(2007), Kugler and Lotti (2007) and Le (2008) have shown potential benefits from high-skilled ...... Morrison, Andrew R, Maurice Schiff, and Mirja Sjöblom.
The Impact of Financial Liberalization on the Brain Drain: A Gendered Bilateral Analysis James T. Bang Department of Finance, Economics and Decision Science St. Ambrose University Aniruddha Mitra Economics Program Division of Social Studies Bard College Abstract We use the bilateral data on female and male emigration rates for highly skilled workers from Artuc, et al. (2013) to analyze the impact of financial liberalization on the female and male brain drain, as well as the female to male brain drain gap. To account for the fact that different types of reform may impact migration incentives differently, we use an exploratory factor analysis (EFA) to separate the impacts of reforms that promote greater freedom from those that promote greater robustness and stability. Our results support the hypothesis that both types of financial liberalization increase the rate of brain drain for both women and men. Further, host country reforms impact women by a greater margin than men, especially reforms that emphasize robustness. The latter result is consistent with the notion that women are more impacted by job insecurity during migration. Keywords Financial liberalization, Immigration, Skilled migration, Factor Analysis, Institutions, JEL Codes F22, O15, P48
1. Introduction Globalization has rekindled interest in the causes and consequences of brain drain (Commander et al., 2004). Yet it is only very recently that the gender dimensions of the brain drain have come to be a topic of focus. An initial contribution towards this objective comes from Docquier et al. (2009), who develop a dataset of total migration to OECD countries by country of residence, place of birth, level of education, and gender. They find that emigration rates for highly-skilled women are, on the average, higher than emigration rates for comparably educated men. Subsequent studies, including Bang and Mitra (2013) and Nejad (2013) focus on cultural and institutional factors that might influence the brain drain and the gap in the rates of brain drain between women and men. One factor that has been thus far unexplored in the literature on the female brain drain is the quality of a country’s financial system. A few studies have explored the impact of immigrants’ home countries’ political institutions (Bang and Mitra, 2011) and financial institutions (Mitra et al., 2013) on the aggregate rate brain drain to the OECD. These studies find a positive relationship between factors such as transparency, property rights and financial liberalization and the selection of high-skilled immigrants. However, to our knowledge no studies exist that investigate financial liberalization’s effects on females relative to males, or that investigate the relative importance of the quality of the home countries’ financial systems to those of the host countries’. We propose that the impacts of the effects of financial liberalization on the incentive to migrate may depend upon (1) the skill distribution of potential migrants; (2) the gender of the potential migrants; (3) the nature of the financial liberalization; and (4) whether reform occurs in the immigrant’s home country or prospective host country.
The rationale for the first two hypotheses is grounded in the idea that women may be differently impacted by the opportunity costs of undertaking migration and by the uncertainty of the job search process in the host country. We find evidence in favor of the hypothesis that financial liberalization increases the rate of brain drain for both men and women, and that the impact of financial liberalization tends to be greater for women than for men. We also find that host country reforms that emphasize greater “robustness” of financial markets increase the brain drain by the greatest margin and increase the female brain drain by a greater margin than do those that only emphasize greater “freedom.” The latter result is consistent with the notion that women are more impacted by higher levels of financial insecurity in the host country, which may be related to a women’s need for greater physical security.
2. Conceptual Foundations The object of this paper is to explore the impact of financial liberalization on the selection of female immigrants relative to males. In doing so, we contribute to two distinct areas of the literature. The first is that which analyzes the selection of immigrants, or brain drain, and the budding area of research that explores how institutional and other factors may impact women differently from men. The second area is that of the nature of financial liberalization and its impacts.
