00           
-----------------------------------------------------------

.         list pidp strata psu if n_psu==1

      +-----------------------------+
      |       pidp   strata     psu |
      |-----------------------------|
 126. |  818917885       35     134 |
 172. |  477283165       53     186 |
 278. |  490097085      114     447 |
 309. |  619908498      133     507 |
 567. | 1224002047     2001    2001 |
      |-----------------------------|
 568. |  136008847     2002    2004 |
 571. |  340002047     2004    2007 |
 572. |  476000687     2005    2010 |
 575. |  884010211     2007    2014 |
 576. | 1292021164     2008    2016 |
      |-----------------------------|
 581. | 1360000691     2012    2024 |
 586. |  340016331     2016    2031 |
 591. |  884023811     2019    2038 |
 602. | 1224032659     2025    2049 |
 603. |  136036731     2026    2052 |
      |-----------------------------|
 604. | 1020163964     2027    2054 |
 615. |  612029927     2033    2066 |
 618. |  952176882     2035    2070 |
 623. | 1632040811     2038    2075 |
 624. | 1028486492     2039    2078 |
      |-----------------------------|
 625. | 1428048287     2040    2080 |
 640. |  408038767     2048    2095 |
 643. |  136050331     2050    2100 |
 648. | 1088060527     2053    2105 |
 655. |  612055087     2057    2114 |
      |-----------------------------|
 660. |  408050331     2060    2119 |
 673. |  680072767     2067    2133 |
 686. | 1632081611     2074    2147 |
 687. |  748076871     2075    2149 |
 688. |  340080931     2076    2151 |
      |-----------------------------|
 689. |  476085011     2077    2154 |
 690. | 1496557684     2078    2155 |
 699. |  952064611     2083    2166 |
 706. | 1428095887     2088    2176 |
 709. | 1496435290     2090    2179 |
      |-----------------------------|
 712. |  272091807     2092    2183 |
 721. | 1632103371     2098    2195 |
 724. | 1428110167     2100    2200 |
 725. |   68105407     2102    2204 |
 726. |  272102007     2104    2207 |
      |-----------------------------|
 727. |  204100651     2106    2212 |
 732. | 1224120367     2109    2217 |
 733. | 1632119691     2110    2219 |
 734. |  748113567     2111    2221 |
 751. | 1360117647     2120    2240 |
      |-----------------------------|
 756. |  748134651     2123    2245 |
 757. |  340131247     2124    2247 |
 758. | 1088127171     2125    2249 |
 769. |  816134651     2131    2261 |
 770. |  408523690     2132    2263 |
      |-----------------------------|
 771. | 1224139411     2133    2265 |
 772. | 1632137371     2134    2267 |
 773. |  748143491     2135    2269 |
 776. |  476146207     2137    2274 |
 779. |  680145531     2139    2277 |
      |-----------------------------|
 780. |  272143487     2140    2279 |
 787. | 1360666608     2144    2288 |
 798. | 1156149607     2153    2305 |
 813. |   68163887     2162    2324 |
 814. |  884170011     2163    2326 |
      |-----------------------------|
 831. |  340176127     2172    2343 |
 844. |  952161851     2179    2358 |
 851. | 1428189047     2184    2368 |
 856. |  884194491     2187    2374 |
 859. | 1156797052     2189    2377 |
      |-----------------------------|
 870. | 1021122326     2195    2390 |
 875. |   68197891     2198    2396 |
 876. |  884822890     2199    2398 |
 887. | 1224215571     2205    2409 |
 908. | 1360215567     2216    2432 |
      |-----------------------------|
 909. | 1224224407     2217    2433 |
 918. |   68223727     2222    2444 |
 921. |  272225091     2224    2447 |
 934. | 1020250927     2231    2462 |
 935. | 1428242091     2232    2464 |
      |-----------------------------|
 940. |  680244807     2235    2469 |
 941. | 1360228491     2236    2472 |
 942. | 1157060930     2237    2473 |
 947. |  409400884     2240    2479 |
 956. |  476251611     2245    2490 |
      |-----------------------------|
 957. | 1632258407     2246    2491 |
 968. | 1360250931     2252    2504 |
 973. |  952246847     2255    2510 |
 974. | 1428268619     2256    2512 |
 979. |  884249583     2259    2518 |
      |-----------------------------|
 984. |  204260451     2262    2524 |
 987. |  408263167     2264    2527 |
 988. |  544269971     2265    2530 |
 989. | 1632293087     2266    2531 |
1012. | 1633285330     2278    2555 |
      |-----------------------------|
1017. | 1088281527     2281    2561 |
1034. | 1632317567     2290    2579 |
1035. | 1020300571     2291    2582 |
1038. | 1088293767     2293    2585 |
1045. |  204300571     2298    2596 |
      |-----------------------------|
1054. | 1020317567     2303    2606 |
1055. | 1428316211     2304    2608 |
1056. | 1088297851     2305    2609 |
1057. |   68317567     2306    2612 |
1060. |  272310095     2308    2615 |
      |-----------------------------|
1083. |  272328447     2320    2639 |
1086. |  204321651     2322    2644 |
1087. |  953891166     2323    2646 |
1090. |  545360774     2325    2650 |
1097. | 1088341379     2329    2657 |
      |-----------------------------|
1100. |  680334567     2331    2661 |
1101. | 1292334567     2332    2664 |
1104. | 1564336611     2334    2667 |
1105. |  816342727     2335    2669 |
1110. | 1632363811     2338    2675 |
      |-----------------------------|
1121. | 1292350887     2344    2688 |
1124. | 1564351567     2346    2691 |
1129. |  544361775     2349    2698 |
1132. | 1020366531     2351    2702 |
1135. | 1088362447     2353    2705 |
      |-----------------------------|
1144. |  816357687     2359    2717 |
1147. |  544368571     2361    2722 |
1154. | 1088376047     2365    2729 |
1155. | 1496360411     2366    2731 |
1166. |  408376051     2372    2743 |
      |-----------------------------|
1169. | 1632393047     2374    2747 |
1170. |  748395091     2375    2749 |
1195. | 1428401891     2388    2776 |
1210. |  544406651     2397    2794 |
1243. | 1496408691     2414    2827 |
      |-----------------------------|
1248. |  612422971     2417    2834 |
1269. | 1292435891     2428    2856 |
1270. | 1156433171     2429    2857 |
1271. | 1564433851     2430    2859 |
1272. |  816437927     2431    2861 |
      |-----------------------------|
1275. |  544450847     2433    2866 |
1276. | 1632454247     2434    2867 |
1277. |  748465135     2435    2869 |
1278. |  340449491     2436    2871 |
1281. | 1497653174     2438    2875 |
      |-----------------------------|
1282. |  681782370     2439    2877 |
1283. | 1292444727     2440    2880 |
1286. |  205986372     2442    2884 |
1293. |  340459691     2448    2895 |
1294. | 1088459007     2449    2897 |
      |-----------------------------|
1309. | 1632484167     2458    2915 |
1310. | 1020477367     2459    2918 |
1319. |  612476015     2465    2930 |
1320. |  206584082     2466    2932 |
1321. |  952467847     2467    2934 |
      |-----------------------------|
1322. |  408466487     2468    2935 |
1325. | 1632492331     2470    2939 |
1332. |   68479407     2474    2948 |
1337. | 1156481447     2477    2953 |
1340. |  952483491     2479    2958 |
      |-----------------------------|
1355. |  884494367     2487    2974 |
1356. | 1292497087     2488    2976 |
1361. |  816499807     2491    2981 |
1368. |  748526331     2495    2989 |
1371. | 1088503207     2497    2993 |
      |-----------------------------|
1382. |  816509331     2503    3005 |
1383. |  408505247     2504    3007 |
1386. | 1632538571     2506    3011 |
1391. |  680510007     2511    3021 |
1392. | 1292515447     2512    3024 |
      |-----------------------------|
1397. |  816521567     2515    3029 |
1404. | 1020533807     2519    3038 |
1439. |  612543331     2537    3074 |
1446. | 1224554887     2541    3081 |
1449. |  748563731     2543    3085 |
      |-----------------------------|
1452. | 1088558967     2545    3089 |
1459. |  615523166     2549    3098 |
1464. | 1224572571     2553    3105 |
1475. |  680563051     2559    3117 |
1482. |  411243682     2564    3127 |
      |-----------------------------|
1485. |  136580727     2566    3132 |
1486. | 1020584811     2567    3134 |
1491. | 1496584127     2570    3139 |
1500. |  952579371     2575    3150 |
1503. |  544597731     2577    3154 |
      |-----------------------------|
1504. |  136597727     2578    3156 |
1517. | 1090359694     2585    3169 |
1518. |  204597047     2586    3172 |
1525. |  748616767     2591    3181 |
1532. |  680599767     2595    3189 |
      |-----------------------------|
1535. | 1564593651     2598    3195 |
1544. |  751583686     2603    3205 |
1547. |  476612691     2605    3210 |
1548. |   68624247     2606    3212 |
1567. | 1428631731     2616    3232 |
      |-----------------------------|
1582. |  408635127     2624    3247 |
1585. | 1634862932     2626    3251 |
1588. |  340641927     2628    3255 |
1591. | 1496648047     2630    3259 |
1592. |  884646015     2631    3262 |
      |-----------------------------|
1595. | 1156648047     2633    3265 |
1598. |  952648055     2635    3270 |
1617. |  612649411     2645    3290 |
1630. |  340663691     2652    3303 |
1633. | 1496668451     2654    3307 |
      |-----------------------------|
1640. |  204662331     2658    3316 |
1657. |  884680007     2667    3334 |
1658. | 1292685451     2668    3336 |
1659. | 1156675247     2669    3337 |
1660. | 1564669811     2670    3339 |
      |-----------------------------|
1663. |  408684091     2672    3343 |
1668. | 1020688167     2675    3350 |
1669. | 1428686811     2676    3352 |
1678. |  612690887     2681    3362 |
1699. | 1156709927     2693    3385 |
      |-----------------------------|
1700. | 1564694287     2694    3387 |
1707. |  748735767     2699    3397 |
1716. | 1292716727     2704    3408 |
1717. | 1156721491     2705    3409 |
1718. |  204718771     2706    3412 |
      |-----------------------------|
1719. |  816707211     2707    3413 |
1724. |  136733047     2710    3420 |
1727. | 1430862934     2712    3424 |
1730. |   68728967     2714    3428 |
1741. |  408725571     2720    3439 |
      |-----------------------------|
1746. | 1020745971     2723    3446 |
1751. |   68740527     2726    3452 |
1752. |  884741211     2727    3454 |
1753. |  272739851     2728    3455 |
1766. | 1020754807     2735    3470 |
      |-----------------------------|
1773. |  884755495     2739    3478 |
1780. |  408758887     2744    3487 |
1781. | 1224765687     2745    3489 |
1784. | 1020765687     2747    3494 |
1787. | 1500209966     2750    3499 |
      |-----------------------------|
1800. |  544782019     2757    3514 |
1801. | 1632790847     2758    3515 |
1808. | 1496774527     2762    3523 |
1825. | 1020784731     2771    3542 |
1832. | 1292791527     2776    3552 |
      |-----------------------------|
1833. |  612784047     2777    3554 |
1840. | 1224803087     2781    3561 |
1841. | 1632813971     2782    3563 |
1842. | 1020800371     2783    3566 |
1845. |  476806495     2785    3570 |
      |-----------------------------|
1850. |  272799691     2788    3575 |
1851. | 1156801055     2789    3577 |
1856. | 1224811927     2793    3585 |
1857. |  136819407     2794    3588 |
1858. |  748856807     2795    3589 |
      |-----------------------------|
1859. |  340814647     2796    3591 |
1862. |  884820091     2799    3598 |
1863. | 1156820091     2801    3601 |
1864. |  204812611     2802    3604 |
1865. |  816798327     2803    3605 |
      |-----------------------------|
1866. |  408808527     2804    3607 |
1869. |  340830287     2808    3615 |
1870. |  476830287     2809    3618 |
1871. |   68823487     2810    3620 |
1872. |  884824167     2811    3622 |
      |-----------------------------|
1875. |  204828247     2814    3628 |
1876. |  952830967     2815    3630 |
1879. |  544841167     2817    3634 |
1882. |  748879251     2819    3637 |
1883. | 1428843887     2820    3640 |
      |-----------------------------|
1888. |  952836407     2827    3654 |
1891. |  136847291     2830    3660 |
1892. | 1020850007     2831    3662 |
1895. | 1092046088     2833    3665 |
1896. |   68847295     2834    3668 |
      |-----------------------------|
1899. | 1156852863     2836    3673 |
1902. |  816842527     2839    3677 |
1903. |  408854091     2840    3679 |
1904. |  544868367     2841    3682 |
1911. | 1496864291     2846    3691 |
      |-----------------------------|
1912. |  884867011     2847    3694 |
1913. | 1156867691     2849    3697 |
1914. | 1564852863     2850    3699 |
1917. |  544873807     2853    3706 |
1920. | 1428881287     2856    3712 |
      |-----------------------------|
1921. | 1496876531     2858    3715 |
1922. |  680834367     2859    3717 |
1925. |  612878571     2861    3722 |
1926. | 1564857491     2862    3723 |
1927. |  816865647     2863    3725 |
      |-----------------------------|
1928. |  408870411     2864    3727 |
1929. | 1224899651     2865    3729 |
1930. |  136886047     2866    3732 |
1935. | 1496890131     2870    3739 |
1938. |  612890811     2873    3746 |
      |-----------------------------|
1947. | 1632910531     2878    3755 |
1948. |  748953367     2879    3757 |
1951. | 1292905087     2884    3768 |
1960. | 1224918687     2889    3777 |
1965. |  340913927     2892    3783 |
      |-----------------------------|
1974. | 1564898287     2898    3795 |
1975. |  816906451     2899    3797 |
1980. | 1632937047     2902    3803 |
1981. | 1020944531     2903    3806 |
1982. | 1428928211     2904    3808 |
      |-----------------------------|
1985. | 1500467728     2906    3811 |
1994. | 1224961527     2913    3825 |
1995. |  136942491     2914    3828 |
2002. |  680890811     2919    3837 |
2023. | 1564935011     2934    3867 |
      |-----------------------------|
2026. |  408946571     2936    3871 |
2029. |  136959491     2938    3876 |
2038. |  680929567     2943    3885 |
2039. |  272964247     2944    3887 |
2040. | 1156967647     2945    3889 |
      |-----------------------------|
2049. | 1632989407     2950    3899 |
2058. |  684916482     2955    3909 |
2059. |  272971051     2956    3911 |
2060. |  612972407     2957    3914 |
2071. |  749025451     2963    3925 |
      |-----------------------------|
2080. | 1292991451     2968    3936 |
2091. | 1633012527     2974    3947 |
2096. | 1088956767     2977    3953 |
2099. |  680962207     2979    3957 |
2108. | 1429005047     2984    3968 |
      |-----------------------------|
2111. | 1633031571     2986    3971 |
2120. |  884969687     2991    3982 |
2125. | 1565013891     2994    3987 |
2130. |  545020687     2997    3994 |
2137. | 1225062167     3001    4001 |
      |-----------------------------|
2138. | 1501249726     3002    4003 |
2143. |  613024091     3005    4010 |
2152. |  137033607     3010    4020 |
2153. | 1021061495     3011    4022 |
2162. |  277671282     3016    4031 |
      |-----------------------------|
2163. |  613039731     3017    4034 |
2168. |  409030207     3020    4039 |
2179. |   69047887     3026    4052 |
2180. |  681015931     3027    4053 |
2193. |  137057407     3034    4068 |
      |-----------------------------|
2200. |   72012092     3038    4076 |
2209. |  817054695     3043    4085 |
2216. |  749102975     3047    4093 |
2217. | 1429084607     3048    4096 |
2228. | 1569365282     3054    4107 |
      |-----------------------------|
2233. |  545089371     3057    4114 |
2234. | 1633107731     3058    4115 |
2241. |   69083247     3062    4124 |
2248. |  205078499     3066    4132 |
2253. |  545096851     3069    4138 |
      |-----------------------------|
2256. |  749134931     3071    4141 |
2257. | 1429113167     3072    4144 |
2262. |  885069647     3075    4150 |
2267. |  205100931     3078    4156 |
2272. | 1633131527     3082    4163 |
      |-----------------------------|
2273. | 1023754376     3083    4166 |
2290. |  409110447     3092    4183 |
2293. |  137116567     3094    4188 |
2298. |  477124047     3097    4194 |
2303. |  273117931     3100    4199 |
      |-----------------------------|
2304. |  953132207     3103    4206 |
2305. |  409119967     3104    4207 |
2306. |  545129487     3105    4210 |
2317. |  885106371     3111    4222 |
2320. |  613132891     3113    4226 |
      |-----------------------------|
2325. | 1429151247     3116    4232 |
2326. |  545141727     3117    4234 |
2327. | 1633170287     3118    4235 |
2328. | 1021160087     3119    4238 |
2329. | 1429172327     3120    4240 |
      |-----------------------------|
2334. |  681140371     3123    4245 |
2339. |  205143767     3126    4252 |
2340. |  953161459     3127    4254 |
2341. |  409149207     3128    4255 |
2344. | 1633177087     3130    4259 |
      |-----------------------------|
2347. |  341153287     3132    4263 |
2350. | 1497138331     3134    4267 |
2351. |  681159411     3135    4269 |
2358. |  817148527     3139    4277 |
2359. |  409160091     3140    4279 |
      |-----------------------------|
2360. |  545173011     3141    4282 |
2363. |  341165531     3144    4287 |
2364. | 1089149891     3145    4289 |
2365. |   69173687     3146    4292 |
2366. |  885143087     3147    4294 |
      |-----------------------------|
2367. | 1293156687     3148    4296 |
2370. | 1565173691     3150    4299 |
2371. |  953182531     3151    4302 |
2376. | 1633208367     3154    4307 |
2377. |  817165531     3155    4309 |
      |-----------------------------|
2386. |  273183207     3160    4319 |
2395. |  545188651     3165    4330 |
2402. |  482222082     3169    4338 |
2405. | 1293179807     3172    4344 |
2408. |  205193407     3174    4348 |
      |-----------------------------|
2415. | 1633234207     3178    4355 |
2420. | 1094528084     3181    4361 |
2421. | 1497178447     3182    4363 |
2422. | 1021220611     3183    4366 |
2429. |  953220607     3187    4374 |
      |-----------------------------|
2432. | 1225236247     3189    4377 |
2433. |  137221971     3190    4380 |
2434. |  749221979     3191    4381 |
2435. | 1429243047     3192    4384 |
2438. | 1502283366     3194    4387 |
      |-----------------------------|
2439. |  681213131     3195    4389 |
2440. |  273220611     3196    4391 |
2445. |  953229447     3199    4398 |
2446. | 1361214491     3200    4400 |
2447. | 1225260735     3201    4401 |
      |-----------------------------|
2448. |  137232851     3202    4404 |
2449. |  885211087     3203    4406 |
2450. |  341229451     3204    4407 |
2451. |  477230131     3205    4410 |
2456. | 1293209047     3208    4416 |
      |-----------------------------|
2463. |  545239647     3213    4426 |
2464. | 1633264131     3214    4427 |
2467. |  341237611     3216    4431 |
2468. | 1225278411     3217    4433 |
2471. |  681248487     3219    4437 |
      |-----------------------------|
2472. | 1293221971     3220    4440 |
2473. | 1157242367     3221    4441 |
2474. | 1565248491     3222    4443 |
2475. |  545260727     3225    4450 |
2476. |  137257331     3226    4452 |
      |-----------------------------|
2477. | 1021259367     3227    4454 |
2478. |  205253251     3234    4468 |
2479. |  817255291     3235    4469 |
2480. |  409255971     3236    4471 |
2481. |  545270927     3237    4474 |
      |-----------------------------|
2482. | 1497221291     3238    4475 |
2483. | 1021264131     3239    4478 |
2486. | 1157272967     3241    4481 |
2487. |   69262767     3242    4484 |
2488. |  681272287     3243    4485 |
      |-----------------------------|
2491. | 1157277795     3245    4489 |
2492. | 1565263447     3246    4491 |
2493. |  409272287     3248    4495 |
2502. |  477281127     3253    4506 |
2505. |  885260727     3255    4510 |
      |-----------------------------|
2506. |  273273651     3256    4511 |
2511. |  817275687     3259    4517 |
2518. | 1429321927     3264    4528 |
2519. | 1089247811     3265    4529 |
2522. | 1293271607     3268    4536 |
      |-----------------------------|
2525. | 1565302211     3270    4539 |
2528. | 1361291331     3272    4544 |
2531. |  137309007     3274    4548 |
2532. | 1429330767     3276    4552 |
2533. | 1089259435     3277    4553 |
      |-----------------------------|
2536. |  681304251     3279    4557 |
2555. |  477316487     3289    4578 |
2560. |  273319891     3292    4583 |
2561. |  616515298     3293    4586 |
2570. |  137325327     3298    4596 |
      |-----------------------------|
2571. |  749322615     3299    4597 |
2578. |  885303571     3303    4606 |
2585. |  953334851     3307    4614 |
2588. |  545334851     3309    4618 |
2595. |  477346407     3313    4626 |
      |-----------------------------|
2598. |  681345051     3315    4629 |
2793. |   69391287     3322    7101 |
2799. |   69734691     3332    7614 |
2800. |   69907407     3337    7870 |
2801. |   69955691     3340    7951 |
      |-----------------------------|
2802. |   70058367     3342    8117 |
2805. |   70337167     3349    8537 |
2806. |  137443659     3360    8999 |
2807. |  137446367     3361    9003 |
2808. |  141236174     3364    9252 |
      |-----------------------------|
2809. |  137668731     3366    9332 |
2810. |  137770047     3369    9481 |
2811. |  144405564     3371    9622 |
2814. |  138097811     3377    9963 |
2815. |  138277331     3382   10236 |
      |-----------------------------|
2816. |  138401087     3384   10418 |
2817. |  138519411     3386   10595 |
2818. |  205421891     3393   10964 |
2819. |  205529327     3399   11128 |
2820. |  205566727     3401   11187 |
      |-----------------------------|
2821. |  210426248     3406   11420 |
2824. |  205834651     3410   11533 |
2828. |  206105971     3417   11942 |
2829. |  206149487     3419   12008 |
2830. |  206224287     3421   12118 |
      |-----------------------------|
2831. |  273477651     3433   12632 |
2844. |  274289567     3460   13793 |
2845. |  280112526     3462   13915 |
2850. |  341432091     3468   14139 |
2851. |  341547007     3471   14308 |
      |-----------------------------|
2856. |  341676207     3476   14500 |
2859. |  341821727     3479   14714 |
2863. |  345793850     3483   14868 |
2864. |  341995807     3485   14984 |
2865. |  342167847     3487   15194 |
      |-----------------------------|
2869. |  346039250     3500   15618 |
2870. |  409401487     3504   15735 |
2871. |  409481731     3505   15862 |
2872. |  409647647     3509   16107 |
2876. |  409897207     3519   16476 |
      |-----------------------------|
2877. |  409940055     3520   16540 |
2878. |  410126371     3526   16834 |
2881. |  477377007     3537   17400 |
2882. |  477495327     3543   17576 |
2883. |  477664671     3546   17827 |
      |-----------------------------|
2884. |  477703411     3548   17884 |
2885. |  477746927     3549   17953 |
2886. |  477818327     3551   18058 |
2889. |  483459846     3559   18524 |
2890. |  478381367     3567   18906 |
      |-----------------------------|
2895. |  545501455     3577   19484 |
2906. |  550283288     3587   20063 |
2907. |  546000571     3589   20185 |
2908. |  546285487     3596   20616 |
2911. |  546445287     3599   20857 |
      |-----------------------------|
2912. |  546658127     3604   21172 |
2924. |  613592567     3618   21719 |
2925. |  613688459     3620   21861 |
2926. |  620581844     3625   22097 |
2927. |  613900611     3626   22175 |
      |-----------------------------|
2932. |  614503095     3644   23083 |
2933. |  817362051     3649   23390 |
2934. |  681672127     3655   23632 |
2935. |  681725851     3658   23711 |
2938. |  681889727     3662   23952 |
      |-----------------------------|
2941. |  682171927     3671   24371 |
2942. |  682260327     3675   24501 |
2946. |  682405179     3681   24726 |
2954. |  756051964     3696   25544 |
2958. |  750075371     3702   25931 |
      |-----------------------------|
2961. |  750294327     3708   26254 |
2962. |  750324931     3709   26299 |
2970. |  817489207     3721   26896 |
2971. |  817867287     3730   27411 |
2976. |  818070611     3736   27726 |
      |-----------------------------|
2977. |  818147447     3738   27789 |
2978. |  818279367     3740   27995 |
2979. |  818429647     3747   28219 |
2987. |  890895446     3764   28981 |
2988. |  885945491     3771   29370 |
      |-----------------------------|
2991. |  886139287     3777   29656 |
2992. |  892153364     3783   29891 |
2993. |  886398367     3786   30058 |
2994. |  886458207     3788   30146 |
2998. |  953454531     3793   30322 |
      |-----------------------------|
3002. |  953736047     3800   30747 |
3003. |  956664898     3803   30978 |
3006. |  954040691     3809   31210 |
3007. |  954121607     3811   31344 |
3015. |  954430331     3820   31860 |
      |-----------------------------|
3021. | 1021552447     3830   32215 |
3028. | 1022066527     3843   32999 |
3031. | 1022220887     3847   33230 |
3032. | 1022258971     3848   33286 |
3033. | 1022295691     3849   33343 |
      |-----------------------------|
3034. | 1022430327     3851   33546 |
3037. | 1089583047     3868   34354 |
3041. | 1089706807     3873   34536 |
3045. | 1089942087     3881   34886 |
3046. | 1090218851     3887   35244 |
      |-----------------------------|
3047. | 1090437807     3895   35586 |
3048. | 1157360007     3897   35686 |
3055. | 1157758487     3905   36170 |
3061. | 1157812215     3908   36254 |
3075. | 1090508535     3920   36845 |
      |-----------------------------|
3085. | 1158615971     3933   37490 |
3088. | 1225587807     3938   37873 |
3089. | 1225617051     3941   37916 |
3097. | 1225819759     3946   38166 |
3098. | 1225870691     3947   38267 |
      |-----------------------------|
3105. | 1226579931     3967   39320 |
3108. | 1293594615     3976   39819 |
3116. | 1293812211     3982   40147 |
3120. | 1293863207     3985   40225 |
3121. | 1293931895     3987   40326 |
      |-----------------------------|
3122. | 1294063811     3991   40537 |
3125. | 1361381095     4006   41203 |
3140. | 1362365731     4035   42738 |
3150. | 1429749659     4052   43465 |
3153. | 1429879527     4055   43659 |
      |-----------------------------|
3159. | 1430286171     4069   44244 |
3160. | 1430430327     4074   44527 |
3166. | 1497557887     4081   44934 |
3167. | 1497649715     4086   45070 |
3173. | 1497784331     4090   45271 |
      |-----------------------------|
3174. | 1497850291     4092   45369 |
3175. | 1501895892     4093   45476 |
3176. | 1497999887     4095   45593 |
3177. | 1498062447     4097   45694 |
3182. | 1498265087     4103   45995 |
      |-----------------------------|
3183. | 1498363007     4106   46139 |
3184. | 1498469087     4110   46305 |
3185. | 1565363407     4114   46404 |
3189. | 1568651376     4117   46563 |
3190. | 1565561967     4119   46696 |
      |-----------------------------|
3193. | 1565672819     4122   46860 |
3201. | 1565969287     4131   47311 |
3202. | 1566066527     4135   47469 |
3212. | 1566371855     4144   47927 |
3213. | 1566409247     4145   47982 |
      |-----------------------------|
3214. | 1566436447     4147   48022 |
3215. | 1633486487     4149   48298 |
3216. | 1633691847     4159   48601 |
3219. | 1637569290     4161   48765 |
3224. | 1634125687     4170   49246 |
      |-----------------------------|
3227. | 1634333091     4175   49551 |
3228. | 1634503767     4181   49867 |
3231. | 1090577891     4189   50280 |
3234. | 1294506487     4192   50477 |
3235. | 1430543215     4194   50594 |
      |-----------------------------|
3241. | 1566552071     4196   50766 |
3249. |  273566727     4228   50998 |
3257. | 1293563331     4768   51576 |
3258. | 1497538847     4875   51692 |
      +-----------------------------+

