Female Brain Drains and Women s Rights Gaps: An Empirical Analysis of Bilateral Migration Flows

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1 Female Brain Drains and Women s Rights Gaps: An Empirical Analysis of Bilateral Migration Flows Maryam Naghsh Nejad 1 Andrew Young 2 1 Institute for the Study of Labor(IZA) 2 West Virginia University July 26, 2014 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

2 Outline 1 Motivation 2 Previous work on female brain drain 3 Theoretical framework 4 Empirical Findings 5 Conclusion NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

3 Motivation Female migration rates are higher than male in 88% of non- OECD countries. This di erence is the most pronounced in the case of high-skilled migrants. (Figure 1 ) The female brain drain rates are on average 17 percent higher than those of males The ratio of female to male migration rates is higher in non- OECD countries. (Figure 2) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

4 Gender and education specific migration rates NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

5 Female brain drain ratio continents NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

6 Gender gap in migration rates NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

7 Why does it matter? E ects of high-skilled migration on development: Loss of human capital. Positive e ects through remittance: Lower in case of high-skilled migrants (Niimi, zden, and Schi, 2008), Even lower in case of women. Positive e ects through high human capital formation in the presence of potential of migration. (Docquier (2006) only when rates are between five to ten percent.) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

8 Why does it matter? Positive e ects of high-skilled women on development: Reduced fertility, infant mortality, improved health and increased educational attainment for children (Schultz (1988), Behrman and Deolalikar (1988), Haveman & Wolfe (1995), and Subbarao and Raney (1995). Abu-Ghaida and Klasen (2004) estimate the lost social gains from gender inequality in education 0.1 and 0.3 in income growth per capita. Knowles, Lorgelly, and Owen (2002) increases in female education positively a ect labor productivity, not the same for men NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

9 Few farming women in developing countries have title and control of land in their own names. In many areas of sub-saharan Africa, widows lack even basic rights to inherit marital property [.] In south Asia, women have gained greater legal inheritance rights over time, but inequitable restrictions continue to keep women at a disadvantage, and women s property rights in practice are much less than in the legal code[.] Women may also have less access [to] productive assets such as labor-saving technologies, credit, and extension services (Mammen and Paxson, 2000, p. 161). NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

10 Outline 1 Motivation 2 Previous work on female brain drain 3 Theoretical framework 4 Empirical Findings 5 Conclusion NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

11 Previous work on female brain drain Limited work on female brain drain mainly due to lack of data: Dumont, Martin and Spielvogel (2007) First data on gender-specific brain drain for 109 origin countries. High-skilled women respond di erently to traditional push factors. Docquier, Lowell, and Marfouk (2009) More complete dataset from 195 origin countries in 2000 and 174 countries in High-skilled women respond di erently to traditional push factors Docquier, Marfouk, Salomone, and Sekkat (2012) Suggest that the di erence in female & male response to traditional push factors might be due to gender discrimination. NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

12 Previous work on female brain drain Bang and Mitra (2011) Use equality in access & equality in outcome variables Baudasse and Baziller (2011) Use relative female to male income, education di erentials, and female labor market participation rates Both Studies: Lack of comprehensive proxy for gender discrimination Small number of observations due to the choice of variables Bang & Mitra: Lack of theoretical framework Baudasse & Baziller: Using stock of migrants instead of rates NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

13 Outline 1 Motivation 2 Previous work on female brain drain 3 Theoretical framework 4 Empirical Findings 5 Conclusion NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

14 Theoretical framework Following the work of Borjas(1987) and Grogger and Hansen (2011): The expected utility function of an individual that lives in country i and decides to stay: u ii,g = (W i + E i D i,g )+ ii,g (1) D i,g =0 if g=m and D i,g >0if g=f (gender discrimination) W i : Wages in country i ; E i : Characteristics of country i NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

15 Theoretical framework The expected utility function of an individual that lives in country i migrating to country j: u ij,g = (W j + E j C ij,g D j,g )+ ii,g (2) C ij,g : Cost of migrating from i to country j C ij,g = C (T ij,g, D i,g, D j,g ) (3) Where C ij,g is strictly increasing convex function of D i,g and D j,g Net gain of migrating from i to country j: NG ij,g = (W j W i )+ (E j E i )+ (D i,g D j,g ) C ij,g + ii,g (4) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

