-----------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/log/acsAnalysis.txt
  log type:  text
 opened on:  29 Mar 2019, 14:30:12

. 
. #delimit ;
delimiter now ;
. local estopt cells(b(star fmt(%-9.3f)) se(fmt(%-9.3f) par([ ]) )) stats 
>              (N, fmt(%9.0gc) label(Observations))     
>              collabels(none) label;

. local wt     [pw=perwt];

. local lnote  "Heteroscedasticity robust standard errors are reported in 
>             parentheses. $ ^\ddagger $ Significance based on the Leamer criterion
>             at 5\%.";

. local Fnote  "F-test of age variables refers to the test-statistic on the test that
>               the coefficients on mother's age and age squared are jointly equal
>               to zero. ";

. local Xnote  "$ \chi^2 $ test statistics refer to the test that the coefficients
>               on mother's age and age squared are jointly equal to zero. ";

. local onote  "Optimal age calculates the turning point of the mother's age
>               quadratic. ";

. local qnote  "Birth quarter is based on \emph{actual} birth quarter.";

. #delimit cr
delimiter now cr
. 
. cap which estout

. if _rc!=0 ssc install estout

. 
. ********************************************************************************
. *** (2) Open data subset to sample of interest (from Sonia's import file)
. ********************************************************************************
. insheet using "$DAT/ACS_20052014_cleaned_all.csv", comma names case clear
(20 vars, 146,853 obs)

. 
. keep if motherAge>=20&motherAge<=45&twins==0
(3,509 observations deleted)

. tab year       , gen(_year)

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       2005 |     15,531       10.83       10.83
       2006 |     15,207       10.61       21.44
       2007 |     15,473       10.79       32.24
       2008 |     15,120       10.55       42.79
       2009 |     14,683       10.24       53.03
       2010 |     13,956        9.74       62.77
       2011 |     13,126        9.16       71.92
       2012 |     13,199        9.21       81.13
       2013 |     13,580        9.47       90.60
       2014 |     13,469        9.40      100.00
------------+-----------------------------------
      Total |    143,344      100.00

. tab statefip   , gen(_state)

   statefip |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      2,008        1.40        1.40
          2 |        338        0.24        1.64
          4 |      2,688        1.88        3.51
          5 |      1,157        0.81        4.32
          6 |     16,663       11.62       15.94
          8 |      2,857        1.99       17.94
          9 |      1,802        1.26       19.19
         10 |        385        0.27       19.46
         11 |        358        0.25       19.71
         12 |      7,671        5.35       25.06
         13 |      4,568        3.19       28.25
         15 |        553        0.39       28.64
         16 |        819        0.57       29.21
         17 |      6,184        4.31       33.52
         18 |      2,995        2.09       35.61
         19 |      1,495        1.04       36.65
         20 |      1,357        0.95       37.60
         21 |      2,088        1.46       39.06
         22 |      1,818        1.27       40.33
         23 |        520        0.36       40.69
         24 |      2,909        2.03       42.72
         25 |      3,598        2.51       45.23
         26 |      4,176        2.91       48.14
         27 |      2,590        1.81       49.95
         28 |      1,057        0.74       50.69
         29 |      2,891        2.02       52.70
         30 |        411        0.29       52.99
         31 |        889        0.62       53.61
         32 |      1,165        0.81       54.42
         33 |        617        0.43       54.85
         34 |      4,624        3.23       58.08
         35 |        671        0.47       58.55
         36 |      8,982        6.27       64.81
         37 |      4,643        3.24       68.05
         38 |        339        0.24       68.29
         39 |      5,446        3.80       72.09
         40 |      1,662        1.16       73.25
         41 |      1,874        1.31       74.55
         42 |      5,638        3.93       78.49
         44 |        493        0.34       78.83
         45 |      2,102        1.47       80.30
         46 |        400        0.28       80.58
         47 |      2,987        2.08       82.66
         48 |     11,187        7.80       90.46
         49 |      1,754        1.22       91.69
         50 |        291        0.20       91.89
         51 |      4,286        2.99       94.88
         53 |      3,702        2.58       97.46
         54 |        751        0.52       97.99
         55 |      2,622        1.83       99.82
         56 |        263        0.18      100.00
------------+-----------------------------------
      Total |    143,344      100.00

. 
. bys twoLevelOcc: gen counter = _N

. keep if counter>499
(1,027 observations deleted)

. drop counter

. tab twoLevelOcc, gen(_2occ)

                            twoLevelOcc |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
Architecture and Engineering Occupati.. |      1,471        1.03        1.03
Arts, Design, Entertainment, Sports, .. |      3,774        2.65        3.69
Building and Grounds Cleaning and Mai.. |      2,085        1.47        5.15
        Business Operations Specialists |      4,782        3.36        8.51
Community and Social Services Occupat.. |      3,651        2.57       11.08
  Computer and Mathematical Occupations |      2,826        1.99       13.06
Education, Training, and Library Occu.. |     16,373       11.50       24.57
                  Financial Specialists |      4,869        3.42       27.99
Food Preparation and Serving Occupati.. |      6,567        4.61       32.60
Healthcare Practitioners and Technica.. |     15,141       10.64       43.24
         Healthcare Support Occupations |      4,973        3.49       46.74
                      Legal Occupations |      2,630        1.85       48.58
Life, Physical, and Social Science Oc.. |      2,128        1.50       50.08
                 Management Occupations |     11,200        7.87       57.95
Office and Administrative Support Occ.. |     24,103       16.94       74.88
  Personal Care and Service Occupations |      6,661        4.68       79.56
                 Production Occupations |      3,145        2.21       81.77
         Protective Service Occupations |        951        0.67       82.44
                      Sales Occupations |     15,107       10.62       93.06
Transportation and Material Moving Oc.. |      1,600        1.12       94.18
                             Unemployed |      8,280        5.82      100.00
----------------------------------------+-----------------------------------
                                  Total |    142,317      100.00

. gen motherAge2  = motherAge*motherAge/100

. gen quarter2 = birthQuarter==2

. gen quarter3 = birthQuarter==3

. gen all      = 1

. gen logInc   = log(hhincome)
(1,007 missing values generated)

. gen other    = race!=1&race!=2

. 
. lab var quarter2     "Quarter 2"

. lab var quarter3     "Quarter 3"

. lab var motherAge    "Mother's Age"

. lab var motherAge2   "Mother's Age$^2$ / 100"

. lab var unemployment "Unemployment Rate"

. lab var logInc       "log(household income)"

. lab var highEduc     "Some College +"

. lab var black        "Black"

. lab var white        "White"

. lab var hispanic     "Hispanic"

. 
. **M is mother, F is father
. gen twoLevelOccM = twoLevelOcc

. foreach s in M F {
  2.     replace twoLevelOcc`s' = "No Occupation" if twoLevelOcc`s'=="Unemployed"
  3.     gen oc_Arch_`s' = twoLevelOcc`s'=="Architecture and Engineering Occupations"
  4.     gen oc_Arts_`s' = twoLevelOcc`s'=="Arts, Design, Entertainment, Sports, and Media Occupations"
  5.     gen oc_Build_`s'= twoLevelOcc`s'=="Building and Grounds Cleaning and Maintenance Occupations"
  6.     gen oc_Busi_`s' = twoLevelOcc`s'=="Business Operations Specialists"
  7.     gen oc_Comm_`s' = twoLevelOcc`s'=="Community and Social Services Occupations"
  8.     gen oc_Comp_`s' = twoLevelOcc`s'=="Computer and Mathematical Occupations"
  9.     gen oc_Cons_`s' = twoLevelOcc`s'=="Construction Trades"
 10.     gen oc_Educ_`s' = twoLevelOcc`s'=="Education, Training, and Library Occupations"
 11.     gen oc_Extr_`s' = twoLevelOcc`s'=="Extraction Workers"
 12.     gen oc_Farm_`s' = twoLevelOcc`s'=="Farming, Fishing, and Forestry Occupations"
 13.     gen oc_Fin_`s'  = twoLevelOcc`s'=="Financial Specialists"
 14.     gen oc_Food_`s' = twoLevelOcc`s'=="Food Preparation and Serving Occupations"
 15.     gen oc_HltP_`s' = twoLevelOcc`s'=="Healthcare Practitioners and Technical Occupations"
 16.     gen oc_HltS_`s' = twoLevelOcc`s'=="Healthcare Support Occupations"
 17.     gen oc_Insl_`s' = twoLevelOcc`s'=="Installation, Maintenance, and Repair Workers"
 18.     gen oc_Legl_`s' = twoLevelOcc`s'=="Legal Occupations"
 19.     gen oc_Sci_`s'  = twoLevelOcc`s'=="Life, Physical, and Social Science Occupations"
 20.     gen oc_Mgmt_`s' = twoLevelOcc`s'=="Management Occupations"
 21.     gen oc_Mil_`s'  = twoLevelOcc`s'=="Military Specific Occupations"
 22.     gen oc_Off_`s'  = twoLevelOcc`s'=="Office and Administrative Support Occupations"
 23.     gen oc_Care_`s' = twoLevelOcc`s'=="Personal Care and Service Occupations"
 24.     gen oc_Prod_`s' = twoLevelOcc`s'=="Production Occupations"
 25.     gen oc_Prot_`s' = twoLevelOcc`s'=="Protective Service Occupations"
 26.     gen oc_Sale_`s' = twoLevelOcc`s'=="Sales Occupations"
 27.     gen oc_Tran_`s' = twoLevelOcc`s'=="Transportation and Material Moving Occupations"
 28.     gen oc_Unem_`s' = twoLevelOcc`s'=="No Occupation"
 29. 
.     lab var oc_Arch_`s' "Architecture and Engineering"
 30.     lab var oc_Arts_`s' "Arts, Design, Entertainment, Sports, and Media"
 31.     lab var oc_Build_`s'"Building and Grounds Cleaning and Maintenance"
 32.     lab var oc_Busi_`s' "Business Operations Specialists"
 33.     lab var oc_Comm_`s' "Community and Social Services"
 34.     lab var oc_Comp_`s' "Computer and Mathematical"
 35.     lab var oc_Cons_`s' "Construction Trades"
 36.     lab var oc_Educ_`s' "Education, Training, and Library"
 37.     lab var oc_Extr_`s' "Extraction Workers"
 38.     lab var oc_Farm_`s' "Farming, Fishing, and Forestry"
 39.     lab var oc_Fin_`s'  "Financial Specialists"
 40.     lab var oc_Food_`s' "Food Preparation and Serving"
 41.     lab var oc_HltP_`s' "Healthcare Practitioners and Technical"
 42.     lab var oc_HltS_`s' "Healthcare Support"
 43.     lab var oc_Insl_`s' "Installation, Maintenance, and Repair Workers"
 44.     lab var oc_Legl_`s' "Legal"
 45.     lab var oc_Sci_`s'  "Life, Physical, and Social Science"
 46.     lab var oc_Mgmt_`s' "Management"
 47.     lab var oc_Mil_`s'  "Military Specific"
 48.     lab var oc_Off_`s'  "Office and Administrative Support"
 49.     lab var oc_Care_`s' "Personal Care and Service"
 50.     lab var oc_Prod_`s' "Production"
 51.     lab var oc_Prot_`s' "Protective Service"
 52.     lab var oc_Sale_`s' "Sales"
 53.     lab var oc_Tran_`s' "Transportation and Material Moving"
 54.     lab var oc_Unem_`s' "No Occupation"
 55. }
(8,280 real changes made)
(1,132 real changes made)

. gen oc_Unkn_F = twoLevelOccF==""

. lab var oc_Unkn_F "No Reported Occupation"

. 
. gen oc_OtherOc_M  = oc_Educ_M==0&oc_Unem_M==0

. gen oc_OtherOc1_M = oc_Educ_M==0&oc_Arts_M==0

. 
. ********************************************************************************
. *** (3) regressions: industry (by quarter) [TABLES 3, A15, A16, A17]
. ********************************************************************************
. keep if race<7
(8,234 observations deleted)

. bys twoLevelOcc:  gen counterM = _N

. bys twoLevelOccF: gen counterF = _N

. keep if counterF>499
(479 observations deleted)

. 
. #delimit ;
delimiter now ;
. local se  robust;

. local abs abs(statefip);

. local age motherAge motherAge2;

. local une ;

. local lv2 oc_Arch_M oc_Build_M oc_Busi_M oc_Comm_M oc_Comp_M oc_Educ_M oc_Fin_M
>           oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M oc_Mgmt_M oc_Off_M
>           oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M;

. local lv2b oc_Arch_M oc_Arts_M oc_Build_M oc_Busi_M oc_Comm_M oc_Comp_M oc_Educ_M
>            oc_Fin_M oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M oc_Mgmt_M
>            oc_Off_M oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M ;

. local lv2F oc_Arch_F oc_Build_F oc_Busi_F oc_Comm_F oc_Comp_F oc_Cons_F
>            oc_Educ_F oc_Farm_F oc_Fin_F oc_Food_F oc_HltP_F oc_HltS_F oc_Insl_F
>            oc_Legl_F oc_Sci_F oc_Mgmt_F oc_Mil_F oc_Off_F oc_Care_F oc_Prod_F
>            oc_Prot_F oc_Sale_F oc_Tran_F oc_Unkn_F;

. local lv2Fo oc_Arch_F oc_Build_F oc_Busi_F oc_Comm_F oc_Comp_F oc_Cons_F
>            oc_Educ_F oc_Farm_F oc_Fin_F oc_Food_F oc_HltP_F oc_HltS_F oc_Insl_F
>            oc_Legl_F oc_Sci_F oc_Mgmt_F oc_Mil_F oc_Off_F oc_Care_F oc_Prod_F
>            oc_Prot_F oc_Sale_F oc_Tran_F;

. local lv2bF oc_Arch_F oc_Arts_F oc_Build_F oc_Busi_F oc_Comm_F oc_Comp_F 
>            oc_Cons_F oc_Educ_F oc_Farm_F oc_Fin_F oc_Food_F oc_HltP_F oc_HltS_F 
>            oc_Insl_F oc_Legl_F oc_Sci_F oc_Mgmt_F oc_Mil_F oc_Off_F oc_Care_F 
>            oc_Prod_F oc_Prot_F oc_Sale_F oc_Tran_F oc_Unkn_F;

. #delimit cr
delimiter now cr
. 
. cap drop _2occ2

. gen nowork = workedyr==2

. lab var nowork "Did Not Work Last Year"

. 
. #delimit ;
delimiter now ;
. local add `" "(Married Mothers, 20--45)" "(Unmarried Mothers, 20--45)" "';

. local nam Married Unmarried;

. #delimit cr
delimiter now cr
. tokenize `nam'