.         
.         * Advice from Longhi and Nandi is to merge the strata with the adjace
> nt ones.
.         
.         bysort strata: keep if _n==1
(1,780 observations deleted)

.         sort strata

.         generate newstrata =strata[_n+1] if n_psu==1
(1,044 missing values generated)

.         fre newstrata

newstrata
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   36    |          1       0.06       0.17       0.17
        54    |          1       0.06       0.17       0.33
        115   |          1       0.06       0.17       0.50
        134   |          1       0.06       0.17       0.67
        2002  |          1       0.06       0.17       0.83
        2003  |          1       0.06       0.17       1.00
        2005  |          1       0.06       0.17       1.17
        2006  |          1       0.06       0.17       1.34
        2008  |          1       0.06       0.17       1.50
        2009  |          1       0.06       0.17       1.67
        2013  |          1       0.06       0.17       1.84
        2017  |          1       0.06       0.17       2.00
        2020  |          1       0.06       0.17       2.17
        2026  |          1       0.06       0.17       2.34
        2027  |          1       0.06       0.17       2.50
        2028  |          1       0.06       0.17       2.67
        2034  |          1       0.06       0.17       2.84
        2036  |          1       0.06       0.17       3.01
        2039  |          1       0.06       0.17       3.17
        2040  |          1       0.06       0.17       3.34
        :     |          :          :          :          :
        4121  |          1       0.06       0.17      96.83
        4124  |          1       0.06       0.17      96.99
        4135  |          1       0.06       0.17      97.16
        4137  |          1       0.06       0.17      97.33
        4145  |          1       0.06       0.17      97.50
        4147  |          1       0.06       0.17      97.66
        4149  |          1       0.06       0.17      97.83
        4159  |          1       0.06       0.17      98.00
        4160  |          1       0.06       0.17      98.16
        4162  |          1       0.06       0.17      98.33
        4172  |          1       0.06       0.17      98.50
        4181  |          1       0.06       0.17      98.66
        4184  |          1       0.06       0.17      98.83
        4191  |          1       0.06       0.17      99.00
        4194  |          1       0.06       0.17      99.17
        4195  |          1       0.06       0.17      99.33
        4197  |          1       0.06       0.17      99.50
        4520  |          1       0.06       0.17      99.67
        4875  |          1       0.06       0.17      99.83
        5121  |          1       0.06       0.17     100.00
        Total |        599      36.46     100.00           
Missing .     |       1044      63.54                      
Total         |       1643     100.00                      
-----------------------------------------------------------

.         list pidp strata psu newstrata if n_psu==1

      +----------------------------------------+
      |       pidp   strata     psu   newstr~a |
      |----------------------------------------|
  35. |  818917885       35     134         36 |
  53. |  477283165       53     186         54 |
  89. |  490097085      114     447        115 |
 103. |  619908498      133     507        134 |
 123. | 1224002047     2001    2001       2002 |
      |----------------------------------------|
 124. |  136008847     2002    2004       2003 |
 126. |  340002047     2004    2007       2005 |
 127. |  476000687     2005    2010       2006 |
 129. |  884010211     2007    2014       2008 |
 130. | 1292021164     2008    2016       2009 |
      |----------------------------------------|
 133. | 1360000691     2012    2024       2013 |
 136. |  340016331     2016    2031       2017 |
 139. |  884023811     2019    2038       2020 |
 145. | 1224032659     2025    2049       2026 |
 146. |  136036731     2026    2052       2027 |
      |----------------------------------------|
 147. | 1020163964     2027    2054       2028 |
 153. |  612029927     2033    2066       2034 |
 155. |  952176882     2035    2070       2036 |
 158. | 1632040811     2038    2075       2039 |
 159. | 1028486492     2039    2078       2040 |
      |----------------------------------------|
 160. | 1428048287     2040    2080       2041 |
 168. |  408038767     2048    2095       2049 |
 170. |  136050331     2050    2100       2051 |
 173. | 1088060527     2053    2105       2054 |
 177. |  612055087     2057    2114       2058 |
      |----------------------------------------|
 180. |  408050331     2060    2119       2061 |
 187. |  680072767     2067    2133       2068 |
 194. | 1632081611     2074    2147       2075 |
 195. |  748076871     2075    2149       2076 |
 196. |  340080931     2076    2151       2077 |
      |----------------------------------------|
 197. |  476085011     2077    2154       2078 |
 198. | 1496557684     2078    2155       2079 |
 203. |  952064611     2083    2166       2085 |
 207. | 1428095887     2088    2176       2089 |
 209. | 1496435290     2090    2179       2091 |
      |----------------------------------------|
 211. |  272091807     2092    2183       2093 |
 216. | 1632103371     2098    2195       2099 |
 218. | 1428110167     2100    2200       2102 |
 219. |   68105407     2102    2204       2104 |
 220. |  272102007     2104    2207       2106 |
      |----------------------------------------|
 221. |  204100651     2106    2212       2107 |
 224. | 1224120367     2109    2217       2110 |
 225. | 1632119691     2110    2219       2111 |
 226. |  748113567     2111    2221       2112 |
 235. | 1360117647     2120    2240       2121 |
      |----------------------------------------|
 238. |  748134651     2123    2245       2124 |
 239. |  340131247     2124    2247       2125 |
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1303. |  273273651     3256    4511       3257 |
1306. |  817275687     3259    4517       3261 |
1310. | 1429321927     3264    4528       3265 |
1311. | 1089247811     3265    4529       3267 |
1313. | 1293271607     3268    4536       3269 |
      |----------------------------------------|
1315. | 1565302211     3270    4539       3271 |
1317. | 1361291331     3272    4544       3273 |
1319. |  137309007     3274    4548       3276 |
1320. | 1429330767     3276    4552       3277 |
1321. | 1089259435     3277    4553       3278 |
      |----------------------------------------|
1323. |  681304251     3279    4557       3280 |
1333. |  477316487     3289    4578       3290 |
1336. |  273319891     3292    4583       3293 |
1337. |  616515298     3293    4586       3294 |
1342. |  137325327     3298    4596       3299 |
      |----------------------------------------|
1343. |  749322615     3299    4597       3300 |
1347. |  885303571     3303    4606       3304 |
1351. |  953334851     3307    4614       3308 |
1353. |  545334851     3309    4618       3310 |
1357. |  477346407     3313    4626       3314 |
      |----------------------------------------|
1359. |  681345051     3315    4629       3317 |
1365. |   69391287     3322    7101       3324 |
1368. |   69734691     3332    7614       3337 |
1369. |   69907407     3337    7870       3340 |
1370. |   69955691     3340    7951       3342 |
      |----------------------------------------|
1371. |   70058367     3342    8117       3343 |
1373. |   70337167     3349    8537       3360 |
1374. |  137443659     3360    8999       3361 |
1375. |  137446367     3361    9003       3364 |
1376. |  141236174     3364    9252       3366 |
      |----------------------------------------|
1377. |  137668731     3366    9332       3369 |
1378. |  137770047     3369    9481       3371 |
1379. |  144405564     3371    9622       3372 |
1381. |  138097811     3377    9963       3382 |
1382. |  138277331     3382   10236       3384 |
      |----------------------------------------|
1383. |  138401087     3384   10418       3386 |
1384. |  138519411     3386   10595       3393 |
1385. |  205421891     3393   10964       3399 |
1386. |  205529327     3399   11128       3401 |
1387. |  205566727     3401   11187       3406 |
      |----------------------------------------|
1388. |  210426248     3406   11420       3409 |
1390. |  205834651     3410   11533       3411 |
1392. |  206105971     3417   11942       3419 |
1393. |  206149487     3419   12008       3421 |
1394. |  206224287     3421   12118       3433 |
      |----------------------------------------|
1395. |  273477651     3433   12632       3438 |
1401. |  274289567     3460   13793       3462 |
1402. |  280112526     3462   13915       3464 |
1404. |  341432091     3468   14139       3471 |
1405. |  341547007     3471   14308       3473 |
      |----------------------------------------|
1408. |  341676207     3476   14500       3477 |
1410. |  341821727     3479   14714       3481 |
1412. |  345793850     3483   14868       3485 |
1413. |  341995807     3485   14984       3487 |
1414. |  342167847     3487   15194       3495 |
      |----------------------------------------|
1416. |  346039250     3500   15618       3504 |
1417. |  409401487     3504   15735       3505 |
1418. |  409481731     3505   15862       3509 |
1419. |  409647647     3509   16107       3511 |
1421. |  409897207     3519   16476       3520 |
      |----------------------------------------|
1422. |  409940055     3520   16540       3526 |
1423. |  410126371     3526   16834       3531 |
1425. |  477377007     3537   17400       3543 |
1426. |  477495327     3543   17576       3546 |
1427. |  477664671     3546   17827       3548 |
      |----------------------------------------|
1428. |  477703411     3548   17884       3549 |
1429. |  477746927     3549   17953       3551 |
1430. |  477818327     3551   18058       3556 |
1432. |  483459846     3559   18524       3567 |
1433. |  478381367     3567   18906       3571 |
      |----------------------------------------|
1435. |  545501455     3577   19484       3580 |
1440. |  550283288     3587   20063       3589 |
1441. |  546000571     3589   20185       3596 |
1442. |  546285487     3596   20616       3597 |
1444. |  546445287     3599   20857       3604 |
      |----------------------------------------|
1445. |  546658127     3604   21172       3607 |
1450. |  613592567     3618   21719       3620 |
1451. |  613688459     3620   21861       3625 |
1452. |  620581844     3625   22097       3626 |
1453. |  613900611     3626   22175       3633 |
      |----------------------------------------|
1456. |  614503095     3644   23083       3649 |
1457. |  817362051     3649   23390       3655 |
1458. |  681672127     3655   23632       3658 |
1459. |  681725851     3658   23711       3660 |
1461. |  681889727     3662   23952       3664 |
      |----------------------------------------|
1463. |  682171927     3671   24371       3675 |
1464. |  682260327     3675   24501       3678 |
1466. |  682405179     3681   24726       3685 |
1470. |  756051964     3696   25544       3698 |
1472. |  750075371     3702   25931       3707 |
      |----------------------------------------|
1474. |  750294327     3708   26254       3709 |
1475. |  750324931     3709   26299       3714 |
1479. |  817489207     3721   26896       3730 |
1480. |  817867287     3730   27411       3732 |
1483. |  818070611     3736   27726       3738 |
      |----------------------------------------|
1484. |  818147447     3738   27789       3740 |
1485. |  818279367     3740   27995       3747 |
1486. |  818429647     3747   28219       3750 |
1490. |  890895446     3764   28981       3771 |
1491. |  885945491     3771   29370       3775 |
      |----------------------------------------|
1493. |  886139287     3777   29656       3783 |
1494. |  892153364     3783   29891       3786 |
1495. |  886398367     3786   30058       3788 |
1496. |  886458207     3788   30146       3792 |
1498. |  953454531     3793   30322       3797 |
      |----------------------------------------|
1500. |  953736047     3800   30747       3803 |
1501. |  956664898     3803   30978       3806 |
1503. |  954040691     3809   31210       3811 |
1504. |  954121607     3811   31344       3813 |
1507. |  954430331     3820   31860       3825 |
      |----------------------------------------|
1510. | 1021552447     3830   32215       3832 |
1514. | 1022066527     3843   32999       3846 |
1516. | 1022220887     3847   33230       3848 |
1517. | 1022258971     3848   33286       3849 |
1518. | 1022295691     3849   33343       3851 |
      |----------------------------------------|
1519. | 1022430327     3851   33546       3853 |
1521. | 1089583047     3868   34354       3871 |
1523. | 1089706807     3873   34536       3880 |
1525. | 1089942087     3881   34886       3887 |
1526. | 1090218851     3887   35244       3895 |
      |----------------------------------------|
1527. | 1090437807     3895   35586       3897 |
1528. | 1157360007     3897   35686       3900 |
1532. | 1157758487     3905   36170       3907 |
1534. | 1157812215     3908   36254       3910 |
1540. | 1090508535     3920   36845       3922 |
      |----------------------------------------|
1544. | 1158615971     3933   37490       3936 |
1546. | 1225587807     3938   37873       3941 |
1547. | 1225617051     3941   37916       3943 |
1550. | 1225819759     3946   38166       3947 |
1551. | 1225870691     3947   38267       3948 |
      |----------------------------------------|
1555. | 1226579931     3967   39320       3973 |
1557. | 1293594615     3976   39819       3978 |
1560. | 1293812211     3982   40147       3984 |
1562. | 1293863207     3985   40225       3987 |
1563. | 1293931895     3987   40326       3991 |
      |----------------------------------------|
1564. | 1294063811     3991   40537       3999 |
1566. | 1361381095     4006   41203       4009 |
1573. | 1362365731     4035   42738       4039 |
1577. | 1429749659     4052   43465       4053 |
1579. | 1429879527     4055   43659       4057 |
      |----------------------------------------|
1582. | 1430286171     4069   44244       4074 |
1583. | 1430430327     4074   44527       4078 |
1586. | 1497557887     4081   44934       4086 |
1587. | 1497649715     4086   45070       4087 |
1590. | 1497784331     4090   45271       4092 |
      |----------------------------------------|
1591. | 1497850291     4092   45369       4093 |
1592. | 1501895892     4093   45476       4095 |
1593. | 1497999887     4095   45593       4097 |
1594. | 1498062447     4097   45694       4099 |
1597. | 1498265087     4103   45995       4106 |
      |----------------------------------------|
1598. | 1498363007     4106   46139       4110 |
1599. | 1498469087     4110   46305       4114 |
1600. | 1565363407     4114   46404       4116 |
1602. | 1568651376     4117   46563       4119 |
1603. | 1565561967     4119   46696       4121 |
      |----------------------------------------|
1605. | 1565672819     4122   46860       4124 |
1608. | 1565969287     4131   47311       4135 |
1609. | 1566066527     4135   47469       4137 |
1613. | 1566371855     4144   47927       4145 |
1614. | 1566409247     4145   47982       4147 |
      |----------------------------------------|
1615. | 1566436447     4147   48022       4149 |
1616. | 1633486487     4149   48298       4159 |
1617. | 1633691847     4159   48601       4160 |
1619. | 1637569290     4161   48765       4162 |
1622. | 1634125687     4170   49246       4172 |
      |----------------------------------------|
1624. | 1634333091     4175   49551       4181 |
1625. | 1634503767     4181   49867       4184 |
1627. | 1090577891     4189   50280       4191 |
1629. | 1294506487     4192   50477       4194 |
1630. | 1430543215     4194   50594       4195 |
      |----------------------------------------|
1632. | 1566552071     4196   50766       4197 |
1635. |  273566727     4228   50998       4520 |
1638. | 1293563331     4768   51576       4875 |
1639. | 1497538847     4875   51692       5121 |
      +----------------------------------------+

.         keep if n_psu==1
(1,044 observations deleted)

.         keep strata newstrata n_psu

.         merge 1:m strata using "$workingdata/gender.dta"
(label strata already defined)
(label psu already defined)
(label ethnic_lbl already defined)
(label nssec already defined)
(label generations already defined)
(label cohort already defined)
(label sex already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         8,565
        from master                         0  (_merge==1)
        from using                      8,565  (_merge==2)

    Matched                             1,510  (_merge==3)
    -----------------------------------------

.         fre n_psu

n_psu
-------------------------------------------------------
          |      Freq.    Percent      Valid       Cum.
----------+--------------------------------------------
Valid   1 |       1510      14.99     100.00     100.00
Missing . |       8565      85.01                      
Total     |      10075     100.00                      
-------------------------------------------------------

.         replace strata = newstrata if n_psu==1
(1,510 real changes made)

.         drop _merge

.         
. svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

Sampling weights: j_indinui_xw
             VCE: linearized
     Single unit: scaled
        Strata 1: strata
 Sampling unit 1: psu
           FPC 1: <zero>

. svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cg
> m c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,297                          Number of obs   =     10,067
Number of PSUs   = 3,416                          Population size = 9,295.5002
                                                  Design df       =      2,119
                                                  F(19, 2101)     =     179.42
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3167

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2702328   .0268493    10.06   0.000      .217579    .3228865
gender_per~e |   .1026319   .0139788     7.34   0.000     .0752184    .1300454
gender_per~2 |  -.0189667    .009576    -1.98   0.048     -.037746   -.0001874
     age_cgm |  -.0127472   .0010931   -11.66   0.000    -.0148909   -.0106034
    age2_cgm |  -.0009016   .0000711   -12.68   0.000     -.001041   -.0007621
             |
      ethnic |
Mixed/Mul..  |   .0510384   .0622059     0.82   0.412    -.0709525    .1730294
     Indian  |   .0218302   .0362251     0.60   0.547    -.0492102    .0928707
Pakistani..  |  -.1956906   .0541249    -3.62   0.000    -.3018341   -.0895471
    Chinese  |   .1751179   .0910223     1.92   0.055    -.0033845    .3536203
Any other..  |  -.0646881   .0723997    -0.89   0.372    -.2066699    .0772938
Black/Afr..  |  -.0287417   .0614767    -0.47   0.640    -.1493028    .0918193
      Other  |   .2733452   .1400929     1.95   0.051    -.0013889    .5480792
             |
       nssec |
1.2 Highe..  |  -.1917456   .0349158    -5.49   0.000    -.2602185   -.1232727
2 Lower m..  |   -.365915   .0273119   -13.40   0.000    -.4194761    -.312354
3 Interme..  |  -.6313358   .0319606   -19.75   0.000    -.6940133   -.5686583
4 Small e..  |  -1.202829   .0565396   -21.27   0.000    -1.313708    -1.09195
5 Lower s..  |  -.5574836    .031449   -17.73   0.000    -.6191577   -.4958094
6 Semi-ro..  |   -.844068   .0283019   -29.82   0.000    -.8995705   -.7885655
7 Routine..  |  -.9473985   .0359188   -26.38   0.000    -1.017838   -.8769586
             |
       _cons |   7.795486   .0302443   257.75   0.000     7.736175    7.854798
------------------------------------------------------------------------------
Note: 7 strata omitted because they contain no population members.
Note: Variance scaled to handle strata with a single sampling unit.

. 

%%stata

save "$workingdata/TEMPFILE.dta", replace

    bysort strata psu: keep if _n==1
    bysort strata: generate n_psu2 = _N
    fre n_psu2
    list pidp strata psu if n_psu2==1
    
    bysort strata: keep if _n==1
    sort strata
    generate newstrata2 =strata[_n+1] if n_psu2==1
    fre newstrata2
    list pidp strata psu newstrata2 if n_psu2==1
    keep if n_psu2==1
    keep strata newstrata2 n_psu2
    merge 1:m strata using "$workingdata/TEMPFILE.dta"
    drop _merge
    fre n_psu2
    replace strata = newstrata2 if n_psu2==1
    
svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)
svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cgm c.age2_cgm i.ethnic i.nssec 

. 
. save "$workingdata/TEMPFILE.dta", replace
file /Users/scottoatley/Documents/Stata Data and Do/Working Data/TEMPFILE.dta
    saved

. 
.         bysort strata psu: keep if _n==1
(6,652 observations deleted)

.         bysort strata: generate n_psu2 = _N

.         fre n_psu2

n_psu2
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   1     |        260       7.60       7.60       7.60
        2     |       1194      34.88      34.88      42.48
        3     |       1146      33.48      33.48      75.96
        4     |        160       4.67       4.67      80.63
        5     |         50       1.46       1.46      82.09
        6     |         18       0.53       0.53      82.62
        7     |         28       0.82       0.82      83.44
        8     |         16       0.47       0.47      83.90
        18    |         18       0.53       0.53      84.43
        29    |         29       0.85       0.85      85.28
        34    |         34       0.99       0.99      86.27
        85    |         85       2.48       2.48      88.75
        186   |        186       5.43       5.43      94.19
        199   |        199       5.81       5.81     100.00
        Total |       3423     100.00     100.00           
-----------------------------------------------------------

