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ФЕДЕРАЛЬНОЕ ГОСУДАРСТВЕННОЕ АВТОНОМНОЕ ОБРАЗОВАТЕЛЬНОЕ УЧРЕЖДЕНИЕ ВЫСШЕГО ОБРАЗОВАНИЯ

«НАЦИОНАЛЬНЫЙ ИССЛЕДОВАТЕЛЬСКИЙ УНИВЕРСИТЕТ

«ВЫСШАЯ ШКОЛА ЭКОНОМИКИ»

Факультет «Санкт-Петербургская школа экономики и менеджмента»

Департамент экономики

Final qualifying work-BACHELOR's WORK

Impact of international migration on gender inequality and demographic indicators

по направлению подготовки 38.03.01 «Экономика»

Ekaterina Ivanova

Санкт-Петербург

2020



ABSTRACT

Problems of gender inequality and the amount of population are widely discussed nowadays,and many countries are currently trying to solveit. This research raises a question about possible effects that international migration, specifically emigration, has on gender inequality and demographical indicators. The hypothesis that we suggest is that emigrants may assimilate and transfer norms from destination country to their country of origin, including ones associated with gender inequality and mortality, namely we consider Gender Inequality Indexand Neonatal Mortality Rate and assume that emigration reducesgender inequality and neonatal mortality. In order to test our hypothesis, we collected data and made panel dataset for 232 countries from 1990 to 2015 with 5-year intervals, estimate fixed effects models and implement the instrumental variables estimation. The results show that the number of emigrants decreasesGender Inequality Index and Neonatal Mortality Rate and the effect is significant.

Key words: migration, gender inequality, demography, neonatal mortality,developing countries, developed countries



CONTENTS

• INTRODUCTION
• CHAPTER 1. THEORETICAL FRAMEWORK
• CHAPTER 2.EMPERICAL RESULTS
• 2.1 Data
• 2.2 Methodology
• 2.3 Results and Discussions
• 2.4 Robustness check
• CONCLUSION
• REFERENCES
• APPENDIX


INTRODUCTION

Сurrently, we are living in the era of globalization when culture and customs go beyond their places of origin, cross the borders of countries and we are witnessing the process of acculturation. The development of societyand open borders made it possible for people to change their place of residence more and more easily. For instance, according to The World Bankthe international migrant stock in the world raised from 2,89% in 1990 to 3.35% in 2015. The increase in migration flows raises questions about how migrants transfer and assimilate norms from one country to another. This research examines the impact of emigrants on demography and gender inequality due to the increased relevance of these areas of society. gender inequality migration mortality

Women discrimination and equal rights are highly discussed topics due to the raise in human developmentand understanding that old rules by which society functioned are obsolete. These conditions make countries to change their social and political foundations to ensure women's equal rights. For instance, the average number of women in parliament rose from 11% to 19% in 2010, but the difference between developing and developed countries remained high (Lodigiani&Salomone, 2015). For researchers, the question of how international migration effects gender inequality is more relevant than ever. Migrants can assimilate norms that becamenormal in destination country and transfer them to homecountry where the same norms are unusualandnew.

Currently, a sharp increase in the population can be observed in developing countries and a slow, and sometimes even negative, increase in the population in developed countries, so the policies of many countries are being developed toward increasing or decreasing its population. For instance, according to The World Bank, countries of Central Europe and The Baltics face decrease in population, having 110 million people in 1989 and 102 million people in 2018. It raises interest, can migrants from countries with one traditions and norms about childbirth and childcaring, that may include attitudes towards pregnancy and the receiving of necessary assistance, transfer norms to another countries and influence neonatal mortality that has a direct impact on the amount of population.

In this research we estimate FE models for Gender Inequality Index (GII) and Neonatal Mortality Rate (NMR)due to the existence of country-specific characteristics and implement instrumental variable estimation to consider possibleendogeneity due to the inclusion of the total number of emigrants in regressors. Additionally, we check whether emigrants have long-term effect as well as short-term effect by making independent variables lagged by 5 years. Finally, we check our results for stability.