. 
. local k=1

. foreach type of local add {
  2.     if `k'==1 local gg  motherAge>=20&motherAge<=45&married==1
  3.     if `k'==2 local gg  motherAge>=20&motherAge<=45&married==0
  4.     if `k'==1 local partner spouse
  5.     if `k'==2 local partner partner
  6.     
.     local edu highEduc black white hispanic 
  7.     local mar  unmarried
  8.     if `k'==1 local mar married
  9. 
.     preserve
 10.     keep if `gg'
 11.      
.     drop if occ2010 == 9920
 12.     drop _2occ21
 13.     eststo: areg quarter2 `age' `edu' `une' _year* `lv2' `wt', `se' `abs'
 14.     test `lv2'
 15.     estadd scalar F1 = r(F)
 16.     local L1   = string((e(df_r)/r(df))*(e(N)^(r(df)/e(N))-1), "%5.3f")
 17.     local L1   = string(invFtail(r(df),e(N),0.05), "%5.3f")
 18.     test `age'
 19.     estadd scalar F1a = r(F)
 20.     local L2   = string((e(df_r)/r(df))*(e(N)^(r(df)/e(N))-1), "%5.3f")
 21.     local L2   = string(invFtail(r(df),e(N),0.05), "%5.3f")
 22.     local opt1 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 23.     local tL1  = string(sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)), "%5.3f")
 24.     local pvL = ttail(e(N),sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)))
 25.     
.     eststo: areg quarter3 `age' `edu' `une' _year* `lv2' `wt', `se' `abs'
 26.     test `lv2'
 27.     estadd scalar F1 = r(F)
 28.     test `age'
 29.     estadd scalar F1a = r(F)
 30.     local opt2 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 31.     
.     #delimit ;
delimiter now ;
.     esttab est1 est2 using "$OUT/IPUMSIndustry_``k''.tex", replace 
>     keep(`age' highEduc black white hispanic `une' `lv2')
>     b(%-9.3f) se(%-9.3f) brackets nonotes nogaps noline
>     mlabels(, none) starlevel("$ ^\ddagger $ " `pvL')
>     nonumbers style(tex) fragment label mtitles(, none)
>     stats(N F1 F1a, fmt(%9.0gc %5.3f %5.3f)
>           label("\midrule Observations" "F-test of Mother's Occupation Dummies"
>                 "F-test of Age Variables"))
>     postfoot("\bottomrule \multicolumn{3}{p{13.6cm}}{\begin{footnotesize}      "
>              "Sample consists of"
>              " all singleton first-born children in the US born to `race' `mar'"
>              " mothers aged 20-45 included in 2005-2014 ACS data where the     "
>              "mother is either the head of the household or the `partner' of the "
>              "head of the household and works in an occupation with at least   "
>              "500 workers in the full sample. `qnote' Occupation classification"
>              " is provided by the 2 digit occupation codes from the census. The"
>              "omitted occupational category is Arts, Design, Entertainment,    "
>              "Sports, and Media, as this occupation has Q2+Q3=0.500(0.500).    "
>              "F-tests for occupation report test-statistics for the joint      "
>              "significance of the dummies and `Fnote' Critical values are `L1' "
>              "and `L2' for occupational and age tests respectively. The Leamer "
>              "critical value for the t-statistic is `tL1'. `lnote' "
>              "\end{footnotesize}}");
 32.     #delimit cr
delimiter now cr
.     estimates clear
 33. 
. 
.     logit quarter2 `age' `edu' `une' _year* `lv2' i.statefip `wt', `se'
 34.     test `lv2'
 35.     local F1 = r(chi2)
 36.     local L1   = string(invchi2tail(r(df),0.05), "%5.3f")
 37.     test `age'
 38.     local F1a = r(chi2)
 39.     local L2   = string(invchi2tail(r(df),0.05), "%5.3f")
 40.     local opt1 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 41.     local dfr = e(N)-e(rank)
 42.     local tL1  = string(sqrt((`dfr'/1)*(e(N)^(1/e(N))-1)), "%5.3f")
 43.     local pvL = ttail(e(N),sqrt((`dfr'/1)*(e(N)^(1/e(N))-1)))
 44.     margins, dydx(`age' `edu' `une' black white hispanic `lv2') post
 45.     estimates store m1
 46.     estadd scalar F1  = `F1'
 47.     estadd scalar F1a = `F1a'
 48.     
.     logit quarter3 `age' `edu' `une' _year* `lv2' i.statefip `wt', `se'
 49.     test `lv2'
 50.     local F1 = r(chi2)
 51.     test `age'
 52.     local F1a = r(chi2)
 53.     local opt2 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 54.     margins, dydx(`age' `edu' `une' black white hispanic `lv2') post
 55.     estimates store m2
 56.     estadd scalar F1  = `F1'
 57.     estadd scalar F1a = `F1a'
 58.     
.     #delimit ;
delimiter now ;
.     esttab m1 m2 using "$OUT/IPUMSIndustryLogit_``k''.tex", replace 
>     keep(`age' highEduc black white hispanic `une' `lv2')
>     b(%-9.3f) se(%-9.3f) brackets nonotes nogaps noline
>     mlabels(, none) starlevel("$ ^\ddagger $ " `pvL')
>     nonumbers style(tex) fragment label mtitles(, none)
>     stats(N F1 F1a, fmt(%9.0gc %5.3f %5.3f)
>           label("\midrule Observations" "$ \chi^2 $ test of Mother's Occupation Dummies"
>                 "$ \chi^2 $ test of Age Variables"))
>     postfoot("\bottomrule "
>              "\multicolumn{3}{p{13.6cm}}{\begin{footnotesize}  Refer to notes   "
>              "in Table \ref{ACS``k''}.  Results are replicated here using a     "
>              "Logit regression and reporting average marginal effects. `qnote'  "
>              "$\chi^2$ tests for occupation report test statistics for the      "
>              "joint significance of the dummies, and `Xnote' Critical values    "
>              "are `L1' and `L2' for occupational and age tests respectively. The"
>              " Leamer critical value for the t-statistic is `tL1'. `lnote' "
>              "\end{footnotesize}}");
 59.     #delimit cr
delimiter now cr
.     estimates clear
 60.     
.     lab var oc_Arch_F "\multicolumn{3}{l}{\textbf{Husband's Occupations}}\\ \\Architecture, Engineering"
 61.     lab var oc_Arch_M "\multicolumn{3}{l}{\textbf{Mother's Occupations}}\\ \\ Architecture and Enginee
> ring"
 62.     drop if twoLevelOccF=="No Occupation"
 63.     eststo: areg quarter2 `age' `edu' `une' _year* `lv2' `lv2F' `wt', `se' `abs'
 64.     test `lv2'
 65.     estadd scalar F2 = r(F)
 66.     test `lv2F'
 67.     estadd scalar F2b = r(F)
 68.     local L1   = string((e(df_r)/r(df))*(e(N)^(r(df)/e(N))-1), "%5.3f")
 69.     local L1   = string(invFtail(r(df),e(N),0.05), "%5.3f")
 70.     test `age'
 71.     estadd scalar F2a = r(F)
 72.     local L2   = string((e(df_r)/r(df))*(e(N)^(r(df)/e(N))-1), "%5.3f")
 73.     local L2   = string(invFtail(r(df),e(N),0.05), "%5.3f")
 74.     local opt1 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 75.     local tL1  = string(sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)), "%5.3f")
 76.     local pvL = ttail(e(N),sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1)))
 77.     
.     eststo: areg quarter3 `age' `edu' `une' _year* `lv2' `lv2F' `wt', `se' `abs'
 78.     test `lv2'
 79.     estadd scalar F2 = r(F)
 80.     test `lv2F'
 81.     estadd scalar F2b = r(F)
 82.     test `age'
 83.     estadd scalar F2a = r(F)
 84.     local opt2 = round((-_b[motherAge]/(0.02*_b[motherAge2]))*100)/100
 85.     
.     #delimit ;
delimiter now ;
.     esttab est1 est2 using "$OUT/IPUMSIndustry_``k''-both.tex", replace 
>     keep(`age' highEduc black white hispanic `une' `lv2' `lv2Fo')
>     b(%-9.3f) se(%-9.3f) brackets nonotes nogaps noline
>     mlabels(, none) starlevel("$ ^\ddagger $ " `pvL')
>     nonumbers style(tex) fragment label mtitles(, none)
>     stats(N F2 F2b F2a, fmt(%9.0gc %5.3f %5.3f %5.3f)
>           label("\midrule Observations" "F-test of Mother's Occupation Dummies"
>                 "F-test of Husband's Occupation Dummies" "F-test of Age Variables"))
>     postfoot("\bottomrule \multicolumn{3}{p{13.6cm}}{\begin{footnotesize}       "
>              "Sample consists of all singleton first-born children in the US    "
>              "born to `race' `mar' mothers aged 20-45 included in 2005-2014 ACS "
>              "data where the mother is either the head of the household or the  "
>              "wife of the head of the household and both the mother and the     "
>              "husband work in an occupation with at least 500 workers in the    "
>              "full sample. `qnote' Occupation classification is provided by the "
>              "2 digit occupation codes from the census. The omitted occupational"
>              " category is Arts, Design, Entertainment, Sports, and Media, as   "
>              "this occupation has Q2+Q3=0.500(0.500). F-tests for occupation    "
>              "report test-statistics for the joint significance of the dummies  "
>              "(for mothers and husbands separately) and `Fnote' Critical values "
>              "are `L1' and `L2' for occupational and age tests respectively.    "
>              "The Leamer critical value for the t-statistic is `tL1'. `lnote'  "
>              "\end{footnotesize}}");
 86.     #delimit cr
delimiter now cr
.     estimates clear
 87.     
.     restore
 88.     local ++k
 89. }
(19,332 observations deleted)
(6,029 observations deleted)
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =    108,243
Absorbed variable: statefip                     No. of categories =         51
                                                F(  34, 108158)   =       3.31
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0027
                                                Adj R-squared     =     0.0019
                                                Root MSE          =     0.4340

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0079935   .0034442     2.32   0.020     .0012429    .0147442
  motherAge2 |  -.0129386   .0054728    -2.36   0.018    -.0236652   -.0022119
    highEduc |   .0125512   .0049768     2.52   0.012     .0027968    .0223057
       black |   .0150215   .0103755     1.45   0.148    -.0053144    .0353574
       white |   .0130162   .0060335     2.16   0.031     .0011906    .0248418
    hispanic |  -.0077564   .0066214    -1.17   0.241    -.0207342    .0052214
      _year1 |   .0000525   .0073863     0.01   0.994    -.0144247    .0145296
      _year2 |  -.0009998   .0073021    -0.14   0.891    -.0153118    .0133122
      _year3 |   .0021979   .0072684     0.30   0.762    -.0120481    .0164439
      _year4 |   .0029449   .0073748     0.40   0.690    -.0115096    .0173994
      _year5 |   .0117322   .0074349     1.58   0.115      -.00284    .0263045
      _year6 |    .002712   .0074604     0.36   0.716    -.0119103    .0173343
      _year7 |  -.0098822   .0077845    -1.27   0.204    -.0251397    .0053753
      _year8 |  -.0149558   .0076853    -1.95   0.052    -.0300188    .0001073
      _year9 |  -.0069182   .0076297    -0.91   0.365    -.0218723     .008036
     _year10 |          0  (omitted)
   oc_Arch_M |   .0102062   .0160473     0.64   0.525    -.0212463    .0416586
  oc_Build_M |   .0280639   .0193204     1.45   0.146    -.0098038    .0659316
   oc_Busi_M |   .0260497   .0119046     2.19   0.029     .0027169    .0493825
   oc_Comm_M |   .0399266   .0131209     3.04   0.002     .0142099    .0656433
   oc_Comp_M |   .0286421   .0136578     2.10   0.036     .0018729    .0554113
   oc_Educ_M |   .0493028   .0098804     4.99   0.000     .0299373    .0686682
    oc_Fin_M |   .0274528   .0119339     2.30   0.021     .0040626    .0508431
   oc_Food_M |   .0294094   .0130214     2.26   0.024     .0038876    .0549312
   oc_HltP_M |   .0182036   .0099487     1.83   0.067    -.0012957    .0377028
   oc_HltS_M |    .011253   .0128792     0.87   0.382    -.0139899     .036496
   oc_Legl_M |   .0088714   .0136281     0.65   0.515    -.0178395    .0355823
    oc_Sci_M |   .0183507   .0148428     1.24   0.216    -.0107411    .0474425
   oc_Mgmt_M |   .0222684   .0102814     2.17   0.030     .0021169    .0424198
    oc_Off_M |   .0214555   .0097309     2.20   0.027     .0023831    .0405278
   oc_Care_M |   .0358427   .0119298     3.00   0.003     .0124605     .059225
   oc_Prod_M |   .0144887   .0147896     0.98   0.327    -.0144987    .0434761
   oc_Prot_M |      .0547   .0244375     2.24   0.025     .0068028    .1025972
   oc_Sale_M |   .0118844   .0102089     1.16   0.244    -.0081249    .0318936
   oc_Tran_M |   .0375601   .0220872     1.70   0.089    -.0057305    .0808507
       _cons |    .087126   .0542398     1.61   0.108    -.0191833    .1934352
------------------------------------------------------------------------------
(est1 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19,108158) =    3.23
            Prob > F =    0.0000

added scalar:
                 e(F1) =  3.2270939

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2,108158) =    2.84
            Prob > F =    0.0587

added scalar:
                e(F1a) =  2.8352656
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =    108,243
Absorbed variable: statefip                     No. of categories =         51
                                                F(  34, 108158)   =       1.21
                                                Prob > F          =     0.1893
                                                R-squared         =     0.0014
                                                Adj R-squared     =     0.0007
                                                Root MSE          =     0.4405

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0024319   .0034865     0.70   0.485    -.0044017    .0092654
  motherAge2 |  -.0043218   .0055208    -0.78   0.434    -.0151424    .0064988
    highEduc |  -.0107691   .0050465    -2.13   0.033    -.0206602   -.0008781
       black |   .0067428   .0103787     0.65   0.516    -.0135993    .0270849
       white |   .0107981   .0060551     1.78   0.075    -.0010698     .022666
    hispanic |  -.0094321   .0068262    -1.38   0.167    -.0228113    .0039472
      _year1 |   -.007458    .007386    -1.01   0.313    -.0219345    .0070185
      _year2 |  -.0090852   .0072918    -1.25   0.213     -.023377    .0052067
      _year3 |  -.0007264   .0073592    -0.10   0.921    -.0151504    .0136975
      _year4 |   .0026994   .0074727     0.36   0.718    -.0119469    .0173458
      _year5 |  -.0073301   .0073807    -0.99   0.321    -.0217963     .007136
      _year6 |   .0005522    .007486     0.07   0.941    -.0141203    .0152246
      _year7 |  -.0102652   .0078654    -1.31   0.192    -.0256814    .0051509
      _year8 |  -.0126789   .0077123    -1.64   0.100    -.0277949    .0024372
      _year9 |  -.0038293   .0077371    -0.49   0.621     -.018994    .0113353
     _year10 |          0  (omitted)
   oc_Arch_M |   .0020734   .0178904     0.12   0.908    -.0329915    .0371383
  oc_Build_M |  -.0204719    .018906    -1.08   0.279    -.0575275    .0165836
   oc_Busi_M |   .0025039   .0124409     0.20   0.840    -.0218801    .0268879
   oc_Comm_M |  -.0155505   .0133778    -1.16   0.245    -.0417708    .0106698
   oc_Comp_M |  -.0038706   .0139242    -0.28   0.781    -.0311619    .0234207
   oc_Educ_M |  -.0142805   .0103073    -1.39   0.166    -.0344827    .0059217
    oc_Fin_M |  -.0041014   .0122444    -0.33   0.738    -.0281002    .0198973
   oc_Food_M |   .0010215   .0134314     0.08   0.939    -.0253038    .0273468
   oc_HltP_M |   .0072449   .0104522     0.69   0.488    -.0132413     .027731
   oc_HltS_M |  -.0130833   .0133419    -0.98   0.327    -.0392333    .0130667
   oc_Legl_M |  -.0012882   .0143456    -0.09   0.928    -.0294054    .0268291
    oc_Sci_M |   -.005737   .0151887    -0.38   0.706    -.0355066    .0240327
   oc_Mgmt_M |   .0037947   .0108467     0.35   0.726    -.0174646    .0250541
    oc_Off_M |  -.0038277   .0102557    -0.37   0.709    -.0239289    .0162734
   oc_Care_M |  -.0088517   .0123324    -0.72   0.473     -.033023    .0153197
   oc_Prod_M |  -.0114614   .0155856    -0.74   0.462     -.042009    .0190862
   oc_Prot_M |  -.0238975   .0226317    -1.06   0.291    -.0682552    .0204603
   oc_Sale_M |  -.0015595   .0108202    -0.14   0.885    -.0227669    .0196479
   oc_Tran_M |   -.038385   .0201337    -1.91   0.057    -.0778468    .0010767
       _cons |   .2392672   .0554369     4.32   0.000     .1306117    .3479227
------------------------------------------------------------------------------
(est2 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19,108158) =    1.24
            Prob > F =    0.2142

added scalar:
                 e(F1) =  1.2392667

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2,108158) =    0.61
            Prob > F =    0.5442

added scalar:
                e(F1a) =  .60841854
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> Industry_Married.tex)

note: _year10 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -6058582.5  
Iteration 1:   log pseudolikelihood = -6044171.9  
Iteration 2:   log pseudolikelihood = -6044136.3  
Iteration 3:   log pseudolikelihood = -6044136.3  

Logistic regression                             Number of obs     =    108,243
                                                Wald chi2(84)     =     192.09
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -6044136.3               Pseudo R2         =     0.0024