Brain Drain and Gender Issues in Immigration and Development The issue of the female-male "brain drain gap" and its policy implications is important to a number of other issues in international and development economics,
including the relationship between gender discrimination and economic development; the relationship between the emigration of high skilled workers (brain drain) and economic development; and gender-specific issues related to immigration. While the review of these issues provided here is by no means comprehensive, it will highlight the recent contributions to these topics as they relate to the issue at hand. Gender Discrimination and Development In 2006 the World Bank set equity as one of the main topics of focus for its World Development Report (World Bank, 2006) and its subsequent Global Monitoring Report (World Bank, 2007) set gender equality as one of its development goals. In it, strong evidence is presented in support of the notion that discriminatory attitudes inhibit economic growth and development. The report also advocates for proactive policies to promote justice and fairness in markets. These findings are supported and reinforced in the case of gender equity by preexisting studies and subsequent research by Zhang, Zhang, and Li (1999), Klasen (1999), Knowles, Lorgelly, and Owen (2002), Lagerlof (2003), Blackden et al. (2006), and Elson (2009). For the most part, these studies find that gender inequality in general, and inequality in educational attainment in particular, impedes economic growth and development. Additional studies by Blackden and Bhanu (1999) and Loshkin and Mroz (2003) have shown that gender inequality can also have a negative impact on a country's poverty rate. Measuring gender equality has been a difficult task. To help with this, Jutting et al. (2008) have compiled a list of indicators that are correlated with gender inequity. One drawback to their dataset is that it only focuses on non-OECD countries, whereas it would be ideal to have data that allow us to compare both OECD and non-OECD
countries. One variable that Jutting et al. do not include in their list, but is both widely available and highly correlated with gender inequality is the fertility rate. In more traditional societies, women tend to begin childbearing early, have more children, and be expected to stay at home rather than pursue a career. Women in these cultures will also be less likely to have formal education. Research on fertility rates by Basu (2002), Petropoulos and Balourdos (2002), Roig-Vila and Martin (2007), and Naufal and Vargas-Silva (2009) has shown that education and immigration can help a society evolve towards more progressive views of women, with the latter taking an effect through the return migration of women who have lived in more modern cultures, and through remittances from women who still are. Also, if migration increases the incentive for women to pursue higher education, it is possible that this will also lead to significant cultural change. Brain Drain and Development Another channel through which inequality can have an impact is through the brain drain. For example, Docquier and Rapoport (2003a and 2003b) outline a theoretical model that predicts that when discrimination against a minority is pervasive, it will be the educated members of the minority who have the greatest incentive to migrate. There has been a vast literature on the topic of the brain drain. Commander, Knagasniemi, and Winters (2004) provide a thorough and detailed review of this literature. This section highlights some of the key contributions that relate well to this study. Early theoretical analyses of the brain drain, such as those by Bhagwati and Rodriguez (1975) and Rodriguez (1975), found that the emigration of a country's highly
educated workers would be detrimental to economic growth and development. The immiserizing effects of the brain drain in these early models appear through the effect of skilled migration on wages and employment of natives who do not migrate, as well as through its burden on the fiscal budget. If skilled labor and unskilled labor are viewed as complementary inputs in a country's production, then a reduction in the supply of one input will tend to reduce the income earned by its complement. Thus, with less skilled labor in the labor force, unskilled labor's wages fall. In addition, if the education of the skilled workers was financed or subsidized from by the government, the migration of these workers would represent a lost investment in human capital. However, there has been a large body of recent research that has questioned whether the brain drain is in fact detrimental to a country's economic welfare and development. Newer theoretical models, such as those by Stark (2004) and Lien and Wang (2005) suggest that there may in fact be a brain gain, or beneficial brain drain. In these newer models, brain drain can have a beneficial long-run impact on a country because a positive probability of migration increases the incentives for natives to invest in education, and thus can have a positive impact on the number of people who have invested who also stay. This positive effect of the brain drain has been demonstrated empirically by Beine, Docquier and Rapoport (2001). In addition, studies by Faini (2007), Kugler and Lotti (2007) and Le (2008) have shown potential benefits from high-skilled emigration coming from return migration and remittances. Cattaneo (2009) has found that high emigration rates of highly educated workers have been associated with reductions in the incidence of poverty.
Gender and Immigration A growing literature has focused on some of the gender-specific issues relating to immigration. This section will review some of the issues that pertain most closely to the factors affecting the educational differences in the migration patterns of men and women and will briefly summarize some of the studies of the labor market disparities between male and female immigrants. For a more thorough review, we refer the reader to Morrison, Schiff, and Sjöblom (2008). Using case studies of migrant women in developed countries, Zlotnik (1990 and 1995) find two significant trends that have affected the migration and labor market opportunities for immigrant women in developed countries, especially European countries. First, they find that since 1974, when many countries shifted the focus of their immigration policies away from guest worker programs and toward family unification, there has been a substantial increase in the number of female immigrants. Second, they find that there have been significant barriers to entry into the formal labor market for many immigrant women in developed countries, especially immigrant women in European countries who have migrated on family unification visas. They find that many of these women eventually find informal work in less skilled occupations (for example as maids and nannies) than would normally suit their skills. Cerrutti and Massey (2001) confirm this finding empirically using data on Mexican immigrants to the US and find that women are much more likely to be "tied" immigrants in the sense that they follow a spouse or parent. Similar stories are documented anecdotally by Ehrenreich and Hochschild (2004).