.         list pidp strata psu if n_psu2==1

      +-----------------------------+
      |       pidp   strata     psu |
      |-----------------------------|
 567. | 1224002047     2002    2001 |
 571. |  340010891     2005    2007 |
 575. |  884010211     2008    2014 |
 602. | 1224030607     2026    2049 |
 603. |  136036731     2027    2052 |
      |-----------------------------|
 623. | 1632224528     2039    2075 |
 624. | 1020037411     2040    2078 |
 686. | 1632081611     2075    2147 |
 687. |  748272098     2076    2149 |
 688. |  340080931     2077    2151 |
      |-----------------------------|
 689. |  476085011     2078    2154 |
 724. | 1428110167     2102    2200 |
 725. |   68105407     2104    2204 |
 726. |  272102007     2106    2207 |
 732. | 1224120367     2110    2217 |
      |-----------------------------|
 733. | 1632119691     2111    2219 |
 756. |  748134647     2124    2245 |
 757. |  340131247     2125    2247 |
 769. |  816134651     2132    2261 |
 770. |  408123767     2133    2263 |
      |-----------------------------|
 771. | 1224137371     2134    2265 |
 772. | 1632137367     2135    2267 |
 779. |  680136691     2140    2277 |
 813. |   68167971     2163    2324 |
 875. |   68197891     2199    2396 |
      |-----------------------------|
 908. | 1360218291     2217    2432 |
 934. | 1020254331     2232    2462 |
 940. |  680244807     2236    2469 |
 941. | 1360228491     2237    2472 |
 956. |  476251611     2246    2490 |
      |-----------------------------|
 973. |  952255695     2256    2510 |
 987. |  408259767     2265    2527 |
 988. |  544269971     2266    2530 |
1034. | 1632317567     2291    2579 |
1054. | 1020317567     2304    2606 |
      |-----------------------------|
1055. | 1429496168     2305    2608 |
1056. | 1088297847     2306    2609 |
1086. |  205298976     2323    2644 |
1100. |  680337291     2332    2661 |
1104. | 1564336611     2335    2667 |
      |-----------------------------|
1154. | 1088376051     2366    2729 |
1169. | 1633965968     2375    2747 |
1269. | 1292435891     2429    2856 |
1270. | 1156430447     2430    2857 |
1271. | 1564433851     2431    2859 |
      |-----------------------------|
1275. |  544441327     2434    2866 |
1276. | 1632459007     2435    2867 |
1277. |  750033972     2436    2869 |
1281. | 1496429095     2439    2875 |
1282. |  681782370     2440    2877 |
      |-----------------------------|
1293. |  340459691     2449    2895 |
1309. | 1634169970     2459    2915 |
1319. |  612477367     2466    2930 |
1320. |  206584082     2467    2932 |
1321. |  952467847     2468    2934 |
      |-----------------------------|
1355. |  884494367     2488    2974 |
1382. |  816503207     2504    3005 |
1391. |  680510007     2512    3021 |
1485. |  136580727     2567    3132 |
1503. |  544597731     2578    3154 |
      |-----------------------------|
1517. | 1090359694     2586    3169 |
1547. |  476615407     2606    3210 |
1591. | 1496648051     2631    3259 |
1657. |  887631324     2668    3334 |
1658. | 1292685447     2669    3336 |
      |-----------------------------|
1659. | 1156675247     2670    3337 |
1668. | 1020689531     2676    3350 |
1699. | 1156698371     2694    3385 |
1716. | 1292716731     2705    3408 |
1717. | 1156711291     2706    3409 |
      |-----------------------------|
1718. |  204718771     2707    3412 |
1751. |   68740599     2727    3452 |
1752. |  884741211     2728    3454 |
1780. |  408750047     2745    3487 |
1800. |  544782007     2758    3514 |
      |-----------------------------|
1832. | 1292791531     2777    3552 |
1840. | 1224796967     2782    3561 |
1841. | 1632815331     2783    3563 |
1850. |  272799691     2789    3575 |
1856. | 1224808531     2794    3585 |
      |-----------------------------|
1857. |  136819407     2795    3588 |
1858. |  748856807     2796    3589 |
1862. |  884820087     2801    3598 |
1863. | 1156820091     2802    3601 |
1864. |  204812611     2803    3604 |
      |-----------------------------|
1865. |  819202894     2804    3605 |
1869. |  340830287     2809    3615 |
1870. |  476830291     2810    3618 |
1871. |   68823487     2811    3620 |
1875. |  204828247     2815    3628 |
      |-----------------------------|
1882. |  748879251     2820    3637 |
1891. |  136847287     2831    3660 |
1895. | 1092046088     2834    3665 |
1902. |  816842527     2840    3677 |
1903. |  408854091     2841    3679 |
      |-----------------------------|
1911. | 1496864291     2847    3691 |
1912. |  884867011     2849    3694 |
1913. | 1156867691     2850    3697 |
1920. | 1428881287     2858    3712 |
1921. | 1496877211     2859    3715 |
      |-----------------------------|
1925. |  612878567     2862    3722 |
1926. | 1564857499     2863    3723 |
1927. |  816865647     2864    3725 |
1928. |  408877211     2865    3727 |
1929. | 1224899651     2866    3729 |
      |-----------------------------|
1947. | 1632910531     2879    3755 |
1974. | 1564898287     2899    3795 |
1980. | 1632937047     2903    3803 |
1981. | 1020944531     2904    3806 |
1994. | 1224961527     2914    3825 |
      |-----------------------------|
2038. |  680929567     2944    3885 |
2039. |  272964247     2945    3887 |
2058. |  684916482     2956    3909 |
2059. |  272973087     2957    3911 |
2137. | 1225056731     3002    4001 |
      |-----------------------------|
2152. |  137033607     3011    4020 |
2162. |  277671282     3017    4031 |
2179. |   69047887     3027    4052 |
2216. |  749102975     3048    4093 |
2233. |  545084607     3058    4114 |
      |-----------------------------|
2256. |  749134931     3072    4141 |
2272. | 1633131527     3083    4163 |
2303. |  273122691     3103    4199 |
2304. |  957195328     3104    4206 |
2305. |  409119967     3105    4207 |
      |-----------------------------|
2325. | 1431693858     3117    4232 |
2326. |  545141727     3118    4234 |
2327. | 1633164847     3119    4235 |
2328. | 1021160087     3120    4238 |
2339. |  205144447     3127    4252 |
      |-----------------------------|
2340. |  953158055     3128    4254 |
2350. | 1497138331     3135    4267 |
2358. |  822582484     3140    4277 |
2359. |  409158731     3141    4279 |
2363. |  341165531     3145    4287 |
      |-----------------------------|
2364. | 1089149891     3146    4289 |
2365. |   69173687     3147    4292 |
2366. |  885143087     3148    4294 |
2370. | 1565166211     3151    4299 |
2376. | 1633204967     3155    4307 |
      |-----------------------------|
2420. | 1094528084     3182    4361 |
2421. | 1497178447     3183    4363 |
2432. | 1225236247     3190    4377 |
2433. |  140175376     3191    4380 |
2434. |  749219247     3192    4381 |
      |-----------------------------|
2438. | 1502283366     3195    4387 |
2439. |  681213131     3196    4389 |
2445. |  953224703     3200    4398 |
2446. | 1361213807     3201    4400 |
2447. | 1225260735     3202    4401 |
      |-----------------------------|
2448. |  137232851     3203    4404 |
2449. |  885211091     3204    4406 |
2450. |  341229451     3205    4407 |
2463. |  545247127     3214    4426 |
2467. |  345284368     3217    4431 |
      |-----------------------------|
2471. |  681247807     3220    4437 |
2472. | 1293222647     3221    4440 |
2473. | 1157242367     3222    4441 |
2474. | 1565248495     3225    4443 |
2475. |  545260727     3226    4450 |
      |-----------------------------|
2476. |  137257331     3227    4452 |
2477. | 1021253931     3234    4454 |
2478. |  205253247     3235    4468 |
2479. |  817255291     3236    4469 |
2480. |  409255971     3237    4471 |
      |-----------------------------|
2481. |  545270927     3238    4474 |
2482. | 1497215167     3239    4475 |
2486. | 1163078884     3242    4481 |
2487. |   69269567     3243    4484 |
2491. | 1157277795     3246    4489 |
      |-----------------------------|
2492. | 1565263447     3248    4491 |
2505. |  885260727     3256    4510 |
2518. | 1429321927     3265    4528 |
2531. |  137309007     3276    4548 |
2532. | 1429333487     3277    4552 |
      |-----------------------------|
2560. |  273319891     3293    4583 |
2570. |  137322611     3299    4596 |
2799. |   69734691     3337    7614 |
2800. |   69907407     3340    7870 |
2801. |   69955691     3342    7951 |
      |-----------------------------|
2805. |   70337167     3360    8537 |
2806. |  137443659     3361    8999 |
2807. |  137446367     3364    9003 |
2808. |  141236174     3366    9252 |
2809. |  137668731     3369    9332 |
      |-----------------------------|
2810. |  137770047     3371    9481 |
2814. |  138097807     3382    9963 |
2815. |  138277331     3384   10236 |
2816. |  138401087     3386   10418 |
2817. |  138519411     3393   10595 |
      |-----------------------------|
2818. |  205421891     3399   10964 |
2819. |  205529327     3401   11128 |
2820. |  205566727     3406   11187 |
2828. |  206105971     3419   11942 |
2829. |  206149487     3421   12008 |
      |-----------------------------|
2830. |  206224291     3433   12118 |
2844. |  274289567     3462   13793 |
2850. |  341432091     3471   14139 |
2863. |  345793850     3485   14868 |
2864. |  341995807     3487   14984 |
      |-----------------------------|
2869. |  342455487     3504   15618 |
2870. |  409401487     3505   15735 |
2871. |  409481731     3509   15862 |
2876. |  409897207     3520   16476 |
2877. |  409940055     3526   16540 |
      |-----------------------------|
2881. |  477377007     3543   17400 |
2882. |  477495327     3546   17576 |
2883. |  477664671     3548   17827 |
2884. |  477703411     3549   17884 |
2885. |  477746927     3551   17953 |
      |-----------------------------|
2889. |  483459846     3567   18524 |
2906. |  545918975     3589   20063 |
2907. |  546000567     3596   20185 |
2911. |  546445287     3604   20857 |
2924. |  613592567     3620   21719 |
      |-----------------------------|
2925. |  613688459     3625   21861 |
2926. |  620581844     3626   22097 |
2932. |  614503095     3649   23083 |
2933. |  817362051     3655   23390 |
2934. |  681672127     3658   23632 |
      |-----------------------------|
2941. |  682171927     3675   24371 |
2961. |  750294327     3709   26254 |
2970. |  817489207     3730   26896 |
2976. |  818070611     3738   27726 |
2977. |  818147447     3740   27789 |
      |-----------------------------|
2978. |  818279367     3747   27995 |
2987. |  890895446     3771   28981 |
2991. |  886139287     3783   29656 |
2992. |  892153364     3786   29891 |
2993. |  886398367     3788   30058 |
      |-----------------------------|
3002. |  953736047     3803   30747 |
3006. |  954040691     3811   31210 |
3031. | 1022220887     3848   33230 |
3032. | 1022258967     3849   33286 |
3033. | 1022295691     3851   33343 |
      |-----------------------------|
3045. | 1089942087     3887   34886 |
3046. | 1090218851     3895   35244 |
3047. | 1090437807     3897   35586 |
3088. | 1225587807     3941   37873 |
3097. | 1225819759     3947   38166 |
      |-----------------------------|
3120. | 1293863207     3987   40225 |
3121. | 1293931887     3991   40326 |
3159. | 1430286167     4074   44244 |
3166. | 1497557887     4086   44934 |
3173. | 1497784331     4092   45271 |
      |-----------------------------|
3174. | 1497850291     4093   45369 |
3175. | 1501895892     4095   45476 |
3176. | 1497999887     4097   45593 |
3182. | 1498265087     4106   45995 |
3183. | 1498363007     4110   46139 |
      |-----------------------------|
3184. | 1498469087     4114   46305 |
3189. | 1565471543     4119   46563 |
3201. | 1565969287     4135   47311 |
3212. | 1566371855     4145   47927 |
3213. | 1566409247     4147   47982 |
      |-----------------------------|
3214. | 1566436447     4149   48022 |
3215. | 1633486491     4159   48298 |
3227. | 1634333087     4181   49551 |
3234. | 1294506487     4194   50477 |
3257. | 1293563331     4875   51576 |
      +-----------------------------+

.         
.         bysort strata: keep if _n==1
(2,119 observations deleted)

.         sort strata

.         generate newstrata2 =strata[_n+1] if n_psu2==1
(1,044 missing values generated)

.         fre newstrata2

newstrata2
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   2003  |          1       0.08       0.38       0.38
        2006  |          1       0.08       0.38       0.77
        2009  |          1       0.08       0.38       1.15
        2027  |          1       0.08       0.38       1.54
        2028  |          1       0.08       0.38       1.92
        2040  |          1       0.08       0.38       2.31
        2041  |          1       0.08       0.38       2.69
        2076  |          1       0.08       0.38       3.08
        2077  |          1       0.08       0.38       3.46
        2078  |          1       0.08       0.38       3.85
        2079  |          1       0.08       0.38       4.23
        2104  |          1       0.08       0.38       4.62
        2106  |          1       0.08       0.38       5.00
        2107  |          1       0.08       0.38       5.38
        2111  |          1       0.08       0.38       5.77
        2112  |          1       0.08       0.38       6.15
        2125  |          1       0.08       0.38       6.54
        2126  |          1       0.08       0.38       6.92
        2133  |          1       0.08       0.38       7.31
        2134  |          1       0.08       0.38       7.69
        :     |          :          :          :          :
        3991  |          1       0.08       0.38      92.69
        3999  |          1       0.08       0.38      93.08
        4078  |          1       0.08       0.38      93.46
        4087  |          1       0.08       0.38      93.85
        4093  |          1       0.08       0.38      94.23
        4095  |          1       0.08       0.38      94.62
        4097  |          1       0.08       0.38      95.00
        4099  |          1       0.08       0.38      95.38
        4110  |          1       0.08       0.38      95.77
        4114  |          1       0.08       0.38      96.15
        4116  |          1       0.08       0.38      96.54
        4121  |          1       0.08       0.38      96.92
        4137  |          1       0.08       0.38      97.31
        4147  |          1       0.08       0.38      97.69
        4149  |          1       0.08       0.38      98.08
        4159  |          1       0.08       0.38      98.46
        4160  |          1       0.08       0.38      98.85
        4184  |          1       0.08       0.38      99.23
        4195  |          1       0.08       0.38      99.62
        5121  |          1       0.08       0.38     100.00
        Total |        260      19.94     100.00           
Missing .     |       1044      80.06                      
Total         |       1304     100.00                      
-----------------------------------------------------------

.         list pidp strata psu newstrata2 if n_psu2==1

      +----------------------------------------+
      |       pidp   strata     psu   newstr~2 |
      |----------------------------------------|
 119. | 1224002047     2002    2001       2003 |
 121. |  340010891     2005    2007       2006 |
 123. |  884010211     2008    2014       2009 |
 135. | 1224030607     2026    2049       2027 |
 136. |  136036731     2027    2052       2028 |
      |----------------------------------------|
 145. | 1632224528     2039    2075       2040 |
 146. | 1020037411     2040    2078       2041 |
 174. | 1632081611     2075    2147       2076 |
 175. |  748272098     2076    2149       2077 |
 176. |  340080931     2077    2151       2078 |
      |----------------------------------------|
 177. |  476085011     2078    2154       2079 |
 192. | 1428110167     2102    2200       2104 |
 193. |   68105407     2104    2204       2106 |
 194. |  272102007     2106    2207       2107 |
 197. | 1224120367     2110    2217       2111 |
      |----------------------------------------|
 198. | 1632119691     2111    2219       2112 |
 209. |  748134647     2124    2245       2125 |
 210. |  340131247     2125    2247       2126 |
 216. |  816134651     2132    2261       2133 |
 217. |  408123767     2133    2263       2134 |
      |----------------------------------------|
 218. | 1224137371     2134    2265       2135 |
 219. | 1632137367     2135    2267       2136 |
 222. |  680136691     2140    2277       2141 |
 238. |   68167971     2163    2324       2164 |
 266. |   68197891     2199    2396       2200 |
      |----------------------------------------|
 282. | 1360218291     2217    2432       2218 |
 294. | 1020254331     2232    2462       2233 |
 297. |  680244807     2236    2469       2237 |
 298. | 1360228491     2237    2472       2238 |
 305. |  476251611     2246    2490       2247 |
      |----------------------------------------|
 313. |  952255695     2256    2510       2257 |
 319. |  408259767     2265    2527       2266 |
 320. |  544269971     2266    2530       2267 |
 342. | 1632317567     2291    2579       2292 |
 351. | 1020317567     2304    2606       2305 |
      |----------------------------------------|
 352. | 1429496168     2305    2608       2306 |
 353. | 1088297847     2306    2609       2307 |
 367. |  205298976     2323    2644       2324 |
 373. |  680337291     2332    2661       2333 |
 375. | 1564336611     2335    2667       2336 |
      |----------------------------------------|
 396. | 1088376051     2366    2729       2367 |
 403. | 1633965968     2375    2747       2376 |
 451. | 1292435891     2429    2856       2430 |
 452. | 1156430447     2430    2857       2431 |
 453. | 1564433851     2431    2859       2432 |
      |----------------------------------------|
 455. |  544441327     2434    2866       2435 |
 456. | 1632459007     2435    2867       2436 |
 457. |  750033972     2436    2869       2437 |
 459. | 1496429095     2439    2875       2440 |
 460. |  681782370     2440    2877       2441 |
      |----------------------------------------|
 465. |  340459691     2449    2895       2450 |
 473. | 1634169970     2459    2915       2460 |
 478. |  612477367     2466    2930       2467 |
 479. |  206584082     2467    2932       2468 |
 480. |  952467847     2468    2934       2469 |
      |----------------------------------------|
 495. |  884494367     2488    2974       2489 |
 507. |  816503207     2504    3005       2505 |
 511. |  680510007     2512    3021       2513 |
 553. |  136580727     2567    3132       2568 |
 561. |  544597731     2578    3154       2579 |
      |----------------------------------------|
 568. | 1090359694     2586    3169       2587 |
 581. |  476615407     2606    3210       2607 |
 601. | 1496648051     2631    3259       2632 |
 631. |  887631324     2668    3334       2669 |
 632. | 1292685447     2669    3336       2670 |
      |----------------------------------------|
 633. | 1156675247     2670    3337       2671 |
 637. | 1020689531     2676    3350       2677 |
 652. | 1156698371     2694    3385       2695 |
 660. | 1292716731     2705    3408       2706 |
 661. | 1156711291     2706    3409       2707 |
      |----------------------------------------|
 662. |  204718771     2707    3412       2708 |
 676. |   68740599     2727    3452       2728 |
 677. |  884741211     2728    3454       2729 |
 690. |  408750047     2745    3487       2746 |
 699. |  544782007     2758    3514       2759 |
      |----------------------------------------|
 714. | 1292791531     2777    3552       2778 |
 718. | 1224796967     2782    3561       2783 |
 719. | 1632815331     2783    3563       2784 |
 723. |  272799691     2789    3575       2790 |
 726. | 1224808531     2794    3585       2795 |
      |----------------------------------------|
 727. |  136819407     2795    3588       2796 |
 728. |  748856807     2796    3589       2798 |
 730. |  884820087     2801    3598       2802 |
 731. | 1156820091     2802    3601       2803 |
 732. |  204812611     2803    3604       2804 |
      |----------------------------------------|
 733. |  819202894     2804    3605       2805 |
 735. |  340830287     2809    3615       2810 |
 736. |  476830291     2810    3618       2811 |
 737. |   68823487     2811    3620       2813 |
 739. |  204828247     2815    3628       2816 |
      |----------------------------------------|
 742. |  748879251     2820    3637       2823 |
 746. |  136847287     2831    3660       2832 |
 748. | 1092046088     2834    3665       2835 |
 751. |  816842527     2840    3677       2841 |
 752. |  408854091     2841    3679       2842 |
      |----------------------------------------|
 756. | 1496864291     2847    3691       2849 |
 757. |  884867011     2849    3694       2850 |
 758. | 1156867691     2850    3697       2851 |
 761. | 1428881287     2858    3712       2859 |
 762. | 1496877211     2859    3715       2860 |
      |----------------------------------------|
 764. |  612878567     2862    3722       2863 |
 765. | 1564857499     2863    3723       2864 |
 766. |  816865647     2864    3725       2865 |
 767. |  408877211     2865    3727       2866 |
 768. | 1224899651     2866    3729       2867 |
      |----------------------------------------|
 776. | 1632910531     2879    3755       2882 |
 788. | 1564898287     2899    3795       2900 |
 791. | 1632937047     2903    3803       2904 |
 792. | 1020944531     2904    3806       2905 |
 798. | 1224961527     2914    3825       2915 |
      |----------------------------------------|
 818. |  680929567     2944    3885       2945 |
 819. |  272964247     2945    3887       2946 |
 828. |  684916482     2956    3909       2957 |
 829. |  272973087     2957    3911       2958 |
 863. | 1225056731     3002    4001       3003 |
      |----------------------------------------|
 870. |  137033607     3011    4020       3012 |
 875. |  277671282     3017    4031       3018 |
 883. |   69047887     3027    4052       3028 |
 900. |  749102975     3048    4093       3049 |
 908. |  545084607     3058    4114       3059 |
      |----------------------------------------|
 918. |  749134931     3072    4141       3073 |
 925. | 1633131527     3083    4163       3084 |
 939. |  273122691     3103    4199       3104 |
 940. |  957195328     3104    4206       3105 |
 941. |  409119967     3105    4207       3106 |
      |----------------------------------------|
 950. | 1431693858     3117    4232       3118 |
 951. |  545141727     3118    4234       3119 |
 952. | 1633164847     3119    4235       3120 |
 953. | 1021160087     3120    4238       3121 |
 958. |  205144447     3127    4252       3128 |
      |----------------------------------------|
 959. |  953158055     3128    4254       3129 |
 963. | 1497138331     3135    4267       3136 |
 967. |  822582484     3140    4277       3141 |
 968. |  409158731     3141    4279       3142 |
 970. |  341165531     3145    4287       3146 |
      |----------------------------------------|
 971. | 1089149891     3146    4289       3147 |
 972. |   69173687     3147    4292       3148 |
 973. |  885143087     3148    4294       3149 |
 975. | 1565166211     3151    4299       3152 |
 978. | 1633204967     3155    4307       3156 |
      |----------------------------------------|
 997. | 1094528084     3182    4361       3183 |
 998. | 1497178447     3183    4363       3184 |
1003. | 1225236247     3190    4377       3191 |
1004. |  140175376     3191    4380       3192 |
1005. |  749219247     3192    4381       3193 |
      |----------------------------------------|
1007. | 1502283366     3195    4387       3196 |
1008. |  681213131     3196    4389       3197 |
1011. |  953224703     3200    4398       3201 |
1012. | 1361213807     3201    4400       3202 |
1013. | 1225260735     3202    4401       3203 |
      |----------------------------------------|
1014. |  137232851     3203    4404       3204 |
1015. |  885211091     3204    4406       3205 |
1016. |  341229451     3205    4407       3206 |
1022. |  545247127     3214    4426       3215 |
1024. |  345284368     3217    4431       3218 |
      |----------------------------------------|
1026. |  681247807     3220    4437       3221 |
1027. | 1293222647     3221    4440       3222 |
1028. | 1157242367     3222    4441       3225 |
1029. | 1565248495     3225    4443       3226 |
1030. |  545260727     3226    4450       3227 |
      |----------------------------------------|
1031. |  137257331     3227    4452       3234 |
1032. | 1021253931     3234    4454       3235 |
1033. |  205253247     3235    4468       3236 |
1034. |  817255291     3236    4469       3237 |
1035. |  409255971     3237    4471       3238 |
      |----------------------------------------|
1036. |  545270927     3238    4474       3239 |
1037. | 1497215167     3239    4475       3240 |
1039. | 1163078884     3242    4481       3243 |
1040. |   69269567     3243    4484       3244 |
1042. | 1157277795     3246    4489       3248 |
      |----------------------------------------|
1043. | 1565263447     3248    4491       3249 |
1049. |  885260727     3256    4510       3257 |
1055. | 1429321927     3265    4528       3267 |
1060. |  137309007     3276    4548       3277 |
1061. | 1429333487     3277    4552       3278 |
      |----------------------------------------|
1074. |  273319891     3293    4583       3294 |
1079. |  137322611     3299    4596       3300 |
1098. |   69734691     3337    7614       3340 |
1099. |   69907407     3340    7870       3342 |
1100. |   69955691     3342    7951       3343 |
      |----------------------------------------|
1102. |   70337167     3360    8537       3361 |
1103. |  137443659     3361    8999       3364 |
1104. |  137446367     3364    9003       3366 |
1105. |  141236174     3366    9252       3369 |
1106. |  137668731     3369    9332       3371 |
      |----------------------------------------|
1107. |  137770047     3371    9481       3372 |
1109. |  138097807     3382    9963       3384 |
1110. |  138277331     3384   10236       3386 |
1111. |  138401087     3386   10418       3393 |
1112. |  138519411     3393   10595       3399 |
      |----------------------------------------|
1113. |  205421891     3399   10964       3401 |
1114. |  205529327     3401   11128       3406 |
1115. |  205566727     3406   11187       3409 |
1118. |  206105971     3419   11942       3421 |
1119. |  206149487     3421   12008       3433 |
      |----------------------------------------|
1120. |  206224291     3433   12118       3438 |
1126. |  274289567     3462   13793       3464 |
1128. |  341432091     3471   14139       3473 |
1133. |  345793850     3485   14868       3487 |
1134. |  341995807     3487   14984       3495 |
      |----------------------------------------|
1136. |  342455487     3504   15618       3505 |
1137. |  409401487     3505   15735       3509 |
1138. |  409481731     3509   15862       3511 |
1140. |  409897207     3520   16476       3526 |
1141. |  409940055     3526   16540       3531 |
      |----------------------------------------|
1143. |  477377007     3543   17400       3546 |
1144. |  477495327     3546   17576       3548 |
1145. |  477664671     3548   17827       3549 |
1146. |  477703411     3549   17884       3551 |
1147. |  477746927     3551   17953       3556 |
      |----------------------------------------|
1149. |  483459846     3567   18524       3571 |
1155. |  545918975     3589   20063       3596 |
1156. |  546000567     3596   20185       3597 |
1158. |  546445287     3604   20857       3607 |
1163. |  613592567     3620   21719       3625 |
      |----------------------------------------|
1164. |  613688459     3625   21861       3626 |
1165. |  620581844     3626   22097       3633 |
1168. |  614503095     3649   23083       3655 |
1169. |  817362051     3655   23390       3658 |
1170. |  681672127     3658   23632       3660 |
      |----------------------------------------|
1173. |  682171927     3675   24371       3678 |
1180. |  750294327     3709   26254       3714 |
1184. |  817489207     3730   26896       3732 |
1187. |  818070611     3738   27726       3740 |
1188. |  818147447     3740   27789       3747 |
      |----------------------------------------|
1189. |  818279367     3747   27995       3750 |
1193. |  890895446     3771   28981       3775 |
1195. |  886139287     3783   29656       3786 |
1196. |  892153364     3786   29891       3788 |
1197. |  886398367     3788   30058       3792 |
      |----------------------------------------|
1200. |  953736047     3803   30747       3806 |
1202. |  954040691     3811   31210       3813 |
1211. | 1022220887     3848   33230       3849 |
1212. | 1022258967     3849   33286       3851 |
1213. | 1022295691     3851   33343       3853 |
      |----------------------------------------|
1217. | 1089942087     3887   34886       3895 |
1218. | 1090218851     3895   35244       3897 |
1219. | 1090437807     3897   35586       3900 |
1233. | 1225587807     3941   37873       3943 |
1236. | 1225819759     3947   38166       3948 |
      |----------------------------------------|
1244. | 1293863207     3987   40225       3991 |
1245. | 1293931887     3991   40326       3999 |
1259. | 1430286167     4074   44244       4078 |
1262. | 1497557887     4086   44934       4087 |
1265. | 1497784331     4092   45271       4093 |
      |----------------------------------------|
1266. | 1497850291     4093   45369       4095 |
1267. | 1501895892     4095   45476       4097 |
1268. | 1497999887     4097   45593       4099 |
1271. | 1498265087     4106   45995       4110 |
1272. | 1498363007     4110   46139       4114 |
      |----------------------------------------|
1273. | 1498469087     4114   46305       4116 |
1275. | 1565471543     4119   46563       4121 |
1279. | 1565969287     4135   47311       4137 |
1283. | 1566371855     4145   47927       4147 |
1284. | 1566409247     4147   47982       4149 |
      |----------------------------------------|
1285. | 1566436447     4149   48022       4159 |
1286. | 1633486491     4159   48298       4160 |
1291. | 1634333087     4181   49551       4184 |
1294. | 1294506487     4194   50477       4195 |
1300. | 1293563331     4875   51576       5121 |
      +----------------------------------------+

