The E ffect of H ome - country Gender Status on the Labor Supply of Immigrants
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Transcript of The E ffect of H ome - country Gender Status on the Labor Supply of Immigrants
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The Effect of Home-country
Gender Status on the Labor
Supply of Immigrants
November 4th, 2011
Yunsun Huh
University of Wisconsin, Green
Bay
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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.
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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.
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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
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Hypothesis 1
Women from more egalitarian societies
have more opportunities to work than
women from less egalitarian societies
More: confidence, positive attitude
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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.
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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)
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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
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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.
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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.
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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.
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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
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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.)
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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
HigherEduRacegion
EnglishMarrEDUsizeFamChild
GEMGDIYrusYrusAgeAgeWorkhr
Re
_5_ 87
652
432
210
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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
Odd ratio from logit regression (LFP)
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Independent
Variables
Model A Model B Model C
Female Male Female Male Female Male
GEM 7.1379** 1.9419**
70.6956**
45.1703**
GDI 1.0118 0.1670** 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** 1.0025** 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
Estimation Coefficients for Labor Supply
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Independent
Variables
Model A Model B Model C
Female Male Female Male Female Male
GEM 6.3491** 10.5337**
8.0786** 7.5884**
GDI 4.2319** 10.6959** -2.6508 4.4806**
YrusGEM -0.2814 -1.2749** -0.6382**
-1.4816**
YrusGDI 0.0211 -0.9082** 0.5677* 0.2859
Yrus2GEM 0.0027 0.0269** 0.0105 0.0333**
Yrus2GDI -0.0028 0.0185** -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
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Estimation coefficients for Model A Coefficients Female immigrants Male immigrants
GEM 6.3491** 10.5337**
YrUSGEM -0.2814 -1.2749**
YrUS2GEM 0.0027 0.0269**
** 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)
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Estimation coefficients for Model BCoefficients Female immigrants Male immigrants
GDI 4.2319** 10.6959**
YrUSGDI 0.0211 -0.9082**
YrUS2GDI -0.0028 0.0185**
** 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)
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Estimation coefficients for Model C
Coefficients Female immigrants Male immigrants
GEM 8.0786** 7.5884**GDI -2.6508 4.4806**YrUSGEM -0.6382** -1.4816**YrUS2GEM 0.0105 0.0333**YrUSGDI 0.5677* 0.2859YrUS2GDI -0.0119 -0.0084
** denotes statistically significant at 5% level
* denotes statistically significant at 10% level
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The Effect of GEM on Labor Supply over timeBased on Model A, including
only GEM in the regressionBased on Model C, including both GEM& GDI in the regression
Effect on w
orking hours
YrUS YrUS
Effect n w
orking hours
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The Effect of GDI on Labor Supply over time Based on Model B, including
only GDI in the regression
YrUS YrUS
Effect on log w
ages
Effect on log w
ages
Based on Model C, including both GEM & GDI in the regression
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Robustness Test: A model for all immigrants
Variables Coefficients P-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
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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
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
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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
Additional Results Labor Force Participation1)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 Supply1) 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.
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.
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Questions?
Countries of origin and the number of immigrants
Birth place(ACS)Labor force
FemaleTotal
Female
Labor forceMale
TotalMale
Australia 159 220 199 217
Bangladesh 141 322 373 413
Brazil 620 980 693 778
Bulgaria 135 176 141 160
Canada 1,216 1,988 1,419 1,643
Chile 145 221 173 195
China 2,771 4,025 2,802 3,280
Colombia 1,204 1,789 1,117 1,314
Dominican Republic 1,181 1,756 921 1,154
Ecuador 475 789 653 757
Egypt/United Arab Rep. 164 273 365 436
El Salvador 1,510 2,175 2,039 2,269
France 253 392 321 358
Germany 941 1,495 635 762
Guatemala 798 1,250 1,528 1,692
Guyana/British Guiana 474 660 466 546
Honduras 553 811 734 853
India 2,721 4,508 4,461 4,912
Indonesia 158 277 168 190
Iran 510 840 780 923
Ireland 185 303 299 333
Israel/Palestine 165 313 347 391
Italy 283 498 444 566
