1. 1 The Effect of Home-country Gender Status on the Labor Supply of Immigrants November 4 th, 2011 Yunsun Huh University of Wisconsin, Green Bay

2. 2 Motivation  Women have a different socio-economic position from men and this difference varies across different cultures and institutions Huh, Y.(2011) : The Effect of Home-country Gender Status on Labor Market Success of Immigrants.  The differential effect of gender status in the home country on wages of female and male immigrants in the U.S.

3. 3 Question & Objective How cultural background (e.g. gender status) affect women’s decision for LFP and LS different from men? Analyze dynamics of labor supply for women immigrants relative to men across different countries of orign.

4. 4 Question & Objective How does cultural background (i.e. gender status) affect women’s labor participation different from men? Analyze the dynamics of labor supply behavior of women immigrants relative to men

5. 5 Hypothesis 1 Women from more egalitarian societies have more opportunities to work than women from less egalitarian societies  More: confidence, positive attitude

6. 6 Hypothesis 2 Women from more egalitarian societies have less opportunities to work than women from less egalitarian societies  Less : more challenges, more aggressive for job searching, deal with inferior working condition etc.

7. 7 Prior Literature Labor & Immigration Literature  No consideration of the impact of home-country conditions on the labor supply of immigrants women Labor Supply literature  Focuses on gender wage gap or fertility behavior: Antecol (2001, 2003), Fernandez and Fogli(2006), Latt and Sevilla-Sanz (2011) Immigration literature  Focuses on human capital factors or female labor force activity in home country : Blau, Kahn, and Papps (2008)

8. 8 Contribution Consider both women & men Add gendered perspective on why origins of immigrants matter Provide insights for Policy  Findings:  Higher gender equality increases labor supply of both sexes  A greater effect of gender status on women  Higher development status increases reservation wages of both sexes

9. 9 Data source and description Individual Immigrant Data: IPUMS-USA (The Integrated Public Use Microdata Series), 1 % sample of the 2006 ACS (American Community Survey)  Restricted sample: Foreign born Individuals between 25 & 65, who arrived in the U.S over age of 18.

10. 10 Data source and description Home country gender status : GDI (Gender Development Index) GEM (Gender Empowerment Measure) : Human Development Reports, UN  42 countries selected: - 2001GDI &1999 GEM: both based on 1999 observations - Enough observations of female immigrant workers in U.S.

11. 11 Data source and description GDI (Gender Development Index) : An indication of the standard of living in a country  HDI (Human Development Index) modified for gender inequality  Health, education, and a decent standard of living.

12. 12 Data source and description GEM (Gender Empowerment Measure) :A measure of the gender inequality of opportunities in a country.  Economic and political participation & decision making

13. 13 Approach Labor Market Participation: binary logit regression with GEM and GDI Labor Supply Behavior : OLS only for labor market participants including zero income earners with GEM and GDI Separate sample group by sex Robustness test (likelihood ratio test, multicollinearity, heteroskedasticity, etc.)

14. 14 Bench Mark Model Labor Supply Labor force participation : Binary Dependent variable  Controlled for the number of children under5, family size, education, marital status, language, region, race

15. 15 Estimation Model Model A: GEM and interaction term btwn. GEM & Yrus Model B: GDI and interaction term btwn. GDI & Yrus Model C: GEM, GDI, and interaction with Yrus for both

16. Odd ratio from logit regression (LFP) 16 Independent Variables Model AModel BModel C FemaleMaleFemaleMaleFemale Male GEM 7.1379**1.9419** 70.6956** 45.1703** GDI 1.01180.1670**0.0328** 0.0093** YrusGEM 0.8863**0.8846** 0.8242** 0.6697** YrusGDI 0.9152** 1.1185**1.0998** 1.5215** Yrus2GEM 1.0025** 1.0027** 1.0074** Yrus2GDI 1.0027**0.9981**0.9998 0.9925** Nchunder5 0.5764**1.1560**0.5803**1.1600**0.5759** 1.1627** Family size 0.9587**1.0695**0.9554**1.0693**0.9541** 1.0698** Marriage 0.5120**1.2940**0.5085**1.2933**0.5138**1.2937** ** denotes statistically significant at 5% level * denotes statistically significant at 10% level