.         keep if n_psu2==1
(1,044 observations deleted)

.         keep strata newstrata2 n_psu2

.         merge 1:m strata using "$workingdata/TEMPFILE.dta"
(label sex already defined)
(label cohort already defined)
(label generations already defined)
(label nssec already defined)
(label ethnic_lbl already defined)
(label psu already defined)
(label strata already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         9,463
        from master                         0  (_merge==1)
        from using                      9,463  (_merge==2)

    Matched                               612  (_merge==3)
    -----------------------------------------

.         drop _merge

.         fre n_psu2

n_psu2
-------------------------------------------------------
          |      Freq.    Percent      Valid       Cum.
----------+--------------------------------------------
Valid   1 |        612       6.07     100.00     100.00
Missing . |       9463      93.93                      
Total     |      10075     100.00                      
-------------------------------------------------------

.         replace strata = newstrata2 if n_psu2==1
(612 real changes made)

.         
. svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

Sampling weights: j_indinui_xw
             VCE: linearized
     Single unit: scaled
        Strata 1: strata
 Sampling unit 1: psu
           FPC 1: <zero>

. svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cg
> m c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,167                          Number of obs   =     10,073
Number of PSUs   = 3,421                          Population size = 9,295.5002
                                                  Design df       =      2,254
                                                  F(19, 2236)     =     190.32
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3167

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2702328   .0259903    10.40   0.000     .2192654    .3212001
gender_per~e |   .1026319   .0133929     7.66   0.000     .0763681    .1288957
gender_per~2 |  -.0189667   .0092777    -2.04   0.041    -.0371605   -.0007729
     age_cgm |  -.0127472   .0010481   -12.16   0.000    -.0148025   -.0106918
    age2_cgm |  -.0009016   .0000687   -13.12   0.000    -.0010364   -.0007668
             |
      ethnic |
Mixed/Mul..  |   .0510384    .059241     0.86   0.389    -.0651342     .167211
     Indian  |   .0218302   .0353664     0.62   0.537    -.0475239    .0911844
Pakistani..  |  -.1956906   .0517353    -3.78   0.000    -.2971445   -.0942367
    Chinese  |   .1751179   .0892427     1.96   0.050     .0001115    .3501243
Any other..  |  -.0646881   .0688436    -0.94   0.348    -.1996916    .0703155
Black/Afr..  |  -.0287417   .0585051    -0.49   0.623    -.1434711    .0859877
      Other  |   .2733452   .1416901     1.93   0.054    -.0045115    .5512018
             |
       nssec |
1.2 Highe..  |  -.1917456   .0337928    -5.67   0.000    -.2580139   -.1254773
2 Lower m..  |   -.365915   .0265227   -13.80   0.000    -.4179265   -.3139036
3 Interme..  |  -.6313358    .030901   -20.43   0.000    -.6919331   -.5707385
4 Small e..  |  -1.202829   .0560045   -21.48   0.000    -1.312655   -1.093003
5 Lower s..  |  -.5574836   .0304359   -18.32   0.000    -.6171688   -.4977983
6 Semi-ro..  |   -.844068   .0278291   -30.33   0.000    -.8986414   -.7894947
7 Routine..  |  -.9473985   .0347923   -27.23   0.000    -1.015627   -.8791702
             |
       _cons |   7.795486   .0295271   264.01   0.000     7.737583    7.853389
------------------------------------------------------------------------------
Note: 2 strata omitted because they contain no population members.
Note: Variance scaled to handle strata with a single sampling unit.

. 

%%stata

save "$workingdata/TEMPFILE.dta", replace

    bysort strata psu: keep if _n==1
    bysort strata: generate n_psu3 = _N
    fre n_psu3
    list pidp strata psu if n_psu3==1
    
    bysort strata: keep if _n==1
    sort strata
    generate newstrata3 =strata[_n+1] if n_psu3==1
    fre newstrata3
    list pidp strata psu newstrata3 if n_psu3==1
    keep if n_psu3==1
    keep strata newstrata3 n_psu3
    merge 1:m strata using "$workingdata/TEMPFILE.dta"
    drop _merge
    fre n_psu3
    replace strata = newstrata3 if n_psu3==1
    
svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)
svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cgm c.age2_cgm i.ethnic i.nssec 

. 
. save "$workingdata/TEMPFILE.dta", replace
file /Users/scottoatley/Documents/Stata Data and Do/Working Data/TEMPFILE.dta
    saved

. 
.         bysort strata psu: keep if _n==1
(6,652 observations deleted)

.         bysort strata: generate n_psu3 = _N

.         fre n_psu3

n_psu3
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   1     |        125       3.65       3.65       3.65
        2     |       1194      34.88      34.88      38.53
        3     |        786      22.96      22.96      61.50
        4     |        604      17.65      17.65      79.14
        5     |         75       2.19       2.19      81.33
        6     |         36       1.05       1.05      82.38
        7     |         35       1.02       1.02      83.41
        8     |         16       0.47       0.47      83.87
        19    |         19       0.56       0.56      84.43
        29    |         29       0.85       0.85      85.28
        34    |         34       0.99       0.99      86.27
        85    |         85       2.48       2.48      88.75
        186   |        186       5.43       5.43      94.19
        199   |        199       5.81       5.81     100.00
        Total |       3423     100.00     100.00           
-----------------------------------------------------------

.         list pidp strata psu if n_psu3==1

      +-----------------------------+
      |       pidp   strata     psu |
      |-----------------------------|
 602. | 1224032659     2027    2049 |
 623. | 1632040811     2040    2075 |
 686. | 1632075491     2076    2147 |
 687. |  748079571     2077    2149 |
 688. |  340080931     2078    2151 |
      |-----------------------------|
 724. | 1428110167     2104    2200 |
 725. |   68105407     2106    2204 |
 732. | 1224120367     2111    2217 |
 756. |  748134651     2125    2245 |
 769. |  816134651     2133    2261 |
      |-----------------------------|
 770. |  408523690     2134    2263 |
 771. | 1224135327     2135    2265 |
 940. |  680244807     2237    2469 |
 987. |  408259767     2266    2527 |
1054. | 1020317567     2305    2606 |
      |-----------------------------|
1055. | 1429496768     2306    2608 |
1269. | 1292431131     2430    2856 |
1270. | 1156434527     2431    2857 |
1275. |  544441327     2435    2866 |
1276. | 1634040092     2436    2867 |
      |-----------------------------|
1281. | 1496429087     2440    2875 |
1319. |  612476007     2467    2930 |
1320. |  206584082     2468    2932 |
1657. |  884680011     2669    3334 |
1658. | 1292685451     2670    3336 |
      |-----------------------------|
1716. | 1292716731     2706    3408 |
1717. | 1156721491     2707    3409 |
1751. |   68740527     2728    3452 |
1840. | 1224796967     2783    3561 |
1856. | 1224808531     2795    3585 |
      |-----------------------------|
1857. |  136819407     2796    3588 |
1862. |  884820091     2802    3598 |
1863. | 1156820091     2803    3601 |
1864. |  204812611     2804    3604 |
1869. |  340830287     2810    3615 |
      |-----------------------------|
1870. |  476830291     2811    3618 |
1902. |  816837087     2841    3677 |
1911. | 1496864291     2849    3691 |
1912. |  884867011     2850    3694 |
1920. | 1428881287     2859    3712 |
      |-----------------------------|
1925. |  612878567     2863    3722 |
1926. | 1564857491     2864    3723 |
1927. |  816865651     2865    3725 |
1928. |  408870411     2866    3727 |
1980. | 1632937047     2904    3803 |
      |-----------------------------|
2038. |  680919367     2945    3885 |
2058. |  684916482     2957    3909 |
2303. |  273117927     3104    4199 |
2304. |  957195328     3105    4206 |
2325. | 1429158055     3118    4232 |
      |-----------------------------|
2326. |  545141727     3119    4234 |
2327. | 1633164847     3120    4235 |
2339. |  205144451     3128    4252 |
2358. |  817148527     3141    4277 |
2363. |  341165531     3146    4287 |
      |-----------------------------|
2364. | 1089149891     3147    4289 |
2365. |   69173687     3148    4292 |
2420. | 1094528084     3183    4361 |
2432. | 1225230127     3191    4377 |
2433. |  137221971     3192    4380 |
      |-----------------------------|
2438. | 1502283366     3196    4387 |
2445. |  953224691     3201    4398 |
2446. | 1361213807     3202    4400 |
2447. | 1225260735     3203    4401 |
2448. |  137229451     3204    4404 |
      |-----------------------------|
2449. |  885211087     3205    4406 |
2471. |  681241687     3221    4437 |
2472. | 1293222647     3222    4440 |
2473. | 1157242367     3225    4441 |
2474. | 1565248491     3226    4443 |
      |-----------------------------|
2475. |  545260727     3227    4450 |
2476. |  137257331     3234    4452 |
2477. | 1021253927     3235    4454 |
2478. |  205253251     3236    4468 |
2479. |  817255287     3237    4469 |
      |-----------------------------|
2480. |  409255971     3238    4471 |
2481. |  545270927     3239    4474 |
2486. | 1163078884     3243    4481 |
2491. | 1157280459     3248    4489 |
2531. |  137309007     3277    4548 |
      |-----------------------------|
2799. |   69734691     3340    7614 |
2800. |   69907407     3342    7870 |
2805. |   70337167     3361    8537 |
2806. |  137443659     3364    8999 |
2807. |  137446367     3366    9003 |
      |-----------------------------|
2808. |  141236174     3369    9252 |
2809. |  137668727     3371    9332 |
2814. |  138097807     3384    9963 |
2815. |  138277331     3386   10236 |
2816. |  138401087     3393   10418 |
      |-----------------------------|
2817. |  138519411     3399   10595 |
2818. |  205421891     3401   10964 |
2819. |  205529327     3406   11128 |
2828. |  206105971     3421   11942 |
2829. |  206149487     3433   12008 |
      |-----------------------------|
2863. |  345793850     3487   14868 |
2869. |  342455491     3505   15618 |
2870. |  409401487     3509   15735 |
2876. |  409897207     3526   16476 |
2881. |  477377007     3546   17400 |
      |-----------------------------|
2882. |  477495327     3548   17576 |
2883. |  477664671     3549   17827 |
2884. |  477703411     3551   17884 |
2906. |  545918975     3596   20063 |
2924. |  613592567     3625   21719 |
      |-----------------------------|
2925. |  613688459     3626   21861 |
2932. |  614503095     3655   23083 |
2933. |  817362051     3658   23390 |
2976. |  818070611     3740   27726 |
2977. |  818147447     3747   27789 |
      |-----------------------------|
2991. |  886139287     3786   29656 |
2992. |  892153364     3788   29891 |
3031. | 1022220887     3849   33230 |
3032. | 1022258967     3851   33286 |
3045. | 1089942087     3895   34886 |
      |-----------------------------|
3046. | 1090218851     3897   35244 |
3120. | 1293863207     3991   40225 |
3173. | 1497784331     4093   45271 |
3174. | 1497850291     4095   45369 |
3175. | 1501895892     4097   45476 |
      |-----------------------------|
3182. | 1498265087     4110   45995 |
3183. | 1498363007     4114   46139 |
3212. | 1566371855     4147   47927 |
3213. | 1566409247     4149   47982 |
3214. | 1566436447     4159   48022 |
      +-----------------------------+

.         
.         bysort strata: keep if _n==1
(2,254 observations deleted)

.         sort strata

.         generate newstrata3 =strata[_n+1] if n_psu3==1
(1,044 missing values generated)

.         fre newstrata3

newstrata3
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   2028  |          1       0.09       0.80       0.80
        2041  |          1       0.09       0.80       1.60
        2077  |          1       0.09       0.80       2.40
        2078  |          1       0.09       0.80       3.20
        2079  |          1       0.09       0.80       4.00
        2106  |          1       0.09       0.80       4.80
        2107  |          1       0.09       0.80       5.60
        2112  |          1       0.09       0.80       6.40
        2126  |          1       0.09       0.80       7.20
        2134  |          1       0.09       0.80       8.00
        2135  |          1       0.09       0.80       8.80
        2136  |          1       0.09       0.80       9.60
        2238  |          1       0.09       0.80      10.40
        2267  |          1       0.09       0.80      11.20
        2306  |          1       0.09       0.80      12.00
        2307  |          1       0.09       0.80      12.80
        2431  |          1       0.09       0.80      13.60
        2432  |          1       0.09       0.80      14.40
        2436  |          1       0.09       0.80      15.20
        2437  |          1       0.09       0.80      16.00
        :     |          :          :          :          :
        3633  |          1       0.09       0.80      84.80
        3658  |          1       0.09       0.80      85.60
        3660  |          1       0.09       0.80      86.40
        3747  |          1       0.09       0.80      87.20
        3750  |          1       0.09       0.80      88.00
        3788  |          1       0.09       0.80      88.80
        3792  |          1       0.09       0.80      89.60
        3851  |          1       0.09       0.80      90.40
        3853  |          1       0.09       0.80      91.20
        3897  |          1       0.09       0.80      92.00
        3900  |          1       0.09       0.80      92.80
        3999  |          1       0.09       0.80      93.60
        4095  |          1       0.09       0.80      94.40
        4097  |          1       0.09       0.80      95.20
        4099  |          1       0.09       0.80      96.00
        4114  |          1       0.09       0.80      96.80
        4116  |          1       0.09       0.80      97.60
        4149  |          1       0.09       0.80      98.40
        4159  |          1       0.09       0.80      99.20
        4160  |          1       0.09       0.80     100.00
        Total |        125      10.69     100.00           
Missing .     |       1044      89.31                      
Total         |       1169     100.00                      
-----------------------------------------------------------

.         list pidp strata psu newstrata3 if n_psu3==1

      +----------------------------------------+
      |       pidp   strata     psu   newstr~3 |
      |----------------------------------------|
 132. | 1224032659     2027    2049       2028 |
 141. | 1632040811     2040    2075       2041 |
 169. | 1632075491     2076    2147       2077 |
 170. |  748079571     2077    2149       2078 |
 171. |  340080931     2078    2151       2079 |
      |----------------------------------------|
 186. | 1428110167     2104    2200       2106 |
 187. |   68105407     2106    2204       2107 |
 190. | 1224120367     2111    2217       2112 |
 201. |  748134651     2125    2245       2126 |
 207. |  816134651     2133    2261       2134 |
      |----------------------------------------|
 208. |  408523690     2134    2263       2135 |
 209. | 1224135327     2135    2265       2136 |
 282. |  680244807     2237    2469       2238 |
 301. |  408259767     2266    2527       2267 |
 331. | 1020317567     2305    2606       2306 |
      |----------------------------------------|
 332. | 1429496768     2306    2608       2307 |
 425. | 1292431131     2430    2856       2431 |
 426. | 1156434527     2431    2857       2432 |
 428. |  544441327     2435    2866       2436 |
 429. | 1634040092     2436    2867       2437 |
      |----------------------------------------|
 431. | 1496429087     2440    2875       2441 |
 447. |  612476007     2467    2930       2468 |
 448. |  206584082     2468    2932       2469 |
 591. |  884680011     2669    3334       2670 |
 592. | 1292685451     2670    3336       2671 |
      |----------------------------------------|
 617. | 1292716731     2706    3408       2707 |
 618. | 1156721491     2707    3409       2708 |
 632. |   68740527     2728    3452       2729 |
 670. | 1224796967     2783    3561       2784 |
 676. | 1224808531     2795    3585       2796 |
      |----------------------------------------|
 677. |  136819407     2796    3588       2798 |
 679. |  884820091     2802    3598       2803 |
 680. | 1156820091     2803    3601       2804 |
 681. |  204812611     2804    3604       2805 |
 683. |  340830287     2810    3615       2811 |
      |----------------------------------------|
 684. |  476830291     2811    3618       2813 |
 694. |  816837087     2841    3677       2842 |
 698. | 1496864291     2849    3691       2850 |
 699. |  884867011     2850    3694       2851 |
 702. | 1428881287     2859    3712       2860 |
      |----------------------------------------|
 704. |  612878567     2863    3722       2864 |
 705. | 1564857491     2864    3723       2865 |
 706. |  816865651     2865    3725       2866 |
 707. |  408870411     2866    3727       2867 |
 728. | 1632937047     2904    3803       2905 |
      |----------------------------------------|
 753. |  680919367     2945    3885       2946 |
 762. |  684916482     2957    3909       2958 |
 864. |  273117927     3104    4199       3105 |
 865. |  957195328     3105    4206       3106 |
 874. | 1429158055     3118    4232       3119 |
      |----------------------------------------|
 875. |  545141727     3119    4234       3120 |
 876. | 1633164847     3120    4235       3121 |
 881. |  205144451     3128    4252       3129 |
 888. |  817148527     3141    4277       3142 |
 890. |  341165531     3146    4287       3147 |
      |----------------------------------------|
 891. | 1089149891     3147    4289       3148 |
 892. |   69173687     3148    4292       3149 |
 914. | 1094528084     3183    4361       3184 |
 919. | 1225230127     3191    4377       3192 |
 920. |  137221971     3192    4380       3193 |
      |----------------------------------------|
 922. | 1502283366     3196    4387       3197 |
 925. |  953224691     3201    4398       3202 |
 926. | 1361213807     3202    4400       3203 |
 927. | 1225260735     3203    4401       3204 |
 928. |  137229451     3204    4404       3205 |
      |----------------------------------------|
 929. |  885211087     3205    4406       3206 |
 937. |  681241687     3221    4437       3222 |
 938. | 1293222647     3222    4440       3225 |
 939. | 1157242367     3225    4441       3226 |
 940. | 1565248491     3226    4443       3227 |
      |----------------------------------------|
 941. |  545260727     3227    4450       3234 |
 942. |  137257331     3234    4452       3235 |
 943. | 1021253927     3235    4454       3236 |
 944. |  205253251     3236    4468       3237 |
 945. |  817255287     3237    4469       3238 |
      |----------------------------------------|
 946. |  409255971     3238    4471       3239 |
 947. |  545270927     3239    4474       3240 |
 949. | 1163078884     3243    4481       3244 |
 951. | 1157280459     3248    4489       3249 |
 966. |  137309007     3277    4548       3278 |
      |----------------------------------------|
1001. |   69734691     3340    7614       3342 |
1002. |   69907407     3342    7870       3343 |
1004. |   70337167     3361    8537       3364 |
1005. |  137443659     3364    8999       3366 |
1006. |  137446367     3366    9003       3369 |
      |----------------------------------------|
1007. |  141236174     3369    9252       3371 |
1008. |  137668727     3371    9332       3372 |
1010. |  138097807     3384    9963       3386 |
1011. |  138277331     3386   10236       3393 |
1012. |  138401087     3393   10418       3399 |
      |----------------------------------------|
1013. |  138519411     3399   10595       3401 |
1014. |  205421891     3401   10964       3406 |
1015. |  205529327     3406   11128       3409 |
1018. |  206105971     3421   11942       3433 |
1019. |  206149487     3433   12008       3438 |
      |----------------------------------------|
1030. |  345793850     3487   14868       3495 |
1032. |  342455491     3505   15618       3509 |
1033. |  409401487     3509   15735       3511 |
1035. |  409897207     3526   16476       3531 |
1037. |  477377007     3546   17400       3548 |
      |----------------------------------------|
1038. |  477495327     3548   17576       3549 |
1039. |  477664671     3549   17827       3551 |
1040. |  477703411     3551   17884       3556 |
1047. |  545918975     3596   20063       3597 |
1053. |  613592567     3625   21719       3626 |
      |----------------------------------------|
1054. |  613688459     3626   21861       3633 |
1057. |  614503095     3655   23083       3658 |
1058. |  817362051     3658   23390       3660 |
1072. |  818070611     3740   27726       3747 |
1073. |  818147447     3747   27789       3750 |
      |----------------------------------------|
1078. |  886139287     3786   29656       3788 |
1079. |  892153364     3788   29891       3792 |
1091. | 1022220887     3849   33230       3851 |
1092. | 1022258967     3851   33286       3853 |
1096. | 1089942087     3895   34886       3897 |
      |----------------------------------------|
1097. | 1090218851     3897   35244       3900 |
1120. | 1293863207     3991   40225       3999 |
1138. | 1497784331     4093   45271       4095 |
1139. | 1497850291     4095   45369       4097 |
1140. | 1501895892     4097   45476       4099 |
      |----------------------------------------|
1143. | 1498265087     4110   45995       4114 |
1144. | 1498363007     4114   46139       4116 |
1152. | 1566371855     4147   47927       4149 |
1153. | 1566409247     4149   47982       4159 |
1154. | 1566436447     4159   48022       4160 |
      +----------------------------------------+

.         keep if n_psu3==1
(1,044 observations deleted)

.         keep strata newstrata3 n_psu3

.         merge 1:m strata using "$workingdata/TEMPFILE.dta"
(label strata already defined)
(label psu already defined)
(label ethnic_lbl already defined)
(label nssec already defined)
(label generations already defined)
(label cohort already defined)
(label sex already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         9,797
        from master                         0  (_merge==1)
        from using                      9,797  (_merge==2)

    Matched                               278  (_merge==3)
    -----------------------------------------

.         drop _merge

.         fre n_psu3

n_psu3
-------------------------------------------------------
          |      Freq.    Percent      Valid       Cum.
----------+--------------------------------------------
Valid   1 |        278       2.76     100.00     100.00
Missing . |       9797      97.24                      
Total     |      10075     100.00                      
-------------------------------------------------------

.         replace strata = newstrata3 if n_psu3==1
(278 real changes made)

.         
. svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

Sampling weights: j_indinui_xw
             VCE: linearized
     Single unit: scaled
        Strata 1: strata
 Sampling unit 1: psu
           FPC 1: <zero>

. svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cg
> m c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,107                          Number of obs   =     10,073
Number of PSUs   = 3,421                          Population size = 9,295.5002
                                                  Design df       =      2,314
                                                  F(19, 2296)     =     198.31
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3167

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2702328   .0253759    10.65   0.000     .2204709    .3199946
gender_per~e |   .1026319   .0130643     7.86   0.000      .077013    .1282508
gender_per~2 |  -.0189667   .0091345    -2.08   0.038    -.0368794   -.0010541
     age_cgm |  -.0127472   .0010227   -12.46   0.000    -.0147527   -.0107416
    age2_cgm |  -.0009016   .0000672   -13.42   0.000    -.0010333   -.0007699
             |
      ethnic |
Mixed/Mul..  |   .0510384    .057997     0.88   0.379     -.062693    .1647699
     Indian  |   .0218302   .0345699     0.63   0.528    -.0459609    .0896214
Pakistani..  |  -.1956906   .0505159    -3.87   0.000    -.2947517   -.0966295
    Chinese  |   .1751179   .0909621     1.93   0.054    -.0032578    .3534936
Any other..  |  -.0646881   .0670603    -0.96   0.335    -.1961926    .0668165
Black/Afr..  |  -.0287417   .0598541    -0.48   0.631    -.1461149    .0886315
      Other  |   .2733452   .1380133     1.98   0.048     .0027026    .5439877
             |
       nssec |
1.2 Highe..  |  -.1917456   .0333967    -5.74   0.000    -.2572363   -.1262549
2 Lower m..  |   -.365915   .0260294   -14.06   0.000    -.4169585   -.3148716
3 Interme..  |  -.6313358     .03036   -20.79   0.000    -.6908715   -.5718001
4 Small e..  |  -1.202829   .0547206   -21.98   0.000    -1.310135   -1.095522
5 Lower s..  |  -.5574836   .0299491   -18.61   0.000    -.6162134   -.4987537
6 Semi-ro..  |   -.844068   .0273472   -30.86   0.000    -.8976956   -.7904405
7 Routine..  |  -.9473985   .0340184   -27.85   0.000    -1.014108   -.8806887
             |
       _cons |   7.795486   .0289719   269.07   0.000     7.738673      7.8523
------------------------------------------------------------------------------
Note: 2 strata omitted because they contain no population members.
Note: Variance scaled to handle strata with a single sampling unit.