The structure of the research is the following: the first chapterintroduce literature review of the works that examine the effect of migration on variables that are closely connected to gender inequality and neonatal mortality, the second chapter includes empirical analysis, specifically we represent the data that we use to conduct our analysis, explain the chosenmethodology, describe and discuss the obtained results and test the results for stability. The research ends with a conclusionthat include the overall results of the study.



CHAPTER 1. THEORETICAL FRAMEWORK

The impact of migration on gender inequality and democracy was considered in a large number ofworks. Currently, there is no research that examines the effect of migration on the Gender Inequality Index that is used in this study. The only work that considers the Gender Inequality Index as the dependent variable is the research that was conducted by Karabaeva (2014). That research examined the impact of remittances on the Gender Inequality Index and did not consider the number of emigrants as explanatory variable that is done in this research. Also, Karabaeva (2014) used received personal remittances per capita in the countryas the explanatory variable measured in current US$, not in constant US$, that can give misleading results in regressions. In her work remittances had significant negative effect on the Gender Inequality Index just in 2 out of 4 regressions while in regressions where all variables were lagged by 10years remittances had significant negative impact in all cases. Additionally, works that examine the effect of migration on variables that reflect women's discrimination can be also considered. For instance, the number of women in parliament, female participation in labor force,women's accessto education and their position is society. Consequently, studies that examine the impact of migration on these variables and that are based on econometric estimation methods will be analyzed.

Women's discrimination can be reflected by the number of seats held by women in parliament of the country. Lodigiani and Salomone (2015) conducted the study on this issue and used data on bilateral international migration from 1960 to 2000 and the number of women in parliament from 1945 to 2003 for 204 countries. The conclusion that they made from their analysis is that international migration to countries where women have more rights in the political sphere remarkably increases the number of women parliamentarians in countries of origin. Authors explain that it happensdue to the migrants who have close cultural values as those in the country of destination and who arewilling to transform current political conditions in their native country when they witness more developed political conditions in the country of arrival. In this case, migrants maybe the channels that transfer these norms.

Some works that are related to the gender inequality topic consider the impact of migration on position that woman has in society, change in the gender gap and includeanalysis on country-specific data.Hadi (2001) examined the effect of male international migration on women's position in the family based on data for 1996 from 70 villages in Bangladesh. The author made a conclusion that male migration increases the probability that women will make independent decisions while the probability of receiving education by daughters in the family is 96% higher compared to families that did not have male migration experience. Amuedo-Dorantes and Pozo (2006) conducted their analysis on data on Mexican population for the 1980s and examinedpossible existence of positive or negative effect of remittances from relatives abroad on female labor participation.The authors found that for women who lived in urban areasincreased remittances do not have any effects on their working hours while for women who lived in rural areas and worked in the informal sector increased remittances are connected with a decline in the number of hours worked by them. A similar research was conducted by Lokshin and Glinskaya (2008) who useddata on Nepal household survey for 2004 for their analysis. Based on their results the authors made a conclusion that male migration experience in family decreases the probabilitythat woman will participate in the labor market because of increased cash receipts through remittances. Abdulloev, Gang & Yun (2014) conducted an analysis on data for Tajikistan and found that emigration from this country decreases the difference in male and female labor participation and increases the number of women who receive education. Antman (2012) found from his analysis on the data for United States that father's experience of migration increases the duration of daughter's education.