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0433426   .0188175     2.30   0.021      .006461    .0802243
  motherAge2 |  -.0701213   .0299073    -2.34   0.019    -.1287385   -.0115042
    highEduc |   .0678171   .0270435     2.51   0.012     .0148129    .1208213
       black |   .0809536   .0555544     1.46   0.145     -.027931    .1898382
       white |   .0701865   .0329221     2.13   0.033     .0056603    .1347127
    hispanic |  -.0421245   .0361571    -1.17   0.244    -.1129911    .0287421
      _year1 |   .0003175   .0391005     0.01   0.994    -.0763181    .0769531
      _year2 |  -.0052771   .0386989    -0.14   0.892    -.0811256    .0705714
      _year3 |   .0117032   .0383991     0.30   0.761    -.0635577    .0869641
      _year4 |   .0155783   .0388651     0.40   0.689     -.060596    .0917526
      _year5 |   .0613975    .038819     1.58   0.114    -.0146863    .1374813
      _year6 |   .0143069    .039327     0.36   0.716    -.0627726    .0913863
      _year7 |  -.0528959   .0417094    -1.27   0.205    -.1346449    .0288531
      _year8 |   -.080751   .0414957    -1.95   0.052     -.162081    .0005791
      _year9 |  -.0368797   .0406891    -0.91   0.365    -.1166288    .0428695
     _year10 |          0  (omitted)
   oc_Arch_M |   .0566127   .0889831     0.64   0.525    -.1177909    .2310163
  oc_Build_M |   .1530847   .1050785     1.46   0.145    -.0528654    .3590349
   oc_Busi_M |   .1419177   .0652063     2.18   0.030     .0141158    .2697197
   oc_Comm_M |   .2131218   .0699987     3.04   0.002      .075927    .3503167
   oc_Comp_M |   .1558862   .0741825     2.10   0.036     .0104911    .3012812
   oc_Educ_M |   .2603581   .0545132     4.78   0.000     .1535141    .3672021
    oc_Fin_M |    .149268   .0652431     2.29   0.022     .0213939    .2771421
   oc_Food_M |   .1603342   .0713411     2.25   0.025     .0205081    .3001602
   oc_HltP_M |   .1001811   .0556021     1.80   0.072     -.008797    .2091592
   oc_HltS_M |   .0616781   .0720461     0.86   0.392    -.0795297    .2028859
   oc_Legl_M |   .0495868   .0757715     0.65   0.513    -.0989226    .1980962
    oc_Sci_M |   .1009748   .0810393     1.25   0.213    -.0578592    .2598088
   oc_Mgmt_M |   .1219159     .05719     2.13   0.033     .0098257    .2340062
    oc_Off_M |   .1176181   .0544864     2.16   0.031     .0108268    .2244095
   oc_Care_M |    .193908   .0650623     2.98   0.003     .0663884    .3214277
   oc_Prod_M |     .07887   .0827482     0.95   0.341    -.0833136    .2410535
   oc_Prot_M |   .2893577   .1236268     2.34   0.019     .0470536    .5316619
   oc_Sale_M |   .0650924   .0573337     1.14   0.256    -.0472795    .1774644
   oc_Tran_M |   .2033712   .1170183     1.74   0.082    -.0259805    .4327229
             |
    statefip |
          2  |   .0465404   .2079351     0.22   0.823     -.361005    .4540857
          4  |  -.1793925   .0981301    -1.83   0.068    -.3717239    .0129389
          5  |  -.1372345   .1244721    -1.10   0.270    -.3811953    .1067264
          6  |  -.1283697   .0788916    -1.63   0.104    -.2829944     .026255
          8  |  -.0938692   .0941073    -1.00   0.319     -.278316    .0905777
          9  |   .0260185   .1011775     0.26   0.797    -.1722856    .2243227
         10  |   .1188837    .178014     0.67   0.504    -.2300174    .4677847
         11  |   .0241454   .1703649     0.14   0.887    -.3097636    .3580545
         12  |  -.0438074   .0830014    -0.53   0.598    -.2064872    .1188724
         13  |  -.1558829   .0880501    -1.77   0.077    -.3284579    .0166922
         15  |  -.2118747   .1867622    -1.13   0.257    -.5779219    .1541725
         16  |  -.1085605   .1307738    -0.83   0.406    -.3648724    .1477514
         17  |  -.0406993    .084626    -0.48   0.631    -.2065632    .1251646
         18  |   .0038099   .0942536     0.04   0.968    -.1809238    .1885437
         19  |  -.0985494   .1114489    -0.88   0.377    -.3169852    .1198864
         20  |  -.2060939   .1152969    -1.79   0.074    -.4320716    .0198838
         21  |   -.292294   .1037779    -2.82   0.005     -.495695    -.088893
         22  |  -.2218564   .1098884    -2.02   0.043    -.4372336   -.0064792
         23  |  -.1170192   .1669928    -0.70   0.483     -.444319    .2102807
         24  |  -.0915367   .0933695    -0.98   0.327    -.2745375    .0914642
         25  |   .0152072   .0893732     0.17   0.865     -.159961    .1903754
         26  |   -.080468   .0883232    -0.91   0.362    -.2535784    .0926424
         27  |  -.0674545   .0957651    -0.70   0.481    -.2551506    .1202416
         28  |  -.1855718   .1293495    -1.43   0.151    -.4390922    .0679485
         29  |  -.0662915   .0942897    -0.70   0.482     -.251096     .118513
         30  |   .1368523   .1807065     0.76   0.449     -.217326    .4910306
         31  |  -.0678796   .1270418    -0.53   0.593     -.316877    .1811178
         32  |   -.041332   .1214092    -0.34   0.734    -.2792896    .1966255
         33  |  -.1081007   .1424062    -0.76   0.448    -.3872117    .1710102
         34  |  -.0760376   .0875655    -0.87   0.385    -.2476628    .0955876
         35  |  -.2654832    .170631    -1.56   0.120    -.5999138    .0689475
         36  |  -.0379341   .0818613    -0.46   0.643    -.1983793    .1225111
         37  |  -.1213666   .0874531    -1.39   0.165    -.2927716    .0500384
         38  |    -.08126   .2120594    -0.38   0.702    -.4968888    .3343689
         39  |  -.1100585   .0856631    -1.28   0.199     -.277955     .057838
         40  |  -.0240405   .1166856    -0.21   0.837    -.2527401     .204659
         41  |   .0933037   .1034749     0.90   0.367    -.1095034    .2961109
         42  |  -.0582177   .0853225    -0.68   0.495    -.2254468    .1090113
         44  |  -.0674304   .1503183    -0.45   0.654    -.3620489    .2271881
         45  |  -.0805803   .1043792    -0.77   0.440    -.2851597    .1239992
         46  |  -.1492305   .1745228    -0.86   0.393    -.4912889    .1928279
         47  |  -.0604886   .0947268    -0.64   0.523    -.2461497    .1251724
         48  |  -.0934002    .080921    -1.15   0.248    -.2520024     .065202
         49  |   -.053708    .102009    -0.53   0.599    -.2536419    .1462259
         50  |   .3543708    .210281     1.69   0.092    -.0577723     .766514
         51  |  -.0969673   .0877638    -1.10   0.269    -.2689811    .0750465
         53  |  -.0280312   .0901775    -0.31   0.756    -.2047758    .1487135
         54  |  -.4016269   .1473887    -2.72   0.006    -.6905034   -.1127503
         55  |   .0394922    .097155     0.41   0.684    -.1509281    .2299126
         56  |    .421382   .2118317     1.99   0.047     .0061995    .8365645
             |
       _cons |  -1.906663   .3034485    -6.28   0.000    -2.501411   -1.311915
------------------------------------------------------------------------------

 ( 1)  [quarter2]oc_Arch_M = 0
 ( 2)  [quarter2]oc_Build_M = 0
 ( 3)  [quarter2]oc_Busi_M = 0
 ( 4)  [quarter2]oc_Comm_M = 0
 ( 5)  [quarter2]oc_Comp_M = 0
 ( 6)  [quarter2]oc_Educ_M = 0
 ( 7)  [quarter2]oc_Fin_M = 0
 ( 8)  [quarter2]oc_Food_M = 0
 ( 9)  [quarter2]oc_HltP_M = 0
 (10)  [quarter2]oc_HltS_M = 0
 (11)  [quarter2]oc_Legl_M = 0
 (12)  [quarter2]oc_Sci_M = 0
 (13)  [quarter2]oc_Mgmt_M = 0
 (14)  [quarter2]oc_Off_M = 0
 (15)  [quarter2]oc_Care_M = 0
 (16)  [quarter2]oc_Prod_M = 0
 (17)  [quarter2]oc_Prot_M = 0
 (18)  [quarter2]oc_Sale_M = 0
 (19)  [quarter2]oc_Tran_M = 0

           chi2( 19) =   62.21
         Prob > chi2 =    0.0000

 ( 1)  [quarter2]motherAge = 0
 ( 2)  [quarter2]motherAge2 = 0

           chi2(  2) =    5.57
         Prob > chi2 =    0.0617

Average marginal effects                        Number of obs     =    108,243
Model VCE    : Robust

Expression   : Pr(quarter2), predict()
dy/dx w.r.t. : motherAge motherAge2 highEduc black white hispanic oc_Arch_M oc_Build_M oc_Busi_M
               oc_Comm_M oc_Comp_M oc_Educ_M oc_Fin_M oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M
               oc_Mgmt_M oc_Off_M oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0081559   .0035396     2.30   0.021     .0012184    .0150934
  motherAge2 |  -.0131949   .0056257    -2.35   0.019    -.0242211   -.0021688
    highEduc |   .0127613   .0050871     2.51   0.012     .0027907    .0227319
       black |   .0152333   .0104563     1.46   0.145    -.0052607    .0357273
       white |   .0132072   .0061931     2.13   0.033     .0010689    .0253455
    hispanic |  -.0079267   .0068029    -1.17   0.244    -.0212602    .0054068
   oc_Arch_M |    .010653   .0167435     0.64   0.525    -.0221638    .0434697
  oc_Build_M |   .0288064   .0197747     1.46   0.145    -.0099514    .0675642
   oc_Busi_M |   .0267051   .0122684     2.18   0.029     .0026595    .0507507
   oc_Comm_M |   .0401038   .0131691     3.05   0.002     .0142929    .0659146
   oc_Comp_M |   .0293336   .0139577     2.10   0.036      .001977    .0566902
   oc_Educ_M |   .0489924   .0102519     4.78   0.000      .028899    .0690857
    oc_Fin_M |   .0280882   .0122756     2.29   0.022     .0040285    .0521479
   oc_Food_M |   .0301706   .0134256     2.25   0.025     .0038569    .0564842
   oc_HltP_M |   .0188514    .010462     1.80   0.072    -.0016538    .0393566
   oc_HltS_M |   .0116062   .0135571     0.86   0.392    -.0149653    .0381776
   oc_Legl_M |   .0093309   .0142578     0.65   0.513    -.0186138    .0372756
    oc_Sci_M |   .0190007   .0152489     1.25   0.213    -.0108865    .0488879
   oc_Mgmt_M |   .0229413   .0107607     2.13   0.033     .0018508    .0440318
    oc_Off_M |   .0221326   .0102525     2.16   0.031     .0020381     .042227
   oc_Care_M |   .0364882   .0122426     2.98   0.003     .0124932    .0604833
   oc_Prod_M |   .0148412   .0155708     0.95   0.341    -.0156769    .0453593
   oc_Prot_M |   .0544493   .0232636     2.34   0.019     .0088535    .1000451
   oc_Sale_M |   .0122486   .0107886     1.14   0.256    -.0088967    .0333939
   oc_Tran_M |    .038269   .0220228     1.74   0.082    -.0048948    .0814328
------------------------------------------------------------------------------

added scalar:
                 e(F1) =  62.214987

added scalar:
                e(F1a) =  5.5720979

note: _year10 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -6187087.5  
Iteration 1:   log pseudolikelihood = -6179277.4  
Iteration 2:   log pseudolikelihood = -6179262.6  
Iteration 3:   log pseudolikelihood = -6179262.6  

Logistic regression                             Number of obs     =    108,243
                                                Wald chi2(84)     =      99.57
                                                Prob > chi2       =     0.1180
Log pseudolikelihood = -6179262.6               Pseudo R2         =     0.0013

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0125803   .0180373     0.70   0.486    -.0227721    .0479327
  motherAge2 |  -.0223668    .028596    -0.78   0.434    -.0784139    .0336804
    highEduc |  -.0552637   .0257877    -2.14   0.032    -.1058066   -.0047208
       black |   .0352462   .0540759     0.65   0.515    -.0707406     .141233
       white |   .0561696   .0317678     1.77   0.077     -.006094    .1184333
    hispanic |  -.0491228   .0358528    -1.37   0.171     -.119393    .0211474
      _year1 |   -.038362   .0379643    -1.01   0.312    -.1127707    .0360467
      _year2 |  -.0468316   .0375347    -1.25   0.212    -.1203982     .026735
      _year3 |  -.0037642   .0375385    -0.10   0.920    -.0773383    .0698099
      _year4 |   .0136517   .0379898     0.36   0.719    -.0608069    .0881103
      _year5 |  -.0377321   .0379596    -0.99   0.320    -.1121315    .0366673
      _year6 |   .0028224   .0381717     0.07   0.941    -.0719928    .0776375
      _year7 |   -.053005   .0406435    -1.30   0.192    -.1326648    .0266549
      _year8 |  -.0657227   .0399934    -1.64   0.100    -.1441083    .0126629
      _year9 |  -.0196655   .0396387    -0.50   0.620    -.0973559    .0580249
     _year10 |          0  (omitted)
   oc_Arch_M |   .0107326   .0915015     0.12   0.907    -.1686071    .1900724
  oc_Build_M |  -.1066376   .0998944    -1.07   0.286    -.3024271    .0891519
   oc_Busi_M |   .0128189   .0634841     0.20   0.840    -.1116075    .1372454
   oc_Comm_M |  -.0810826   .0698081    -1.16   0.245    -.2179039    .0557388
   oc_Comp_M |  -.0199035   .0719196    -0.28   0.782    -.1608634    .1210564
   oc_Educ_M |  -.0742757   .0530257    -1.40   0.161    -.1782042    .0296527
    oc_Fin_M |  -.0210736   .0629637    -0.33   0.738    -.1444802     .102333
   oc_Food_M |   .0050349   .0684048     0.07   0.941    -.1290361    .1391059
   oc_HltP_M |   .0368461   .0532845     0.69   0.489    -.0675897    .1412819
   oc_HltS_M |  -.0677413   .0691355    -0.98   0.327    -.2032443    .0677618
   oc_Legl_M |  -.0065403   .0735203    -0.09   0.929    -.1506374    .1375569
    oc_Sci_M |  -.0296376   .0784835    -0.38   0.706    -.1834624    .1241872
   oc_Mgmt_M |   .0193765   .0553373     0.35   0.726    -.0890826    .1278357
    oc_Off_M |  -.0197532   .0524934    -0.38   0.707    -.1226384    .0831321
   oc_Care_M |  -.0456758   .0634871    -0.72   0.472    -.1701082    .0787567
   oc_Prod_M |  -.0590997   .0806502    -0.73   0.464    -.2171711    .0989717
   oc_Prot_M |  -.1257258   .1215213    -1.03   0.301    -.3639032    .1124516
   oc_Sale_M |  -.0080822   .0553261    -0.15   0.884    -.1165193    .1003549
   oc_Tran_M |  -.2044474   .1106448    -1.85   0.065    -.4213072    .0124123
             |
    statefip |
          2  |  -.1916472    .210684    -0.91   0.363    -.6045803    .2212859
          4  |    .024113   .1004218     0.24   0.810    -.1727101    .2209362
          5  |   .0111193   .1257162     0.09   0.930    -.2352799    .2575185
          6  |  -.0263661   .0837315    -0.31   0.753    -.1904769    .1377446
          8  |  -.0079929   .0970818    -0.08   0.934    -.1982697    .1822838
          9  |   .0671466   .1070946     0.63   0.531     -.142755    .2770483
         10  |  -.0814884   .1823536    -0.45   0.655     -.438895    .2759182
         11  |   .0111442   .1760313     0.06   0.950    -.3338709    .3561593
         12  |  -.0683867   .0875335    -0.78   0.435    -.2399493    .1031759
         13  |  -.0780354   .0925863    -0.84   0.399    -.2595011    .1034304
         15  |  -.1530455   .1619786    -0.94   0.345    -.4705178    .1644268
         16  |  -.0580939   .1337815    -0.43   0.664    -.3203008     .204113
         17  |  -.0262184   .0890179    -0.29   0.768    -.2006902    .1482534
         18  |   -.043796   .0976019    -0.45   0.654    -.2350922    .1475003
         19  |  -.0183587   .1147089    -0.16   0.873    -.2431839    .2064666
         20  |  -.0320659   .1190382    -0.27   0.788    -.2653765    .2012447
         21  |  -.0549502   .1055431    -0.52   0.603    -.2618109    .1519105
         22  |   .0417169   .1129675     0.37   0.712    -.1796953    .2631291
         23  |   .0973541   .1564871     0.62   0.534    -.2093549    .4040631
         24  |  -.0463707   .0967804    -0.48   0.632    -.2360569    .1433155
         25  |  -.0928415   .0932633    -1.00   0.320    -.2756342    .0899511
         26  |    -.00308   .0927773    -0.03   0.974    -.1849202    .1787602
         27  |  -.0523454   .1015028    -0.52   0.606    -.2512872    .1465963
         28  |  -.0112258   .1267806    -0.09   0.929    -.2597112    .2372596
         29  |  -.0870248   .1002015    -0.87   0.385    -.2834161    .1093666
         30  |  -.4597922   .1967875    -2.34   0.019    -.8454887   -.0740958
         31  |   -.333013   .1404664    -2.37   0.018     -.608322   -.0577039
         32  |  -.1297835   .1234091    -1.05   0.293    -.3716609    .1120938
         33  |   .0719971   .1406003     0.51   0.609    -.2035744    .3475687
         34  |  -.0848262   .0920758    -0.92   0.357    -.2652915    .0956391
         35  |  -.0114843   .1592167    -0.07   0.942    -.3235434    .3005748
         36  |  -.0084198   .0861714    -0.10   0.922    -.1773126    .1604729
         37  |  -.0790239   .0914709    -0.86   0.388    -.2583036    .1002557
         38  |  -.0649667   .2129736    -0.31   0.760    -.4823874    .3524539
         39  |  -.0122911   .0897498    -0.14   0.891    -.1881976    .1636153
         40  |  -.2550318   .1173067    -2.17   0.030    -.4849487   -.0251149
         41  |  -.0303034   .1067929    -0.28   0.777    -.2396136    .1790068
         42  |  -.0144706   .0894824    -0.16   0.872    -.1898529    .1609117
         44  |   .2156756    .160201     1.35   0.178    -.0983126    .5296639
         45  |  -.0553308   .1053076    -0.53   0.599    -.2617299    .1510683
         46  |  -.1375948   .1875001    -0.73   0.463    -.5050883    .2298987
         47  |   .0183091    .097813     0.19   0.852    -.1734008     .210019
         48  |   -.112616   .0854818    -1.32   0.188    -.2801572    .0549252
         49  |   .0200318   .1094102     0.18   0.855    -.1944082    .2344718
         50  |  -.1379065   .2205717    -0.63   0.532    -.5702191    .2944062
         51  |   .0028004    .092087     0.03   0.976    -.1776869    .1832877
         53  |  -.0965414   .0941469    -1.03   0.305     -.281066    .0879832
         54  |   .0994952   .1504498     0.66   0.508     -.195381    .3943715
         55  |  -.1841533   .1017667    -1.81   0.070    -.3836124    .0153058
         56  |  -.4544043   .2306406    -1.97   0.049    -.9064516    -.002357
             |
       _cons |  -1.107365   .2960217    -3.74   0.000    -1.687557   -.5271737
------------------------------------------------------------------------------

