the e ffect of h ome - country gender status on the labor supply of immigrants

1

The Effect of Home-country

Gender Status on the Labor

Supply of Immigrants

November 4th, 2011

Yunsun Huh

University of Wisconsin, Green

Bay

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

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

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

Hypothesis 1

Women from more egalitarian societies

have more opportunities to work than

women from less egalitarian societies

More: confidence, positive attitude

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

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

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

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


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


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


GEM (Gender Empowerment Measure)

:A measure of the gender inequality of opportunities

in a country.

Economic and political participation & decision making

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

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

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

Odd ratio from logit regression (LFP)

16

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

17

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


18

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**



Ex) Thailand (25th percentile) Dominican Rep(75thpercentile)

Women’s working hours: 0.77hr (46min),

Men’s working hours: 1.27hr (76min)

19

Estimation coefficients for Model BCoefficients Female immigrants Male immigrants

GDI 4.2319** 10.6959**

YrUSGDI 0.0211 -0.9082**

YrUS2GDI -0.0028 0.0185**



Ex) Iran (25th percentile) Israel (75th percentile)

Women’s working hours: 0.81hr (48min)

Men’s working hours: 2.1hr(126min)

20

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



21

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

22

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

23

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



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

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

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.

29

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

30

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


immigrants


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**

31

32

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

33

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)

34

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

35

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

36

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

37

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

38

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%

39

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**

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%

the e ffect of h ome - country gender status on the labor supply of immigrants

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