Currently, there are works that consider the effect of migration on child mortality and related topics. However, most works concentrate on a specific country analysis while keeping the question about cross-country analysis open. Kanaiaupuni and Donato (1999) conducted their analysis on data on emigration of Mexican population to the United States, specifically 25 Mexican communities during the periods 1987-1988 and 1992-1993, to examine the effect of migration on infants' survival. The authors foundthat migration has various effects at different stages of its institutionalization: initially, migration increases the number of infants' deaths, but after a while migration starts to have a negativeimpact, decreasing the number of infants who die. Authors explained that it may happen because of the assimilation of new norms in the community that takes time. Additionally, authors identifiedthat when the migration's intensity increases the infant mortality becomes lower.Furthermore, both individual human factors and community-level factors showed the significant impact on infants' survival.Beine, Docquier, and Schiff (2013) conducted an analysis on cross-country data, namely on bilateral migration for 208countries. Data included both developing and developed countries. The authors found that international migration decreases the fertility rate in the country of origin if it is higher than the fertility rate in the country of destination and increases if the fertility rate in the country of origin is lower. This process is explained due to the transfer of norms that are connected with fertility rate. Lindstrom and Munoz-Franco (2006) used data on Guatemalan Survey of Family for 1995 that contained information on rural women in Guatemala tostudy the impact of migration on the probability of women to use formal antenatal and childbirth care. The authors found that migration to cities and presence of relatives abroad have a positive impact on the likelihood that woman will take prenatal care. Migration to cities and international migration, according to the authors, help population through the transfer of norms and cash receipts to overcomefinancial, geographical and cultural barriersthat can be observed in the population and represent the obstacle to accept prenatal care.Omariba and Boyle (2010) conducted an analysis for less developed countries to check the effect of migration from villages to cities on infant mortality and found that migration status of family members does not significantly influenceinfant mortality when education and marital status of family members are taken into account.Yabiku, Agadjanian&Cau (2012) examined the effect of male labor emigration on child mortality in the country of origin in Mozambique. They found that the impact is significant just in case when male migration was considered successful by family members and it decreased child mortality under 5 years. However, the overall impact is not significant and there is no difference in child mortality whether the family had male emigration experience or not. Lindstrom and Saucedo (2002) conducted analysis on data on migration from Mexico to the United States to examine the impact of migration on fertility of Mexican women. The results of their study show that migration and separation of the one member of a couple does not influence the long term fertility of a woman, the effect is significant and negative only in short period of time and decreases the likelihood that woman will give birth. Nevertheless, families where the men had experience of migration to United States are connected with higher woman's fertility.

To sum up, gender inequality and mortality among children and infants are relevant topicsfor many researchers. However, there is lack of works that conduct cross-country analysis. To fill the gap in the number of such studies we examine the effect of emigration on gender inequality and neonatal mortality.



CHAPTER 2.EMPERICAL RESULTS

2.1 Data

Data on migration was collected from the United Nations database, in particular the “International Migrant Stock 2019” dataset which contain information for the total number of migrants for 1990, 1995, 2000, 2005, 2010, 2015 and 2019 years for 232 countries based on official statistics on the foreign-born or the foreign population and represent a bilateral migration matrix. Data on the Neonatal Mortality Rateand Mortality Rate under 5 years was collected from The World Bank, World Development Indicators,and contained annual information from 1960 to 2018. Data on the Gender Inequality Index was drawn from The Human Development Reports database and includes annual data on the index from 1995 to 2018.The Gender Inequality Index reflectscurrent women's inequality in the country and represent it in three main spheres: health, politics and economic status. Namely, the indicators that are used to compute the Gender Inequality Index are maternal mortality ratio, adolescent birth rate, share of parliamentary seats held by each sex, population with at least some secondary education and labor force participation rate by each sex. High index represents fewer equal rights and opportunities forwomen in the country. Most data on country-specific information, specifically, GDP per capita, GNI per capita, remittances, net official development assistance received, import and export of good and services, tertiary school enrollment rate, compulsory education duration, trade, female unemployment rate and labor participation rate, current health expenditure, female life expectancy and total life expectancy was drawnfrom The Word Bank database. Data on the Globalization Index was collected from KOF Time Series Database. Data on Shadow economy was drawn from The Global Economy, the variable was estimated by Medina and Schneider (2018). The governance indicators like Government Effectiveness and Political Stability and Absence of Violence were obtained from The Worldwide Governance Indicators (WGI) dataset. The final dataset representsa panel dataset for 232 countries from 1995 to 2015 with 5-year intervals for Gender Inequality Index and from 1990 to 2015 with 5-year intervals for Neonatal Mortality Rate.

Since World Bank does not contain data on remittance in constant terms, we calculated it using CPI index. Namely, we divided the amount of remittance in current US$ by the value of CPI index for each year and multiplied by the CPI in 2010, specifically, 100. By doing this and dividing by the amount of the total population of each year we got remittances per capita in constant 2010 US$. Also, net official development assistance received was calculated by the same method to make the measurements consistent.