17. Estimation Coefficients for Labor Supply 17 Independent Variables Model AModel BModel C FemaleMaleFemaleMaleFemale Male GEM 6.3491**10.5337** 8.0786**7.5884** GDI 4.2319**10.6959**-2.65084.4806** YrusGEM -0.2814-1.2749** -0.6382**-1.4816** YrusGDI 0.0211-0.9082**0.5677*0.2859 Yrus2GEM 0.00270.0269** 0.01050.0333** Yrus2GDI -0.00280.0185**-0.0119-0.0084 Nchunder5 -1.6620**-0.1354-1.6517**-0.1458-1.6665**-0.1376 Family size -0.2041**-0.0569*-0.1974**-0.0538*-0.1980**-0.0539* Marriage -1.2772**1.1922** -1.2876** 0.3295**-1.2977**1.1810** ** denotes statistically significant at 5% level * denotes statistically significant at 10% level

18. 18 Estimation coefficients for Model A CoefficientsFemale immigrantsMale immigrants GEM 6.3491**10.5337** YrUSGEM -0.2814-1.2749** YrUS2GEM 0.00270.0269** ** denotes statistically significant at 5% level * denotes statistically significant at 10% level Ex) Thailand (25th percentile)  Dominican Rep(75thpercentile) Women’s working hours: 0.77hr (46min), Men’s working hours: 1.27hr (76min)

19. 19 Estimation coefficients for Model B CoefficientsFemale immigrantsMale immigrants GDI 4.2319**10.6959** YrUSGDI 0.0211-0.9082** YrUS2GDI -0.00280.0185** ** denotes statistically significant at 5% level * denotes statistically significant at 10% level Ex) Iran (25th percentile)  Israel (75th percentile) Women’s working hours: 0.81hr (48min) Men’s working hours: 2.1hr(126min)

20. 20 Estimation coefficients for Model C CoefficientsFemale immigrantsMale immigrants GEM 8.0786**7.5884** GDI -2.65084.4806** YrUSGEM -0.6382**-1.4816** YrUS2GEM 0.01050.0333** YrUSGDI 0.5677*0.2859 YrUS2GDI -0.0119-0.0084 ** denotes statistically significant at 5% level * denotes statistically significant at 10% level

21. 21 The Effect of GEM on Labor Supply over time Based on Model A, including only GEM in the regression Based on Model C, including both GEM& GDI in the regression Effect on working hours YrUS Effect n working hours

22. 22 The Effect of GDI on Labor Supply over time Based on Model B, including only GDI in the regression YrUS Effect on log wages Based on Model C, including both GEM & GDI in the regression

23. 23 Robustness Test: A model for all immigrants VariablesCoefficientsP-value Female -9.6804 0.000 GEM 28.9448 0.000 GDI -16.5668 0.000 FemaleGEM 8.1028 0.000 FemaleGDI -11.9647 0.000 Controlling for all human capital factors, GEM, GDI, and gender ** denotes statistically significant at 5% level * denotes statistically significant at 10% level

24. 24 Conclusion: Results 1. Substantial cultural effect on labor participation and labor supply of immigrants even after controlling for human capital factors  Different Effect of GDI and GEM on labor participation  GEM increase working hours of both women and men, but it has greater effect on women

25. Result 2. Different effects of GEM by sex.  Strong positive impact of GEM on labor participation and labor supply of female immigrants  Support H1 3. Small effect of GDI  Small negative impact of GDI on labor participation  Stronger GDI effect on labor supply of men 25

26. 26 Conclusion: Implication The more empowered the women in a society are, the higher gains in terms of labor supply for both women and men. Economic development status helps men more. Importance of socio-political factors on capability

27. Additional Results Labor Force Participation 1)Race : Compared to Hispanic  Black, American Indian, Asian men less likely in LFP  Balck and Asian women more likely in LFP 2) Region : Affect men’s LFP only.  Compared to West, South men more likely to be in LFP, while Mwest, East men are less likely to be in LFP Labor Supply 1) Race: Compared to Hispanic  White men work more, Black, AI, Asian men work less  Black and Asian women work more 2) Region: Affect women’s LS only.  Compared to West, South women work less than women in the West while East Mwest women work more than West women.

28. Additional Results Education:  More education has positive impact on both LFP & LS. Greater impact on women than men. English Fluency:  Helps more women than men.  Fluency increase probability to be in LFP of women but not affect men. Self-selection  Higher level of education than home country population doesn’t affect on Job Market Participation, but it increases working hours.

29. 29 Questions?