A growing number of economic studies have found evidence that the wages of immigrant women lag behind the wages of immigrant men as well as native women on average. Other studies have isolated more specific ways in which the earnings of foreign born women differ from their foreign born men and native women. For example, Chiswick and Miller (1999) find that foreign born women who do not speak the local language suffer a greater penalty to their earnings than foreign born men do. Similarly, Hayfron (2002) finds that immigrant women face a "double penalty" – first by earning lower wages on average than immigrant men or native women, and second by suffering a higher "marriage penalty" on their earnings than native women. Goyette (1999) finds that foreign born female scientists are less likely to be promoted than foreign males and native women with similar education and experience. Chattopadhyay (1998) finds similar trends in occupational mobility for immigrants to Malaysia and Curran and Rivero-Fuentes (2006) and Massey, Fischer, and Capoferro (2006) find them for Mexican and Latin American women immigrating to the US. Many of these studies focus on the outcomes for women once they arrive in their destination. The question here is more focused on the "push and pull" factors that serve as a motive for women to move. In particular, on the selection pattern of women in immigration, whether there is a "female brain drain," and what factors might be at the root of it. Two papers that have approached the specific issue of the brain drain as a gendered topic stand out as having special significance for the remainder of the discussion in this . Docquier, Lowell and Marfouk (2009) develop a dataset of emigration rates for emigrants to OECD countries by level of education and gender and
find that for most developing countries the high skilled emigration rates of women are higher than the rates for men. Using the same data, Dumont, Martin, and Spielvogel (2007) reach a similar conclusion, but also find a significant relationship between the female-male gap in tertiary educated emigration rates and several social development indicators such as school enrollment rates and infant mortality rates. They find that the higher the gap between female tertiary-educated emigration rates and male tertiary-educated emigration rates is, the higher the mortality rates of infants and children under five are, and the lower the secondary school enrollment rates for both boys and girls are. This suggests that while, aggregated studies of the brain drain may reveal a beneficial brain drain effect, the same does not hold for female brain drain. These findings strongly suggest that there are unique consequences to the emigration of educated women that differ from the economic impacts of the emigration of educated men. The female brain drain, in turn, may be linked to some of the disparities in wages and employment opportunities arise. Bang and Mitra . In the short run this may have negative consequences for economic and social development. However, in the long run, migration and return migration of highly educated women may provide an effective channel through which more equitable social norms can be transmitted.
Financial Liberalization The financial system of a country performs a number of functions that promote economic performance. First, it improves the allocation of capital by reducing information cost about investment opportunities. Second, it strengthens corporate
governance by reducing the transactions costs faced by the providers of capital. Third, it increases the availability of funds by facilitating the trading and risk management. Fourth, it mobilizes savings by reducing the cost of collecting savings from disparate sources as well as the informational asymmetries that prevent households from investing their savings. Finally, it facilitates the exchange of goods and services in the economy. The liberalization of equity and capital markets improves the ability of the financial system to perform these functions (Bekaert et al., 2005, 2011; Chinn and Ito, 2006) and improves the accumulation of capital, productivity, and growth. The increase in economic prosperity as a result of financial liberalization may be expected to reduce the returns to migration over the entirety of the domestic skill distribution. However, the relative returns to migration may be greater for high-skill workers than for low-skill workers. In other words, the disincentive to migrate induced by growth is less for the more highly skilled than for the less skilled. This hypothesis is supported by empirical evidence that financial development reduces poverty and improves the distribution of income (Clarke et al., 2006; Beck et al., 2007; Claessens and Perotti, 2007; Perez-Moreno, 2011). As such, if the net marginal benefit from migration was the sole determinant of selection, one would expect financial development in the home country to increase the fraction of high skilled immigrants. However, financial development will also impact the marginal cost of migration; and while it makes the migration venture easier to finance for both high and low skilled workers, it is not difficult to see that low skilled workers gain more with respect to this: high skill workers are a priori more likely to have accumulated savings that can defray the cost of relocation. Even if this was not so, they are more likely to own assets that can
be advanced as collateral to borrow the funds needed to finance migration. In reducing the credit market imperfections that effectively deny the poor access to credit, financial liberalization is therefore likely to reduce the marginal cost of migration more for the low skilled than for the high skilled and hence exert a negative impact on selection. On the balance, therefore, it is not clear what the net impact on selection would be and it is this ambiguity that places the subject in the domain of empirical inquiry. Further, as previously mentioned and as has been emphasized so often in the literature (Rajan and Zingales, 2003; Chinn and Ito, 2006; Claessens and Perotti, 2007), there is no reason to believe that liberalization will inevitably lead to financial development. Unsound institutions lead to financial sector reforms being captured by the ruling political elite, in which case liberalization may well reduce growth (Ang, 2011) and worsen the existing distribution of income (Ang, 2010). This, in conjunction with the fact that institutions in their own right play an important role in determining the selection of immigrants (Bang and Mitra, 2011; Mitra et al., 2013), underlines the need to look at the impact of financial liberalization in conjunction with institutional structure.