The variables that influence gender inequality and neonatal mortality were chosen according existing studies on connected issues, economic theory and our assumptions. So, studies for gender inequality consider development and openness of the country (Arora, 2012; Chen, Ge, Lai & Wan, 2013), education (Kane, 1995), the quality of government (Brown, 2004). For child and infant mortality existing researchers examine the possible effects of education (Pamuk, Fuchs & Lutz, 2011; Kravdal, 2004), government health expenditures and income of population(Wang, 2003).The explanatory variable was chosen to be the logarithm of the total number of emigrants. The control variables in model for Gender Inequality Indexwere chosen to be the logarithm of remittances per capita, the logarithm of GDP per capita, the logarithm of net official development assistance received per capita, Globalization Index, tertiary school enrollment and indicators that represent the development of government and society, namely Government Effectiveness and Political Stabilityand Absence of Violence.For model for Neonatal Mortality Rate the control variables were chosen to be the logarithm of remittances per capita, the logarithm of GDP per capita, Government Effectiveness,Political Stability and Absence of Violence,trade ( as % of GDP), female life expectancy, female unemployment rate, shadow economy (as % of GDP), compulsory education duration and current health expenditures (as % of GDP).

The table below represent the description of variables that are used in regressions and their expected effects on dependent variables.

Table 1. Description of variables and their expected effects

Variable	Description and expected effects
GenderInequalityIndexGII (GII)	Index that reflects gender discrimination in three spheres: health, politics and economic status and takes values between 0 and 1. Higher index reflects higher gender discrimination in the country.
lnNeonatalMortalityRate(lnNMR)	The logarithm of the number of newborn children who dies during the first 28 days of their life, measured in number of deaths per 1,000 live births.
lnMortalityRateUnder5	The logarithm of the number of children who will probably die during their first 5 years of living, measured in deaths per 1,000 live births
lnTotalnumberofemigrants	The logarithm of the total number of emigrants, measured in number of people. Reflects the assimilation and transfer of norms from the destination country to the country of origin. The excepted impact is negative.
lnRemittancespercapita	The logarithm of the remittances per capita, received, measured in constants 2010 US$. Reflects possible increase in wealth of population. The excepted impact is negative.
lngdppercap2010	The logarithm of the Gross Domestic Product per capita, measured in constants 2010 US$. Reflects the development and wealth of population. The excepted impact is negative.
lnNetODAreceivedpercapitacon	The logarithm of the net official development assistance received per capita, measured in constants 2010 US$. Reflects possible increase in development and wealth of the society. The excepted impact is negative.

Table 1 continued

Variable	Description and expected effects
GlobalizationIndex	Index that reflects the level of globalization of the country in economic, social and political spheres. Index takes values between 0 and 100. Higher index represents more globalized country. Reflects the development and openness of the country. The excepted impact is negative.
SchoolEnrollmentTertiary	The percentage of the population of that age group that officially corresponds to the tertiary level of education who receives tertiary education. Reflects the development of society. The excepted impact is negative.
GovernmentEffectiveness	Index that reflects the quality of services that are provided by government. Index takes values between -2.5 and 2.5. High index is associated with better government services in the country. Better government services are associated with higher wellbeing and development of society. The excepted impact is negative.
PoliticalStability	Index that reflects the probability of political instabilities and manifestations of terrorist acts. Index takes values between -2.5 and 2.5. High index is associated with more stable political situation in the countryis. Society may develop more efficient and receive better public services in case of political stability and absence of terrorism. The excepted impact is negative.
TradepercentofGDP	The sum of exports and imports of goods and services, measured as percent of GDP. Reflects the openness of the country. The excepted impact is negative.
Lifeexpectancyatbirthfemale	The expected number of years that a newborn female child would live if the conditions of mortality would remain constant during his life. Higher female life expectancy reflects betterliving conditionsinthe society. The excepted impact is negative.
FemaleUnemployment	Share of female labor force that currently does not have job but that is capable to work and searching for a job. Higher female unemployment may be associated with lowermonetary wealth of society. The excepted impact is positive.