30. Countries of origin and the number of immigrants Birth place(ACS) Labor force Female Total Female Labor force Male Total Male Australia 159 220199217 Bangladesh 141 322373413 Brazil 620 980693778 Bulgaria 135 176141160 Canada 1,216 1,9881,4191,643 Chile 145 221173195 China 2,771 4,0252,8023,280 Colombia 1,204 1,7891,1171,314 Dominican Republic 1,181 1,7569211,154 Ecuador 475 789653757 Egypt/United Arab Rep. 164 273365436 El Salvador 1,510 2,1752,0392,269 France 253 392321358 Germany 941 1,495635762 Guatemala 798 1,2501,5281,692 Guyana/British Guiana 474 660466546 Honduras 553 811734853 India 2,721 4,5084,4614,912 Indonesia 158 277168190 Iran 510 840780923 Ireland 185 303299333 Israel/Palestine 165 313347391 Italy 283 498444566 30 Birth place(ACS) Labor force Female Total Female Labor force Male Total Male Japan 633 1,296612718 Korea 1,583 2,9381,4781,864 Malaysia 150 218157172 Mexico 10,660 21,17320,84023,574 Netherlands 110 187172193 Pakistan 267 615703784 Panama 227 333142175 Peru 743 1,036766848 Philippines 4,626 6,1232,9373,654 Poland 765 1,1788801,007 Portugal 165 304271339 Romania 285 421335384 South Africa (Union of) 183 271257268 Spain 152 242178206 Thailand 381 643217259 Trinidad and Tobago 481 650390464 Turkey 145 232278314 UK(England + Scotland +northern Ireland +ns) 1,127 1,8151,5701,789 Venezuela 303 495341382 Total 39,748 66,23253,60261,536

31. Odds Ratio in Logit Regressions (Labor force participation) Basic ModelModel AModel BModel C Independent variables Female immigrants Male Immigrants Female immigrants Male Immigrants Female immigrants Male Immigrants Female immigrants Male Immigrants Age 1.2051**1.1899**1.1978**1.1886**1.2068**1.1934**1.2064**1.1999** Age2 0.9976** 0.9977**0.9976** 0.9975** Yrus 1.0822**1.0610**1.1471**1.1253**1.1580**0.97391.1043**0.9300** Yrus2 0.9984**0.9986**0.9971**0.9974**0.9963**1.00000.9972**1.0008 GEM 9.0316**1.33387.1379**1.9419** 70.6956**45.1703** GDI 0.1427**0.3807 1.01180.1670**0.0328**0.0093** YrusGEM 0.8863**0.8846** 0.8242**0.6697** YrusGDI 0.9152** 1.1185**1.0998**1.5251** Yrus2GEM 1.0025** 1.0027**1.0074** Yrus2GDI 1.0027**0.9981**0.99980.9925** Nchunder5 0.5787**1.1582**0.5764**1.1560**0.5803**1.1600**0.5759**1.1627** Famsize 0.9540**1.0686**0.9587**1.0695**0.9554**1.0693**0.9541**1.0698** Marriage 0.5134**1.2951**0.5120**1.2940**0.5085**1.2933**0.5138**1.2937** English Fluency 1.4122**0.9471*1.4461**0.95921.4550**0.95221.4064**0.9585 Under 8 th grade 0.8082**0.8439**0.8787**0.8759**0.8428**0.8419**0.8142**0.8444** Some high school 0.8009**0.7382**0.8143**0.7440**0.8127**0.7385**0.7995**0.7410** 31

32. 32 Some college study 1.1146**0.95301.1169**0.94951.1302**0.95541.1221**0.9570 Associated degree 1.2488**0.98821.2406**0.98381.2713**0.99481.2626**0.9922 Bachelor’s degree 1.3630**1.2452**1.3776**1.2432**1.3573**1.2428**1.3918**1.2626** Master’s degree 1.6688**1.7729**1.7492**1.7929**1.6549**1.7711**1.7117**1.8199** Prof/doc degree 2.2908**2.1201**2.3645**2.1263**2.3030**2.1495**2.3261**2.1667** White-non Hispanic 0.98090.94810.8587**0.8851**0.9220**0.93340.97790.9195* Black-non Hispanic 1.3741**0.7406**1.3215**0.7333**1.3345**0.7388**1.3900**0.7483** American Indian/Alaska Native-non Hispanic 1.30580.4701**1.33710.4734**1.19320.4710**1.31370.4626** Asian and pacific Islander-non Hispanic 1.1687**0.6515**1.1055**0.6419**0.97690.6364**1.1574**0.6532** Other-non Hispanic 1.05930.7935*1.03430.7885*0.91230.7768*1.07870.8214 East 1.0594**0.9341*1.1028**0.95131.0679**0.9334*1.0553**0.9359* Mwest 1.0914**1.05571.1062**1.06211.0858**1.05501.0878**1.0540 South 0.9579**1.03570.96971.04300.9522**1.03530.9516**1.0263 More_EDU 1.1560**1.1569**1.2743**1.1994**1.1986**1.1577**1.1635**1.1522** Odds Ratio (Cont’d)