3. Empirical Specification and Data Several recent studies of migration take into account the importance of conditions in both the home and the host country in modeling and estimating the impacts of migration (Crozet, 2004; Mayda, 2010; Ramos and Suriñach, 2013). For the most part, these studies model migration flows based on a gravity model of bilateral migration commonly employed to estimate bilateral international trade flows. In the remainder of this section we summarize the theoretical gravity model proposed by
Crozet (2004) and describe our empirical application of it for measuring the effects of financial liberalization.
Gravity Model of Migration Among the various applications of the gravity model to international migration, Crozet (2004) stands out for its formal derivation from theoretical microfoundations. Using a model with three sectors (one manufactured good, x, one non-traded “service” sector, y, and one “traditional” good, z), they derive the following gravity model for migration flows of workers from home country i to host country j in time t, Mjit as: (1)
(∑
)
[( [
where
))
(
(
)
(
]
[(∑
)]
[
)(
(
(
(
(
))]
is the employment in sector l in country k at time s;
k at time s; d is the distance between the two countries;
)
)
) ̃(
] ),
is the wage in country
is the probability that a
migrant will find employment in country k at time s; B is and indicator equal to one if the two countries share a common border; and ̃
(
)
is a function of the sum over all
possible destinations of the difference between the value function of staying in the home country relative to migrating to each destination. The values of the rest of the terms in (1) are parameters that arise out of the (Cobb-Douglass) function assumed by Crozet to describe consumer utility. The upshot of this equation is that we can predict that migration levels to each destination country will be: (1) increasing in the size of the destination labor market; (2) decreasing in the size and wages available in other markets; (3) increasing in the expected wage in the destination labor market; and (4) decreasing in moving costs. Our
hypothesis is that it may be the case that women may be more impacted by factors such as employment uncertainty and trade costs. For example, even though the explicit moving costs may be equal across genders, the perceived moving costs may be more for women than for men in the sense that women face higher implicit opportunity costs. This difference might manifest itself through differences in the impacts of distance and various “border” effects as well as a differential for women on institutional factors that influence access to credit. The remainder of this section introduces our empirical model for estimating these differences and describes our data sources.
Empirical Specification and Variables As an empirical approximation of the theoretical model for bilateral migration given by Crozet (2004), we estimate the following equation: (1) ln(Selection Rate)ijt = 0 + ln(Migrants)ijt + ln(Distance)ij + Borderij + Colonyij + Languageij + SameCountryij + 7ln(GDP p.c. jt/GDP p.c.it) + 8ln(Populationit) + 9ln(Populationit) + 10ln(Areait) + 11ln(Areajt) + 1'Institutionsit + 2'Institutionsjt + 1'Liberalizationit + 2'Liberalizationjt + uit. In this specification, Selection Rate is measured as the proportion of the stock of all emigrants from home country i to OECD host country j in year t who have attained a tertiary level of education. Data for emigrant stocks by educational attainment and gender come from the “Migration to OECD Destination Countries” bilateral dataset published by Docqiuer et al. (2007).1 This dataset documents foreign-born populations living in 31 OECD destination countries who have migrated from 195 home countries during the 1990 and 2000 census periods, yielding a total sample of 12,090 bilateral 1
http://perso.uclouvain.be/frederic.docquier/oxlight.htm
panel observations. We also obtain data for the total size of the bilateral migrant stock between those countries (in millions), which we include as our first control variable from the same source. The next set of controls in our specification captures various measure of distance and contiguity between the bilateral pairs of countries in our sample, and which are standard components of most bilateral gravity models of trade and migration. We have downloaded these data from the Centre d’Etudes Prospectives et d’Informations Internationales (CEPII).2 The first, Distance, measures the geographical distance between the two countries. Next, we add a group of dummy variables which include: Border, which equals one if the countries share a land border; Colony, which takes the value one if the countries share a common colonial history; Language, which indicates whether the country’s citizens speak a common ethnic language; and Same Country, which equals one if the countries have historically been part of the same country. After these, we include several economic and demographic controls from the World Development Indicators. To capture the relative economic incentive to migrate, we include the log of the ratio of the host country’s GDP to the home country’s GDP. We also include the log of the home and host countries’ total population (in millions) to control for both size as well as the differential impacts of immigration quotas on larger countries relative to small ones. We also include the total land area (in km2) of the home and host countries as an additional control for size. Our last vector of controls, Institutions, measures the quality of the home and host countries’ institutions. As measures of institutional quality we consider variables from multiple sources, including the World Bank’s Database of Political Institutions 2
http://www.cepii.fr/CEPII/en/bdd_modele/bdd.asp