Table 1 continued

Variable	Description and expected effects
ShadoweconomypercentofGDP	Shadow economy computed as a percent of GDP, based on model that consists the exogenous variables that affect shadow economy, like share of direct and indirect taxation, tax moralee.t.c. Reflects additional benefits from production of good and services that are not officially counted. The excepted impact is negative.
Compulsoryeducationduration	The number of years that every child had to spend in school to complete compulsory education. Reflects the development of society. The excepted impact is negative.
CurrentHealthExpenditure	The current expenditure on health, measured as percent of GDP. Reflects the level of health care receivedin the country and the health of the population. The excepted impact is negative.
lnGNIpercapitaconstant2010US	The logarithm of the Gross National Income per capita, measured in constants 2010 US$. Reflects the development and wealth of population. The excepted impact is negative.
lnImportofgoodsandserv	The logarithm of the value of goods and services that are imported to the country, measured in constants 2010 US$. Reflects the openness of the country. The excepted impact is negative.
lnExportsofgoodsandservicesc	The logarithm of the value of goods and services that are exported from the country, measured in constants 2010 US$. Reflects the openness of the country. The excepted impact is negative.
Lifeexpectancyatbirthtotal	The expected number of years that a newborn child would live if the conditions of mortality would remain constant during his life. Higher life expectancy reflects better living conditions in the society. The excepted impact is negative.
Laborforceparticipationrate	The percent of female population with the age above 15 who are currently working. Reflects the economic prosperity of the society. The excepted impact is negative.

The main hypothesis of this study is that migration to other countries reduces Gender Inequality Index and Neonatal Mortality Rate due to the assimilation and transfer of norms.

From the descriptive statistics (Table 2, Table 3) it can be seen that higher number of people migrate to high and upper middleincome countries that are associated with better living conditions, including health and economic status. Classification of countries is taken from The World Bank, namely countries that had in 2017 GNI per capita$12,055 or more are classified as high income countries, between $996 and $12,055 - as middle income countries, less than $996 as low income, between $3,896 and $12,055 as uppermiddle income counties and between $996 and $3,896 as lower middle income countries. Also, the Gender Inequality Index (Table 4, Table 5) is higher in low and lower middle income countries which means that women there suffer more from discrimination compared to women in high and upper middle income countries and more neonates (Table 6, Table 7) diein low and lower middle income countries compared to high and upper middle income countries. People who migrate to high and upper high income countries can assimilate norms in the country of destination and transfer them to their country of origin due to different channels including communication with relatives left behind through the Internet, social networks and phone calls. So, this research is going to concentrate on overall impact of the total number of emigrants on the Gender Inequality Index and Neonatal Mortality Rate. The description statistics of variables that are used in regressions are presented in APPENDIX 1.



Table 2. Descriptive statistics for total number of emigrants to high and upper middle incomecountries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Number of emigrants	232	451 000	850 000	0	5 270 000	1990
Number of emigrants	232	506 000	955 000	0	6 940 000	1995
Number of emigrants	232	572000	1110000	1	9550000	2000
Number of emigrants	232	653000	1260000	1	10 800 000	2005
Number of emigrants	232	769000	1510000	2	12 400 000	2010
Number of emigrants	232	871000	1690000	3	13 600 000	2015

Table 3. Descriptive statistics for total number of emigrants to low and lower middle incomecountries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Number of emigrants	232	169 000	675000	0	7 390 000	1990
Number of emigrants	232	154 000	578000	0	6 350 000	1995
Number of emigrants	232	143 000	517000	0	5 430 000	2000
Number of emigrants	232	137 000	473000	0	4 930 000	2005
Number of emigrants	232	143 000	468000	0	4 650 000	2010
Number of emigrants	232	153 000	468000	0	4 590 000	2015

Table 4. Descriptive statistics of Gender Inequality Index for high and upper middleincome countries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Gender Inequality Index	253	0.40	0.14	0.09	0.69	1995
Gender Inequality Index	248	0.40	0.14	0.06	0.69	2000
Gender Inequality Index	256	0.39	0.15	0.05	0.69	2005
Gender Inequality Index	257	0.39	0.16	0.05	0.69	2010
Gender Inequality Index	257	0.38	0.16	0.04	0.69	2015