. 

%%stata

save "$workingdata/TEMPFILE.dta", replace

    bysort strata psu: keep if _n==1
    bysort strata: generate n_psu4 = _N
    fre n_psu4
    list pidp strata psu if n_psu4==1
    
    bysort strata: keep if _n==1
    sort strata
    generate newstrata4 =strata[_n+1] if n_psu4==1
    fre newstrata4
    list pidp strata psu newstrata4 if n_psu4==1
    keep if n_psu4==1
    keep strata newstrata4 n_psu4
    merge 1:m strata using "$workingdata/TEMPFILE.dta"
    drop _merge
    fre n_psu4
    replace strata = newstrata4 if n_psu4==1
    
svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)
svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cgm c.age2_cgm i.ethnic i.nssec 

. 
. save "$workingdata/TEMPFILE.dta", replace
file /Users/scottoatley/Documents/Stata Data and Do/Working Data/TEMPFILE.dta
    saved

. 
.         bysort strata psu: keep if _n==1
(6,652 observations deleted)

.         bysort strata: generate n_psu4 = _N

.         fre n_psu4

n_psu4
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   1     |         65       1.90       1.90       1.90
        2     |       1194      34.88      34.88      36.78
        3     |        786      22.96      22.96      59.74
        4     |        380      11.10      11.10      70.84
        5     |        335       9.79       9.79      80.63
        6     |         60       1.75       1.75      82.38
        7     |         35       1.02       1.02      83.41
        8     |         16       0.47       0.47      83.87
        19    |         19       0.56       0.56      84.43
        29    |         29       0.85       0.85      85.28
        34    |         34       0.99       0.99      86.27
        85    |         85       2.48       2.48      88.75
        186   |        186       5.43       5.43      94.19
        199   |        199       5.81       5.81     100.00
        Total |       3423     100.00     100.00           
-----------------------------------------------------------

.         list pidp strata psu if n_psu4==1

      +-----------------------------+
      |       pidp   strata     psu |
      |-----------------------------|
 686. | 1632076847     2077    2147 |
 687. |  748076871     2078    2149 |
 724. | 1428110167     2106    2200 |
 769. |  816134651     2134    2261 |
 770. |  408523690     2135    2263 |
      |-----------------------------|
1054. | 1020317567     2306    2606 |
1269. | 1292435891     2431    2856 |
1275. |  544450847     2436    2866 |
1319. |  612477367     2468    2930 |
1657. |  884680007     2670    3334 |
      |-----------------------------|
1716. | 1292716727     2707    3408 |
1856. | 1224816007     2796    3585 |
1862. |  884820087     2803    3598 |
1863. | 1156820091     2804    3601 |
1869. |  340830287     2811    3615 |
      |-----------------------------|
1911. | 1496864291     2850    3691 |
1925. |  612878567     2864    3722 |
1926. | 1564857499     2865    3723 |
1927. |  816865651     2866    3725 |
2303. |  273117931     3105    4199 |
      |-----------------------------|
2325. | 1429158055     3119    4232 |
2326. |  545141727     3120    4234 |
2363. |  341165531     3147    4287 |
2364. | 1089149891     3148    4289 |
2432. | 1225230127     3192    4377 |
      |-----------------------------|
2445. |  953229447     3202    4398 |
2446. | 1361216527     3203    4400 |
2447. | 1225260735     3204    4401 |
2448. |  137229447     3205    4404 |
2471. |  681248491     3222    4437 |
      |-----------------------------|
2472. | 1293221967     3225    4440 |
2473. | 1157242367     3226    4441 |
2474. | 1565248491     3227    4443 |
2475. |  545260727     3234    4450 |
2476. |  137257331     3235    4452 |
      |-----------------------------|
2477. | 1021257331     3236    4454 |
2478. |  205253251     3237    4468 |
2479. |  817255291     3238    4469 |
2480. |  409255971     3239    4471 |
2799. |   69734691     3342    7614 |
      |-----------------------------|
2805. |   70337167     3364    8537 |
2806. |  137443659     3366    8999 |
2807. |  137446367     3369    9003 |
2808. |  141236174     3371    9252 |
2814. |  138097807     3386    9963 |
      |-----------------------------|
2815. |  138277331     3393   10236 |
2816. |  138401087     3399   10418 |
2817. |  138519411     3401   10595 |
2818. |  205421891     3406   10964 |
2828. |  206105971     3433   11942 |
      |-----------------------------|
2869. |  346039250     3509   15618 |
2881. |  477377007     3548   17400 |
2882. |  477495327     3549   17576 |
2883. |  477664671     3551   17827 |
2924. |  613592567     3626   21719 |
      |-----------------------------|
2932. |  621132604     3658   23083 |
2976. |  818070611     3747   27726 |
2991. |  886139287     3788   29656 |
3031. | 1022220887     3851   33230 |
3045. | 1089942087     3897   34886 |
      |-----------------------------|
3173. | 1497784331     4095   45271 |
3174. | 1497850291     4097   45369 |
3182. | 1498265091     4114   45995 |
3212. | 1566371855     4149   47927 |
3213. | 1566409247     4159   47982 |
      +-----------------------------+

.         
.         bysort strata: keep if _n==1
(2,314 observations deleted)

.         sort strata

.         generate newstrata4 =strata[_n+1] if n_psu4==1
(1,044 missing values generated)

.         fre newstrata4

newstrata4
-----------------------------------------------------------
              |      Freq.    Percent      Valid       Cum.
--------------+--------------------------------------------
Valid   2078  |          1       0.09       1.54       1.54
        2079  |          1       0.09       1.54       3.08
        2107  |          1       0.09       1.54       4.62
        2135  |          1       0.09       1.54       6.15
        2136  |          1       0.09       1.54       7.69
        2307  |          1       0.09       1.54       9.23
        2432  |          1       0.09       1.54      10.77
        2437  |          1       0.09       1.54      12.31
        2469  |          1       0.09       1.54      13.85
        2671  |          1       0.09       1.54      15.38
        2708  |          1       0.09       1.54      16.92
        2798  |          1       0.09       1.54      18.46
        2804  |          1       0.09       1.54      20.00
        2805  |          1       0.09       1.54      21.54
        2813  |          1       0.09       1.54      23.08
        2851  |          1       0.09       1.54      24.62
        2865  |          1       0.09       1.54      26.15
        2866  |          1       0.09       1.54      27.69
        2867  |          1       0.09       1.54      29.23
        3106  |          1       0.09       1.54      30.77
        :     |          :          :          :          :
        3399  |          1       0.09       1.54      70.77
        3401  |          1       0.09       1.54      72.31
        3406  |          1       0.09       1.54      73.85
        3409  |          1       0.09       1.54      75.38
        3438  |          1       0.09       1.54      76.92
        3511  |          1       0.09       1.54      78.46
        3549  |          1       0.09       1.54      80.00
        3551  |          1       0.09       1.54      81.54
        3556  |          1       0.09       1.54      83.08
        3633  |          1       0.09       1.54      84.62
        3660  |          1       0.09       1.54      86.15
        3750  |          1       0.09       1.54      87.69
        3792  |          1       0.09       1.54      89.23
        3853  |          1       0.09       1.54      90.77
        3900  |          1       0.09       1.54      92.31
        4097  |          1       0.09       1.54      93.85
        4099  |          1       0.09       1.54      95.38
        4116  |          1       0.09       1.54      96.92
        4159  |          1       0.09       1.54      98.46
        4160  |          1       0.09       1.54     100.00
        Total |         65       5.86     100.00           
Missing .     |       1044      94.14                      
Total         |       1109     100.00                      
-----------------------------------------------------------

.         list pidp strata psu newstrata4 if n_psu4==1

      +----------------------------------------+
      |       pidp   strata     psu   newstr~4 |
      |----------------------------------------|
 167. | 1632076847     2077    2147       2078 |
 168. |  748076871     2078    2149       2079 |
 183. | 1428110167     2106    2200       2107 |
 201. |  816134651     2134    2261       2135 |
 202. |  408523690     2135    2263       2136 |
      |----------------------------------------|
 322. | 1020317567     2306    2606       2307 |
 415. | 1292435891     2431    2856       2432 |
 417. |  544450847     2436    2866       2437 |
 434. |  612477367     2468    2930       2469 |
 577. |  884680007     2670    3334       2671 |
      |----------------------------------------|
 602. | 1292716727     2707    3408       2708 |
 658. | 1224816007     2796    3585       2798 |
 660. |  884820087     2803    3598       2804 |
 661. | 1156820091     2804    3601       2805 |
 663. |  340830287     2811    3615       2813 |
      |----------------------------------------|
 676. | 1496864291     2850    3691       2851 |
 680. |  612878567     2864    3722       2865 |
 681. | 1564857499     2865    3723       2866 |
 682. |  816865651     2866    3725       2867 |
 836. |  273117931     3105    4199       3106 |
      |----------------------------------------|
 845. | 1429158055     3119    4232       3120 |
 846. |  545141727     3120    4234       3121 |
 858. |  341165531     3147    4287       3148 |
 859. | 1089149891     3148    4289       3149 |
 885. | 1225230127     3192    4377       3193 |
      |----------------------------------------|
 889. |  953229447     3202    4398       3203 |
 890. | 1361216527     3203    4400       3204 |
 891. | 1225260735     3204    4401       3205 |
 892. |  137229447     3205    4404       3206 |
 900. |  681248491     3222    4437       3225 |
      |----------------------------------------|
 901. | 1293221967     3225    4440       3226 |
 902. | 1157242367     3226    4441       3227 |
 903. | 1565248491     3227    4443       3234 |
 904. |  545260727     3234    4450       3235 |
 905. |  137257331     3235    4452       3236 |
      |----------------------------------------|
 906. | 1021257331     3236    4454       3237 |
 907. |  205253251     3237    4468       3238 |
 908. |  817255291     3238    4469       3239 |
 909. |  409255971     3239    4471       3240 |
 960. |   69734691     3342    7614       3343 |
      |----------------------------------------|
 962. |   70337167     3364    8537       3366 |
 963. |  137443659     3366    8999       3369 |
 964. |  137446367     3369    9003       3371 |
 965. |  141236174     3371    9252       3372 |
 967. |  138097807     3386    9963       3393 |
      |----------------------------------------|
 968. |  138277331     3393   10236       3399 |
 969. |  138401087     3399   10418       3401 |
 970. |  138519411     3401   10595       3406 |
 971. |  205421891     3406   10964       3409 |
 974. |  206105971     3433   11942       3438 |
      |----------------------------------------|
 986. |  346039250     3509   15618       3511 |
 989. |  477377007     3548   17400       3549 |
 990. |  477495327     3549   17576       3551 |
 991. |  477664671     3551   17827       3556 |
1003. |  613592567     3626   21719       3633 |
      |----------------------------------------|
1006. |  621132604     3658   23083       3660 |
1020. |  818070611     3747   27726       3750 |
1025. |  886139287     3788   29656       3792 |
1037. | 1022220887     3851   33230       3853 |
1041. | 1089942087     3897   34886       3900 |
      |----------------------------------------|
1081. | 1497784331     4095   45271       4097 |
1082. | 1497850291     4097   45369       4099 |
1085. | 1498265091     4114   45995       4116 |
1093. | 1566371855     4149   47927       4159 |
1094. | 1566409247     4159   47982       4160 |
      +----------------------------------------+

.         keep if n_psu4==1
(1,044 observations deleted)

.         keep strata newstrata4 n_psu4

.         merge 1:m strata using "$workingdata/TEMPFILE.dta"
(label sex already defined)
(label cohort already defined)
(label generations already defined)
(label nssec already defined)
(label ethnic_lbl already defined)
(label psu already defined)
(label strata already defined)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         9,942
        from master                         0  (_merge==1)
        from using                      9,942  (_merge==2)

    Matched                               133  (_merge==3)
    -----------------------------------------

.         drop _merge

.         fre n_psu4

n_psu4
-------------------------------------------------------
          |      Freq.    Percent      Valid       Cum.
----------+--------------------------------------------
Valid   1 |        133       1.32     100.00     100.00
Missing . |       9942      98.68                      
Total     |      10075     100.00                      
-------------------------------------------------------

.         replace strata = newstrata4 if n_psu4==1
(133 real changes made)

.         
. svyset psu [pweight=j_indinui_xw], strata(strata) single(scaled)

Sampling weights: j_indinui_xw
             VCE: linearized
     Single unit: scaled
        Strata 1: strata
 Sampling unit 1: psu
           FPC 1: <zero>

. svy: regress logincome i.sex gender_performance gender_performance_2 c.age_cg
> m c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(19, 2331)     =     202.33
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3167

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2702328   .0250182    10.80   0.000     .2211727    .3192928
gender_per~e |   .1026319   .0129006     7.96   0.000     .0773342    .1279296
gender_per~2 |  -.0189667   .0090351    -2.10   0.036    -.0366843   -.0012491
     age_cgm |  -.0127472   .0010107   -12.61   0.000    -.0147291   -.0107652
    age2_cgm |  -.0009016   .0000662   -13.62   0.000    -.0010314   -.0007717
             |
      ethnic |
Mixed/Mul..  |   .0510384   .0573494     0.89   0.374    -.0614222    .1634991
     Indian  |   .0218302   .0342279     0.64   0.524    -.0452898    .0889503
Pakistani..  |  -.1956906   .0499823    -3.92   0.000    -.2937046   -.0976766
    Chinese  |   .1751179   .0896023     1.95   0.051    -.0005898    .3508256
Any other..  |  -.0646881   .0660494    -0.98   0.327    -.1942093    .0648332
Black/Afr..  |  -.0287417   .0589643    -0.49   0.626    -.1443693    .0868858
      Other  |   .2733452   .1359451     2.01   0.044     .0067602    .5399301
             |
       nssec |
1.2 Highe..  |  -.1917456   .0329133    -5.83   0.000    -.2562878   -.1272034
2 Lower m..  |   -.365915   .0256558   -14.26   0.000    -.4162254   -.3156047
3 Interme..  |  -.6313358   .0299101   -21.11   0.000    -.6899887   -.5726829
4 Small e..  |  -1.202829   .0539238   -22.31   0.000    -1.308572   -1.097086
5 Lower s..  |  -.5574836   .0294968   -18.90   0.000    -.6153259   -.4996412
6 Semi-ro..  |   -.844068   .0271185   -31.13   0.000    -.8972468   -.7908892
7 Routine..  |  -.9473985   .0337399   -28.08   0.000    -1.013561   -.8812354
             |
       _cons |   7.795486   .0285518   273.03   0.000     7.739497    7.851476
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. 

Analytical Models

%%stata

cd"$outputs"

regress logincome i.sex c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable

svy: regress logincome i.sex c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable, append 

regress logincome i.sex c.gender_performance c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable, append 

svy: regress logincome i.sex c.gender_performance c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable, append 

regress logincome i.sex c.gender_performance c.gender_performance_2 c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable, append

svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 
etable, append 

collect style showbase all

collect label levels etable_depvar 1 "OLS Regression (Gender Only)" ///
                                        2 "SVY OLS Regression (Gender Only)" ///
                                        3 "OLS Regression (Gender + Performance)" ///
                                        4 "SVY OLS Regression (Gender + Performance)" ///
                                        5 "OLS Regression (Performance Squared)" ///
                                        6 "SVY OLS Regression (Performance Squared)", modify

collect style cell, font(Book Antiqua)

etable, replay column(depvar) ///
cstat(_r_b, nformat(%4.2f))  ///
        cstat(_r_se, nformat(%6.2f))  ///
        showstars showstarsnote  ///
        stars(.05 "*" .01 "**" .001 "***", attach(_r_b)) ///
        mstat(N) mstat(aic) mstat(bic) mstat(r2_a)  ///
        title("Table 1.4: Regression Models of relationship between gender and performance on log income") ///
        titlestyles(font(Times New Roman, size(10) bold)) ///
        note("Data Source: UKHLS Wave 10. Adjusted for Complex Sample. n=10,307") ///
        notestyles(font(Times New Roman, size(8) italic)) ///
        export("incomegendermodels.docx", replace)

. 
. cd"$outputs"
/Users/scottoatley/Documents/Stata Data and Do/Output

. 
. regress logincome i.sex c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 

      Source |       SS           df       MS      Number of obs   =    10,075
-------------+----------------------------------   F(18, 10056)    =    246.92
       Model |  1892.48612        18  105.138118   Prob > F        =    0.0000
    Residual |  4281.87957    10,056  .425803458   R-squared       =    0.3065
-------------+----------------------------------   Adj R-squared   =    0.3053
       Total |  6174.36569    10,074  .612901101   Root MSE        =    .65254

------------------------------------------------------------------------------
   logincome | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .3319267   .0147849    22.45   0.000     .3029454    .3609081
  househours |  -.0105026   .0009589   -10.95   0.000    -.0123823   -.0086228
     age_cgm |  -.0119548   .0006767   -17.67   0.000    -.0132812   -.0106284
    age2_cgm |  -.0008497    .000045   -18.87   0.000     -.000938   -.0007615
             |
      ethnic |
Mixed/Mul..  |  -.0448588   .0541705    -0.83   0.408    -.1510438    .0613263
     Indian  |   .0293837   .0336394     0.87   0.382    -.0365563    .0953237
Pakistani..  |  -.2497511   .0350685    -7.12   0.000    -.3184924   -.1810097
    Chinese  |   .1785353   .0999442     1.79   0.074    -.0173753    .3744459
Any other..  |  -.0078087   .0653697    -0.12   0.905    -.1359464    .1203291
Black/Afr..  |   .0009316   .0421712     0.02   0.982    -.0817324    .0835955
      Other  |   .0863596   .1214634     0.71   0.477    -.1517329    .3244522
             |
       nssec |
1.2 Highe..  |  -.1662495   .0354672    -4.69   0.000    -.2357723   -.0967267
2 Lower m..  |   -.340152    .031109   -10.93   0.000    -.4011319   -.2791722
3 Interme..  |  -.6242994   .0343482   -18.18   0.000    -.6916288     -.55697
4 Small e..  |  -1.187561   .0355275   -33.43   0.000    -1.257202    -1.11792
5 Lower s..  |  -.5460945   .0380203   -14.36   0.000    -.6206219   -.4715672
6 Semi-ro..  |  -.8283425   .0339866   -24.37   0.000    -.8949631    -.761722
7 Routine..  |  -.9132829   .0372592   -24.51   0.000    -.9863184   -.8402474
             |
       _cons |   7.821307   .0322595   242.45   0.000     7.758071    7.884542
------------------------------------------------------------------------------

. etable

---------------------------------------------------------------------------
                                                                  logincome
---------------------------------------------------------------------------
Sex                                                                        
  Male                                                                0.332
                                                                    (0.015)
Hours per week Spent on Housework                                    -0.011
                                                                    (0.001)
Age Centered at Grand Mean                                           -0.012
                                                                    (0.001)
Age Squared Centered at Grand Mean                                   -0.001
                                                                    (0.000)
Ethnicity                                                                  
  Mixed/Multiple Ethnic Groups                                       -0.045
                                                                    (0.054)
  Indian                                                              0.029
                                                                    (0.034)
  Pakistani and Bangladeshi                                          -0.250
                                                                    (0.035)
  Chinese                                                             0.179
                                                                    (0.100)
  Any other Asian Background                                         -0.008
                                                                    (0.065)
  Black/African/Caribbean/Black British                               0.001
                                                                    (0.042)
  Other                                                               0.086
                                                                    (0.121)
Current Social Class (NS-SEC)                                              
  1.2 Higher professional occupations                                -0.166
                                                                    (0.035)
  2 Lower managerial, administrative and professional occupations    -0.340
                                                                    (0.031)
  3 Intermediate occupations                                         -0.624
                                                                    (0.034)
  4 Small employers and own account workers                          -1.188
                                                                    (0.036)
  5 Lower supervisory and technical occupations                      -0.546
                                                                    (0.038)
  6 Semi-routine occupations                                         -0.828
                                                                    (0.034)
  7 Routine occupations                                              -0.913
                                                                    (0.037)
Intercept                                                             7.821
                                                                    (0.032)
Number of observations                                                10075
---------------------------------------------------------------------------

. 
. svy: regress logincome i.sex c.househours c.age_cgm c.age2_cgm i.ethnic i.nss
> ec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(18, 2332)     =     212.02
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3168

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |    .364727   .0175352    20.80   0.000      .330341    .3991131
  househours |  -.0100449   .0013384    -7.51   0.000    -.0126695   -.0074204
     age_cgm |  -.0123597   .0010164   -12.16   0.000    -.0143529   -.0103665
    age2_cgm |  -.0009082   .0000664   -13.69   0.000    -.0010383   -.0007781
             |
      ethnic |
Mixed/Mul..  |   .0438463   .0579748     0.76   0.450    -.0698408    .1575335
     Indian  |   .0431181   .0343794     1.25   0.210    -.0242991    .1105353
Pakistani..  |  -.1818844   .0494282    -3.68   0.000    -.2788117    -.084957
    Chinese  |    .164613   .0836962     1.97   0.049     .0004869    .3287391
Any other..  |  -.0404982     .06157    -0.66   0.511    -.1612354     .080239
Black/Afr..  |   -.027199   .0578028    -0.47   0.638    -.1405488    .0861509
      Other  |    .315947   .1352867     2.34   0.020     .0506533    .5812407
             |
       nssec |
1.2 Highe..  |  -.1875161   .0329569    -5.69   0.000    -.2521438   -.1228884
2 Lower m..  |  -.3645071   .0256727   -14.20   0.000    -.4148506   -.3141636
3 Interme..  |  -.6349013    .029811   -21.30   0.000      -.69336   -.5764426
4 Small e..  |  -1.193071   .0539704   -22.11   0.000    -1.298906   -1.087236
5 Lower s..  |  -.5491226   .0295474   -18.58   0.000    -.6070643   -.4911809
6 Semi-ro..  |  -.8334001   .0271086   -30.74   0.000    -.8865594   -.7802409
7 Routine..  |   -.937043   .0338148   -27.71   0.000    -1.003353   -.8707332
             |
       _cons |   7.818266   .0294943   265.08   0.000     7.760429    7.876104
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. etable, append 

-------------------------------------------------------------------------------------
                                                                  logincome logincome
-------------------------------------------------------------------------------------
Sex                                                                                  
  Male                                                                0.332     0.365
                                                                    (0.015)   (0.018)
Hours per week Spent on Housework                                    -0.011    -0.010
                                                                    (0.001)   (0.001)
Age Centered at Grand Mean                                           -0.012    -0.012
                                                                    (0.001)   (0.001)
Age Squared Centered at Grand Mean                                   -0.001    -0.001
                                                                    (0.000)   (0.000)
Ethnicity                                                                            
  Mixed/Multiple Ethnic Groups                                       -0.045     0.044
                                                                    (0.054)   (0.058)
  Indian                                                              0.029     0.043
                                                                    (0.034)   (0.034)
  Pakistani and Bangladeshi                                          -0.250    -0.182
                                                                    (0.035)   (0.049)
  Chinese                                                             0.179     0.165
                                                                    (0.100)   (0.084)
  Any other Asian Background                                         -0.008    -0.040
                                                                    (0.065)   (0.062)
  Black/African/Caribbean/Black British                               0.001    -0.027
                                                                    (0.042)   (0.058)
  Other                                                               0.086     0.316
                                                                    (0.121)   (0.135)
Current Social Class (NS-SEC)                                                        
  1.2 Higher professional occupations                                -0.166    -0.188
                                                                    (0.035)   (0.033)
  2 Lower managerial, administrative and professional occupations    -0.340    -0.365
                                                                    (0.031)   (0.026)
  3 Intermediate occupations                                         -0.624    -0.635
                                                                    (0.034)   (0.030)
  4 Small employers and own account workers                          -1.188    -1.193
                                                                    (0.036)   (0.054)
  5 Lower supervisory and technical occupations                      -0.546    -0.549
                                                                    (0.038)   (0.030)
  6 Semi-routine occupations                                         -0.828    -0.833
                                                                    (0.034)   (0.027)
  7 Routine occupations                                              -0.913    -0.937
                                                                    (0.037)   (0.034)
Intercept                                                             7.821     7.818
                                                                    (0.032)   (0.029)
Number of observations                                                10075     10075
-------------------------------------------------------------------------------------

. 
. regress logincome i.sex c.gender_performance c.househours c.age_cgm c.age2_cg
> m i.ethnic i.nssec 

      Source |       SS           df       MS      Number of obs   =    10,075
-------------+----------------------------------   F(19, 10055)    =    236.10
       Model |  1904.80244        19   100.25276   Prob > F        =    0.0000
    Residual |  4269.56325    10,055   .42462091   R-squared       =    0.3085
-------------+----------------------------------   Adj R-squared   =    0.3072
       Total |  6174.36569    10,074  .612901101   Root MSE        =    .65163