 ( 1)  [quarter3]oc_Arch_M = 0
 ( 2)  [quarter3]oc_Build_M = 0
 ( 3)  [quarter3]oc_Busi_M = 0
 ( 4)  [quarter3]oc_Comm_M = 0
 ( 5)  [quarter3]oc_Comp_M = 0
 ( 6)  [quarter3]oc_Educ_M = 0
 ( 7)  [quarter3]oc_Fin_M = 0
 ( 8)  [quarter3]oc_Food_M = 0
 ( 9)  [quarter3]oc_HltP_M = 0
 (10)  [quarter3]oc_HltS_M = 0
 (11)  [quarter3]oc_Legl_M = 0
 (12)  [quarter3]oc_Sci_M = 0
 (13)  [quarter3]oc_Mgmt_M = 0
 (14)  [quarter3]oc_Off_M = 0
 (15)  [quarter3]oc_Care_M = 0
 (16)  [quarter3]oc_Prod_M = 0
 (17)  [quarter3]oc_Prot_M = 0
 (18)  [quarter3]oc_Sale_M = 0
 (19)  [quarter3]oc_Tran_M = 0

           chi2( 19) =   23.13
         Prob > chi2 =    0.2319

 ( 1)  [quarter3]motherAge = 0
 ( 2)  [quarter3]motherAge2 = 0

           chi2(  2) =    1.20
         Prob > chi2 =    0.5474

Average marginal effects                        Number of obs     =    108,243
Model VCE    : Robust

Expression   : Pr(quarter3), predict()
dy/dx w.r.t. : motherAge motherAge2 highEduc black white hispanic oc_Arch_M oc_Build_M oc_Busi_M
               oc_Comm_M oc_Comp_M oc_Educ_M oc_Fin_M oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M
               oc_Mgmt_M oc_Off_M oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0024392    .003497     0.70   0.485    -.0044148    .0092933
  motherAge2 |  -.0043368   .0055441    -0.78   0.434    -.0152031    .0065295
    highEduc |  -.0107152   .0050009    -2.14   0.032    -.0205169   -.0009136
       black |    .006834    .010486     0.65   0.515    -.0137182    .0273862
       white |   .0108909    .006158     1.77   0.077    -.0011785    .0229603
    hispanic |  -.0095246   .0069498    -1.37   0.171     -.023146    .0040968
   oc_Arch_M |    .002081   .0177415     0.12   0.907    -.0326918    .0368537
  oc_Build_M |  -.0206763   .0193679    -1.07   0.286    -.0586366     .017284
   oc_Busi_M |   .0024855    .012309     0.20   0.840    -.0216398    .0266108
   oc_Comm_M |  -.0157213   .0135352    -1.16   0.245    -.0422499    .0108072
   oc_Comp_M |  -.0038592   .0139449    -0.28   0.782    -.0311906    .0234723
   oc_Educ_M |  -.0144015   .0102816    -1.40   0.161    -.0345532    .0057501
    oc_Fin_M |   -.004086   .0122084    -0.33   0.738     -.028014     .019842
   oc_Food_M |   .0009762   .0132633     0.07   0.941    -.0250193    .0269717
   oc_HltP_M |   .0071442   .0103311     0.69   0.489    -.0131045    .0273929
   oc_HltS_M |  -.0131346    .013405    -0.98   0.327    -.0394078    .0131387
   oc_Legl_M |  -.0012681   .0142551    -0.09   0.929    -.0292076    .0266714
    oc_Sci_M |  -.0057465   .0152175    -0.38   0.706    -.0355723    .0240793
   oc_Mgmt_M |    .003757   .0107295     0.35   0.726    -.0172724    .0247864
    oc_Off_M |    -.00383   .0101782    -0.38   0.707    -.0237789    .0161189
   oc_Care_M |  -.0088562   .0123094    -0.72   0.472    -.0329821    .0152697
   oc_Prod_M |   -.011459   .0156374    -0.73   0.464    -.0421078    .0191898
   oc_Prot_M |  -.0243774   .0235614    -1.03   0.301    -.0705569    .0218022
   oc_Sale_M |  -.0015671   .0107273    -0.15   0.884    -.0225922     .019458
   oc_Tran_M |  -.0396409   .0214505    -1.85   0.065    -.0816832    .0024014
------------------------------------------------------------------------------

added scalar:
                 e(F1) =  23.125392

added scalar:
                e(F1a) =  1.2049746
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> IndustryLogit_Married.tex)
note: label truncated to 80 characters
(616 observations deleted)
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =    107,627
Absorbed variable: statefip                     No. of categories =         51
                                                F(  58, 107518)   =       2.33
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0031
                                                Adj R-squared     =     0.0021
                                                Root MSE          =     0.4341

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0072476   .0034956     2.07   0.038     .0003963    .0140989
  motherAge2 |  -.0117112   .0055479    -2.11   0.035     -.022585   -.0008375
    highEduc |   .0103857   .0050677     2.05   0.040     .0004532    .0203183
       black |    .016414   .0105486     1.56   0.120    -.0042611    .0370892
       white |   .0129299    .006151     2.10   0.036     .0008739    .0249858
    hispanic |  -.0076524   .0066708    -1.15   0.251    -.0207271    .0054224
      _year1 |   .0007277   .0073895     0.10   0.922    -.0137556    .0152111
      _year2 |   .0001753   .0073223     0.02   0.981    -.0141762    .0145269
      _year3 |   .0035507   .0072885     0.49   0.626    -.0107347    .0178361
      _year4 |   .0039869   .0073902     0.54   0.590    -.0104977    .0184716
      _year5 |   .0128529   .0074508     1.73   0.085    -.0017506    .0274564
      _year6 |   .0037963   .0074769     0.51   0.612    -.0108584     .018451
      _year7 |  -.0086281   .0078077    -1.11   0.269    -.0239311    .0066748
      _year8 |  -.0136663   .0077035    -1.77   0.076     -.028765    .0014325
      _year9 |  -.0053649   .0076553    -0.70   0.483    -.0203693    .0096394
     _year10 |          0  (omitted)
   oc_Arch_M |   .0079454   .0161989     0.49   0.624    -.0238041     .039695
  oc_Build_M |   .0318878   .0196804     1.62   0.105    -.0066854     .070461
   oc_Busi_M |   .0250842   .0119824     2.09   0.036     .0015989    .0485696
   oc_Comm_M |   .0397439   .0132104     3.01   0.003     .0138518     .065636
   oc_Comp_M |    .027232   .0138125     1.97   0.049     .0001597    .0543043
   oc_Educ_M |   .0485985   .0099868     4.87   0.000     .0290245    .0681725
    oc_Fin_M |   .0273348   .0120149     2.28   0.023     .0037857    .0508838
   oc_Food_M |   .0293031    .013221     2.22   0.027     .0033901     .055216
   oc_HltP_M |   .0177946   .0100961     1.76   0.078    -.0019936    .0375828
   oc_HltS_M |   .0133971   .0129951     1.03   0.303    -.0120731    .0388673
   oc_Legl_M |   .0101837   .0138542     0.74   0.462    -.0169703    .0373378
    oc_Sci_M |   .0183504   .0149847     1.22   0.221    -.0110193    .0477201
   oc_Mgmt_M |   .0212663   .0103497     2.05   0.040      .000981    .0415516
    oc_Off_M |   .0219715   .0098319     2.23   0.025     .0027012    .0412418
   oc_Care_M |   .0374255   .0120428     3.11   0.002     .0138217    .0610292
   oc_Prod_M |   .0144839   .0149296     0.97   0.332    -.0147778    .0437457
   oc_Prot_M |   .0547791   .0246118     2.23   0.026     .0065403    .1030179
   oc_Sale_M |   .0114634   .0103082     1.11   0.266    -.0087406    .0316674
   oc_Tran_M |   .0420579   .0223321     1.88   0.060    -.0017127    .0858285
   oc_Arch_F |   .0227447   .0121634     1.87   0.061    -.0010955    .0465848
  oc_Build_F |    .010959    .015342     0.71   0.475    -.0191112    .0410291
   oc_Busi_F |   .0236336   .0138263     1.71   0.087    -.0034657    .0507329
   oc_Comm_F |   .0231822      .0171     1.36   0.175    -.0103335    .0566979
   oc_Comp_F |   .0162769   .0116377     1.40   0.162    -.0065328    .0390866
   oc_Cons_F |   .0139948   .0115587     1.21   0.226    -.0086601    .0366496
   oc_Educ_F |    .027518   .0122105     2.25   0.024     .0035855    .0514504
   oc_Farm_F |  -.0144355   .0233648    -0.62   0.537    -.0602301    .0313591
    oc_Fin_F |   .0037089    .012742     0.29   0.771    -.0212653    .0286831
   oc_Food_F |   .0184896    .016233     1.14   0.255    -.0133267     .050306
   oc_HltP_F |   .0186873   .0126314     1.48   0.139    -.0060701    .0434447
   oc_HltS_F |   .0051599   .0276487     0.19   0.852    -.0490312     .059351
   oc_Insl_F |   .0067001   .0121256     0.55   0.581    -.0170659     .030466
   oc_Legl_F |   .0054618   .0147049     0.37   0.710    -.0233596    .0342832
    oc_Sci_F |   .0072486   .0160094     0.45   0.651    -.0241296    .0386269
   oc_Mgmt_F |   .0240308   .0106772     2.25   0.024     .0031036    .0449581
    oc_Mil_F |   .0295941   .0189925     1.56   0.119    -.0076309     .066819
    oc_Off_F |   .0148387    .012132     1.22   0.221    -.0089398    .0386173
   oc_Care_F |   .0003723   .0219677     0.02   0.986    -.0426841    .0434288
   oc_Prod_F |   .0119008   .0119959     0.99   0.321     -.011611    .0354127
   oc_Prot_F |   .0091854   .0126754     0.72   0.469    -.0156582     .034029
   oc_Sale_F |   .0130968   .0108879     1.20   0.229    -.0082433    .0344369
   oc_Tran_F |   .0025675   .0121252     0.21   0.832    -.0211976    .0263327
   oc_Unkn_F |    .024419   .0190852     1.28   0.201    -.0129878    .0618258
       _cons |   .0843836   .0557293     1.51   0.130     -.024845    .1936122
------------------------------------------------------------------------------
(est1 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19,107518) =    3.11
            Prob > F =    0.0000

added scalar:
                 e(F2) =  3.1142717

 ( 1)  oc_Arch_F = 0
 ( 2)  oc_Build_F = 0
 ( 3)  oc_Busi_F = 0
 ( 4)  oc_Comm_F = 0
 ( 5)  oc_Comp_F = 0
 ( 6)  oc_Cons_F = 0
 ( 7)  oc_Educ_F = 0
 ( 8)  oc_Farm_F = 0
 ( 9)  oc_Fin_F = 0
 (10)  oc_Food_F = 0
 (11)  oc_HltP_F = 0
 (12)  oc_HltS_F = 0
 (13)  oc_Insl_F = 0
 (14)  oc_Legl_F = 0
 (15)  oc_Sci_F = 0
 (16)  oc_Mgmt_F = 0
 (17)  oc_Mil_F = 0
 (18)  oc_Off_F = 0
 (19)  oc_Care_F = 0
 (20)  oc_Prod_F = 0
 (21)  oc_Prot_F = 0
 (22)  oc_Sale_F = 0
 (23)  oc_Tran_F = 0
 (24)  oc_Unkn_F = 0

       F( 24,107518) =    0.98
            Prob > F =    0.4911

added scalar:
                e(F2b) =  .97874684

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2,107518) =    2.26
            Prob > F =    0.1046

added scalar:
                e(F2a) =  2.2576093
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =    107,627
Absorbed variable: statefip                     No. of categories =         51
                                                F(  58, 107518)   =       1.21
                                                Prob > F          =     0.1266
                                                R-squared         =     0.0019
                                                Adj R-squared     =     0.0009
                                                Root MSE          =     0.4404

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0029136   .0035333     0.82   0.410    -.0040117    .0098389
  motherAge2 |  -.0049504   .0055839    -0.89   0.375    -.0158947    .0059939
    highEduc |  -.0105146   .0051484    -2.04   0.041    -.0206055   -.0004237
       black |    .005006   .0104963     0.48   0.633    -.0155665    .0255785
       white |   .0108364   .0061443     1.76   0.078    -.0012063    .0228791
    hispanic |   -.010113   .0068695    -1.47   0.141    -.0235772    .0033512
      _year1 |  -.0079757   .0074087    -1.08   0.282    -.0224966    .0065452
      _year2 |  -.0100343   .0073145    -1.37   0.170    -.0243706    .0043021
      _year3 |  -.0021053   .0073834    -0.29   0.776    -.0165765     .012366
      _year4 |   .0026404   .0075008     0.35   0.725     -.012061    .0173418
      _year5 |  -.0080727   .0074017    -1.09   0.275    -.0225798    .0064345
      _year6 |   .0000958   .0075112     0.01   0.990    -.0146261    .0148178
      _year7 |  -.0114019    .007891    -1.44   0.148    -.0268681    .0040644
      _year8 |  -.0134705   .0077379    -1.74   0.082    -.0286366    .0016956
      _year9 |  -.0049816   .0077637    -0.64   0.521    -.0201984    .0102351
     _year10 |          0  (omitted)
   oc_Arch_M |   .0043695   .0179853     0.24   0.808    -.0308814    .0396204
  oc_Build_M |  -.0192679   .0190595    -1.01   0.312    -.0566242    .0180884
   oc_Busi_M |   .0018318   .0124874     0.15   0.883    -.0226433    .0263068
   oc_Comm_M |  -.0151769   .0134329    -1.13   0.259    -.0415053    .0111514
   oc_Comp_M |  -.0037969   .0139763    -0.27   0.786    -.0311903    .0235964
   oc_Educ_M |  -.0138966   .0103827    -1.34   0.181    -.0342466    .0064533
    oc_Fin_M |  -.0045113   .0123118    -0.37   0.714    -.0286422    .0196197
   oc_Food_M |   .0004427   .0135426     0.03   0.974    -.0261007    .0269861
   oc_HltP_M |   .0053796   .0105472     0.51   0.610    -.0152928    .0260519
   oc_HltS_M |  -.0164012   .0134124    -1.22   0.221    -.0426893     .009887
   oc_Legl_M |  -.0000687   .0145491    -0.00   0.996    -.0285847    .0284473
    oc_Sci_M |  -.0043224   .0153386    -0.28   0.778    -.0343858     .025741
   oc_Mgmt_M |   .0046868   .0109009     0.43   0.667    -.0166788    .0260524
    oc_Off_M |   -.004717   .0103292    -0.46   0.648     -.024962    .0155281
   oc_Care_M |  -.0082849   .0124051    -0.67   0.504    -.0325987     .016029
   oc_Prod_M |  -.0120912   .0157384    -0.77   0.442    -.0429383    .0187558
   oc_Prot_M |  -.0271316   .0228097    -1.19   0.234    -.0718383     .017575
   oc_Sale_M |  -.0023492   .0109065    -0.22   0.829    -.0237257    .0190273
   oc_Tran_M |  -.0428905   .0202385    -2.12   0.034    -.0825576   -.0032234
   oc_Arch_F |  -.0137776   .0124125    -1.11   0.267     -.038106    .0105507
  oc_Build_F |  -.0307327   .0154792    -1.99   0.047    -.0610718   -.0003936
   oc_Busi_F |    -.00419   .0140617    -0.30   0.766    -.0317508    .0233708
   oc_Comm_F |  -.0252374   .0166665    -1.51   0.130    -.0579035    .0074288
   oc_Comp_F |  -.0066585   .0120497    -0.55   0.581    -.0302757    .0169586
   oc_Cons_F |  -.0073003   .0119581    -0.61   0.542     -.030738    .0161373
   oc_Educ_F |  -.0091446   .0125817    -0.73   0.467    -.0338045    .0155154
   oc_Farm_F |   .0099495   .0275223     0.36   0.718    -.0439938    .0638928
    oc_Fin_F |   .0055011    .013563     0.41   0.685    -.0210821    .0320843
   oc_Food_F |   .0035494   .0164054     0.22   0.829    -.0286049    .0357037
   oc_HltP_F |   .0007775   .0129516     0.06   0.952    -.0246074    .0261624
   oc_HltS_F |  -.0220282   .0261272    -0.84   0.399     -.073237    .0291807
   oc_Insl_F |   .0003491   .0127143     0.03   0.978    -.0245707    .0252688
   oc_Legl_F |  -.0119565   .0153894    -0.78   0.437    -.0421194    .0182065
    oc_Sci_F |  -.0142689   .0158513    -0.90   0.368    -.0453373    .0167995
   oc_Mgmt_F |  -.0188887   .0110336    -1.71   0.087    -.0405144     .002737
    oc_Mil_F |   .0105643   .0194345     0.54   0.587    -.0275271    .0486557
    oc_Off_F |   .0032351   .0125089     0.26   0.796    -.0212821    .0277523
   oc_Care_F |  -.0141047   .0207908    -0.68   0.498    -.0548543     .026645
   oc_Prod_F |  -.0045651   .0124623    -0.37   0.714    -.0289911    .0198608
   oc_Prot_F |   .0062172   .0131425     0.47   0.636    -.0195418    .0319762
   oc_Sale_F |  -.0071595   .0113392    -0.63   0.528    -.0293841     .015065
   oc_Tran_F |  -.0028174   .0126302    -0.22   0.823    -.0275725    .0219376
   oc_Unkn_F |   -.001421   .0195644    -0.07   0.942     -.039767    .0369249
       _cons |   .2381955   .0571777     4.17   0.000      .126128     .350263
------------------------------------------------------------------------------
(est2 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19,107518) =    1.21
            Prob > F =    0.2372

added scalar:
                 e(F2) =  1.2107417

 ( 1)  oc_Arch_F = 0
 ( 2)  oc_Build_F = 0
 ( 3)  oc_Busi_F = 0
 ( 4)  oc_Comm_F = 0
 ( 5)  oc_Comp_F = 0
 ( 6)  oc_Cons_F = 0
 ( 7)  oc_Educ_F = 0
 ( 8)  oc_Farm_F = 0
 ( 9)  oc_Fin_F = 0
 (10)  oc_Food_F = 0
 (11)  oc_HltP_F = 0
 (12)  oc_HltS_F = 0
 (13)  oc_Insl_F = 0
 (14)  oc_Legl_F = 0
 (15)  oc_Sci_F = 0
 (16)  oc_Mgmt_F = 0
 (17)  oc_Mil_F = 0
 (18)  oc_Off_F = 0
 (19)  oc_Care_F = 0
 (20)  oc_Prod_F = 0
 (21)  oc_Prot_F = 0
 (22)  oc_Sale_F = 0
 (23)  oc_Tran_F = 0
 (24)  oc_Unkn_F = 0