Table 5. Descriptive statistics of Gender Inequality Index for lowand lower middle income countries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Gender Inequality Index	162	0.56	0.12	0.23	0.81	1995
Gender Inequality Index	166	0.56	0.12	0.23	0.82	2000
Gender Inequality Index	172	0.56	0.12	0.23	0.81	2005
Gender Inequality Index	169	0.55	0.12	0.23	0.82	2010
Gender Inequality Index	172	0.55	0.12	0.23	0.84	2015

Table 6. Descriptive statistics for Neonatal Mortality Rate, per 1000 live births for high and upper middleincome countries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Neonatal Mortality Rate	404	14.25	8.31	1.6	48.1	1990
Neonatal Mortality Rate	404	14.04	8.44	1.6	48.1	1995
Neonatal Mortality Rate	404	13.86	8.57	1.6	48.1	2000
Neonatal Mortality Rate	404	13.75	8.67	1.3	48.1	2005
Neonatal Mortality Rate	404	13.66	8.74	1.1	48.1	2010
Neonatal Mortality Rate	404	13.61	8.79	.9	48.1	2015

Table 7. Descriptive statistics for Neonatal Mortality Rate, per 1000 live births for lowand lower middle income countries

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Neonatal Mortality Rate	301	30.35	13.81	5.6	74.7	1990
Neonatal Mortality Rate	301	30.02	13.26	5.6	67.3	1995
Neonatal Mortality Rate	301	29.57	12.72	5.6	65.6	2000
Neonatal Mortality Rate	301	29.06	12.35	5.6	65.6	2005
Neonatal Mortality Rate	301	28.65	12.14	5.6	65.6	2010

Table 7 continued

Variable	Obs	Mean	Std.Dev.	Min	Max	Year
Neonatal Mortality Rate	301	28.28	12.06	5.6	65.6	2015



Firstly, we did the graphical visualization to show the correlation between dependent variables and the total number of emigrants to confirm our hypothesis of negative correlation between variables. From Figure 1 we can support our assumption that Gender Inequality Index and the total number of emigrants have negative relationship. Figure 2 shows the negative correlation between Neonatal Mortality Rate and the total number of emigrants, butthis correlation is just slightly negative. We assume that such relationships between variables willremain in our regressions.

Figure 1. Relationship between Gender Inequality Index and the total number of emigrants

Figure 2. Relationship between Neonatal Mortality Rate and the total number of emigrants

2.2 Methodology

We assume that the right model specification is Fixed Effects (FE) model due to the existence of country-specific characteristics. Also, we transform our models and make the explanatory variable and control variables with lagsfor 5 yearsdue to the fact that migrants can have a long-run impact on the dependent variables.

GIIit=бi+Xitв + uit

lnNMRit=бi+Xitв + uit

GIIit=бi+Xit-5в + uit

lnNMRit=бi+Xit-5в + uit

To deal with the problem of endogeneity, which is associated with the inclusion of migrants in regressors that can cause reverse causality, the chosen modelsare estimated by the instrumental variable method. High gender discrimination and high neonatalmortality can be the factors that influence people's decision to emigrate from the country, so the Gender Inequality Index and Neonatal Mortality Ratecan influence the number of emigrants.We use the predicted number of emigrants as instrument by estimatingthe pseudo-gravity model for migration according the previous research that dealt with the same problem (Alesina, Harnoss& Rapoport, 2016; Docquier, Lodigiani, Rapoport & Schiff, 2016).The pseudo-gravity model is estimated on data that contain such bilateral geographical and cultural variables that supposedly do not affect directly gender inequality and neonatal mortality. Data for pseudo-gravity model was collected from Dynamic Gravity dataset provided by UnitedStates International Trade Commission. Table below contains a description of chosen variables for pseudo-gravity model.