------------------------------------------------------------------------------
   logincome | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2564705   .0203539    12.60   0.000     .2165727    .2963682
gender_per~e |   .0575801   .0106914     5.39   0.000     .0366229    .0785373
  househours |  -.0087432   .0010118    -8.64   0.000    -.0107265   -.0067599
     age_cgm |  -.0122274   .0006776   -18.04   0.000    -.0135557   -.0108992
    age2_cgm |  -.0008559    .000045   -19.03   0.000    -.0009441   -.0007677
             |
      ethnic |
Mixed/Mul..  |  -.0472228    .054097    -0.87   0.383    -.1532638    .0588183
     Indian  |   .0221119   .0336198     0.66   0.511    -.0437897    .0880134
Pakistani..  |  -.2614308   .0350869    -7.45   0.000    -.3302082   -.1926535
    Chinese  |   .1866129   .0998166     1.87   0.062    -.0090476    .3822733
Any other..  |  -.0146466   .0652912    -0.22   0.823    -.1426305    .1133373
Black/Afr..  |  -.0006679   .0421136    -0.02   0.987    -.0832191    .0818833
      Other  |   .0712762   .1213269     0.59   0.557    -.1665489    .3091013
             |
       nssec |
1.2 Highe..  |  -.1669308   .0354181    -4.71   0.000    -.2363575   -.0975042
2 Lower m..  |  -.3389994   .0310665   -10.91   0.000     -.399896   -.2781028
3 Interme..  |  -.6184191   .0343179   -18.02   0.000     -.685689   -.5511492
4 Small e..  |  -1.190252   .0354816   -33.55   0.000    -1.259803   -1.120701
5 Lower s..  |  -.5464718   .0379675   -14.39   0.000    -.6208957   -.4720479
6 Semi-ro..  |  -.8252712   .0339442   -24.31   0.000    -.8918086   -.7587338
7 Routine..  |  -.9127938   .0372075   -24.53   0.000    -.9857281   -.8398596
             |
       _cons |   7.842242   .0324484   241.68   0.000     7.778636    7.905847
------------------------------------------------------------------------------

. etable, append 

-----------------------------------------------------------------------------------------------
                                                                  logincome logincome logincome
-----------------------------------------------------------------------------------------------
Sex                                                                                            
  Male                                                                0.332     0.365     0.256
                                                                    (0.015)   (0.018)   (0.020)
Hours per week Spent on Housework                                    -0.011    -0.010    -0.009
                                                                    (0.001)   (0.001)   (0.001)
Age Centered at Grand Mean                                           -0.012    -0.012    -0.012
                                                                    (0.001)   (0.001)   (0.001)
Age Squared Centered at Grand Mean                                   -0.001    -0.001    -0.001
                                                                    (0.000)   (0.000)   (0.000)
Ethnicity                                                                                      
  Mixed/Multiple Ethnic Groups                                       -0.045     0.044    -0.047
                                                                    (0.054)   (0.058)   (0.054)
  Indian                                                              0.029     0.043     0.022
                                                                    (0.034)   (0.034)   (0.034)
  Pakistani and Bangladeshi                                          -0.250    -0.182    -0.261
                                                                    (0.035)   (0.049)   (0.035)
  Chinese                                                             0.179     0.165     0.187
                                                                    (0.100)   (0.084)   (0.100)
  Any other Asian Background                                         -0.008    -0.040    -0.015
                                                                    (0.065)   (0.062)   (0.065)
  Black/African/Caribbean/Black British                               0.001    -0.027    -0.001
                                                                    (0.042)   (0.058)   (0.042)
  Other                                                               0.086     0.316     0.071
                                                                    (0.121)   (0.135)   (0.121)
Current Social Class (NS-SEC)                                                                  
  1.2 Higher professional occupations                                -0.166    -0.188    -0.167
                                                                    (0.035)   (0.033)   (0.035)
  2 Lower managerial, administrative and professional occupations    -0.340    -0.365    -0.339
                                                                    (0.031)   (0.026)   (0.031)
  3 Intermediate occupations                                         -0.624    -0.635    -0.618
                                                                    (0.034)   (0.030)   (0.034)
  4 Small employers and own account workers                          -1.188    -1.193    -1.190
                                                                    (0.036)   (0.054)   (0.035)
  5 Lower supervisory and technical occupations                      -0.546    -0.549    -0.546
                                                                    (0.038)   (0.030)   (0.038)
  6 Semi-routine occupations                                         -0.828    -0.833    -0.825
                                                                    (0.034)   (0.027)   (0.034)
  7 Routine occupations                                              -0.913    -0.937    -0.913
                                                                    (0.037)   (0.034)   (0.037)
Gender Performance                                                                        0.058
                                                                                        (0.011)
Intercept                                                             7.821     7.818     7.842
                                                                    (0.032)   (0.029)   (0.032)
Number of observations                                                10075     10075     10075
-----------------------------------------------------------------------------------------------

. 
. svy: regress logincome i.sex c.gender_performance c.househours c.age_cgm c.ag
> e2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(19, 2331)     =     205.07
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3200

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |    .269727    .025184    10.71   0.000     .2203418    .3191122
gender_per~e |   .0734491   .0132266     5.55   0.000     .0475121    .0993862
  househours |  -.0076379   .0013263    -5.76   0.000    -.0102387   -.0050371
     age_cgm |  -.0127288   .0010129   -12.57   0.000     -.014715   -.0107425
    age2_cgm |  -.0009151   .0000662   -13.82   0.000    -.0010449   -.0007852
             |
      ethnic |
Mixed/Mul..  |   .0477798   .0573943     0.83   0.405     -.064769    .1603286
     Indian  |   .0334775   .0346198     0.97   0.334     -.034411     .101366
Pakistani..  |  -.1955548   .0491943    -3.98   0.000    -.2920235    -.099086
    Chinese  |   .1822778   .0854866     2.13   0.033     .0146408    .3499148
Any other..  |  -.0495103   .0634381    -0.78   0.435    -.1739109    .0748902
Black/Afr..  |  -.0301361   .0584565    -0.52   0.606    -.1447678    .0844955
      Other  |   .2973404   .1334233     2.23   0.026     .0357008    .5589801
             |
       nssec |
1.2 Highe..  |  -.1860217   .0328001    -5.67   0.000    -.2503419   -.1217015
2 Lower m..  |  -.3602929    .025478   -14.14   0.000    -.4102547   -.3103311
3 Interme..  |  -.6242341   .0296712   -21.04   0.000    -.6824186   -.5660497
4 Small e..  |  -1.194075   .0540222   -22.10   0.000    -1.300011   -1.088139
5 Lower s..  |   -.548383   .0294312   -18.63   0.000     -.606097   -.4906691
6 Semi-ro..  |   -.828699   .0269444   -30.76   0.000    -.8815364   -.7758617
7 Routine..  |  -.9337714   .0334504   -27.92   0.000    -.9993668   -.8681761
             |
       _cons |   7.839896   .0296874   264.08   0.000      7.78168    7.898112
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. etable, append 

---------------------------------------------------------------------------------------------------------
                                                                  logincome logincome logincome logincome
---------------------------------------------------------------------------------------------------------
Sex                                                                                                      
  Male                                                                0.332     0.365     0.256     0.270
                                                                    (0.015)   (0.018)   (0.020)   (0.025)
Hours per week Spent on Housework                                    -0.011    -0.010    -0.009    -0.008
                                                                    (0.001)   (0.001)   (0.001)   (0.001)
Age Centered at Grand Mean                                           -0.012    -0.012    -0.012    -0.013
                                                                    (0.001)   (0.001)   (0.001)   (0.001)
Age Squared Centered at Grand Mean                                   -0.001    -0.001    -0.001    -0.001
                                                                    (0.000)   (0.000)   (0.000)   (0.000)
Ethnicity                                                                                                
  Mixed/Multiple Ethnic Groups                                       -0.045     0.044    -0.047     0.048
                                                                    (0.054)   (0.058)   (0.054)   (0.057)
  Indian                                                              0.029     0.043     0.022     0.033
                                                                    (0.034)   (0.034)   (0.034)   (0.035)
  Pakistani and Bangladeshi                                          -0.250    -0.182    -0.261    -0.196
                                                                    (0.035)   (0.049)   (0.035)   (0.049)
  Chinese                                                             0.179     0.165     0.187     0.182
                                                                    (0.100)   (0.084)   (0.100)   (0.085)
  Any other Asian Background                                         -0.008    -0.040    -0.015    -0.050
                                                                    (0.065)   (0.062)   (0.065)   (0.063)
  Black/African/Caribbean/Black British                               0.001    -0.027    -0.001    -0.030
                                                                    (0.042)   (0.058)   (0.042)   (0.058)
  Other                                                               0.086     0.316     0.071     0.297
                                                                    (0.121)   (0.135)   (0.121)   (0.133)
Current Social Class (NS-SEC)                                                                            
  1.2 Higher professional occupations                                -0.166    -0.188    -0.167    -0.186
                                                                    (0.035)   (0.033)   (0.035)   (0.033)
  2 Lower managerial, administrative and professional occupations    -0.340    -0.365    -0.339    -0.360
                                                                    (0.031)   (0.026)   (0.031)   (0.025)
  3 Intermediate occupations                                         -0.624    -0.635    -0.618    -0.624
                                                                    (0.034)   (0.030)   (0.034)   (0.030)
  4 Small employers and own account workers                          -1.188    -1.193    -1.190    -1.194
                                                                    (0.036)   (0.054)   (0.035)   (0.054)
  5 Lower supervisory and technical occupations                      -0.546    -0.549    -0.546    -0.548
                                                                    (0.038)   (0.030)   (0.038)   (0.029)
  6 Semi-routine occupations                                         -0.828    -0.833    -0.825    -0.829
                                                                    (0.034)   (0.027)   (0.034)   (0.027)
  7 Routine occupations                                              -0.913    -0.937    -0.913    -0.934
                                                                    (0.037)   (0.034)   (0.037)   (0.033)
Gender Performance                                                                        0.058     0.073
                                                                                        (0.011)   (0.013)
Intercept                                                             7.821     7.818     7.842     7.840
                                                                    (0.032)   (0.029)   (0.032)   (0.030)
Number of observations                                                10075     10075     10075     10075
---------------------------------------------------------------------------------------------------------

. 
. regress logincome i.sex c.gender_performance c.gender_performance_2 c.househo
> urs c.age_cgm c.age2_cgm i.ethnic i.nssec 

      Source |       SS           df       MS      Number of obs   =    10,075
-------------+----------------------------------   F(20, 10054)    =    225.47
       Model |  1911.83708        20   95.591854   Prob > F        =    0.0000
    Residual |  4262.52861    10,054  .423963458   R-squared       =    0.3096
-------------+----------------------------------   Adj R-squared   =    0.3083
       Total |  6174.36569    10,074  .612901101   Root MSE        =    .65112

------------------------------------------------------------------------------
   logincome | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2537441   .0203492    12.47   0.000     .2138556    .2936325
gender_per~e |   .0582717   .0106844     5.45   0.000     .0373281    .0792153
gender_per~2 |  -.0297259   .0072976    -4.07   0.000    -.0440306   -.0154212
  househours |  -.0089307   .0010121    -8.82   0.000    -.0109145   -.0069469
     age_cgm |  -.0120031   .0006793   -17.67   0.000    -.0133347   -.0106715
    age2_cgm |  -.0008635    .000045   -19.20   0.000    -.0009517   -.0007753
             |
      ethnic |
Mixed/Mul..  |  -.0526234   .0540714    -0.97   0.330    -.1586142    .0533674
     Indian  |   .0223161   .0335938     0.66   0.507    -.0435345    .0881667
Pakistani..  |  -.2544197    .035102    -7.25   0.000    -.3232266   -.1856129
    Chinese  |   .1819252   .0997459     1.82   0.068    -.0135968    .3774471
Any other..  |  -.0209381    .065259    -0.32   0.748    -.1488587    .1069825
Black/Afr..  |  -.0082169   .0421218    -0.20   0.845    -.0907841    .0743503
      Other  |   .0739938   .1212348     0.61   0.542    -.1636507    .3116383
             |
       nssec |
1.2 Highe..  |  -.1692417   .0353953    -4.78   0.000    -.2386235   -.0998599
2 Lower m..  |   -.341235   .0310473   -10.99   0.000    -.4020939    -.280376
3 Interme..  |  -.6182045   .0342913   -18.03   0.000    -.6854224   -.5509866
4 Small e..  |  -1.184603   .0354813   -33.39   0.000    -1.254153   -1.115053
5 Lower s..  |  -.5461982   .0379382   -14.40   0.000    -.6205646   -.4718318
6 Semi-ro..  |  -.8268426   .0339201   -24.38   0.000    -.8933328   -.7603525
7 Routine..  |  -.9116519   .0371798   -24.52   0.000    -.9845317   -.8387721
             |
       _cons |   7.877381   .0335512   234.79   0.000     7.811614    7.943148
------------------------------------------------------------------------------

. etable, append

-------------------------------------------------------------------------------------------------------------------
                                                                  logincome logincome logincome logincome logincome
-------------------------------------------------------------------------------------------------------------------
Sex                                                                                                                
  Male                                                                0.332     0.365     0.256     0.270     0.254
                                                                    (0.015)   (0.018)   (0.020)   (0.025)   (0.020)
Hours per week Spent on Housework                                    -0.011    -0.010    -0.009    -0.008    -0.009
                                                                    (0.001)   (0.001)   (0.001)   (0.001)   (0.001)
Age Centered at Grand Mean                                           -0.012    -0.012    -0.012    -0.013    -0.012
                                                                    (0.001)   (0.001)   (0.001)   (0.001)   (0.001)
Age Squared Centered at Grand Mean                                   -0.001    -0.001    -0.001    -0.001    -0.001
                                                                    (0.000)   (0.000)   (0.000)   (0.000)   (0.000)
Ethnicity                                                                                                          
  Mixed/Multiple Ethnic Groups                                       -0.045     0.044    -0.047     0.048    -0.053
                                                                    (0.054)   (0.058)   (0.054)   (0.057)   (0.054)
  Indian                                                              0.029     0.043     0.022     0.033     0.022
                                                                    (0.034)   (0.034)   (0.034)   (0.035)   (0.034)
  Pakistani and Bangladeshi                                          -0.250    -0.182    -0.261    -0.196    -0.254
                                                                    (0.035)   (0.049)   (0.035)   (0.049)   (0.035)
  Chinese                                                             0.179     0.165     0.187     0.182     0.182
                                                                    (0.100)   (0.084)   (0.100)   (0.085)   (0.100)
  Any other Asian Background                                         -0.008    -0.040    -0.015    -0.050    -0.021
                                                                    (0.065)   (0.062)   (0.065)   (0.063)   (0.065)
  Black/African/Caribbean/Black British                               0.001    -0.027    -0.001    -0.030    -0.008
                                                                    (0.042)   (0.058)   (0.042)   (0.058)   (0.042)
  Other                                                               0.086     0.316     0.071     0.297     0.074
                                                                    (0.121)   (0.135)   (0.121)   (0.133)   (0.121)
Current Social Class (NS-SEC)                                                                                      
  1.2 Higher professional occupations                                -0.166    -0.188    -0.167    -0.186    -0.169
                                                                    (0.035)   (0.033)   (0.035)   (0.033)   (0.035)
  2 Lower managerial, administrative and professional occupations    -0.340    -0.365    -0.339    -0.360    -0.341
                                                                    (0.031)   (0.026)   (0.031)   (0.025)   (0.031)
  3 Intermediate occupations                                         -0.624    -0.635    -0.618    -0.624    -0.618
                                                                    (0.034)   (0.030)   (0.034)   (0.030)   (0.034)
  4 Small employers and own account workers                          -1.188    -1.193    -1.190    -1.194    -1.185
                                                                    (0.036)   (0.054)   (0.035)   (0.054)   (0.035)
  5 Lower supervisory and technical occupations                      -0.546    -0.549    -0.546    -0.548    -0.546
                                                                    (0.038)   (0.030)   (0.038)   (0.029)   (0.038)
  6 Semi-routine occupations                                         -0.828    -0.833    -0.825    -0.829    -0.827
                                                                    (0.034)   (0.027)   (0.034)   (0.027)   (0.034)
  7 Routine occupations                                              -0.913    -0.937    -0.913    -0.934    -0.912
                                                                    (0.037)   (0.034)   (0.037)   (0.033)   (0.037)
Gender Performance                                                                        0.058     0.073     0.058
                                                                                        (0.011)   (0.013)   (0.011)
Gender Performance Squared                                                                                   -0.030
                                                                                                            (0.007)
Intercept                                                             7.821     7.818     7.842     7.840     7.877
                                                                    (0.032)   (0.029)   (0.032)   (0.030)   (0.034)
Number of observations                                                10075     10075     10075     10075     10075
-------------------------------------------------------------------------------------------------------------------

. 
. svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.ho
> usehours c.age_cgm c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(20, 2330)     =     194.89
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3206

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2659332   .0250108    10.63   0.000     .2168878    .3149786
gender_per~e |   .0752805   .0131093     5.74   0.000     .0495736    .1009874
gender_per~2 |  -.0219879    .009074    -2.42   0.015    -.0397818    -.004194
  househours |  -.0078036   .0013378    -5.83   0.000     -.010427   -.0051803
     age_cgm |  -.0125408   .0010116   -12.40   0.000    -.0145245   -.0105571
    age2_cgm |  -.0009201   .0000663   -13.89   0.000    -.0010501   -.0007902
             |
      ethnic |
Mixed/Mul..  |   .0410085   .0577073     0.71   0.477     -.072154     .154171
     Indian  |   .0311704   .0348317     0.89   0.371    -.0371337    .0994745
Pakistani..  |  -.1905147   .0495205    -3.85   0.000    -.2876231   -.0934063
    Chinese  |   .1770656   .0865498     2.05   0.041     .0073436    .3467877
Any other..  |   -.058578   .0635066    -0.92   0.356    -.1831129    .0659568
Black/Afr..  |  -.0347055    .058929    -0.59   0.556    -.1502638    .0808527
      Other  |   .3007145    .136221     2.21   0.027     .0335886    .5678404
             |
       nssec |
1.2 Highe..  |  -.1872532   .0327817    -5.71   0.000    -.2515372   -.1229691
2 Lower m..  |  -.3622905   .0254522   -14.23   0.000    -.4122016   -.3123794
3 Interme..  |   -.623402   .0296907   -21.00   0.000    -.6816248   -.5651792
4 Small e..  |  -1.190102    .054053   -22.02   0.000    -1.296098   -1.084105
5 Lower s..  |  -.5481736   .0294964   -18.58   0.000    -.6060154   -.4903318
6 Semi-ro..  |    -.83036   .0269328   -30.83   0.000    -.8831745   -.7775456
7 Routine..  |  -.9341831   .0334079   -27.96   0.000     -.999695   -.8686711
             |
       _cons |   7.867162   .0312074   252.09   0.000     7.805965    7.928359
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. etable, append 

-----------------------------------------------------------------------------------------------------------------------------
                                                                  logincome logincome logincome logincome logincome logincome
-----------------------------------------------------------------------------------------------------------------------------
Sex                                                                                                                          
  Male                                                                0.332     0.365     0.256     0.270     0.254     0.266
                                                                    (0.015)   (0.018)   (0.020)   (0.025)   (0.020)   (0.025)
Hours per week Spent on Housework                                    -0.011    -0.010    -0.009    -0.008    -0.009    -0.008
                                                                    (0.001)   (0.001)   (0.001)   (0.001)   (0.001)   (0.001)
Age Centered at Grand Mean                                           -0.012    -0.012    -0.012    -0.013    -0.012    -0.013
                                                                    (0.001)   (0.001)   (0.001)   (0.001)   (0.001)   (0.001)
Age Squared Centered at Grand Mean                                   -0.001    -0.001    -0.001    -0.001    -0.001    -0.001
                                                                    (0.000)   (0.000)   (0.000)   (0.000)   (0.000)   (0.000)
Ethnicity                                                                                                                    
  Mixed/Multiple Ethnic Groups                                       -0.045     0.044    -0.047     0.048    -0.053     0.041
                                                                    (0.054)   (0.058)   (0.054)   (0.057)   (0.054)   (0.058)
  Indian                                                              0.029     0.043     0.022     0.033     0.022     0.031
                                                                    (0.034)   (0.034)   (0.034)   (0.035)   (0.034)   (0.035)
  Pakistani and Bangladeshi                                          -0.250    -0.182    -0.261    -0.196    -0.254    -0.191
                                                                    (0.035)   (0.049)   (0.035)   (0.049)   (0.035)   (0.050)
  Chinese                                                             0.179     0.165     0.187     0.182     0.182     0.177
                                                                    (0.100)   (0.084)   (0.100)   (0.085)   (0.100)   (0.087)
  Any other Asian Background                                         -0.008    -0.040    -0.015    -0.050    -0.021    -0.059
                                                                    (0.065)   (0.062)   (0.065)   (0.063)   (0.065)   (0.064)
  Black/African/Caribbean/Black British                               0.001    -0.027    -0.001    -0.030    -0.008    -0.035
                                                                    (0.042)   (0.058)   (0.042)   (0.058)   (0.042)   (0.059)
  Other                                                               0.086     0.316     0.071     0.297     0.074     0.301
                                                                    (0.121)   (0.135)   (0.121)   (0.133)   (0.121)   (0.136)
Current Social Class (NS-SEC)                                                                                                
  1.2 Higher professional occupations                                -0.166    -0.188    -0.167    -0.186    -0.169    -0.187
                                                                    (0.035)   (0.033)   (0.035)   (0.033)   (0.035)   (0.033)
  2 Lower managerial, administrative and professional occupations    -0.340    -0.365    -0.339    -0.360    -0.341    -0.362
                                                                    (0.031)   (0.026)   (0.031)   (0.025)   (0.031)   (0.025)
  3 Intermediate occupations                                         -0.624    -0.635    -0.618    -0.624    -0.618    -0.623
                                                                    (0.034)   (0.030)   (0.034)   (0.030)   (0.034)   (0.030)
  4 Small employers and own account workers                          -1.188    -1.193    -1.190    -1.194    -1.185    -1.190
                                                                    (0.036)   (0.054)   (0.035)   (0.054)   (0.035)   (0.054)
  5 Lower supervisory and technical occupations                      -0.546    -0.549    -0.546    -0.548    -0.546    -0.548
                                                                    (0.038)   (0.030)   (0.038)   (0.029)   (0.038)   (0.029)
  6 Semi-routine occupations                                         -0.828    -0.833    -0.825    -0.829    -0.827    -0.830
                                                                    (0.034)   (0.027)   (0.034)   (0.027)   (0.034)   (0.027)
  7 Routine occupations                                              -0.913    -0.937    -0.913    -0.934    -0.912    -0.934
                                                                    (0.037)   (0.034)   (0.037)   (0.033)   (0.037)   (0.033)
Gender Performance                                                                        0.058     0.073     0.058     0.075
                                                                                        (0.011)   (0.013)   (0.011)   (0.013)
Gender Performance Squared                                                                                   -0.030    -0.022
                                                                                                            (0.007)   (0.009)
Intercept                                                             7.821     7.818     7.842     7.840     7.877     7.867
                                                                    (0.032)   (0.029)   (0.032)   (0.030)   (0.034)   (0.031)
Number of observations                                                10075     10075     10075     10075     10075     10075
-----------------------------------------------------------------------------------------------------------------------------

. 
. collect style showbase all

. 
. collect label levels etable_depvar 1 "OLS Regression (Gender Only)" ///
>                                                                              
>    2 "SVY OLS Regression (Gender Only)" ///
>                                                                              
>    3 "OLS Regression (Gender + Performance)" ///
>                                                                              
>    4 "SVY OLS Regression (Gender + Performance)" ///
>                                         5 "OLS Regression (Performance Square
> d)" ///
>                                         6 "SVY OLS Regression (Performance Sq
> uared)", modify

. 
. collect style cell, font(Book Antiqua)

. 
. etable, replay column(depvar) ///
> cstat(_r_b, nformat(%4.2f))  ///
>                 cstat(_r_se, nformat(%6.2f))  ///
>                 showstars showstarsnote  ///
>                 stars(.05 "*" .01 "**" .001 "***", attach(_r_b)) ///
>                 mstat(N) mstat(aic) mstat(bic) mstat(r2_a)      ///
>                 title("Table 1.4: Regression Models of relationship between g
> ender and performance on log income") ///
>                 titlestyles(font(Times New Roman, size(10) bold)) ///
>                 note("Data Source: UKHLS Wave 10. Adjusted for Complex Sample
> . n=10,307") ///
>                 notestyles(font(Times New Roman, size(8) italic)) ///
>                 export("incomegendermodels.docx", replace)