       F( 24,107518) =    1.12
            Prob > F =    0.3078

added scalar:
                e(F2b) =  1.1220544

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2,107518) =    0.54
            Prob > F =    0.5817

added scalar:
                e(F2a) =  .54184327
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> Industry_Married-both.tex)
(114,272 observations deleted)
(1,165 observations deleted)
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =     18,167
Absorbed variable: statefip                     No. of categories =         51
                                                F(  34,  18082)   =       1.03
                                                Prob > F          =     0.4225
                                                R-squared         =     0.0070
                                                Adj R-squared     =     0.0024
                                                Root MSE          =     0.4260

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |  -.0015735   .0062471    -0.25   0.801    -.0138183    .0106714
  motherAge2 |   .0030706   .0103296     0.30   0.766    -.0171765    .0233176
    highEduc |   .0033282   .0090954     0.37   0.714    -.0144996     .021156
       black |  -.0050127   .0244625    -0.20   0.838    -.0529616    .0429362
       white |  -.0058561   .0231351    -0.25   0.800    -.0512032     .039491
    hispanic |  -.0021469   .0128646    -0.17   0.867    -.0273628     .023069
      _year1 |   .0126974   .0178326     0.71   0.476    -.0222562    .0476509
      _year2 |   .0302702   .0179513     1.69   0.092     -.004916    .0654565
      _year3 |   .0060014   .0173929     0.35   0.730    -.0280903    .0400932
      _year4 |   .0114146   .0170295     0.67   0.503    -.0219649     .044794
      _year5 |  -.0022049   .0169775    -0.13   0.897    -.0354824    .0310726
      _year6 |   .0171717   .0175292     0.98   0.327    -.0171873    .0515306
      _year7 |    .035071   .0183247     1.91   0.056    -.0008472    .0709892
      _year8 |   .0139421   .0181492     0.77   0.442    -.0216322    .0495163
      _year9 |  -.0053281   .0176194    -0.30   0.762    -.0398638    .0292076
     _year10 |          0  (omitted)
   oc_Arch_M |    -.08504   .0614067    -1.38   0.166    -.2054029    .0353229
  oc_Build_M |  -.0552496   .0432017    -1.28   0.201     -.139929    .0294298
   oc_Busi_M |  -.0077373   .0471365    -0.16   0.870    -.1001293    .0846547
   oc_Comm_M |  -.0333073    .048793    -0.68   0.495    -.1289463    .0623317
   oc_Comp_M |   .0214478   .0586856     0.37   0.715    -.0935815    .1364772
   oc_Educ_M |  -.0301759   .0420231    -0.72   0.473    -.1125452    .0521933
    oc_Fin_M |   -.034929   .0476404    -0.73   0.463    -.1283087    .0584507
   oc_Food_M |   .0009118   .0396265     0.02   0.982    -.0767598    .0785835
   oc_HltP_M |  -.0328449   .0411838    -0.80   0.425    -.1135691    .0478793
   oc_HltS_M |  -.0121084   .0408842    -0.30   0.767    -.0922454    .0680286
   oc_Legl_M |   .0400189   .0561461     0.71   0.476    -.0700327    .1500706
    oc_Sci_M |  -.0292606   .0650765    -0.45   0.653    -.1568168    .0982956
   oc_Mgmt_M |  -.0633613   .0406764    -1.56   0.119    -.1430909    .0163683
    oc_Off_M |  -.0292643    .038811    -0.75   0.451    -.1053376     .046809
   oc_Care_M |  -.0301159   .0408853    -0.74   0.461     -.110255    .0500233
   oc_Prod_M |   -.045287   .0420904    -1.08   0.282    -.1277881    .0372142
   oc_Prot_M |   .0007303   .0527163     0.01   0.989    -.1025986    .1040592
   oc_Sale_M |  -.0257429   .0389977    -0.66   0.509    -.1021822    .0506964
   oc_Tran_M |   .0120225   .0459941     0.26   0.794    -.0781304    .1021753
       _cons |   .2747001   .1011173     2.72   0.007     .0765006    .4728996
------------------------------------------------------------------------------
(est1 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19, 18082) =    1.27
            Prob > F =    0.1890

added scalar:
                 e(F1) =  1.2733815

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2, 18082) =    0.11
            Prob > F =    0.8934

added scalar:
                e(F1a) =  .11270279
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =     18,167
Absorbed variable: statefip                     No. of categories =         51
                                                F(  34,  18082)   =       1.34
                                                Prob > F          =     0.0886
                                                R-squared         =     0.0078
                                                Adj R-squared     =     0.0031
                                                Root MSE          =     0.4384

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0106578   .0063909     1.67   0.095    -.0018691    .0231847
  motherAge2 |  -.0178138   .0105068    -1.70   0.090    -.0384082    .0027806
    highEduc |   .0000579   .0093755     0.01   0.995     -.018319    .0184349
       black |   .0340723   .0236334     1.44   0.149    -.0122514     .080396
       white |   .0346137   .0221651     1.56   0.118    -.0088321    .0780595
    hispanic |   .0027896   .0138706     0.20   0.841    -.0243981    .0299774
      _year1 |   .0017066   .0193152     0.09   0.930     -.036153    .0395663
      _year2 |  -.0021946   .0186577    -0.12   0.906    -.0387655    .0343763
      _year3 |   .0303693   .0187314     1.62   0.105     -.006346    .0670845
      _year4 |  -.0261715   .0179415    -1.46   0.145    -.0613385    .0089955
      _year5 |   .0050106   .0184617     0.27   0.786    -.0311761    .0411972
      _year6 |   .0092683    .018685     0.50   0.620     -.027356    .0458926
      _year7 |   -.008923   .0187827    -0.48   0.635     -.045739    .0278929
      _year8 |  -.0116434   .0184387    -0.63   0.528    -.0477849    .0244981
      _year9 |   .0058953   .0191422     0.31   0.758    -.0316252    .0434157
     _year10 |          0  (omitted)
   oc_Arch_M |   .1718592   .0774893     2.22   0.027     .0199728    .3237455
  oc_Build_M |   .0263575   .0419657     0.63   0.530    -.0558992    .1086143
   oc_Busi_M |   .0069574   .0463196     0.15   0.881    -.0838334    .0977483
   oc_Comm_M |   .0138549   .0475846     0.29   0.771    -.0794154    .1071253
   oc_Comp_M |  -.0295264   .0532681    -0.55   0.579     -.133937    .0748842
   oc_Educ_M |   .0144236   .0403514     0.36   0.721     -.064669    .0935161
    oc_Fin_M |  -.0250496   .0461263    -0.54   0.587    -.1154614    .0653623
   oc_Food_M |  -.0189235    .037226    -0.51   0.611      -.09189     .054043
   oc_HltP_M |  -.0047765   .0391875    -0.12   0.903    -.0815877    .0720347
   oc_HltS_M |  -.0022486   .0387678    -0.06   0.954    -.0782373    .0737401
   oc_Legl_M |  -.0501112   .0480322    -1.04   0.297    -.1442589    .0440364
    oc_Sci_M |   .0644649   .0704224     0.92   0.360    -.0735697    .2024995
   oc_Mgmt_M |  -.0174982   .0390709    -0.45   0.654    -.0940809    .0590844
    oc_Off_M |   .0010637   .0364464     0.03   0.977    -.0703748    .0725021
   oc_Care_M |    .026399   .0395933     0.67   0.505    -.0512076    .1040056
   oc_Prod_M |  -.0163025   .0404296    -0.40   0.687    -.0955483    .0629434
   oc_Prot_M |  -.0338703   .0495704    -0.68   0.494     -.131033    .0632925
   oc_Sale_M |   .0070141   .0367662     0.19   0.849    -.0650513    .0790794
   oc_Tran_M |  -.0545399   .0414932    -1.31   0.189    -.1358706    .0267908
       _cons |   .0765279   .1018427     0.75   0.452    -.1230935    .2761494
------------------------------------------------------------------------------
(est2 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19, 18082) =    1.43
            Prob > F =    0.0994

added scalar:
                 e(F1) =  1.4336534

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2, 18082) =    1.45
            Prob > F =    0.2354

added scalar:
                e(F1a) =  1.4467514
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> Industry_Unmarried.tex)

note: _year10 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1261972.8  
Iteration 1:   log pseudolikelihood = -1254048.6  
Iteration 2:   log pseudolikelihood = -1253974.3  
Iteration 3:   log pseudolikelihood = -1253974.2  
Iteration 4:   log pseudolikelihood = -1253974.2  

Logistic regression                             Number of obs     =     18,167
                                                Wald chi2(84)     =      78.78
                                                Prob > chi2       =     0.6406
Log pseudolikelihood = -1253974.2               Pseudo R2         =     0.0063

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |  -.0089358   .0344025    -0.26   0.795    -.0763635    .0584918
  motherAge2 |   .0173701   .0568715     0.31   0.760    -.0940961    .1288363
    highEduc |   .0183043   .0500972     0.37   0.715    -.0798844    .1164929
       black |  -.0276043   .1324071    -0.21   0.835    -.2871174    .2319089
       white |  -.0322064   .1248507    -0.26   0.796    -.2769093    .2124965
    hispanic |  -.0116675   .0714574    -0.16   0.870    -.1517213    .1283864
      _year1 |   .0710126   .1001365     0.71   0.478    -.1252513    .2672765
      _year2 |   .1653196   .0983534     1.68   0.093    -.0274496    .3580887
      _year3 |   .0340458   .0987418     0.34   0.730    -.1594846    .2275763
      _year4 |   .0640912   .0960179     0.67   0.504    -.1241004    .2522828
      _year5 |  -.0125762   .0973148    -0.13   0.897    -.2033097    .1781573
      _year6 |     .09564   .0978395     0.98   0.328    -.0961219    .2874019
      _year7 |   .1904157   .0997269     1.91   0.056    -.0050455    .3858769
      _year8 |   .0776829   .1016441     0.76   0.445    -.1215359    .2769016
      _year9 |  -.0309955   .1016611    -0.30   0.760    -.2302475    .1682566
     _year10 |          0  (omitted)
   oc_Arch_M |  -.4917212   .3751753    -1.31   0.190    -1.227051    .2436089
  oc_Build_M |  -.3109462   .2332808    -1.33   0.183    -.7681682    .1462758
   oc_Busi_M |  -.0401789   .2444251    -0.16   0.869    -.5192434    .4388855
   oc_Comm_M |  -.1796208   .2611074    -0.69   0.492    -.6913818    .3321403
   oc_Comp_M |   .1092861   .2946698     0.37   0.711    -.4682562    .6868283
   oc_Educ_M |  -.1614869   .2201285    -0.73   0.463    -.5929309     .269957
    oc_Fin_M |  -.1886209    .255727    -0.74   0.461    -.6898366    .3125949
   oc_Food_M |    .005265    .204391     0.03   0.979     -.395334     .405864
   oc_HltP_M |  -.1766729   .2154492    -0.82   0.412    -.5989455    .2455998
   oc_HltS_M |  -.0626684   .2119433    -0.30   0.767    -.4780696    .3527328
   oc_Legl_M |   .1955117   .2759814     0.71   0.479    -.3454019    .7364252
    oc_Sci_M |  -.1575887   .3560314    -0.44   0.658    -.8553975    .5402201
   oc_Mgmt_M |  -.3582398   .2162882    -1.66   0.098    -.7821568    .0656772
    oc_Off_M |  -.1567124   .2009389    -0.78   0.435    -.5505454    .2371207
   oc_Care_M |  -.1616753   .2134793    -0.76   0.449    -.5800871    .2567365
   oc_Prod_M |  -.2488371   .2230306    -1.12   0.265     -.685969    .1882948
   oc_Prot_M |   .0061219   .2738586     0.02   0.982     -.530631    .5428748
   oc_Sale_M |  -.1372566   .2020072    -0.68   0.497    -.5331834    .2586701
   oc_Tran_M |   .0619606   .2353619     0.26   0.792    -.3993401    .5232614
             |
    statefip |
          2  |  -.9870849   .5261364    -1.88   0.061    -2.018293    .0441236
          4  |  -.2054724   .2329342    -0.88   0.378     -.662015    .2510703
          5  |  -.2860988   .2877479    -0.99   0.320    -.8500743    .2778767
          6  |  -.0058689   .1806335    -0.03   0.974    -.3599041    .3481662
          8  |   .1065506   .2351735     0.45   0.650     -.354381    .5674822
          9  |   .0571355   .2619216     0.22   0.827    -.4562214    .5704924
         10  |   .2079081    .404865     0.51   0.608    -.5856127    1.001429
         11  |     .06832   .3700599     0.18   0.854     -.656984     .793624
         12  |  -.1354416   .1821123    -0.74   0.457    -.4923752    .2214921
         13  |  -.0662024   .2002408    -0.33   0.741    -.4586671    .3262623
         15  |   .5089428   .4661971     1.09   0.275    -.4047867    1.422672
         16  |   .1353942   .3246816     0.42   0.677    -.5009701    .7717585
         17  |   -.144917   .1930546    -0.75   0.453     -.523297    .2334631
         18  |  -.2924983    .214017    -1.37   0.172    -.7119639    .1269673
         19  |   .2166793   .2705102     0.80   0.423     -.313511    .7468697
         20  |  -.1173929   .2792613    -0.42   0.674    -.6647349    .4299491
         21  |   .0026448   .2365804     0.01   0.991    -.4610442    .4663338
         22  |  -.0014611   .2300735    -0.01   0.995    -.4523968    .4494746
         23  |  -.3013577   .3711486    -0.81   0.417    -1.028796    .4260802
         24  |  -.2913116   .2197122    -1.33   0.185    -.7219396    .1393164
         25  |  -.0413472   .2141979    -0.19   0.847    -.4611673    .3784728
         26  |   .0295575   .2028189     0.15   0.884    -.3679601    .4270752
         27  |   .1644714   .2364975     0.70   0.487    -.2990552    .6279979
         28  |   -.404908   .2656036    -1.52   0.127    -.9254816    .1156655
         29  |  -.0305761   .2107404    -0.15   0.885    -.4436196    .3824675
         30  |   .1477521    .367427     0.40   0.688    -.5723915    .8678958
         31  |   .5118475    .319591     1.60   0.109    -.1145393    1.138234
         32  |  -.2504474   .2677023    -0.94   0.350    -.7751343    .2742395
         33  |     .17275   .3495284     0.49   0.621    -.5123132    .8578131
         34  |  -.1734472   .2142395    -0.81   0.418    -.5933488    .2464544
         35  |   -.327303   .3389548    -0.97   0.334    -.9916423    .3370363
         36  |  -.1989734   .1857521    -1.07   0.284    -.5630409     .165094
         37  |   .0098139   .1983303     0.05   0.961    -.3789062    .3985341
         38  |  -.8082029   .5898602    -1.37   0.171    -1.964308     .347902
         39  |  -.0626818   .1889943    -0.33   0.740    -.4331039    .3077403
         40  |   -.274595    .253051    -1.09   0.278    -.7705657    .2213758
         41  |   .0221946   .2406088     0.09   0.927    -.4493901    .4937792
         42  |  -.1401132   .1956866    -0.72   0.474    -.5236519    .2434256
         44  |  -.4021588   .3841063    -1.05   0.295    -1.154993    .3506758
         45  |   -.043934   .2208036    -0.20   0.842    -.4767011    .3888332
         46  |   .2958987   .4044256     0.73   0.464     -.496761    1.088558
         47  |  -.2383827   .2135927    -1.12   0.264    -.6570167    .1802514
         48  |  -.0858979   .1804183    -0.48   0.634    -.4395112    .2677154
         49  |   .1400197   .3387649     0.41   0.679    -.5239472    .8039867
         50  |    .063784   .4036638     0.16   0.874    -.7273825    .8549505
         51  |  -.2431222   .2162073    -1.12   0.261    -.6668807    .1806363
         53  |  -.1164738   .2175306    -0.54   0.592     -.542826    .3098784
         54  |  -.1770297   .2997844    -0.59   0.555    -.7645962    .4105369
         55  |  -.1984973   .2344036    -0.85   0.397    -.6579199    .2609254
         56  |  -.0795992   .5154503    -0.15   0.877    -1.089863     .930665
             |
       _cons |  -.8770416   .5750576    -1.53   0.127    -2.004134    .2500506
------------------------------------------------------------------------------