Table 8. Description of variables for pseudo-gravity model

Variable	Describtion
contig	Dummy variable that takes value 1if countries have common border
comlang_ethno	Dummy variable that takes value 1if countries have common ethnic language, namely, language is spoken by at least 9% of population
distw	Weighted distance between two countries, weighted by population, in kilometres
curcol	Dummy variable that takes value 1if countries are currently in colonial relationships
tdiff	Number of hours difference between two countries
pop_d	Total amount of population in country of destination in million people
pop_o	Total amount of population in country of origin in million people

The data of bilateral migration contains many zeroes of such variable as the number of migrants. To estimate pseudo-gravity model (Table 9) the logarithm of the number of migrants is taken as the dependent variable, however it can cause the bias in the OLS estimations. To eliminate this problem the PPML estimator is used instead. Then the number of migrantsis predicted using the PPML estimates and from the estimated results the number of emigrants is calculated.

Table 9. Pseudo-gravitymodel

	(1)	(2)
VARIABLES	OLS	PPML
contig	1.290***	2.201***
	(0.141)	(0.206)
comlang_ethno	1.101***	0.806***
	(0.0997)	(0.166)
distw	-1.346***	-0.000305***
	(0.0686)	(6.04e-05)
curcol	2.892***	1.491***
	(0.359)	(0.285)
tdiff	0.0684***	0.150**
	(0.0195)	(0.0729)
pop_d	0.578***	0.00212***
	(0.0233)	(0.000253)
pop_o	-0.439***	-0.00235***
	(0.0752)	(0.000417)
Constant	14.83***	11.21***
	(0.883)	(0.574)
Observations	56,350	217,047
R-squared	0.663	0.319
Country FE	YES	YES
Year FE	YES	YES

The dependent variable in OLS regression is a logarithm of the total number of migrants, in PPML regression - the total number of migrants. The distw, pop_d and pop_o in OLS regression are in logs. Clustered standard errors by country of destination in parentheses

*** p<0.01, ** p<0.05, * p<0.1

2.3 Results and Discussions

We start the analysis from estimating pooled OLS models. The results show that the number of emigrants negatively effects the Gender Inequality Index (Table 10) while the effect on Neonatal Mortality Rate is positive (Table 11). Then we test model (4) for GII and model (5) for NMR for random effects. Breusch and Pagan Lagrangian multiplier test for random effects shows that we reject the null hypothesis that the variance of the random effect is zero and that the intercept is the same for all units, so the panel effect exists and we cannot choose pooled OLS model as right model specification(APPENDIX 2).Then we test model (4) for GII and model (5) for NMR whether the right model specification should be FE or RE model. Cluster-Robust Hausman Tests reject the null hypothesis that there is no systematic difference between RE and FE, so the FE was chosen(APPENDIX 3). Estimated RE regressions can be found in APPENDIX 4. Also, we check the variation inflation factor (APPENDIX 5) that shows that there is no multicollinearity problem in model (4) for GII and model (5) for NMR.

Table 10. OLS regressions for Gender Inequality Index

VARIABLES	(1)	(2)	(3)	(4)
lnTotalnumberofemigrants	-0.00792	-0.00607	-0.00667	-0.0117
	(0.00738)	(0.00689)	(0.00741)	(0.00870)
lnRemittancespercapita	-0.000524	-0.000581	-0.000877	-0.000373
	(0.00439)	(0.00429)	(0.00434)	(0.00429)
lngdppercap2010	-0.0489***	-0.0456***	-0.0402***	-0.0396***
	(0.0111)	(0.0100)	(0.0114)	(0.0110)
lnNetODAreceivedpercapitacon	-0.00685	-0.00686	-0.00648	-0.00604
	(0.00766)	(0.00747)	(0.00750)	(0.00733)
GlobalizationIndex	-0.00509***	-0.00374***	-0.00353***	-0.00352***
	(0.000926)	(0.000984)	(0.000980)	(0.000965)
SchoolEnrollmentTertiary		-0.00184***	-0.00176***	-0.00166***
		(0.000449)	(0.000456)	(0.000456)
GovernmentEffectiveness			-0.0192	-0.00523
			(0.0152)	(0.0159)
PoliticalStability				-0.0209
				(0.0130)
Constant	1.290***	1.193***	1.139***	1.194***
	(0.154)	(0.144)	(0.150)	(0.155)
Observations	388	388	385	384
R-squared	0.486	0.524	0.525	0.530
r2_a	0.479	0.517	0.516	0.520