33. Regression for Labor Supply of Immigrants 33 Basic ModelModel AModel BModel C Independent variables Female immigrants Male Immigrants Female immigrants Male Immigrants Female immigrants Male Immigrants Female immigrants Male Immigrants Age 0.3409**0.4798**0.3407**0.4780**0.3408**0.4589**0.3410**0.4726** Age2 -0.0041**-0.0063**-0.0041**-0.0063**-0.0041**-0.0061**-0.0041**-0.0062** Yrus 0.2079**0.2089**0.3356**0.8166**0.18440.8991**0.07570.7021** Yrus2 -0.0044**-0.0038**-0.0055**-0.0166**-0.0021-0.0178**-0.0002-0.0134** GEM 1.4767-3.5769**6.3491**10.5337** 8.0786**7.5884** GDI 2.5157**6.1480** 4.2319**10.6959**-2.65084.4806** YrusGEM -0.2814-1.2749** -0.6382**-1.4816** YrusGDI 0.0211-0.9082**0.5677*0.2859 Yrus2GEM 0.00270.0269** 0.01050.0333** Yrus2GDI -0.00280.0185**-0.0119-0.0084 Nchunder5 -1.6628**-0.1305-1.6620**-0.1354-1.6517**-0.1458-1.6665**-0.1376 Famsize -0.1994**-0.0479-0.2041**-0.0569*-0.1974**-0.0538*-0.1980**-0.0539* Marriage -1.2807**1.1651**-1.2772**1.1922**-1.2876**1.1832**-1.2797**1.1810** English Fluency 1.0152**0.3594**0.9897**0.3312**1.0284**0.3295**1.0180**0.3882** Under 8 th grade -0.0477-0.2631-0.1487-0.4388-0.0153-0.2927-0.0471-0.2383 Some high school 0.0285-0.26480.0029-0.25870.0375-0.26820.0263-0.2414 Some college study 0.36100.3629*0.35360.3680*0.36070.3580*0.35780.3534*

34. Labor Supply (cont’d) 34 Associated degree 0.13750.03810.1262-0.00210.13140.00810.1257-0.0123 Bachelor’s degree 1.2892**0.6481**1.2623**0.6929**1.2696**0.6808**1.2873**0.6736** Master’s degree 1.4069**0.8053**1.3754**0.7995**1.3964**0.8496**1.4236**0.8366** Prof/doc degree 5.3026**3.3532**5.2318**3.2705**5.2800**3.2944**5.2811**3.3091** White-non Hispanic -0.09601.6565**0.12212.0246**-0.09891.7956**-0.04231.6137** Black-non Hispanic 1.5235**-1.4326**1.5814**-1.3195**1.5047**-1.4020**1.5340**-1.4030** American Indian/Alaska Native- non Hispanic 0.1966-0.26180.2935-0.32250.1645-0.19030.2740-0.2774 Asian and pacific Islander-non Hispanic 1.9369**-0.18072.0077**-0.10641.8547**0.08071.9401**-0.1804 Other-non Hispanic 1.7834**-0.66931.8803**-0.4405**1.6998**-0.42051.8547**-0.5852 East -0.02950.9251**-0.06930.7982**-0.01370.8959**-0.02940.9176** Mwest 0.19800.9246**0.17800.8867**0.19480.9335**0.18640.9251** South 0.2864*1.3214**0.2732*1.2394**0.2879*1.3167**0.2816*1.2983** More_EDI 0.35770.09660.2259-0.16810.39040.03870.35250.0765 Constant 25.2002**27.4881**24.9619**25.7028**24.6394**22.7885**26.0018**23.6074** Adjusted R² 0.03080.02970.03090.03040.03080.03010.03100.0314 Observation 3974853602397485360239748536023974853602

35. 35 Education of female & male Immigrants

36. 36 Year in Migration

37. 37 Race of immigrants Female Male

38. 38 Descriptive Statistics - Marriage Female Male Total Immigrants 66,23161,536 Labor Force Participation 60%87% Married among Non LFP 70%71% Married among LFP 82%76%

39. 39 Basic Sensitivity Test GEM coefficientsFemale Male Model with GEM & GDI 8.0786**7.5884** Model with GEM only 6.3491**10.5337** GDI coefficientsFemaleMale Model with GEM & GDI -2.65084.4806** Model with GDI only 4.2319**10.6959**

40. 40 EX) Portugal vs. Korea Similar GDI (0.870 vs. 0.868) & Very Different GEM (0.571 vs. 0.336) Moving from Korea to Portugal  Model A (Only GEM): Women 20 % Men 15 %  Model B (Only GDI) : Women 0.11% Men 0.16%  Model C (Both GEM & GDI): Women 26.6% Men 6.08%