 ( 1)  [quarter2]oc_Arch_M = 0
 ( 2)  [quarter2]oc_Build_M = 0
 ( 3)  [quarter2]oc_Busi_M = 0
 ( 4)  [quarter2]oc_Comm_M = 0
 ( 5)  [quarter2]oc_Comp_M = 0
 ( 6)  [quarter2]oc_Educ_M = 0
 ( 7)  [quarter2]oc_Fin_M = 0
 ( 8)  [quarter2]oc_Food_M = 0
 ( 9)  [quarter2]oc_HltP_M = 0
 (10)  [quarter2]oc_HltS_M = 0
 (11)  [quarter2]oc_Legl_M = 0
 (12)  [quarter2]oc_Sci_M = 0
 (13)  [quarter2]oc_Mgmt_M = 0
 (14)  [quarter2]oc_Off_M = 0
 (15)  [quarter2]oc_Care_M = 0
 (16)  [quarter2]oc_Prod_M = 0
 (17)  [quarter2]oc_Prot_M = 0
 (18)  [quarter2]oc_Sale_M = 0
 (19)  [quarter2]oc_Tran_M = 0

           chi2( 19) =   24.14
         Prob > chi2 =    0.1907

 ( 1)  [quarter2]motherAge = 0
 ( 2)  [quarter2]motherAge2 = 0

           chi2(  2) =    0.23
         Prob > chi2 =    0.8904

Average marginal effects                        Number of obs     =     18,167
Model VCE    : Robust

Expression   : Pr(quarter2), predict()
dy/dx w.r.t. : motherAge motherAge2 highEduc black white hispanic oc_Arch_M oc_Build_M oc_Busi_M
               oc_Comm_M oc_Comp_M oc_Educ_M oc_Fin_M oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M
               oc_Mgmt_M oc_Off_M oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |  -.0016142   .0062145    -0.26   0.795    -.0137944     .010566
  motherAge2 |   .0031378   .0102732     0.31   0.760    -.0169974     .023273
    highEduc |   .0033065   .0090495     0.37   0.715    -.0144302    .0210432
       black |  -.0049865   .0239163    -0.21   0.835    -.0518616    .0418886
       white |  -.0058179   .0225534    -0.26   0.796    -.0500216    .0383859
    hispanic |  -.0021076    .012908    -0.16   0.870    -.0274069    .0231916
   oc_Arch_M |  -.0888259   .0677944    -1.31   0.190    -.2217005    .0440488
  oc_Build_M |  -.0561702   .0421346    -1.33   0.182    -.1387525    .0264121
   oc_Busi_M |   -.007258   .0441553    -0.16   0.869    -.0938009    .0792848
   oc_Comm_M |  -.0324472    .047168    -0.69   0.492    -.1248949    .0600005
   oc_Comp_M |   .0197417   .0532258     0.37   0.711     -.084579    .1240625
   oc_Educ_M |  -.0291715   .0397658    -0.73   0.463     -.107111    .0487681
    oc_Fin_M |   -.034073    .046202    -0.74   0.461    -.1246272    .0564812
   oc_Food_M |   .0009511   .0369216     0.03   0.979     -.071414    .0733162
   oc_HltP_M |  -.0319147    .038925    -0.82   0.412    -.1082063    .0443769
   oc_HltS_M |  -.0113206   .0382869    -0.30   0.767    -.0863615    .0637204
   oc_Legl_M |   .0353178   .0498432     0.71   0.479    -.0623731    .1330086
    oc_Sci_M |  -.0284673   .0643175    -0.44   0.658    -.1545273    .0975928
   oc_Mgmt_M |  -.0647134   .0390784    -1.66   0.098    -.1413056    .0118787
    oc_Off_M |   -.028309   .0363002    -0.78   0.435     -.099456     .042838
   oc_Care_M |  -.0292055    .038564    -0.76   0.449    -.1047895    .0463785
   oc_Prod_M |  -.0449506   .0402911    -1.12   0.265    -.1239197    .0340185
   oc_Prot_M |   .0011059   .0494704     0.02   0.982    -.0958544    .0980662
   oc_Sale_M |  -.0247944   .0364923    -0.68   0.497    -.0963181    .0467292
   oc_Tran_M |   .0111927   .0425157     0.26   0.792    -.0721365     .094522
------------------------------------------------------------------------------

added scalar:
                 e(F1) =  24.144118

added scalar:
                e(F1a) =  .23213477

note: _year10 omitted because of collinearity
Iteration 0:   log pseudolikelihood = -1316700.4  
Iteration 1:   log pseudolikelihood = -1307940.3  
Iteration 2:   log pseudolikelihood = -1307878.6  
Iteration 3:   log pseudolikelihood = -1307878.6  

Logistic regression                             Number of obs     =     18,167
                                                Wald chi2(84)     =      93.69
                                                Prob > chi2       =     0.2201
Log pseudolikelihood = -1307878.6               Pseudo R2         =     0.0067

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0562311   .0338954     1.66   0.097    -.0102027    .1226649
  motherAge2 |  -.0940756   .0559274    -1.68   0.093    -.2036913    .0155401
    highEduc |  -.0000481   .0488536    -0.00   0.999    -.0957995    .0957032
       black |   .1843689   .1304184     1.41   0.157    -.0712465    .4399843
       white |   .1867413    .123058     1.52   0.129    -.0544479    .4279306
    hispanic |    .014406   .0722452     0.20   0.842    -.1271919     .156004
      _year1 |   .0082172   .1006433     0.08   0.935    -.1890402    .2054745
      _year2 |  -.0118063   .0974721    -0.12   0.904    -.2028482    .1792355
      _year3 |   .1535092   .0945195     1.62   0.104    -.0317455    .3387639
      _year4 |  -.1414778   .0962463    -1.47   0.142     -.330117    .0471614
      _year5 |   .0255063   .0955543     0.27   0.790    -.1617768    .2127893
      _year6 |    .048148   .0961187     0.50   0.616    -.1402412    .2365372
      _year7 |  -.0469838    .098596    -0.48   0.634    -.2402285    .1462608
      _year8 |  -.0621331   .0972327    -0.64   0.523    -.2527057    .1284394
      _year9 |   .0303203    .098685     0.31   0.759    -.1630987    .2237393
     _year10 |          0  (omitted)
   oc_Arch_M |   .7819724   .3392602     2.30   0.021     .1170346     1.44691
  oc_Build_M |   .1330971   .2151598     0.62   0.536    -.2886083    .5548026
   oc_Busi_M |   .0364357   .2395488     0.15   0.879    -.4330713    .5059426
   oc_Comm_M |   .0712154   .2439306     0.29   0.770    -.4068798    .5493105
   oc_Comp_M |  -.1628644   .2937539    -0.55   0.579    -.7386115    .4128828
   oc_Educ_M |   .0741191   .2085251     0.36   0.722    -.3345826    .4828207
    oc_Fin_M |  -.1345562   .2477389    -0.54   0.587    -.6201155    .3510031
   oc_Food_M |  -.0999265   .1948247    -0.51   0.608    -.4817759    .2819229
   oc_HltP_M |   -.024333   .2046024    -0.12   0.905    -.4253464    .3766804
   oc_HltS_M |  -.0114934   .2020325    -0.06   0.955    -.4074698     .384483
   oc_Legl_M |  -.2803249   .2699866    -1.04   0.299    -.8094889     .248839
    oc_Sci_M |   .3097518   .3319207     0.93   0.351    -.3408008    .9603043
   oc_Mgmt_M |  -.0927882   .2055197    -0.45   0.652    -.4955995    .3100231
    oc_Off_M |   .0058953   .1899997     0.03   0.975    -.3664973    .3782879
   oc_Care_M |   .1335542   .2040054     0.65   0.513    -.2662892    .5333975
   oc_Prod_M |  -.0864942    .212569    -0.41   0.684    -.5031217    .3301333
   oc_Prot_M |  -.1833286   .2698257    -0.68   0.497    -.7121773    .3455201
   oc_Sale_M |   .0368342   .1914843     0.19   0.847    -.3384682    .4121365
   oc_Tran_M |  -.3055756   .2268003    -1.35   0.178    -.7500961    .1389449
             |
    statefip |
          2  |   .0222436   .4543209     0.05   0.961    -.8682091    .9126963
          4  |   .0550488   .2173461     0.25   0.800    -.3709418    .4810394
          5  |   .1490587   .2499539     0.60   0.551    -.3408419    .6389593
          6  |  -.2578892   .1745455    -1.48   0.140    -.5999922    .0842137
          8  |  -.4238071   .2362491    -1.79   0.073    -.8868468    .0392326
          9  |  -.1027587   .2456899    -0.42   0.676    -.5843021    .3787848
         10  |   .3574953   .4144367     0.86   0.388    -.4547857    1.169776
         11  |  -.2650507   .4112637    -0.64   0.519    -1.071113    .5410114
         12  |  -.2341548   .1756557    -1.33   0.183    -.5784336    .1101239
         13  |  -.1656008   .1935232    -0.86   0.392    -.5448993    .2136977
         15  |  -.1009457   .4678245    -0.22   0.829    -1.017865    .8159735
         16  |  -.4462656   .3383804    -1.32   0.187    -1.109479    .2169477
         17  |  -.2338691   .1903202    -1.23   0.219    -.6068899    .1391516
         18  |  -.0582965   .1969437    -0.30   0.767    -.4442991    .3277061
         19  |   -.481551   .2560971    -1.88   0.060    -.9834921    .0203902
         20  |  -.0652707   .2763379    -0.24   0.813     -.606883    .4763417
         21  |  -.2376993   .2288019    -1.04   0.299    -.6861428    .2107441
         22  |  -.4418584   .2215139    -1.99   0.046    -.8760177   -.0076992
         23  |   -.441655   .3340214    -1.32   0.186    -1.096325    .2130149
         24  |  -.2942255   .2130891    -1.38   0.167    -.7118724    .1234215
         25  |  -.0874139    .210449    -0.42   0.678    -.4998864    .3250586
         26  |  -.2078489   .1915172    -1.09   0.278    -.5832156    .1675178
         27  |  -.0077019    .230384    -0.03   0.973    -.4592462    .4438425
         28  |  -.3468508   .2731126    -1.27   0.204    -.8821417      .18844
         29  |  -.1161155   .2116377    -0.55   0.583    -.5309178    .2986869
         30  |   .2266018   .3928593     0.58   0.564    -.5433882    .9965918
         31  |   .1113993   .3248522     0.34   0.732    -.5252995     .748098
         32  |  -.1629285    .267597    -0.61   0.543    -.6874091     .361552
         33  |  -.7043491   .3596723    -1.96   0.050    -1.409294    .0005957
         34  |  -.2222348    .210731    -1.05   0.292      -.63526    .1907904
         35  |   .0607594   .3151734     0.19   0.847    -.5569691    .6784879
         36  |  -.2042614   .1779342    -1.15   0.251    -.5530061    .1444832
         37  |   -.185389   .1949163    -0.95   0.342     -.567418      .19664
         38  |   .6009146    .434212     1.38   0.166    -.2501253    1.451955
         39  |  -.0347448   .1816988    -0.19   0.848    -.3908679    .3213782
         40  |   .0816151   .2436475     0.33   0.738    -.3959253    .5591554
         41  |  -.0141722   .2421589    -0.06   0.953     -.488795    .4604506
         42  |  -.2888388   .1889493    -1.53   0.126    -.6591726    .0814951
         44  |   .2629739   .3546005     0.74   0.458    -.4320303     .957978
         45  |  -.2437409   .2174775    -1.12   0.262     -.669989    .1825071
         46  |   .1859555   .3619784     0.51   0.607    -.5235091    .8954201
         47  |  -.1184397   .2002956    -0.59   0.554    -.5110118    .2741324
         48  |  -.1436144   .1747852    -0.82   0.411    -.4861871    .1989583
         49  |  -.5502646   .3623061    -1.52   0.129    -1.260372    .1598424
         50  |  -.1109089   .3924492    -0.28   0.777    -.8800952    .6582773
         51  |  -.3279189   .2118297    -1.55   0.122    -.7430975    .0872597
         53  |  -.0655195   .2034798    -0.32   0.747    -.4643325    .3332935
         54  |  -.0808701   .2881395    -0.28   0.779    -.6456132     .483873
         55  |  -.2463046   .2227988    -1.11   0.269    -.6829822     .190373
         56  |  -.6361476   .5834513    -1.09   0.276    -1.779691    .5073959
             |
       _cons |  -1.849593   .5612573    -3.30   0.001    -2.949637   -.7495485
------------------------------------------------------------------------------

 ( 1)  [quarter3]oc_Arch_M = 0
 ( 2)  [quarter3]oc_Build_M = 0
 ( 3)  [quarter3]oc_Busi_M = 0
 ( 4)  [quarter3]oc_Comm_M = 0
 ( 5)  [quarter3]oc_Comp_M = 0
 ( 6)  [quarter3]oc_Educ_M = 0
 ( 7)  [quarter3]oc_Fin_M = 0
 ( 8)  [quarter3]oc_Food_M = 0
 ( 9)  [quarter3]oc_HltP_M = 0
 (10)  [quarter3]oc_HltS_M = 0
 (11)  [quarter3]oc_Legl_M = 0
 (12)  [quarter3]oc_Sci_M = 0
 (13)  [quarter3]oc_Mgmt_M = 0
 (14)  [quarter3]oc_Off_M = 0
 (15)  [quarter3]oc_Care_M = 0
 (16)  [quarter3]oc_Prod_M = 0
 (17)  [quarter3]oc_Prot_M = 0
 (18)  [quarter3]oc_Sale_M = 0
 (19)  [quarter3]oc_Tran_M = 0

           chi2( 19) =   27.75
         Prob > chi2 =    0.0883

 ( 1)  [quarter3]motherAge = 0
 ( 2)  [quarter3]motherAge2 = 0

           chi2(  2) =    2.84
         Prob > chi2 =    0.2418

Average marginal effects                        Number of obs     =     18,167
Model VCE    : Robust

Expression   : Pr(quarter3), predict()
dy/dx w.r.t. : motherAge motherAge2 highEduc black white hispanic oc_Arch_M oc_Build_M oc_Busi_M
               oc_Comm_M oc_Comp_M oc_Educ_M oc_Fin_M oc_Food_M oc_HltP_M oc_HltS_M oc_Legl_M oc_Sci_M
               oc_Mgmt_M oc_Off_M oc_Care_M oc_Prod_M oc_Prot_M oc_Sale_M oc_Tran_M