Table 1.4: Regression Models of relationship between gender and performance on log income
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> ----------------------------------------------------------------------------------------------
                                                                           OLS Regression (Gender Only) SVY OLS Regression (Gender Only) OLS Regression (Gender + Performance) SVY OLS Regression (Gende
> r + Performance) OLS Regression (Performance Squared) SVY OLS Regression (Performance Squared)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> ----------------------------------------------------------------------------------------------
Sex                                                                                                                                                                                                     
>                                                                                               
  Female                                                                               0.00                          0.00                                 0.00                                   0.00   
>                                  0.00                                  0.00                   
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
  Male                                                                                 0.33 ***                      0.36 ***                             0.26 ***                               0.27 **
> *                                0.25 ***                              0.27 ***               
                                                                                     (0.01)                        (0.02)                               (0.02)                                 (0.03)   
>                                (0.02)                                (0.03)                   
Hours per week Spent on Housework                                                     -0.01 ***                     -0.01 ***                            -0.01 ***                              -0.01 **
> *                               -0.01 ***                             -0.01 ***               
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
Age Centered at Grand Mean                                                            -0.01 ***                     -0.01 ***                            -0.01 ***                              -0.01 **
> *                               -0.01 ***                             -0.01 ***               
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
Age Squared Centered at Grand Mean                                                    -0.00 ***                     -0.00 ***                            -0.00 ***                              -0.00 **
> *                               -0.00 ***                             -0.00 ***               
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
Ethnicity                                                                                                                                                                                               
>                                                                                               
  White                                                                                0.00                          0.00                                 0.00                                   0.00   
>                                  0.00                                  0.00                   
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
  Mixed/Multiple Ethnic Groups                                                        -0.04                          0.04                                -0.05                                   0.05   
>                                 -0.05                                  0.04                   
                                                                                     (0.05)                        (0.06)                               (0.05)                                 (0.06)   
>                                (0.05)                                (0.06)                   
  Indian                                                                               0.03                          0.04                                 0.02                                   0.03   
>                                  0.02                                  0.03                   
                                                                                     (0.03)                        (0.03)                               (0.03)                                 (0.03)   
>                                (0.03)                                (0.03)                   
  Pakistani and Bangladeshi                                                           -0.25 ***                     -0.18 ***                            -0.26 ***                              -0.20 **
> *                               -0.25 ***                             -0.19 ***               
                                                                                     (0.04)                        (0.05)                               (0.04)                                 (0.05)   
>                                (0.04)                                (0.05)                   
  Chinese                                                                              0.18                          0.16 *                               0.19                                   0.18 * 
>                                  0.18                                  0.18 *                 
                                                                                     (0.10)                        (0.08)                               (0.10)                                 (0.09)   
>                                (0.10)                                (0.09)                   
  Any other Asian Background                                                          -0.01                         -0.04                                -0.01                                  -0.05   
>                                 -0.02                                 -0.06                   
                                                                                     (0.07)                        (0.06)                               (0.07)                                 (0.06)   
>                                (0.07)                                (0.06)                   
  Black/African/Caribbean/Black British                                                0.00                         -0.03                                -0.00                                  -0.03   
>                                 -0.01                                 -0.03                   
                                                                                     (0.04)                        (0.06)                               (0.04)                                 (0.06)   
>                                (0.04)                                (0.06)                   
  Other                                                                                0.09                          0.32 *                               0.07                                   0.30 * 
>                                  0.07                                  0.30 *                 
                                                                                     (0.12)                        (0.14)                               (0.12)                                 (0.13)   
>                                (0.12)                                (0.14)                   
Current Social Class (NS-SEC)                                                                                                                                                                           
>                                                                                               
  1.1 Large employers and higher managerial and administrative occupations             0.00                          0.00                                 0.00                                   0.00   
>                                  0.00                                  0.00                   
                                                                                     (0.00)                        (0.00)                               (0.00)                                 (0.00)   
>                                (0.00)                                (0.00)                   
  1.2 Higher professional occupations                                                 -0.17 ***                     -0.19 ***                            -0.17 ***                              -0.19 **
> *                               -0.17 ***                             -0.19 ***               
                                                                                     (0.04)                        (0.03)                               (0.04)                                 (0.03)   
>                                (0.04)                                (0.03)                   
  2 Lower managerial, administrative and professional occupations                     -0.34 ***                     -0.36 ***                            -0.34 ***                              -0.36 **
> *                               -0.34 ***                             -0.36 ***               
                                                                                     (0.03)                        (0.03)                               (0.03)                                 (0.03)   
>                                (0.03)                                (0.03)                   
  3 Intermediate occupations                                                          -0.62 ***                     -0.63 ***                            -0.62 ***                              -0.62 **
> *                               -0.62 ***                             -0.62 ***               
                                                                                     (0.03)                        (0.03)                               (0.03)                                 (0.03)   
>                                (0.03)                                (0.03)                   
  4 Small employers and own account workers                                           -1.19 ***                     -1.19 ***                            -1.19 ***                              -1.19 **
> *                               -1.18 ***                             -1.19 ***               
                                                                                     (0.04)                        (0.05)                               (0.04)                                 (0.05)   
>                                (0.04)                                (0.05)                   
  5 Lower supervisory and technical occupations                                       -0.55 ***                     -0.55 ***                            -0.55 ***                              -0.55 **
> *                               -0.55 ***                             -0.55 ***               
                                                                                     (0.04)                        (0.03)                               (0.04)                                 (0.03)   
>                                (0.04)                                (0.03)                   
  6 Semi-routine occupations                                                          -0.83 ***                     -0.83 ***                            -0.83 ***                              -0.83 **
> *                               -0.83 ***                             -0.83 ***               
                                                                                     (0.03)                        (0.03)                               (0.03)                                 (0.03)   
>                                (0.03)                                (0.03)                   
  7 Routine occupations                                                               -0.91 ***                     -0.94 ***                            -0.91 ***                              -0.93 **
> *                               -0.91 ***                             -0.93 ***               
                                                                                     (0.04)                        (0.03)                               (0.04)                                 (0.03)   
>                                (0.04)                                (0.03)                   
Gender Performance                                                                                                                                        0.06 ***                               0.07 **
> *                                0.06 ***                              0.08 ***               
                                                                                                                                                        (0.01)                                 (0.01)   
>                                (0.01)                                (0.01)                   
Gender Performance Squared                                                                                                                                                                              
>                                 -0.03 ***                             -0.02 *                 
                                                                                                                                                                                                        
>                                (0.01)                                (0.01)                   
Intercept                                                                              7.82 ***                      7.82 ***                             7.84 ***                               7.84 **
> *                                7.88 ***                              7.87 ***               
                                                                                     (0.03)                        (0.03)                               (0.03)                                 (0.03)   
>                                (0.03)                                (0.03)                   
Number of observations                                                                10075                         10075                                10075                                  10075   
>                                 10075                                 10075                   
AIC                                                                                20008.79                                                           19981.76                                          
>                              19967.15                                                         
BIC                                                                                20145.92                                                           20126.12                                          
>                              20118.73                                                         
Adjusted R-squared                                                                     0.31                                                               0.31                                          
>                                  0.31                                                         
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> ----------------------------------------------------------------------------------------------
*** p<.001, ** p<.01, * p<.05
Data Source: UKHLS Wave 10. Adjusted for Complex Sample. n=10,307
(collection ETable exported to file incomegendermodels.docx)

. 

eee

NameError: name 'eee' is not defined

%%stata 

svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 

margins sex, atmeans saving(margins3one, replace)
marginsplot, recast(scatter) ///
    plotopts(mcolor(dknavy)) ///
    title("Margins by Sex") ///
    saving(margins3one, replace)


margins, at(gender_performance=(-2(0.1)2)) atmeans saving(margins3two, replace) 
marginsplot, recast(line) recastci(rarea) saving(margins3two, replace) 

margins sex, at(gender_performance=(-2(0.1)2)) saving(margins3three, replace)
marginsplot, recast(line) recastci(rarea) saving(margins3three, replace) 

combomarginsplot margins3two margins3three, ///
    recast(line) recastci(rarea) ///
    title("Margins of Gender Performance by Sex") ///
    saving(margins2comb, replace) ///
    legend(order(1 "GP" 2 "GP Female" 3 "GP Male") ring(0) pos(11)) ///
    plot1opts(lcolor(eltblue)) ///
    plot2opts(lcolor(dknavy)) ///
    plot3opts(lcolor(emerald)) ///
    ci1opts(color(eltblue%30)) ///
    ci2opts(color(dknavy%30)) ///
    ci3opts(color(emerald%30))




margins, at(gender_performance_2=(0(0.1)4)) atmeans saving(margins3four, replace)
marginsplot, saving(margins3four, replace)

margins sex, at(gender_performance_2=(0(0.1)4)) saving(margins3five, replace)
marginsplot, saving(margins3five, replace)

combomarginsplot margins3four margins3five, saving(margins2comb2, replace) legend(off)

. 
. svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.ho
> usehours c.age_cgm c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(20, 2330)     =     194.89
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3206

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2659332   .0250108    10.63   0.000     .2168878    .3149786
gender_per~e |   .0752805   .0131093     5.74   0.000     .0495736    .1009874
gender_per~2 |  -.0219879    .009074    -2.42   0.015    -.0397818    -.004194
  househours |  -.0078036   .0013378    -5.83   0.000     -.010427   -.0051803
     age_cgm |  -.0125408   .0010116   -12.40   0.000    -.0145245   -.0105571
    age2_cgm |  -.0009201   .0000663   -13.89   0.000    -.0010501   -.0007902
             |
      ethnic |
Mixed/Mul..  |   .0410085   .0577073     0.71   0.477     -.072154     .154171
     Indian  |   .0311704   .0348317     0.89   0.371    -.0371337    .0994745
Pakistani..  |  -.1905147   .0495205    -3.85   0.000    -.2876231   -.0934063
    Chinese  |   .1770656   .0865498     2.05   0.041     .0073436    .3467877
Any other..  |   -.058578   .0635066    -0.92   0.356    -.1831129    .0659568
Black/Afr..  |  -.0347055    .058929    -0.59   0.556    -.1502638    .0808527
      Other  |   .3007145    .136221     2.21   0.027     .0335886    .5678404
             |
       nssec |
1.2 Highe..  |  -.1872532   .0327817    -5.71   0.000    -.2515372   -.1229691
2 Lower m..  |  -.3622905   .0254522   -14.23   0.000    -.4122016   -.3123794
3 Interme..  |   -.623402   .0296907   -21.00   0.000    -.6816248   -.5651792
4 Small e..  |  -1.190102    .054053   -22.02   0.000    -1.296098   -1.084105
5 Lower s..  |  -.5481736   .0294964   -18.58   0.000    -.6060154   -.4903318
6 Semi-ro..  |    -.83036   .0269328   -30.83   0.000    -.8831745   -.7775456
7 Routine..  |  -.9341831   .0334079   -27.96   0.000     -.999695   -.8686711
             |
       _cons |   7.867162   .0312074   252.09   0.000     7.805965    7.928359
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. 
. margins sex, atmeans saving(margins3one, replace)

Adjusted predictions

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Subpop. no. obs =      9,959
                                                  Subpop. size    =          .
Model VCE: Linearized                             Design df       =      2,349

Expression: Linear prediction, predict()
At: 0.sex                =  .4740695 (mean)
    1.sex                =  .5259305 (mean)
    gender_performance   =  .0450531 (mean)
    gender_performance_2 =  .9972269 (mean)
    househours           =  8.955378 (mean)
    age_cgm              = -2.914182 (mean)
    age2_cgm             =  133.1745 (mean)
    1.ethnic             =   .927727 (mean)
    2.ethnic             =  .0114951 (mean)
    3.ethnic             =  .0216869 (mean)
    4.ethnic             =  .0127135 (mean)
    5.ethnic             =  .0029637 (mean)
    6.ethnic             =  .0073807 (mean)
    7.ethnic             =  .0139619 (mean)
    8.ethnic             =  .0020713 (mean)
    1.nssec              =   .055246 (mean)
    2.nssec              =  .0980403 (mean)
    3.nssec              =   .324239 (mean)
    4.nssec              =  .1248197 (mean)
    5.nssec              =  .1024653 (mean)
    6.nssec              =  .0697344 (mean)
    7.nssec              =  .1442067 (mean)
    8.nssec              =  .0812487 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
     Female  |   7.122252   .0157225   453.00   0.000      7.09142    7.153083
       Male  |   7.388185    .014123   523.13   0.000      7.36049     7.41588
------------------------------------------------------------------------------

. marginsplot, recast(scatter) ///
>     plotopts(mcolor(dknavy)) ///
>     title("Margins by Sex") ///
>     saving(margins3one, replace)

Variables that uniquely identify margins: sex
file margins3one.gph saved

. 
. 
. margins, at(gender_performance=(-2(0.1)2)) atmeans saving(margins3two, replac
> e) 

Adjusted predictions

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Subpop. no. obs =      9,959
                                                  Subpop. size    =          .
Model VCE: Linearized                             Design df       =      2,349

Expression: Linear prediction, predict()
1._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        -2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
2._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.9
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
3._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.8
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
4._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.7
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
5._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.6
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
6._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.5
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
7._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.4
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
8._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.3
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
9._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
10._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =      -1.1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
11._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        -1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
12._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.9
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
13._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.8
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
14._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.7
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
15._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.6
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
16._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.5
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
17._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.4
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
18._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.3
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
19._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
20._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       -.1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
21._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =         0
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
22._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
23._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
24._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .3
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
25._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .4
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
26._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .5
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
27._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .6
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
28._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .7
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
29._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .8
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
30._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =        .9
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
31._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =         1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
32._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.1
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
33._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
34._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.3
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
35._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.4
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
36._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.5
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
37._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.6
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
38._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.7
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
39._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.8
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
40._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =       1.9
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
41._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =         2
        gender_perform~2 =  .9972269 (mean)
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   7.108161   .0275374   258.13   0.000     7.054161    7.162161
          2  |   7.115689   .0262876   270.69   0.000      7.06414    7.167239
          3  |   7.123217    .025044   284.43   0.000     7.074107    7.172328
          4  |   7.130745   .0238076   299.52   0.000     7.084059    7.177431
          5  |   7.138273   .0225796   316.14   0.000     7.093995    7.182552
          6  |   7.145802   .0213615   334.52   0.000     7.103912    7.187691
          7  |    7.15333   .0201551   354.91   0.000     7.113806    7.192853
          8  |   7.160858   .0189625   377.63   0.000     7.123673    7.198043
          9  |   7.168386   .0177866   403.02   0.000     7.133507    7.203265
         10  |   7.175914   .0166309   431.48   0.000     7.143301    7.208526
         11  |   7.183442   .0154999   463.45   0.000     7.153047    7.213837
         12  |    7.19097   .0143994   499.39   0.000     7.162733    7.219207
         13  |   7.198498   .0133371   539.73   0.000     7.172344    7.224652
         14  |   7.206026   .0123228   584.77   0.000     7.181861    7.230191
         15  |   7.213554   .0113692   634.48   0.000     7.191259    7.235849
         16  |   7.221082   .0104931   688.17   0.000     7.200505    7.241659
         17  |    7.22861   .0097153   744.04   0.000     7.209559    7.247662
         18  |   7.236138   .0090613   798.57   0.000     7.218369    7.253907
         19  |   7.243666   .0085595   846.27   0.000     7.226881    7.260451
         20  |   7.251194   .0082377   880.25   0.000      7.23504    7.267348
         21  |   7.258722   .0081173   894.23   0.000     7.242805     7.27464
         22  |    7.26625   .0082072   885.35   0.000     7.250156    7.282344
         23  |   7.273778   .0085008   855.66   0.000     7.257109    7.290448
         24  |   7.281306   .0089781   811.01   0.000     7.263701    7.298912
         25  |   7.288835   .0096117   758.33   0.000     7.269986    7.307683
         26  |   7.296363   .0103731   703.39   0.000     7.276021    7.316704
         27  |   7.303891   .0112363   650.03   0.000     7.281856    7.325925
         28  |   7.311419   .0121797   600.29   0.000     7.287535    7.335303
         29  |   7.318947   .0131861   555.05   0.000     7.293089    7.344804
         30  |   7.326475   .0142421   514.42   0.000     7.298546    7.354403
         31  |   7.334003   .0153375   478.17   0.000     7.303926    7.364079
         32  |   7.341531   .0164644   445.90   0.000     7.309245    7.373817
         33  |   7.349059   .0176169   417.16   0.000     7.314513    7.383605
         34  |   7.356587   .0187901   391.51   0.000      7.31974    7.393434
         35  |   7.364115   .0199804   368.57   0.000     7.324934    7.403296
         36  |   7.371643    .021185   347.97   0.000       7.3301    7.413186
         37  |   7.379171   .0224015   329.41   0.000     7.335242      7.4231
         38  |   7.386699   .0236281   312.62   0.000     7.340365    7.433033
         39  |   7.394227   .0248634   297.39   0.000     7.345471    7.442984
         40  |   7.401755    .026106   283.53   0.000     7.350562    7.452948
         41  |   7.409283    .027355   270.86   0.000     7.355641    7.462926
------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) saving(margins3two, replace) 

Variables that uniquely identify margins: gender_performance
file margins3two.gph saved

. 
. margins sex, at(gender_performance=(-2(0.1)2)) saving(margins3three, replace)

Predictive margins

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Subpop. no. obs =      9,959
                                                  Subpop. size    =          .
Model VCE: Linearized                             Design df       =      2,349

Expression: Linear prediction, predict()
1._at:  gender_perform~e =   -2
2._at:  gender_perform~e = -1.9
3._at:  gender_perform~e = -1.8
4._at:  gender_perform~e = -1.7
5._at:  gender_perform~e = -1.6
6._at:  gender_perform~e = -1.5
7._at:  gender_perform~e = -1.4
8._at:  gender_perform~e = -1.3
9._at:  gender_perform~e = -1.2
10._at: gender_perform~e = -1.1
11._at: gender_perform~e =   -1
12._at: gender_perform~e =  -.9
13._at: gender_perform~e =  -.8
14._at: gender_perform~e =  -.7
15._at: gender_perform~e =  -.6
16._at: gender_perform~e =  -.5
17._at: gender_perform~e =  -.4
18._at: gender_perform~e =  -.3
19._at: gender_perform~e =  -.2
20._at: gender_perform~e =  -.1
21._at: gender_perform~e =    0
22._at: gender_perform~e =   .1
23._at: gender_perform~e =   .2
24._at: gender_perform~e =   .3
25._at: gender_perform~e =   .4
26._at: gender_perform~e =   .5
27._at: gender_perform~e =   .6
28._at: gender_perform~e =   .7
29._at: gender_perform~e =   .8
30._at: gender_perform~e =   .9
31._at: gender_perform~e =    1
32._at: gender_perform~e =  1.1
33._at: gender_perform~e =  1.2
34._at: gender_perform~e =  1.3
35._at: gender_perform~e =  1.4
36._at: gender_perform~e =  1.5
37._at: gender_perform~e =  1.6
38._at: gender_perform~e =  1.7
39._at: gender_perform~e =  1.8
40._at: gender_perform~e =  1.9
41._at: gender_perform~e =    2

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     _at#sex |
   1#Female  |   6.968299   .0207269   336.20   0.000     6.927654    7.008944
     1#Male  |   7.234232    .036758   196.81   0.000     7.162151    7.306314
   2#Female  |   6.975827   .0196789   354.48   0.000     6.937237    7.014417
     2#Male  |    7.24176   .0355163   203.90   0.000     7.172114    7.311407
   3#Female  |   6.983355   .0186642   374.16   0.000     6.946755    7.019955
     3#Male  |   7.249288   .0342797   211.47   0.000     7.182067     7.31651
   4#Female  |   6.990883   .0176884   395.22   0.000     6.956197     7.02557
     4#Male  |   7.256816   .0330489   219.58   0.000     7.192008    7.321624
   5#Female  |   6.998411   .0167584   417.61   0.000     6.965548    7.031274
     5#Male  |   7.264344   .0318245   228.26   0.000     7.201937    7.326751
   6#Female  |   7.005939   .0158823   441.12   0.000     6.974794    7.037084
     6#Male  |   7.271872   .0306073   237.59   0.000     7.211852    7.331893
   7#Female  |   7.013467   .0150694   465.41   0.000     6.983917    7.043018
     7#Male  |     7.2794   .0293981   247.61   0.000     7.221751    7.337049
   8#Female  |   7.020995   .0143305   489.93   0.000     6.992894    7.049097
     8#Male  |   7.286928   .0281981   258.42   0.000     7.231633    7.342224
   9#Female  |   7.028523   .0136776   513.87   0.000     7.001702    7.055345
     9#Male  |   7.294457   .0270083   270.08   0.000     7.241494    7.347419
  10#Female  |   7.036051   .0131235   536.14   0.000     7.010316    7.061786
    10#Male  |   7.301985   .0258303   282.69   0.000     7.251332    7.352637
  11#Female  |   7.043579   .0126813   555.43   0.000     7.018712    7.068447
    11#Male  |   7.309513   .0246656   296.34   0.000     7.261144    7.357881
  12#Female  |   7.051107   .0123628   570.35   0.000     7.026864    7.075351
    12#Male  |   7.317041   .0235164   311.15   0.000     7.270926    7.363156
  13#Female  |   7.058636   .0121779   579.63   0.000     7.034755    7.082516
    13#Male  |   7.324569    .022385   327.21   0.000     7.280672    7.368465
  14#Female  |   7.066164   .0121326   582.41   0.000     7.042372    7.089955
    14#Male  |   7.332097   .0212742   344.65   0.000     7.290379    7.373815
  15#Female  |   7.073692   .0122285   578.46   0.000     7.049712    7.097671
    15#Male  |   7.339625   .0201874   363.58   0.000     7.300038    7.379212
  16#Female  |    7.08122   .0124623   568.21   0.000     7.056781    7.105658
    16#Male  |   7.347153   .0191287   384.09   0.000     7.309642    7.384664
  17#Female  |   7.088748   .0128265   552.66   0.000     7.063595      7.1139
    17#Male  |   7.354681    .018103   406.27   0.000     7.319181    7.390181
  18#Female  |   7.096276   .0133104   533.14   0.000     7.070174    7.122377
    18#Male  |   7.362209   .0171164   430.13   0.000     7.328644    7.395774
  19#Female  |   7.103804   .0139015   511.01   0.000     7.076543    7.131064
    19#Male  |   7.369737   .0161759   455.60   0.000     7.338017    7.401458
  20#Female  |   7.111332   .0145867   487.52   0.000     7.082728    7.139936
    20#Male  |   7.377265     .01529   482.49   0.000     7.347282    7.407248
  21#Female  |    7.11886   .0153535   463.66   0.000     7.088752    7.148968
    21#Male  |   7.384793   .0144688   510.39   0.000      7.35642    7.413166
  22#Female  |   7.126388   .0161903   440.16   0.000     7.094639    7.158137
    22#Male  |   7.392321   .0137239   538.65   0.000     7.365409    7.419233
  23#Female  |   7.133916   .0170868   417.51   0.000     7.100409    7.167423
    23#Male  |   7.399849   .0130683   566.24   0.000     7.374223    7.425476
  24#Female  |   7.141444   .0180341   396.00   0.000      7.10608    7.176808
    24#Male  |   7.407377   .0125161   591.83   0.000     7.382833    7.431921
  25#Female  |   7.148972   .0190246   375.78   0.000     7.111665    7.186279
    25#Male  |   7.414905   .0120816   613.74   0.000     7.391214    7.438597
  26#Female  |     7.1565   .0200519   356.90   0.000     7.117179    7.195821
    26#Male  |   7.422433   .0117776   630.22   0.000     7.399338    7.445529
  27#Female  |   7.164028   .0211106   339.36   0.000     7.122631    7.205426
    27#Male  |   7.429961   .0116144   639.72   0.000     7.407186    7.452737
  28#Female  |   7.171556   .0221963   323.10   0.000      7.12803    7.215083
    28#Male  |   7.437489   .0115981   641.27   0.000     7.414746    7.460233
  29#Female  |   7.179084   .0233052   308.05   0.000     7.133384    7.224785
    29#Male  |   7.445018   .0117292   634.74   0.000     7.422017    7.468018
  30#Female  |   7.186612    .024434   294.12   0.000     7.138698    7.234527
    30#Male  |   7.452546   .0120029   620.89   0.000     7.429008    7.476083
  31#Female  |    7.19414   .0255803   281.24   0.000     7.143978    7.244303
    31#Male  |   7.460074   .0124098   601.14   0.000     7.435738    7.484409
  32#Female  |   7.201669   .0267417   269.30   0.000     7.149229    7.254108
    32#Male  |   7.467602   .0129373   577.22   0.000     7.442232    7.492971
  33#Female  |   7.209197   .0279163   258.24   0.000     7.154453     7.26394
    33#Male  |    7.47513   .0135713   550.81   0.000     7.448517    7.501743
  34#Female  |   7.216725   .0291026   247.98   0.000     7.159655    7.273794
    34#Male  |   7.482658   .0142976   523.35   0.000      7.45462    7.510695
  35#Female  |   7.224253   .0302991   238.43   0.000     7.164837    7.283668
    35#Male  |   7.490186   .0151031   495.94   0.000     7.460569    7.519803
  36#Female  |   7.231781   .0315048   229.55   0.000     7.170001    7.293561
    36#Male  |   7.497714   .0159756   469.32   0.000     7.466386    7.529042
  37#Female  |   7.239309   .0327185   221.26   0.000     7.175149    7.303469
    37#Male  |   7.505242   .0169049   443.97   0.000     7.472092    7.538392
  38#Female  |   7.246837   .0339395   213.52   0.000     7.180282    7.313391
    38#Male  |    7.51277   .0178821   420.13   0.000     7.477704    7.547836
  39#Female  |   7.254365    .035167   206.28   0.000     7.185403    7.323326
    39#Male  |   7.520298   .0188997   397.91   0.000     7.483236     7.55736
  40#Female  |   7.261893   .0364003   199.50   0.000     7.190513    7.333273
    40#Male  |   7.527826   .0199515   377.31   0.000     7.488702    7.566951
  41#Female  |   7.269421   .0376388   193.14   0.000     7.195612     7.34323
    41#Male  |   7.535354   .0210325   358.27   0.000      7.49411    7.576598
------------------------------------------------------------------------------