16 Theoretical framework The likelihood of a high-skilled individual from group g migrating from i to country j: ln M ij,g M i,g = (W j W i )+ (E j E i )+ (D i,g D j,g ) C ij,g (5) Where M ij,g M i,g is the population share of gender group g in i that migrates to j. M i,g is the population By subtracting the log odds of male from female migration rates: ln M ij,f M i,f ln M ij,m M i,m = (C ij,f C ij,m )+ (D i,g D j,g ) (6) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

17 Theoretical framework d ln M ij,f @D j,g ln M ij,m M i,m ln M ij,f M i,f ln M ij,f M i,f ln M ij,m = M i,m ln M ij,m = M i,m = (dd i,f dd @D j,g + i,f dd i,f j,f dd j,f (9) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

18 Outline 1 Motivation 2 Previous work on female brain drain 3 Theoretical framework 4 Empirical Findings 5 Conclusion NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

19 Empirical framework Dependent Variable: FBR ij = female brain drain rate ij male brain drain rate ij (10) brain drain rate ij,g = stock of migrant ij,g,h,2000 stock of migrant ij,g,h,1990 (11) population of nationals i,g,h,1990 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

20 Measuring Women s rights women s rights (Cingranelli & Richards, 2010) women s rights= women s social rights + women s economic rights + women s political rights Women s social rights: considers gender inequalities in inheritance, marriage, and divorce as well as the women s rights to travel, obtain education, and choose a residence. Varies between 0 and 3. Examples: Saudi Arabia:0, Ethiopia:1, Spain:2, Germany:3. Mean:1.31. Women s economic rights: focuses on the right to get and choose a job without husband or male relatives consent. It also includes the equalities in hiring, pay, promotion, and job securities in workplace. Varies between 0 &3. Examples: Saudi Arabia:0, Ethiopia:1; Italy:2; United States:3 Mean:1.31. NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

21 Measuring Women s rights Women s political rights: includes the right to vote and engage in political activities such as running a political o ce, hold government positions, join political parties, and petition government o cials. Varies between 0 &3. Examples: Saudi Arabia:0, Ethiopia:1; Spain:2; Norway:3 Mean:1.72. NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

22 Empirical framework Independent Variables: Women 0 s rights gap ij = Women 0 s economic rights j + Women 0 s political rights j + Women 0 s social right Women 0 s economic rights i + Women 0 s political rights i + Women 0 s social right Women 0 s economic rights gap ij = Women0 s economic rights j Women 0 s economic rights i (13) Women 0 s politica lrights gap ij = Women0 s political rights j Women 0 s political rights i (14) Women 0 s social rights gap ij = Women0 s social rights j Women 0 s social rights i (15) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

23 Gravity Model and Estimation Techniques The gravity models that we estimate are each of one of three forms: log (FBR ij )= Women 0 s rights gap ij + 2 Women 0 s rights gap ij 2 + z Z ij + ij (16) log (FBR ij + 1) = Women 0 s rights gap ij + 2 Women 0 s rights gap ij + z Z ij + ij (17) FBR ij = Women 0 s rights gap ij + 2 Women 0 s rights gap ij 2 + z Z ij + ij (18) NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

24 E ects of women s rights gaps on FBR (I) (II) (III) (IV) (V) Estimation Method (OLS) (OLS) (Heckman) (PPML) (PPML) Dependent Variable Log(FBR) Log(FBR+1) Log(FBR) FBR FBR>0 Women s Rights Gap (Women s Rights Gap) Orig-landlocked Orig-small island Orig-loggdp Orig-unemployment Dest-loggdp Dest-unemployment Contiguity Common 1st language Common 2nd language Colony Log distance Orig-government instability Mills Test Observations F p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

25 Interpretation of results Hump-shaped relationship peaks at a womens rights gap of about is more than a sample standard deviation greater than the sample mean (1.193). Saudi Arabia, Lesotho, and Sudan as origins relative to the US as a destination Common sense result: Starting from a womens rights gap 1.796, more desirable destination e ect (benefits) Costs of migration: starting from womens rights gap NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