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0107551   .0064816     1.66   0.097    -.0019486    .0234589
  motherAge2 |  -.0179935   .0106955    -1.68   0.093    -.0389563    .0029692
    highEduc |  -9.21e-06   .0093441    -0.00   0.999    -.0183233    .0183048
       black |   .0352637   .0249599     1.41   0.158    -.0136568    .0841841
       white |   .0357174   .0235435     1.52   0.129     -.010427    .0818618
    hispanic |   .0027554   .0138187     0.20   0.842    -.0243287    .0298395
   oc_Arch_M |   .1495654   .0648434     2.31   0.021     .0224746    .2766561
  oc_Build_M |   .0254571   .0411508     0.62   0.536    -.0551971    .1061112
   oc_Busi_M |   .0069689   .0458176     0.15   0.879    -.0828319    .0967697
   oc_Comm_M |   .0136211   .0466552     0.29   0.770    -.0778214    .1050636
   oc_Comp_M |  -.0311505   .0561873    -0.55   0.579    -.1412756    .0789745
   oc_Educ_M |   .0141765   .0398845     0.36   0.722    -.0639957    .0923487
    oc_Fin_M |  -.0257361    .047386    -0.54   0.587     -.118611    .0671387
   oc_Food_M |  -.0191126   .0372626    -0.51   0.608     -.092146    .0539207
   oc_HltP_M |  -.0046541    .039134    -0.12   0.905    -.0813553    .0720471
   oc_HltS_M |  -.0021983    .038642    -0.06   0.955    -.0779352    .0735386
   oc_Legl_M |  -.0536169   .0516516    -1.04   0.299    -.1548522    .0476185
    oc_Sci_M |   .0592452   .0634775     0.93   0.351    -.0651684    .1836588
   oc_Mgmt_M |  -.0177473   .0393085    -0.45   0.652    -.0947906     .059296
    oc_Off_M |   .0011276   .0363406     0.03   0.975    -.0700987    .0723539
   oc_Care_M |   .0255445   .0390218     0.65   0.513    -.0509369    .1020258
   oc_Prod_M |  -.0165435   .0406555    -0.41   0.684    -.0962268    .0631398
   oc_Prot_M |  -.0350647   .0516069    -0.68   0.497    -.1362122    .0660829
   oc_Sale_M |   .0070452    .036625     0.19   0.847    -.0647386    .0788289
   oc_Tran_M |  -.0584465   .0433732    -1.35   0.178    -.1434564    .0265635
------------------------------------------------------------------------------

added scalar:
                 e(F1) =  27.753264

added scalar:
                e(F1a) =  2.8391338
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> IndustryLogit_Unmarried.tex)
note: label truncated to 80 characters
(186 observations deleted)
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =     17,981
Absorbed variable: statefip                     No. of categories =         51
                                                F(  58,  17872)   =       1.13
                                                Prob > F          =     0.2319
                                                R-squared         =     0.0093
                                                Adj R-squared     =     0.0034
                                                Root MSE          =     0.4258

------------------------------------------------------------------------------
             |               Robust
    quarter2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |  -.0015791   .0062761    -0.25   0.801    -.0138808    .0107225
  motherAge2 |   .0033506   .0103759     0.32   0.747    -.0169872    .0236884
    highEduc |   .0014592   .0091491     0.16   0.873    -.0164739    .0193923
       black |     .00359   .0245743     0.15   0.884     -.044578    .0517581
       white |   -.002176   .0231111    -0.09   0.925     -.047476     .043124
    hispanic |   -.002852   .0129822    -0.22   0.826    -.0282984    .0225944
      _year1 |   .0115943   .0179498     0.65   0.518     -.023589    .0467776
      _year2 |   .0295811   .0180492     1.64   0.101    -.0057971    .0649593
      _year3 |   .0056869   .0174747     0.33   0.745    -.0285652    .0399389
      _year4 |   .0092549   .0171404     0.54   0.589    -.0243419    .0428518
      _year5 |  -.0002855   .0171271    -0.02   0.987    -.0338563    .0332852
      _year6 |   .0148563   .0176173     0.84   0.399    -.0196752    .0493879
      _year7 |   .0348995   .0184039     1.90   0.058    -.0011739    .0709729
      _year8 |   .0163321   .0182675     0.89   0.371    -.0194739    .0521382
      _year9 |  -.0053437    .017738    -0.30   0.763    -.0401119    .0294245
     _year10 |          0  (omitted)
   oc_Arch_M |  -.0821598   .0628481    -1.31   0.191     -.205348    .0410285
  oc_Build_M |  -.0623812    .043535    -1.43   0.152     -.147714    .0229517
   oc_Busi_M |  -.0120684   .0472574    -0.26   0.798    -.1046974    .0805606
   oc_Comm_M |  -.0355203   .0491789    -0.72   0.470    -.1319158    .0608751
   oc_Comp_M |   .0177741   .0590613     0.30   0.763    -.0979918      .13354
   oc_Educ_M |   -.034636   .0421344    -0.82   0.411    -.1172236    .0479515
    oc_Fin_M |    -.03917   .0478322    -0.82   0.413    -.1329258    .0545857
   oc_Food_M |    .000015   .0398752     0.00   1.000    -.0781443    .0781742
   oc_HltP_M |  -.0366145   .0414492    -0.88   0.377     -.117859      .04463
   oc_HltS_M |  -.0156652   .0410971    -0.38   0.703    -.0962194     .064889
   oc_Legl_M |    .032127   .0559978     0.57   0.566    -.0776342    .1418882
    oc_Sci_M |   -.032521   .0646496    -0.50   0.615    -.1592404    .0941984
   oc_Mgmt_M |  -.0690445   .0408613    -1.69   0.091    -.1491366    .0110475
    oc_Off_M |  -.0355005   .0389719    -0.91   0.362    -.1118892    .0408883
   oc_Care_M |  -.0330313   .0410939    -0.80   0.422    -.1135793    .0475168
   oc_Prod_M |    -.05163   .0423724    -1.22   0.223    -.1346841     .031424
   oc_Prot_M |   -.007798   .0530035    -0.15   0.883      -.11169    .0960941
   oc_Sale_M |  -.0309671   .0392021    -0.79   0.430    -.1078069    .0458728
   oc_Tran_M |   .0028682   .0462858     0.06   0.951    -.0878564    .0935928
   oc_Arch_F |  -.0798388   .0764687    -1.04   0.296    -.2297247    .0700472
  oc_Build_F |   .0095046   .0632073     0.15   0.880    -.1143878     .133397
   oc_Busi_F |  -.0459295    .085265    -0.54   0.590     -.213057    .1211981
   oc_Comm_F |  -.0840479   .0846845    -0.99   0.321    -.2500377    .0819418
   oc_Comp_F |  -.0266717   .0759568    -0.35   0.725    -.1755544     .122211
   oc_Cons_F |   .0095933   .0589972     0.16   0.871    -.1060469    .1252335
   oc_Educ_F |  -.1076611   .0707849    -1.52   0.128    -.2464064    .0310842
   oc_Farm_F |   .0016903    .079434     0.02   0.983     -.154008    .1573887
    oc_Fin_F |  -.0516487   .0953304    -0.54   0.588    -.2385054    .1352081
   oc_Food_F |  -.0574761   .0599684    -0.96   0.338    -.1750199    .0600677
   oc_HltP_F |  -.0015327   .0816537    -0.02   0.985    -.1615817    .1585164
   oc_HltS_F |   .0169121   .0987764     0.17   0.864    -.1766991    .2105234
   oc_Insl_F |  -.0140312   .0616949    -0.23   0.820    -.1349593    .1068969
   oc_Legl_F |   .2420088   .1647561     1.47   0.142    -.0809292    .5649467
    oc_Sci_F |  -.0185938   .1105228    -0.17   0.866    -.2352292    .1980416
   oc_Mgmt_F |   .0037473   .0632021     0.06   0.953    -.1201349    .1276294
    oc_Mil_F |   .2417871   .1669539     1.45   0.148    -.0854587    .5690328
    oc_Off_F |   .0162382   .0618258     0.26   0.793    -.1049464    .1374228
   oc_Care_F |  -.0292894   .0736551    -0.40   0.691    -.1736605    .1150817
   oc_Prod_F |  -.0043439     .06004    -0.07   0.942    -.1220281    .1133403
   oc_Prot_F |   .0469387   .0711271     0.66   0.509    -.0924773    .1863548
   oc_Sale_F |  -.0171787    .060693    -0.28   0.777    -.1361428    .1017854
   oc_Tran_F |   .0129213   .0599631     0.22   0.829    -.1046122    .1304547
   oc_Unkn_F |  -.0214297    .057223    -0.37   0.708    -.1335923    .0907328
       _cons |   .2887998   .1155646     2.50   0.012      .062282    .5153175
------------------------------------------------------------------------------
(est1 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19, 17872) =    1.35
            Prob > F =    0.1417

added scalar:
                 e(F2) =  1.3476929

 ( 1)  oc_Arch_F = 0
 ( 2)  oc_Build_F = 0
 ( 3)  oc_Busi_F = 0
 ( 4)  oc_Comm_F = 0
 ( 5)  oc_Comp_F = 0
 ( 6)  oc_Cons_F = 0
 ( 7)  oc_Educ_F = 0
 ( 8)  oc_Farm_F = 0
 ( 9)  oc_Fin_F = 0
 (10)  oc_Food_F = 0
 (11)  oc_HltP_F = 0
 (12)  oc_HltS_F = 0
 (13)  oc_Insl_F = 0
 (14)  oc_Legl_F = 0
 (15)  oc_Sci_F = 0
 (16)  oc_Mgmt_F = 0
 (17)  oc_Mil_F = 0
 (18)  oc_Off_F = 0
 (19)  oc_Care_F = 0
 (20)  oc_Prod_F = 0
 (21)  oc_Prot_F = 0
 (22)  oc_Sale_F = 0
 (23)  oc_Tran_F = 0
 (24)  oc_Unkn_F = 0

       F( 24, 17872) =    1.29
            Prob > F =    0.1547

added scalar:
                e(F2b) =  1.2906051

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2, 17872) =    0.22
            Prob > F =    0.8000

added scalar:
                e(F2a) =  .22320032
note: _year10 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =     17,981
Absorbed variable: statefip                     No. of categories =         51
                                                F(  58,  17872)   =       1.28
                                                Prob > F          =     0.0719
                                                R-squared         =     0.0094
                                                Adj R-squared     =     0.0034
                                                Root MSE          =     0.4383

------------------------------------------------------------------------------
             |               Robust
    quarter3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   motherAge |   .0110285   .0064361     1.71   0.087    -.0015868    .0236439
  motherAge2 |  -.0184214    .010577    -1.74   0.082    -.0391533    .0023105
    highEduc |  -.0004897   .0094622    -0.05   0.959    -.0190366    .0180572
       black |   .0332827   .0236876     1.41   0.160    -.0131473    .0797128
       white |   .0357262   .0221703     1.61   0.107    -.0077297    .0791821
    hispanic |   .0030821   .0139809     0.22   0.826    -.0243218     .030486
      _year1 |   .0016452   .0194258     0.08   0.933    -.0364313    .0397216
      _year2 |  -.0014662   .0188163    -0.08   0.938    -.0383481    .0354157
      _year3 |    .030326   .0188638     1.61   0.108    -.0066488    .0673008
      _year4 |  -.0254195   .0180657    -1.41   0.159    -.0608301    .0099911
      _year5 |    .005344   .0186207     0.29   0.774    -.0311543    .0418423
      _year6 |   .0110746   .0188599     0.59   0.557    -.0258927    .0480419
      _year7 |  -.0090549   .0189343    -0.48   0.632    -.0461679    .0280582
      _year8 |  -.0105796   .0186065    -0.57   0.570    -.0470501    .0258909
      _year9 |   .0059897   .0192888     0.31   0.756    -.0318183    .0437977
     _year10 |          0  (omitted)
   oc_Arch_M |   .1636555   .0787824     2.08   0.038     .0092343    .3180766
  oc_Build_M |   .0358519   .0418838     0.86   0.392    -.0462445    .1179483
   oc_Busi_M |   .0173986   .0460178     0.38   0.705    -.0728007    .1075978
   oc_Comm_M |   .0246619   .0476741     0.52   0.605     -.068784    .1181078
   oc_Comp_M |  -.0199953   .0533662    -0.37   0.708    -.1245982    .0846076
   oc_Educ_M |   .0288338   .0401006     0.72   0.472    -.0497674    .1074349
    oc_Fin_M |  -.0157442    .046083    -0.34   0.733    -.1060712    .0745829
   oc_Food_M |  -.0088528   .0369961    -0.24   0.811    -.0813688    .0636632
   oc_HltP_M |    .007406   .0389912     0.19   0.849    -.0690206    .0838327
   oc_HltS_M |   .0104345   .0385572     0.27   0.787    -.0651414    .0860105
   oc_Legl_M |  -.0381028   .0481346    -0.79   0.429    -.1324512    .0562456
    oc_Sci_M |   .0621537   .0717673     0.87   0.386    -.0785172    .2028246
   oc_Mgmt_M |   -.009711   .0387355    -0.25   0.802    -.0856364    .0662143
    oc_Off_M |   .0136931   .0361746     0.38   0.705    -.0572127    .0845989
   oc_Care_M |   .0366411     .03933     0.93   0.352    -.0404496    .1137318
   oc_Prod_M |  -.0048152   .0402532    -0.12   0.905    -.0837155     .074085
   oc_Prot_M |  -.0210391   .0492843    -0.43   0.669     -.117641    .0755628
   oc_Sale_M |   .0177081   .0364916     0.49   0.627     -.053819    .0892351
   oc_Tran_M |  -.0470297   .0412317    -1.14   0.254    -.1278479    .0337885
   oc_Arch_F |   .0247176    .083043     0.30   0.766    -.1380547    .1874898
  oc_Build_F |  -.0138387   .0588353    -0.24   0.814    -.1291615    .1014841
   oc_Busi_F |  -.0122565   .0802809    -0.15   0.879    -.1696149    .1451019
   oc_Comm_F |   .0441263   .1048627     0.42   0.674    -.1614146    .2496673
   oc_Comp_F |    .075498   .0884226     0.85   0.393    -.0978189    .2488149
   oc_Cons_F |  -.0040758   .0541165    -0.08   0.940    -.1101494    .1019977
   oc_Educ_F |  -.0592463    .077493    -0.76   0.445    -.2111401    .0926475
   oc_Farm_F |   .0607803   .0770947     0.79   0.430    -.0903327    .2118933
    oc_Fin_F |   .1343834   .1002881     1.34   0.180    -.0621911    .3309578
   oc_Food_F |   .0466384   .0562619     0.83   0.407    -.0636403    .1569171
   oc_HltP_F |  -.0279282   .0756596    -0.37   0.712    -.1762284     .120372
   oc_HltS_F |  -.0235286   .0915158    -0.26   0.797    -.2029084    .1558512
   oc_Insl_F |   .0384916   .0579823     0.66   0.507    -.0751594    .1521426
   oc_Legl_F |  -.0388808   .1372042    -0.28   0.777    -.3078143    .2300527
    oc_Sci_F |   .2129516   .1230664     1.73   0.084    -.0282704    .4541736
   oc_Mgmt_F |    .049672   .0589933     0.84   0.400    -.0659606    .1653046
    oc_Mil_F |  -.1548489   .0823794    -1.88   0.060    -.3163206    .0066227
    oc_Off_F |  -.0309963   .0558051    -0.56   0.579    -.1403796     .078387
   oc_Care_F |  -.0241313   .0669814    -0.36   0.719    -.1554212    .1071587
   oc_Prod_F |   .0172787   .0552554     0.31   0.755    -.0910272    .1255847
   oc_Prot_F |  -.0179736    .062923    -0.29   0.775    -.1413086    .1053615
   oc_Sale_F |   .0249392   .0569094     0.44   0.661    -.0866087    .1364871
   oc_Tran_F |    .022641   .0550924     0.41   0.681    -.0853453    .1306274
   oc_Unkn_F |   .0180593   .0521492     0.35   0.729    -.0841582    .1202767
       _cons |   .0427451   .1133502     0.38   0.706    -.1794322    .2649225
------------------------------------------------------------------------------
(est2 stored)

 ( 1)  oc_Arch_M = 0
 ( 2)  oc_Build_M = 0
 ( 3)  oc_Busi_M = 0
 ( 4)  oc_Comm_M = 0
 ( 5)  oc_Comp_M = 0
 ( 6)  oc_Educ_M = 0
 ( 7)  oc_Fin_M = 0
 ( 8)  oc_Food_M = 0
 ( 9)  oc_HltP_M = 0
 (10)  oc_HltS_M = 0
 (11)  oc_Legl_M = 0
 (12)  oc_Sci_M = 0
 (13)  oc_Mgmt_M = 0
 (14)  oc_Off_M = 0
 (15)  oc_Care_M = 0
 (16)  oc_Prod_M = 0
 (17)  oc_Prot_M = 0
 (18)  oc_Sale_M = 0
 (19)  oc_Tran_M = 0

       F( 19, 17872) =    1.39
            Prob > F =    0.1175

added scalar:
                 e(F2) =  1.3937395

 ( 1)  oc_Arch_F = 0
 ( 2)  oc_Build_F = 0
 ( 3)  oc_Busi_F = 0
 ( 4)  oc_Comm_F = 0
 ( 5)  oc_Comp_F = 0
 ( 6)  oc_Cons_F = 0
 ( 7)  oc_Educ_F = 0
 ( 8)  oc_Farm_F = 0
 ( 9)  oc_Fin_F = 0
 (10)  oc_Food_F = 0
 (11)  oc_HltP_F = 0
 (12)  oc_HltS_F = 0
 (13)  oc_Insl_F = 0
 (14)  oc_Legl_F = 0
 (15)  oc_Sci_F = 0
 (16)  oc_Mgmt_F = 0
 (17)  oc_Mil_F = 0
 (18)  oc_Off_F = 0
 (19)  oc_Care_F = 0
 (20)  oc_Prod_F = 0
 (21)  oc_Prot_F = 0
 (22)  oc_Sale_F = 0
 (23)  oc_Tran_F = 0
 (24)  oc_Unkn_F = 0

       F( 24, 17872) =    1.29
            Prob > F =    0.1536

added scalar:
                e(F2b) =  1.2922024

 ( 1)  motherAge = 0
 ( 2)  motherAge2 = 0

       F(  2, 17872) =    1.53
            Prob > F =    0.2175

added scalar:
                e(F2a) =  1.5258117
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> Industry_Unmarried-both.tex)