. marginsplot, recast(line) recastci(rarea) saving(margins3three, replace) 

Variables that uniquely identify margins: gender_performance sex
file margins3three.gph saved

. 
. combomarginsplot margins3two margins3three, ///
>     recast(line) recastci(rarea) ///
>     title("Margins of Gender Performance by Sex") ///
>     saving(margins2comb, replace) ///
>     legend(order(1 "GP" 2 "GP Female" 3 "GP Male") ring(0) pos(11)) ///
>     plot1opts(lcolor(eltblue)) ///
>     plot2opts(lcolor(dknavy)) ///
>     plot3opts(lcolor(emerald)) ///
>     ci1opts(color(eltblue%30)) ///
>     ci2opts(color(dknavy%30)) ///
>     ci3opts(color(emerald%30))
Warning: statistics differ for sex: file 1=mean, file 2=asobserved;  using firs
> t (mean)
Warning: statistics differ for gender_performance_2: file 1=mean, file 2=asobse
> rved;  using first (mean)
Warning: statistics differ for househours: file 1=mean, file 2=asobserved;  usi
> ng first (mean)
Warning: statistics differ for age_cgm: file 1=mean, file 2=asobserved;  using 
> first (mean)
Warning: statistics differ for age2_cgm: file 1=mean, file 2=asobserved;  using
>  first (mean)
Warning: statistics differ for ethnic: file 1=mean, file 2=asobserved;  using f
> irst (mean)
Warning: statistics differ for nssec: file 1=mean, file 2=asobserved;  using fi
> rst (mean)

  Variables that uniquely identify margins: gender_performance _filenumber
      sex
file margins2comb.gph saved

. 
. 
. 
. 
. margins, at(gender_performance_2=(0(0.1)4)) atmeans saving(margins3four, repl
> ace)

Adjusted predictions

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Subpop. no. obs =      9,959
                                                  Subpop. size    =          .
Model VCE: Linearized                             Design df       =      2,349

Expression: Linear prediction, predict()
1._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =         0
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
2._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .1
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
3._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .2
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
4._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .3
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
5._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .4
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
6._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .5
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
7._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .6
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
8._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .7
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
9._at:  0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .8
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
10._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =        .9
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
11._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =         1
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
12._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.1
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
13._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.2
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
14._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.3
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
15._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.4
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
16._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.5
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
17._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.6
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
18._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.7
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
19._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.8
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
20._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       1.9
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
21._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =         2
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
22._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.1
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
23._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.2
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
24._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.3
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
25._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.4
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
26._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.5
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
27._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.6
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
28._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.7
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
29._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.8
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
30._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       2.9
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
31._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =         3
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
32._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.1
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
33._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.2
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
34._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.3
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
35._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.4
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
36._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.5
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
37._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.6
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
38._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.7
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
39._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.8
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
40._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =       3.9
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)
41._at: 0.sex            =  .4740695 (mean)
        1.sex            =  .5259305 (mean)
        gender_perform~e =  .0450531 (mean)
        gender_perform~2 =         4
        househours       =  8.955378 (mean)
        age_cgm          = -2.914182 (mean)
        age2_cgm         =  133.1745 (mean)
        1.ethnic         =   .927727 (mean)
        2.ethnic         =  .0114951 (mean)
        3.ethnic         =  .0216869 (mean)
        4.ethnic         =  .0127135 (mean)
        5.ethnic         =  .0029637 (mean)
        6.ethnic         =  .0073807 (mean)
        7.ethnic         =  .0139619 (mean)
        8.ethnic         =  .0020713 (mean)
        1.nssec          =   .055246 (mean)
        2.nssec          =  .0980403 (mean)
        3.nssec          =   .324239 (mean)
        4.nssec          =  .1248197 (mean)
        5.nssec          =  .1024653 (mean)
        6.nssec          =  .0697344 (mean)
        7.nssec          =  .1442067 (mean)
        8.nssec          =  .0812487 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   7.284041   .0120926   602.35   0.000     7.260328    7.307754
          2  |   7.281842   .0114373   636.68   0.000     7.259414     7.30427
          3  |   7.279643   .0108184   672.90   0.000     7.258429    7.300858
          4  |   7.277445   .0102426   710.51   0.000     7.257359     7.29753
          5  |   7.275246   .0097175   748.67   0.000      7.25619    7.294302
          6  |   7.273047   .0092519   786.12   0.000     7.254904     7.29119
          7  |   7.270848    .008855   821.10   0.000     7.253484    7.288213
          8  |   7.268649   .0085364   851.48   0.000      7.25191    7.285389
          9  |   7.266451   .0083053   874.92   0.000     7.250164    7.282737
         10  |   7.264252   .0081689   889.25   0.000     7.248233    7.280271
         11  |   7.262053   .0081321   893.01   0.000     7.246106       7.278
         12  |   7.259854   .0081963   885.75   0.000     7.243782    7.275927
         13  |   7.257655    .008359   868.25   0.000     7.241264    7.274047
         14  |   7.255457   .0086147   842.22   0.000     7.238563     7.27235
         15  |   7.253258   .0089555   809.92   0.000     7.235696    7.270819
         16  |   7.251059    .009372   773.69   0.000     7.232681    7.269437
         17  |    7.24886   .0098547   735.57   0.000     7.229535    7.268185
         18  |   7.246661   .0103944   697.17   0.000     7.226278    7.267045
         19  |   7.244463   .0109826   659.63   0.000     7.222926    7.265999
         20  |   7.242264    .011612   623.69   0.000     7.219493    7.265035
         21  |   7.240065   .0122763   589.76   0.000     7.215992    7.264138
         22  |   7.237866     .01297   558.05   0.000     7.212432      7.2633
         23  |   7.235667   .0136888   528.58   0.000     7.208824    7.262511
         24  |   7.233469   .0144288   501.32   0.000     7.205174    7.261763
         25  |    7.23127    .015187   476.15   0.000     7.201489    7.261051
         26  |   7.229071   .0159608   452.93   0.000     7.197772     7.26037
         27  |   7.226872    .016748   431.51   0.000      7.19403    7.259715
         28  |   7.224674   .0175468   411.74   0.000     7.190265    7.259082
         29  |   7.222475   .0183557   393.47   0.000      7.18648     7.25847
         30  |   7.220276   .0191734   376.58   0.000     7.182677    7.257875
         31  |   7.218077   .0199989   360.92   0.000      7.17886    7.257294
         32  |   7.215878   .0208312   346.40   0.000     7.175029    7.256728
         33  |    7.21368   .0216695   332.90   0.000     7.171186    7.256173
         34  |   7.211481   .0225132   320.32   0.000     7.167333    7.255629
         35  |   7.209282   .0233616   308.60   0.000      7.16347    7.255094
         36  |   7.207083   .0242143   297.64   0.000     7.159599    7.254567
         37  |   7.204884   .0250709   287.38   0.000     7.155721    7.254048
         38  |   7.202686   .0259309   277.76   0.000     7.151836    7.253535
         39  |   7.200487   .0267941   268.73   0.000     7.147944    7.253029
         40  |   7.198288     .02766   260.24   0.000     7.144047    7.252529
         41  |   7.196089   .0285286   252.24   0.000     7.140145    7.252033
------------------------------------------------------------------------------

. marginsplot, saving(margins3four, replace)

Variables that uniquely identify margins: gender_performance_2
file margins3four.gph saved

. 
. margins sex, at(gender_performance_2=(0(0.1)4)) saving(margins3five, replace)

Predictive margins

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Subpop. no. obs =      9,959
                                                  Subpop. size    =          .
Model VCE: Linearized                             Design df       =      2,349

Expression: Linear prediction, predict()
1._at:  gender_perform~2 =   0
2._at:  gender_perform~2 =  .1
3._at:  gender_perform~2 =  .2
4._at:  gender_perform~2 =  .3
5._at:  gender_perform~2 =  .4
6._at:  gender_perform~2 =  .5
7._at:  gender_perform~2 =  .6
8._at:  gender_perform~2 =  .7
9._at:  gender_perform~2 =  .8
10._at: gender_perform~2 =  .9
11._at: gender_perform~2 =   1
12._at: gender_perform~2 = 1.1
13._at: gender_perform~2 = 1.2
14._at: gender_perform~2 = 1.3
15._at: gender_perform~2 = 1.4
16._at: gender_perform~2 = 1.5
17._at: gender_perform~2 = 1.6
18._at: gender_perform~2 = 1.7
19._at: gender_perform~2 = 1.8
20._at: gender_perform~2 = 1.9
21._at: gender_perform~2 =   2
22._at: gender_perform~2 = 2.1
23._at: gender_perform~2 = 2.2
24._at: gender_perform~2 = 2.3
25._at: gender_perform~2 = 2.4
26._at: gender_perform~2 = 2.5
27._at: gender_perform~2 = 2.6
28._at: gender_perform~2 = 2.7
29._at: gender_perform~2 = 2.8
30._at: gender_perform~2 = 2.9
31._at: gender_perform~2 =   3
32._at: gender_perform~2 = 3.1
33._at: gender_perform~2 = 3.2
34._at: gender_perform~2 = 3.3
35._at: gender_perform~2 = 3.4
36._at: gender_perform~2 = 3.5
37._at: gender_perform~2 = 3.6
38._at: gender_perform~2 = 3.7
39._at: gender_perform~2 = 3.8
40._at: gender_perform~2 = 3.9
41._at: gender_perform~2 =   4

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     _at#sex |
   1#Female  |   7.144178   .0174591   409.20   0.000     7.109942    7.178415
     1#Male  |   7.410112   .0173156   427.94   0.000     7.376156    7.444067
   2#Female  |    7.14198   .0170779   418.20   0.000      7.10849    7.175469
     2#Male  |   7.407913   .0168041   440.84   0.000     7.374961    7.440865
   3#Female  |   7.139781   .0167372   426.58   0.000      7.10696    7.172602
     3#Male  |   7.405714    .016327   453.59   0.000     7.373697    7.437731
   4#Female  |   7.137582   .0164397   434.17   0.000     7.105344     7.16982
     4#Male  |   7.403515   .0158875   466.00   0.000      7.37236     7.43467
   5#Female  |   7.135383   .0161875   440.79   0.000      7.10364    7.167127
     5#Male  |   7.401317   .0154887   477.85   0.000     7.370944     7.43169
   6#Female  |   7.133185    .015983   446.30   0.000     7.101842    7.164527
     6#Male  |   7.399118   .0151339   488.91   0.000     7.369441    7.428795
   7#Female  |   7.130986    .015828   450.53   0.000     7.099947    7.162024
     7#Male  |   7.396919   .0148262   498.91   0.000     7.367845    7.425993
   8#Female  |   7.128787   .0157239   453.37   0.000     7.097953    7.159621
     8#Male  |    7.39472   .0145686   507.58   0.000     7.366152    7.423289
   9#Female  |   7.126588   .0156717   454.74   0.000     7.095856     7.15732
     9#Male  |   7.392521   .0143638   514.66   0.000     7.364354    7.420688
  10#Female  |   7.124389    .015672   454.59   0.000     7.093657    7.155122
    10#Male  |   7.390323   .0142141   519.93   0.000     7.362449    7.418196
  11#Female  |   7.122191   .0157247   452.93   0.000     7.091355    7.153026
    11#Male  |   7.388124   .0141212   523.19   0.000     7.360433    7.415815
  12#Female  |   7.119992   .0158293   449.80   0.000     7.088951    7.151033
    12#Male  |   7.385925   .0140863   524.34   0.000     7.358302    7.413548
  13#Female  |   7.117793   .0159848   445.28   0.000     7.086447    7.149139
    13#Male  |   7.383726   .0141097   523.31   0.000     7.356057    7.411395
  14#Female  |   7.115594   .0161898   439.51   0.000     7.083846    7.147342
    14#Male  |   7.381527   .0141913   520.14   0.000     7.353699    7.409356
  15#Female  |   7.113395   .0164424   432.63   0.000     7.081152    7.145639
    15#Male  |   7.379329     .01433   514.96   0.000     7.351228    7.407429
  16#Female  |   7.111197   .0167404   424.79   0.000     7.078369    7.144024
    16#Male  |    7.37713   .0145241   507.92   0.000     7.348648    7.405611
  17#Female  |   7.108998   .0170815   416.18   0.000     7.075502    7.142494
    17#Male  |   7.374931   .0147715   499.27   0.000     7.345964    7.403898
  18#Female  |   7.106799   .0174631   406.96   0.000     7.072554    7.141044
    18#Male  |   7.372732   .0150696   489.24   0.000     7.343181    7.402283
  19#Female  |     7.1046   .0178826   397.29   0.000     7.069533    7.139668
    19#Male  |   7.370533   .0154154   478.13   0.000     7.340304    7.400763
  20#Female  |   7.102401   .0183375   387.32   0.000     7.066442    7.138361
    20#Male  |   7.368335   .0158058   466.18   0.000      7.33734     7.39933
  21#Female  |   7.100203   .0188251   377.17   0.000     7.063287    7.137118
    21#Male  |   7.366136   .0162376   453.65   0.000     7.334294    7.397977
  22#Female  |   7.098004   .0193431   366.95   0.000     7.060073    7.135935
    22#Male  |   7.363937   .0167076   440.75   0.000     7.331174      7.3967
  23#Female  |   7.095805    .019889   356.77   0.000     7.056803    7.134807
    23#Male  |   7.361738   .0172125   427.70   0.000     7.327985    7.395492
  24#Female  |   7.093606   .0204605   346.70   0.000     7.053484    7.133729
    24#Male  |    7.35954   .0177496   414.63   0.000     7.324733    7.394346
  25#Female  |   7.091408   .0210557   336.79   0.000     7.050118    7.132697
    25#Male  |   7.357341   .0183158   401.69   0.000     7.321424    7.393258
  26#Female  |   7.089209   .0216725   327.11   0.000     7.046709    7.131708
    26#Male  |   7.355142   .0189087   388.98   0.000     7.318062    7.392221
  27#Female  |    7.08701   .0223092   317.67   0.000     7.043262    7.130758
    27#Male  |   7.352943   .0195258   376.58   0.000     7.314654    7.391233
  28#Female  |   7.084811   .0229641   308.52   0.000     7.039779    7.129843
    28#Male  |   7.350744   .0201648   364.53   0.000     7.311202    7.390287
  29#Female  |   7.082612   .0236357   299.66   0.000     7.036263    7.128961
    29#Male  |   7.348546   .0208237   352.89   0.000     7.307711     7.38938
  30#Female  |   7.080414   .0243227   291.10   0.000     7.032717     7.12811
    30#Male  |   7.346347   .0215008   341.68   0.000     7.304184    7.388509
  31#Female  |   7.078215   .0250236   282.86   0.000     7.029144    7.127285
    31#Male  |   7.344148   .0221943   330.90   0.000     7.300625     7.38767
  32#Female  |   7.076016   .0257375   274.93   0.000     7.025545    7.126487
    32#Male  |   7.341949   .0229028   320.57   0.000     7.297037    7.386861
  33#Female  |   7.073817   .0264632   267.31   0.000     7.021924    7.125711
    33#Male  |    7.33975   .0236249   310.68   0.000     7.293423    7.386078
  34#Female  |   7.071618   .0271998   259.99   0.000      7.01828    7.124957
    34#Male  |   7.337552   .0243593   301.22   0.000     7.289784     7.38532
  35#Female  |    7.06942   .0279465   252.96   0.000     7.014617    7.124222
    35#Male  |   7.335353   .0251051   292.19   0.000     7.286122    7.384583
  36#Female  |   7.067221   .0287025   246.22   0.000     7.010936    7.123506
    36#Male  |   7.333154   .0258612   283.56   0.000     7.282441    7.383867
  37#Female  |   7.065022    .029467   239.76   0.000     7.007238    7.122806
    37#Male  |   7.330955   .0266268   275.32   0.000     7.278741     7.38317
  38#Female  |   7.062823   .0302394   233.56   0.000     7.003525    7.122122
    38#Male  |   7.328756   .0274011   267.46   0.000     7.275024    7.382489
  39#Female  |   7.060624   .0310191   227.62   0.000     6.999797    7.121452
    39#Male  |   7.326558   .0281832   259.96   0.000     7.271291    7.381824
  40#Female  |   7.058426   .0318056   221.92   0.000     6.996056    7.120796
    40#Male  |   7.324359   .0289727   252.80   0.000     7.267544    7.381174
  41#Female  |   7.056227   .0325983   216.46   0.000     6.992302    7.120151
    41#Male  |    7.32216   .0297689   245.97   0.000     7.263784    7.380536
------------------------------------------------------------------------------

. marginsplot, saving(margins3five, replace)

Variables that uniquely identify margins: gender_performance_2 sex
file margins3five.gph saved

. 
. combomarginsplot margins3four margins3five, saving(margins2comb2, replace) le
> gend(off)
Warning: statistics differ for sex: file 1=mean, file 2=asobserved;  using firs
> t (mean)
Warning: statistics differ for gender_performance: file 1=mean, file 2=asobserv
> ed;  using first (mean)
Warning: statistics differ for househours: file 1=mean, file 2=asobserved;  usi
> ng first (mean)
Warning: statistics differ for age_cgm: file 1=mean, file 2=asobserved;  using 
> first (mean)
Warning: statistics differ for age2_cgm: file 1=mean, file 2=asobserved;  using
>  first (mean)
Warning: statistics differ for ethnic: file 1=mean, file 2=asobserved;  using f
> irst (mean)
Warning: statistics differ for nssec: file 1=mean, file 2=asobserved;  using fi
> rst (mean)

  Variables that uniquely identify margins: gender_performance_2 _filenumber
      sex
file margins2comb2.gph saved

. 

%%stata

graph combine margins3one.gph margins2comb.gph, ycommon saving(marginsmodel3, replace)
graph export "Margins.png", width(2000) replace

. 
. graph combine margins3one.gph margins2comb.gph, ycommon saving(marginsmodel3,
>  replace)
file marginsmodel3.gph saved

. graph export "Margins.png", width(2000) replace
(file Margins.png not found)
file Margins.png written in PNG format

. 

Model Diagnostics

%%stata

regress logincome i.sex c.gender_performance c.gender_performance_2 c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 

vif

predict residuals, residuals 

. 
. regress logincome i.sex c.gender_performance c.gender_performance_2 c.househo
> urs c.age_cgm c.age2_cgm i.ethnic i.nssec 

      Source |       SS           df       MS      Number of obs   =    10,075
-------------+----------------------------------   F(20, 10054)    =    225.47
       Model |  1911.83708        20   95.591854   Prob > F        =    0.0000
    Residual |  4262.52861    10,054  .423963458   R-squared       =    0.3096
-------------+----------------------------------   Adj R-squared   =    0.3083
       Total |  6174.36569    10,074  .612901101   Root MSE        =    .65112

------------------------------------------------------------------------------
   logincome | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2537441   .0203492    12.47   0.000     .2138556    .2936325
gender_per~e |   .0582717   .0106844     5.45   0.000     .0373281    .0792153
gender_per~2 |  -.0297259   .0072976    -4.07   0.000    -.0440306   -.0154212
  househours |  -.0089307   .0010121    -8.82   0.000    -.0109145   -.0069469
     age_cgm |  -.0120031   .0006793   -17.67   0.000    -.0133347   -.0106715
    age2_cgm |  -.0008635    .000045   -19.20   0.000    -.0009517   -.0007753
             |
      ethnic |
Mixed/Mul..  |  -.0526234   .0540714    -0.97   0.330    -.1586142    .0533674
     Indian  |   .0223161   .0335938     0.66   0.507    -.0435345    .0881667
Pakistani..  |  -.2544197    .035102    -7.25   0.000    -.3232266   -.1856129
    Chinese  |   .1819252   .0997459     1.82   0.068    -.0135968    .3774471
Any other..  |  -.0209381    .065259    -0.32   0.748    -.1488587    .1069825
Black/Afr..  |  -.0082169   .0421218    -0.20   0.845    -.0907841    .0743503
      Other  |   .0739938   .1212348     0.61   0.542    -.1636507    .3116383
             |
       nssec |
1.2 Highe..  |  -.1692417   .0353953    -4.78   0.000    -.2386235   -.0998599
2 Lower m..  |   -.341235   .0310473   -10.99   0.000    -.4020939    -.280376
3 Interme..  |  -.6182045   .0342913   -18.03   0.000    -.6854224   -.5509866
4 Small e..  |  -1.184603   .0354813   -33.39   0.000    -1.254153   -1.115053
5 Lower s..  |  -.5461982   .0379382   -14.40   0.000    -.6205646   -.4718318
6 Semi-ro..  |  -.8268426   .0339201   -24.38   0.000    -.8933328   -.7603525
7 Routine..  |  -.9116519   .0371798   -24.52   0.000    -.9845317   -.8387721
             |
       _cons |   7.877381   .0335512   234.79   0.000     7.811614    7.943148
------------------------------------------------------------------------------

. 
. vif

    Variable |       VIF       1/VIF  
-------------+----------------------
       1.sex |      2.46    0.406494
gender_per~e |      2.71    0.368658
gender_per~2 |      1.03    0.968101
  househours |      1.35    0.738659
     age_cgm |      1.35    0.741353
    age2_cgm |      1.30    0.768253
      ethnic |
          2  |      1.01    0.994394
          3  |      1.01    0.987482
          4  |      1.04    0.960422
          5  |      1.00    0.995239
          6  |      1.00    0.995640
          7  |      1.01    0.991739
          8  |      1.00    0.997534
       nssec |
          2  |      2.65    0.377039
          3  |      5.04    0.198261
          4  |      3.16    0.316565
          5  |      2.73    0.366400
          6  |      2.20    0.454627
          7  |      3.39    0.295324
          8  |      2.34    0.427206
-------------+----------------------
    Mean VIF |      1.94

. 
. predict residuals, residuals 

. 

%%stata

svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.househours c.age_cgm c.age2_cgm i.ethnic i.nssec 

coefplot, drop(_cons) xline(0) title("Full Analytical Model")
graph export "coefplot.png", width(2000) replace

. 
. svy: regress logincome i.sex c.gender_performance c.gender_performance_2 c.ho
> usehours c.age_cgm c.age2_cgm i.ethnic i.nssec 
(running regress on estimation sample)

Survey: Linear regression

Number of strata = 1,074                          Number of obs   =     10,075
Number of PSUs   = 3,423                          Population size = 9,295.5002
                                                  Design df       =      2,349
                                                  F(20, 2330)     =     194.89
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.3206

------------------------------------------------------------------------------
             |             Linearized
   logincome | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         sex |
       Male  |   .2659332   .0250108    10.63   0.000     .2168878    .3149786
gender_per~e |   .0752805   .0131093     5.74   0.000     .0495736    .1009874
gender_per~2 |  -.0219879    .009074    -2.42   0.015    -.0397818    -.004194
  househours |  -.0078036   .0013378    -5.83   0.000     -.010427   -.0051803
     age_cgm |  -.0125408   .0010116   -12.40   0.000    -.0145245   -.0105571
    age2_cgm |  -.0009201   .0000663   -13.89   0.000    -.0010501   -.0007902
             |
      ethnic |
Mixed/Mul..  |   .0410085   .0577073     0.71   0.477     -.072154     .154171
     Indian  |   .0311704   .0348317     0.89   0.371    -.0371337    .0994745
Pakistani..  |  -.1905147   .0495205    -3.85   0.000    -.2876231   -.0934063
    Chinese  |   .1770656   .0865498     2.05   0.041     .0073436    .3467877
Any other..  |   -.058578   .0635066    -0.92   0.356    -.1831129    .0659568
Black/Afr..  |  -.0347055    .058929    -0.59   0.556    -.1502638    .0808527
      Other  |   .3007145    .136221     2.21   0.027     .0335886    .5678404
             |
       nssec |
1.2 Highe..  |  -.1872532   .0327817    -5.71   0.000    -.2515372   -.1229691
2 Lower m..  |  -.3622905   .0254522   -14.23   0.000    -.4122016   -.3123794
3 Interme..  |   -.623402   .0296907   -21.00   0.000    -.6816248   -.5651792
4 Small e..  |  -1.190102    .054053   -22.02   0.000    -1.296098   -1.084105
5 Lower s..  |  -.5481736   .0294964   -18.58   0.000    -.6060154   -.4903318
6 Semi-ro..  |    -.83036   .0269328   -30.83   0.000    -.8831745   -.7775456
7 Routine..  |  -.9341831   .0334079   -27.96   0.000     -.999695   -.8686711
             |
       _cons |   7.867162   .0312074   252.09   0.000     7.805965    7.928359
------------------------------------------------------------------------------
Note: Variance scaled to handle strata with a single sampling unit.

. 
. coefplot, drop(_cons) xline(0) title("Full Analytical Model")

. graph export "coefplot.png", width(2000) replace
(file coefplot.png not found)
file coefplot.png written in PNG format

. 

%%stata

hist residuals, normal

. 
. hist residuals, normal
(bin=40, start=-9.9879751, width=.30881041)

. 

Results

Conclusions

Notes

Supplementary Materials

References