26 E ects of women s economic rights gaps on FBR (I) (II) (III) (IV) (V) Estimation Method (OLS) (OLS) (Heckman) (PPML) (PPML) Dependent Variable Log(FBR) Log(FBR+1) Log(FBR) FBR FBR>0 Women s Econ Rights Gap Women s Econ Rights Gap Orig-landlocked Orig small island Orig-loggdp Orig-unemployment Dest-loggdp Dest- unemployment Contiguity Common 1st language Common 2nd language Colony Log distance Orig-government instability Mills Test Observations F p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

27 E ects of women s political rights gaps on FBR (I) (II) (III) (IV) (V) Estimation Method (OLS) (OLS) (Heckman) (PPML) (PPML) Dependent Variable Log(FBR) Log(FBR+1) Log(FBR) FBR FBR>0 Women s Pol Rights Gap Women s Pol Rights Gap Orig-landlocked Orig small island Orig-loggdp Orig-unemployment Dest-loggdp Dest - unemployment Contiguity dummy Common 1st language Common 2nd language Colony Log distance Orig-government instability Mills Test Observations F p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

28 E ects of women s social rights gaps on FBR (I) (II) (III) (IV) (V) Estimation Method (OLS) (OLS) (Heckman) (PPML) (PPML) Dependent Variable Log(FBR) Log(FBR+1) Log(FBR) FBR FBR>0 Women s Soc Rights Gap Women s Soc Rights Gap Orig-landlocked Orig small island Orig-loggdp Orig-unemployment Dest-loggdp Dest- unemployment Contiguity Common 1st language Common 2nd language (0.067) (0.049) (0.086) (0.099) (0.099) Colony Log distance Orig-government instability Mills Test Observations F p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

29 E ects of WR political, economic, and social gaps on FBR (I) (II) (III) (IV) (V) Estimation Method (OLS) (OLS) (Heckman) (PPML) (PPML) Dependent Variable Log(FBR) Log(FBR+1) Log(FBR) FBR FBR>0 Women s Pol Rights Gap Women s Pol Rights Gap Women s Econ Rights Gap Women s Econ Rights Gap Women s Soc Rights Gap Women s Soc Rights Gap Orig-landlocked Orig small island Orig-loggdp Orig-unemployment Dest-loggdp Dest- unemployment Common 1st language Common 2nd language Observations F p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

30 E ects of WR gaps on FBR-gaps Multilateral resistance (I) (II) (III) (IV) Estimation Method (PPML) (PPML) (PPML) (PPML) Dependent Variable FBR FBR FBR FBR Women s Rights Gap Women s Rights Gap Women s Pol Rights Gap Women s Pol Rights Gap Women s Econ Rights Gap Women s Econ Rights Gap Women s Soc Rights Gap Women s Soc Rights Gap Common 1st language Colony Log distance Origin fixed e ect Yes Yes Yes Yes Destination fixed e ect Yes Yes Yes Yes Origin nest fixed e ect Yes Yes Yes Yes Observations RSq p < 0.10, p < 0.05, p <.01 NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

31 Outline 1 Motivation 2 Previous work on female brain drain 3 Theoretical framework 4 Empirical Findings 5 Conclusion NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

32 Conclusion develop a model of migration where both womens expected costs and benefits of migration are a function of womens rights in the origin country relative to those of the destination (the womens rights gap). when womens rights levels are higher in the destination country in comparison with the origin country, high-skilled women are more likely to migrate (compare to men), unless the low levels of womens rights in origin manifests as increased cost of migration for women. Using a panel of over 5,000 bilateral migration flows across OECD and non-oecd countries and the womens rights indices from the CIRI Human Rights Dataset, we report evidence consistent with the theory. NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

33 Thank you Your opinion, suggestions and comments are highly appreciated. Thank You Maryam Naghsh Nejad NaghshNejad & Young (IZA&WVU) Gender Gap in High-Skilled Migration July 26, / 33

Female Brain Drains and Women s Rights Gaps: A Gravity Model Analysis of Bilateral Migration Flows

Female Brain Drains and Women s Rights Gaps 1 Female Brain Drains and Women s Rights Gaps: A Gravity Model Analysis of Bilateral Migration Flows Maryam Naghsh Nejad College of Business and Economics West