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Birth place(ACS)Labor force
Female
TotalFemale
Labor forceMale
TotalMale
Japan 633 1,296 612 718
Korea 1,583 2,938 1,478 1,864
Malaysia 150 218 157 172
Mexico 10,660 21,173 20,840 23,574
Netherlands 110 187 172 193
Pakistan 267 615 703 784
Panama 227 333 142 175
Peru 743 1,036 766 848
Philippines 4,626 6,123 2,937 3,654
Poland 765 1,178 880 1,007
Portugal 165 304 271 339
Romania 285 421 335 384
South Africa (Union of) 183 271 257 268
Spain 152 242 178 206
Thailand 381 643 217 259
Trinidad and Tobago 481 650 390 464
Turkey 145 232 278 314
UK(England + Scotland +northern Ireland +ns)
1,127 1,815 1,570 1,789
Venezuela 303 495 341 382
Total 39,748 66,232 53,602 61,536
Odds Ratio in Logit Regressions(Labor force participation)
Basic Model Model A Model B Model 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** 0.9976** 0.9977** 0.9976** 0.9976** 0.9976** 0.9976** 0.9975**
Yrus 1.0822** 1.0610** 1.1471** 1.1253** 1.1580** 0.9739 1.1043** 0.9300**
Yrus2 0.9984** 0.9986** 0.9971** 0.9974** 0.9963** 1.0000 0.9972** 1.0008
GEM 9.0316** 1.3338 7.1379** 1.9419**
70.6956** 45.1703**
GDI 0.1427** 0.3807
1.0118 0.1670** 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** 1.0025**
1.0027** 1.0074**
Yrus2GDI 1.0027** 0.9981** 0.9998 0.9925**
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** 0.9592 1.4550** 0.9522 1.4064** 0.9585
Under 8th 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**
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Some college study 1.1146** 0.9530 1.1169** 0.9495 1.1302** 0.9554 1.1221** 0.9570
Associated degree 1.2488** 0.9882 1.2406** 0.9838 1.2713** 0.9948 1.2626** 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 0.9809 0.9481 0.8587** 0.8851** 0.9220** 0.9334 0.9779 0.9195*
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
1.3058 0.4701** 1.3371 0.4734** 1.1932 0.4710** 1.3137 0.4626**
Asian and pacific Islander-non
Hispanic
1.1687** 0.6515** 1.1055** 0.6419** 0.9769 0.6364** 1.1574** 0.6532**
Other-non Hispanic 1.0593 0.7935* 1.0343 0.7885* 0.9123 0.7768* 1.0787 0.8214
East 1.0594** 0.9341* 1.1028** 0.9513 1.0679** 0.9334* 1.0553** 0.9359*
Mwest 1.0914** 1.0557 1.1062** 1.0621 1.0858** 1.0550 1.0878** 1.0540
South 0.9579** 1.0357 0.9697 1.0430 0.9522** 1.0353 0.9516** 1.0263
More_EDU 1.1560** 1.1569** 1.2743** 1.1994** 1.1986** 1.1577** 1.1635** 1.1522**
Odds Ratio (Cont’d)
Regression for Labor Supply of Immigrants
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Basic Model Model A Model B Model 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** 0.1844 0.8991** 0.0757 0.7021**
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** -2.6508 4.4806**
YrusGEM -0.2814 -1.2749** -0.6382** -1.4816**
YrusGDI 0.0211 -0.9082** 0.5677* 0.2859
Yrus2GEM 0.0027 0.0269** 0.0105 0.0333**
Yrus2GDI -0.0028 0.0185** -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 8th grade -0.0477 -0.2631 -0.1487 -0.4388 -0.0153 -0.2927 -0.0471 -0.2383
Some high school 0.0285 -0.2648 0.0029 -0.2587 0.0375 -0.2682 0.0263 -0.2414
Some college study 0.3610 0.3629* 0.3536 0.3680* 0.3607 0.3580* 0.3578 0.3534*
Labor Supply (cont’d)
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Associated degree 0.1375 0.0381 0.1262 -0.0021 0.1314 0.0081 0.1257 -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 -0.0960 1.6565** 0.1221 2.0246** -0.0989 1.7956** -0.0423 1.6137**
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 -0.2618 0.2935 -0.3225 0.1645 -0.1903 0.2740 -0.2774
Asian and pacific
Islander-non Hispanic
1.9369** -0.1807 2.0077** -0.1064 1.8547** 0.0807 1.9401** -0.1804
Other-non Hispanic 1.7834** -0.6693 1.8803** -0.4405** 1.6998** -0.4205 1.8547** -0.5852
East -0.0295 0.9251** -0.0693 0.7982** -0.0137 0.8959** -0.0294 0.9176**
Mwest 0.1980 0.9246** 0.1780 0.8867** 0.1948 0.9335** 0.1864 0.9251**
South 0.2864* 1.3214** 0.2732* 1.2394** 0.2879* 1.3167** 0.2816* 1.2983**
More_EDI 0.3577 0.0966 0.2259 -0.1681 0.3904 0.0387 0.3525 0.0765
Constant 25.2002** 27.4881** 24.9619** 25.7028** 24.6394** 22.7885** 26.0018** 23.6074**
Adjusted R² 0.0308 0.0297 0.0309 0.0304 0.0308 0.0301 0.0310 0.0314
Observation 39748 53602 39748 53602 39748 53602 39748 53602
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Education of female & male Immigrants
16.29 % 22.04 %
9.35 %11.27 %
19.49 %19.55 %
11.93 %9.91 %6.74 %3.87 %
21.91 % 16.33 %
9.14 % 10.17 %
5.16 % 6.86 %
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Female Immig Male Immig
Pro/ DocM.AB.SAssociate degreeSome collegHS gradSome HSUnder 8th
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Year in Migration Male immigrants
24.34 %
36.5 %
10.81 %
26.19 %
2.16 %
before 1970 1970-19791980-1989 1990-19992000-2006
Female immigrants
25.75 %
36.96%
12.07%
21.88%
3.34%
before 1970 1970-19791980-1989 1990-19992000-2006
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Race of immigrants0.84%
26.16%
1.3%
0.11%
16.92%
54.68%
Hispanic
White-nonhispanic
Black-nonhistpanic
American Indian/ Alaska Natïve-nonhispan
Asian/ Pacific Icelander-nonhispanic
Other-nonhispanic
Female Male
34.66%
2.07%0.11% 18.49%
43.8%
0.87%
Hispanic
White-nonhispanic
Black-nonhistpanic
American Indian/ Alaska Natïve-nonhispan
Asian/ Pacific Icelander-nonhispanic
Other-nonhispanic
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Descriptive Statistics - Marriage Female Male
Total Immigrants 66,231 61,536Labor Force Participation 60% 87%
Married among Non LFP 70% 71%Married among LFP 82% 76%
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Basic Sensitivity Test
GEM coefficients Female Male
Model with GEM & GDI 8.0786** 7.5884**Model with GEM only 6.3491** 10.5337**
GDI coefficients Female Male
Model with GEM & GDI -2.6508 4.4806**Model with GDI only 4.2319** 10.6959**
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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%