The dependent variable is Gender Inequality Index. Clustered standard errors by country in parentheses

*** p<0.01, ** p<0.05, * p<0.1



Table 11. OLS regressions for Neonatal Mortality Rate

VARIABLES	(1)	(2)	(3)	(4)	(5)
lnTotalnumberofemigrants	-0.0443*	0.0153	0.0361*	0.0378*	0.0386
	(0.0262)	(0.0218)	(0.0213)	(0.0214)	(0.0267)
lnRemittancespercapita	-0.0370**	-0.00308	-0.00444	-0.00463	-0.00347
	(0.0174)	(0.0148)	(0.0141)	(0.0139)	(0.0160)
lngdppercap2010	-0.432***	-0.281***	-0.255***	-0.252***	-0.236***
	(0.0399)	(0.0409)	(0.0396)	(0.0395)	(0.0372)
GovernmentEffectiveness	-0.237***	-0.183***	-0.206***	-0.202***	-0.218***
	(0.0577)	(0.0514)	(0.0460)	(0.0526)	(0.0535)
TradepercentofGDP	-0.00121*	-0.000683	-0.000500	-0.000489	-0.000657
	(0.000715)	(0.000631)	(0.000620)	(0.000614)	(0.000673)
Lifeexpectancyatbirthfemale		-0.0396***	-0.0420***	-0.0418***	-0.0433***
		(0.00468)	(0.00473)	(0.00472)	(0.00446)
FemaleUnemployment		0.00547	0.00846**	0.00779*	0.00267
		(0.00371)	(0.00396)	(0.00398)	(0.00373)
ShadoweconomypercentofGDP			0.00162	0.00177	-0.000824
			(0.00285)	(0.00283)	(0.00290)
PoliticalStability				-0.00635	-0.0139
				(0.0388)	(0.0391)
Compulsoryeducationduration					0.0118
					(0.0125)
CurrentHealthExpenditure					-0.0334***
					(0.0122)
Constant	6.888***	7.433***	7.026***	6.966***	7.125***
	(0.469)	(0.423)	(0.465)	(0.476)	(0.531)
Observations	673	646	576	571	431
R-squared	0.808	0.861	0.884	0.884	0.900
r2_a	0.806	0.859	0.882	0.882	0.898

The dependent variable is the logarithm of Neonatal Mortality Rate. Clustered standard errors by country in parentheses

*** p<0.01, ** p<0.05, * p<0.1

As we expected, FE regressions show that the number of emigrants reducesthe Gender Inequality Index (Table 12) and Neonatal Mortality Rate (Table 13) and the impact is significant for both variables. The significance of the number of emigrants for the Gender Inequality Index (Table 12) and Neonatal Mortality Rate (Table 13) is observed in all model specifications when we add control variables.

Table 12. FE regressions for Gender Inequality Index

VARIABLES	(1)	(2)	(3)	(4)
lnTotalnumberofemigrants	-0.0216*	-0.0214*	-0.0213*	-0.0207*
	(0.0119)	(0.0117)	(0.0118)	(0.0115)
lnRemittancespercapita	0.00407	0.00410	0.00411	0.00418
	(0.00347)	(0.00353)	(0.00350)	(0.00350)
lngdppercap2010	-0.0895***	-0.0863***	-0.0870***	-0.0908***
	(0.0229)	(0.0234)	(0.0255)	(0.0264)
lnNetODAreceivedpercapitacon	0.00310	0.00356	0.00347	0.00339
	(0.00431)	(0.00434)	(0.00441)	(0.00445)
GlobalizationIndex	-0.00347***	-0.00341***	-0.00340***	-0.00329***
	(0.000721)	(0.000734)	(0.000742)	(0.000729)
SchoolEnrollmentTertiary		-0.000203	-0.000203	-0.000224
		(0.000255)	(0.000256)	(0.000241)
GovernmentEffectiveness			0.000314	-0.00397
			(0.0166)	(0.0170)
PoliticalStability				0.00861
				(0.00857)
Constant	1.645***	1.616***	1.619***	1.638***
	(0.185)	(0.188)	(0.194)

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