. 
. 
. ********************************************************************************
. *** (4) Figure 1b
. ********************************************************************************
. gen Qnum = _n in 1/4
(133,600 missing values generated)

. gen Qnum1 = _n+0.05 in 1/4
(133,600 missing values generated)

. gen Qnum2 = _n-0.05 in 1/4
(133,600 missing values generated)

. gen Qnum3 = _n+0.10 in 1/4
(133,600 missing values generated)

. gen EstEduc  = .
(133,604 missing values generated)

. gen EstOther = .
(133,604 missing values generated)

. gen EstUnem  = .
(133,604 missing values generated)

. gen lbEduc  = .
(133,604 missing values generated)

. gen lbOther = .
(133,604 missing values generated)

. gen lbUnem  = .
(133,604 missing values generated)

. gen ubEduc  = .
(133,604 missing values generated)

. gen ubOther = .
(133,604 missing values generated)

. gen ubUnem  = .
(133,604 missing values generated)

. 
. gen EstEduc_u  = .
(133,604 missing values generated)

. gen EstOther_u = .
(133,604 missing values generated)

. gen lbEduc_u  = .
(133,604 missing values generated)

. gen lbOther_u = .
(133,604 missing values generated)

. gen ubEduc_u  = .
(133,604 missing values generated)

. gen ubOther_u = .
(133,604 missing values generated)

. 
. gen Q2 = birthQuarter==2

. reg Q2 oc_Educ_M if motherAge>=20&motherAge<=45&married==1

      Source |       SS           df       MS      Number of obs   =   114,272
-------------+----------------------------------   F(1, 114270)    =     80.56
       Model |  15.1904915         1  15.1904915   Prob > F        =    0.0000
    Residual |  21548.1429   114,270  .188572179   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =    0.0007
       Total |  21563.3334   114,271  .188703463   Root MSE        =    .43425

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   oc_Educ_M |   .0342163   .0038123     8.98   0.000     .0267443    .0416884
       _cons |   .2479466   .0013777   179.97   0.000     .2452463    .2506469
------------------------------------------------------------------------------

. drop Q2

. 
. 
. count if motherAge>=20&motherAge<=45&married==1
  114,272

. local NNm = string(r(N),"%12.0fc")

. 
. count if motherAge>=20&motherAge<=45&married==0
  19,332

. local NNu = string(r(N),"%12.0fc")

. 
. foreach Q of numlist 1(1)4 {
  2.     gen Q`Q'=birthQuarter==`Q'
  3.     reg Q`Q' if oc_Educ_M&motherAge>=20&motherAge<=45&married==1
  4.     local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))
  5.     replace EstEduc = _b[_cons] in `Q'
  6.     replace ubEduc = _b[_cons]+`tL1'*_se[_cons] in `Q'
  7.     replace lbEduc = _b[_cons]-`tL1'*_se[_cons] in `Q'
  8.     
.     reg Q`Q' if oc_OtherOc_M&motherAge>=20&motherAge<=45&married==1
  9.     local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))
 10.     replace EstOther = _b[_cons] in `Q'
 11.     replace ubOther = _b[_cons]+`tL1'*_se[_cons] in `Q'
 12.     replace lbOther = _b[_cons]-`tL1'*_se[_cons] in `Q'
 13. 
.     reg Q`Q' if oc_Unem_M&motherAge>=20&motherAge<=45&married==1
 14.     local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))
 15.     replace EstUnem = _b[_cons] in `Q'
 16.     replace ubUnem = _b[_cons]+`tL1'*_se[_cons] in `Q'
 17.     replace lbUnem = _b[_cons]-`tL1'*_se[_cons] in `Q'
 18. 
.     reg Q`Q' if oc_Educ_M&motherAge>=20&motherAge<=45&married==0
 19.     local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))
 20.     replace EstEduc_u = _b[_cons] in `Q'
 21.     replace ubEduc_u = _b[_cons]+`tL1'*_se[_cons] in `Q'
 22.     replace lbEduc_u = _b[_cons]-`tL1'*_se[_cons] in `Q'
 23.     
.     reg Q`Q' if oc_OtherOc_M&motherAge>=20&motherAge<=45&married==0
 24.     local tL1  = sqrt((e(df_r)/1)*(e(N)^(1/e(N))-1))
 25.     replace EstOther_u = _b[_cons] in `Q'
 26.     replace ubOther_u = _b[_cons]+`tL1'*_se[_cons] in `Q'
 27.     replace lbOther_u = _b[_cons]-`tL1'*_se[_cons] in `Q'
 28. }

      Source |       SS           df       MS      Number of obs   =    14,924
-------------+----------------------------------   F(0, 14923)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  2705.03049    14,923  .181265864   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  2705.03049    14,923  .181265864   Root MSE        =    .42575

------------------------------------------------------------------------------
          Q1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2378049   .0034851    68.23   0.000     .2309737    .2446361
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    93,319
-------------+----------------------------------   F(0, 93318)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |   16931.859    93,318  .181442583   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |   16931.859    93,318  .181442583   Root MSE        =    .42596

------------------------------------------------------------------------------
          Q1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2381616   .0013944   170.80   0.000     .2354286    .2408946
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =     6,029
-------------+----------------------------------   F(0, 6028)      =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  1124.79151     6,028  .186594477   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  1124.79151     6,028  .186594477   Root MSE        =    .43197

------------------------------------------------------------------------------
          Q1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .248134   .0055632    44.60   0.000     .2372281    .2590399
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =       885
-------------+----------------------------------   F(0, 884)       =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  160.693785       884    .1817803   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  160.693785       884    .1817803   Root MSE        =    .42636

------------------------------------------------------------------------------
          Q1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2384181   .0143318    16.64   0.000     .2102897    .2665465
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    17,282
-------------+----------------------------------   F(0, 17281)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  3169.49896    17,281  .183409465   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  3169.49896    17,281  .183409465   Root MSE        =    .42826

------------------------------------------------------------------------------
          Q1 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .241928   .0032577    74.26   0.000     .2355426    .2483135
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    14,924
-------------+----------------------------------   F(0, 14923)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  3022.81178    14,923  .202560596   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  3022.81178    14,923  .202560596   Root MSE        =    .45007

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .282163   .0036841    76.59   0.000     .2749416    .2893843
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    93,319
-------------+----------------------------------   F(0, 93318)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  17428.7371    93,318  .186767152   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  17428.7371    93,318  .186767152   Root MSE        =    .43217

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2485346   .0014147   175.68   0.000     .2457618    .2513074
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =     6,029
-------------+----------------------------------   F(0, 6028)      =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  1096.06237     6,028  .181828528   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  1096.06237     6,028  .181828528   Root MSE        =    .42641

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2388456   .0054917    43.49   0.000     .2280798    .2496113
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =       885
-------------+----------------------------------   F(0, 884)       =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  160.693785       884    .1817803   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  160.693785       884    .1817803   Root MSE        =    .42636

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2384181   .0143318    16.64   0.000     .2102897    .2665465
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    17,282
-------------+----------------------------------   F(0, 17281)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  3156.55919    17,281  .182660679   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  3156.55919    17,281  .182660679   Root MSE        =    .42739

------------------------------------------------------------------------------
          Q2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2404814   .0032511    73.97   0.000      .234109    .2468538
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    14,924
-------------+----------------------------------   F(0, 14923)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  2843.19512    14,923  .190524367   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  2843.19512    14,923  .190524367   Root MSE        =    .43649

------------------------------------------------------------------------------
          Q3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2560976    .003573    71.68   0.000      .249094    .2631011
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    93,319
-------------+----------------------------------   F(0, 93318)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  18198.9616    93,318  .195020913   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  18198.9616    93,318  .195020913   Root MSE        =    .44161

------------------------------------------------------------------------------
          Q3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2655193   .0014456   183.67   0.000     .2626859    .2683528
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =     6,029
-------------+----------------------------------   F(0, 6028)      =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  1136.78554     6,028  .188584197   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  1136.78554     6,028  .188584197   Root MSE        =    .43426

------------------------------------------------------------------------------
          Q3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2521148   .0055928    45.08   0.000     .2411509    .2630787
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =       885
-------------+----------------------------------   F(0, 884)       =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  173.532203       884  .196303397   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  173.532203       884  .196303397   Root MSE        =    .44306

------------------------------------------------------------------------------
          Q3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2677966   .0148933    17.98   0.000     .2385662    .2970271
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    17,282
-------------+----------------------------------   F(0, 17281)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |   3321.5401    17,281  .192207633   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |   3321.5401    17,281  .192207633   Root MSE        =    .43841

------------------------------------------------------------------------------
          Q3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2595764   .0033349    77.84   0.000     .2530396    .2661133
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    14,924
-------------+----------------------------------   F(0, 14923)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  2593.61056    14,923  .173799542   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  2593.61056    14,923  .173799542   Root MSE        =    .41689

------------------------------------------------------------------------------
          Q4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2239346   .0034126    65.62   0.000     .2172455    .2306237
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    93,319
-------------+----------------------------------   F(0, 93318)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  17393.4794    93,318  .186389329   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  17393.4794    93,318  .186389329   Root MSE        =    .43173

------------------------------------------------------------------------------
          Q4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2477845   .0014133   175.33   0.000     .2450145    .2505545
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =     6,029
-------------+----------------------------------   F(0, 6028)      =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  1162.59546     6,028  .192865868   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  1162.59546     6,028  .192865868   Root MSE        =    .43916

------------------------------------------------------------------------------
          Q4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2609056   .0056559    46.13   0.000      .249818    .2719933
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =       885
-------------+----------------------------------   F(0, 884)       =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  168.287006       884  .190369916   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  168.287006       884  .190369916   Root MSE        =    .43631

------------------------------------------------------------------------------
          Q4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2553672   .0146665    17.41   0.000     .2265819    .2841525
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

      Source |       SS           df       MS      Number of obs   =    17,282
-------------+----------------------------------   F(0, 17281)     =      0.00
       Model |           0         0           .   Prob > F        =         .
    Residual |  3308.51504    17,281  .191453911   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  3308.51504    17,281  .191453911   Root MSE        =    .43755

------------------------------------------------------------------------------
          Q4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2580141   .0033284    77.52   0.000     .2514901    .2645381
------------------------------------------------------------------------------
(1 real change made)
(1 real change made)
(1 real change made)

. 
. #delimit ;
delimiter now ;
. format EstEduc %04.2f;

. format ubEduc_u %04.2f;

. format lbEduc_u %04.2f;

. twoway connected EstEduc Qnum in 1/4, lcolor(black) lwidth(medthick) m(Sh) mcolor(red) 
>    ||  rcap ubEduc lbEduc Qnum in 1/4, scheme(s1mono) lcolor(gs12) 
>    || connected EstOther Qnum1 in 1/4, lcolor(black) lwidth(medthick) lpattern(dash) m(Oh) mcolor(blue) 
>    ||  rcap ubOther lbOther Qnum1 in 1/4, lcolor(gs12)  yline(0.25, lcolor(red) lpatter(dash))
>    || connected EstOther_u Qnum2 in 1/4,
>       lcolor(black) lwidth(medthick) lpattern(dash_dot) m(T) mcolor(blue) 
>    ||  rcap ubOther_u lbOther_u Qnum2 in 1/4, lcolor(gs12)
>    || connected EstEduc_u Qnum3 in 1/4,
>        lcolor(black) lwidth(medthick) lpattern(longdash) m(D) mcolor(blue) 
>    ||  rcap ubEduc_u lbEduc_u Qnum3 in 1/4, lcolor(gs12) 
> ytitle("Proportion Births")
> note("Number of married mothers = `NNm'.  Number of unmarried mothers = `NNu'")
> xlabel(1 "Quarter 1" 2 "Quarter 2" 3 "Quarter 3" 4 "Quarter 4")
> legend(order(1 "Education, Library, Training (Married)" 3 "Other Occupations (Married)"   
>              7 "Education, Library, Training (Unmarried)" 5 "Other Occupations (Unmarried)"
>              2 "95% CI") size(small) symxsize(11));

. graph export "$OUT/occupationQuarters_withUnmarried.eps", replace;
(file C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/occupationQuarters
> _withUnmarried.eps written in EPS format)

. #delimit cr
delimiter now cr
. 
. 
. ********************************************************************************
. *** (5) Sumstats [Table A14]
. ********************************************************************************
. generat young     =   motherAge <=39

. gen teacher=twoLevelOcc =="Education, Training, and Library Occupations"

. gen quarter1 = birthQuarter==1

. gen quarter4 = birthQuarter==4

. 
. local rd (1=2) (2=6) (3=9) (4=10) (5=11) (6=12) (7=13) (8=14) (10=15) (11=16)

. recode educ `rd', gen(educYrs)
(133205 differences between educ and educYrs)

.  
. lab var other     "Other Race (Asian/Native American)"

. lab var educYrs   "Years of education"

. lab var married   "Married"

. lab var young     "Young (aged 25-39)"

. lab var highEduc  "Some College +"

. lab var goodQuart "Good Season of Birth"

. lab var motherAge "\midrule Mother's Age"

. lab var teacher   "Works in Education, Training and Library"

. lab var quarter1  "Quarter 1 Birth"

. lab var quarter2  "Quarter 2 Birth"

. lab var quarter3  "Quarter 3 Birth"

. lab var quarter4  "Quarter 4 Birth"

. 
. #delimit ;
delimiter now ;
. local add `" "(Married Mothers, 20--45)" "(Unmarried Mothers, 20--45)" "';

. local nam Married Unmarried;

. #delimit cr
delimiter now cr
. tokenize `nam'

. 
. local k=1

. foreach type of local add {
  2.     if `k'==1 local gg  motherAge>=20&motherAge<=45&married==1
  3.     if `k'==2 local gg  motherAge>=20&motherAge<=45&married==0
  4.     local edu black white other hispanic
  5. 
.     preserve
  6.     keep if `gg'
  7.     drop if occ2010 == 9920
  8.     #delimit ;
delimiter now ;
.     estpost tabstat motherAge `edu' highEduc educYrs teacher
>     quarter1 quarter2 quarter3 quarter4,
>     statistics(count mean sd min max) columns(statistics);
  9.     esttab using "$OUT/IPUMSstats_``k''.tex", replace label noobs
>     cells("count(fmt(%12.0gc) label(N)) mean(fmt(3) label(Mean)) sd(fmt(2)
>            label(Std. Dev.)) min(fmt(0) label(Min.)) max(fmt(0) label(Max.))")
>     fragment tex nonumber nolines nomtitles nonotes;
 10.      #delimit cr
delimiter now cr
.     restore
 11. 
.     local ++k
 12. }
(19,332 observations deleted)
(6,029 observations deleted)

Summary statistics: count mean sd min max
     for variables: motherAge black white other hispanic highEduc educYrs teacher quarter1 quarter2 quarter
> 3 quarter4

             |  e(count)    e(mean)      e(sd)     e(min)     e(max) 
-------------+-------------------------------------------------------
   motherAge |    108243   30.18934   4.910152         20         45 
       black |    108243   .0370463   .1888761          0          1 
       white |    108243   .8754746   .3301815          0          1 
       other |    108243   .0874791   .2825372          0          1 
    hispanic |    108243   .0678658   .2515167          0          1 
    highEduc |    108243   .8159604   .3875183          0          1 
     educYrs |    108243   14.28305   1.705834          0         16 
     teacher |    108243    .137875   .3447703          0          1 
    quarter1 |    108243   .2381124   .4259302          0          1 
    quarter2 |    108243   .2531711   .4348301          0          1 
    quarter3 |    108243   .2642203   .4409192          0          1 
    quarter4 |    108243   .2444962   .4297901          0          1 
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> stats_Married.tex)
(114,272 observations deleted)
(1,165 observations deleted)

Summary statistics: count mean sd min max
     for variables: motherAge black white other hispanic highEduc educYrs teacher quarter1 quarter2 quarter
> 3 quarter4

             |  e(count)    e(mean)      e(sd)     e(min)     e(max) 
-------------+-------------------------------------------------------
   motherAge |     18167   27.11989   5.950856         20         45 
       black |     18167   .2277206   .4193729          0          1 
       white |     18167    .736225   .4406909          0          1 
       other |     18167   .0360544   .1864306          0          1 
    hispanic |     18167   .1086035   .3111497          0          1 
    highEduc |     18167   .4741564   .4993454          0          1 
     educYrs |     18167   12.78169    1.79133          0         16 
     teacher |     18167   .0487147   .2152769          0          1 
    quarter1 |     18167    .241757   .4281596          0          1 
    quarter2 |     18167   .2403809   .4273265          0          1 
    quarter3 |     18167   .2599769   .4386337          0          1 
    quarter4 |     18167   .2578852   .4374825          0          1 
(output written to C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/results/ACS/IPUMS
> stats_Unmarried.tex)

. 
. 
. ********************************************************************************
. *** (6) Close
. ********************************************************************************
. log close
      name:  <unnamed>
       log:  C:/Users/cq224/Dropbox/JAE_replication/replication_DemandSeasonofBirth/log/acsAnalysis.txt
  log type:  text
 closed on:  29 Mar 2019, 14:31:21
-----------------------------------------------------------------------------------------------------------