(DPI),3 the International Country Risk Guide (ICRG) from Political Risk Services,4 and the Polity IV project from the Center for Systematic Peace.5 However, it has been previously noted that, on the one hand, including these variables in tandem can lead to an unacceptable degree of inefficiency due to multicollinearity; on the other hand, including them individually may lead to considerable omitted variable bias. Moreover, although these variables purport to measure distinct dimensions of institutional quality, there is considerable overlap in what they actually capture (Langbein and Knack, 2010; Bang and Mitra 2013 and 2014). To address these concerns, and to address similar concerns with regard to the financial liberalization data, we perform an exploratory factor analysis (EFA) on our measures of political and financial institutions. Finally, our variables of interest, Liberalization, is a vector of factors that capture the extent to which the government adversely intervenes to direct and restrict access to credit or control the cost of acquiring capital, as opposed to the extent to which the government transparently engages in proactive regulations to promote a stable and robust banking sector. The dataset we use to capture these types of policies is the New Database of Financial Reforms by Abiad et al. (2008). To reflect the absence of policies that suppress market allocation of credit, this dataset includes: Directed Credit, which measures the absence of high reserve requirements and mandates requiring banks to supply credit to sectors favored by the regime at subsidized rates; Credit Controls, which The variables we consider from the DPI include: the Legislative index of Electoral Competition, the Executive Index of Electoral Competition, Electoral Fraud, Political Fractionalization, Political Polarization, and Checks. The database and a complete description of the variables in it can be found at: http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20649465 ~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html 4 The ICRG variables we consider are: Government Stability, Investment Profile, Corruption, Bureaucratic Quality, and Democratic Accountability. A complete description of these variables is publicly available at: http://www.prsgroup.com/icrg.aspx 5 The variables we include from Polity IV are the Polity 2 democracy-autocracy index and regime durability. The Polity dataset and a description of its variables can be found at: http://www.systemicpeace.org/polity/polity4.htm 3
additionally captures the absence of ceilings on the expansion of credit; Interest Rate Controls, which reflects the absence of government controls on the market determination of deposit and lending interest rates; and Capital Controls, which measures the absence of dual exchange rates and restrictions on international capital. The next set of indices quantifies the absence of policies that limit free enterprise in the financial sector. These include: Privatization, which captures the absence of state ownership in the banking sector; and Entry Barriers, or the absence of restrictions on entry to the banking sector, the range of banking activities allowable, geographical area, or license requirements. Finally, the dataset includes several measures of the presence of policies that enhance the operation of financial markets, such as: Banking Supervision, which measures the independence of regulatory agencies that oversee banks as well as how adequate their enforcement powers are and breadth of the agencies’ coverage and the adoption of capital requirements in line with the Basle I Accord; and Security Markets, which assesses the extent to which policies are in place to foster the development of security markets, including openness to foreign investors. Including the raw values for these variables in our specification opens the door to the problems of multicollinearity, omitted variables bias, and measurement error mentioned above with respect to our measures of institutional quality. Moreover, in addition to the fact that the various measures of financial reform may be highly correlated with each other, it may also be the case that they are also correlated with the measures of institutional quality, and especially with measures of transparency and property rights enforcement. Therefore, we include the measures of financial reform
and our measures of institutional quality in our EFA, which we describe in the next section.
4. Measuring Financial Liberalization To determine the underlying latent variables that capture the degree of a country’s institutional quality financial liberalization, we consider 21 quantitative measures from the Database of Political Institutions (DPI), the Polity Project, the International Country Risk Guide (ICRG), and Abiad, et al. (2010). However, adding these variables, individually or collectively, may create problems for our models since these measures tend to be correlated with one another. At the same time, the quality of a country’s financial and government institutions is likely to be multidimensional, and it is our hypothesis that different dimensions of institutional character may impact the incentives to migrate differently. Here, we provide a brief overview of some of the different approaches to dealing with this issue. One simple method for doing this is to perform a principal component analysis on the institutional variables, and interpret the first component as institutions. This is the essence of what Alesina and Perotti (1996), Perotti (1996), and Keefer and Knack (1997) do in the context of investigating the influence of institutional quality on economic growth. In a similar vein, and following Alesina et al. (1996), one could construct a one-dimensional index of institutional quality by using logit analysis. However, as argued by Jong-A-Pin (2009) in the context of measuring the economic impact of political instability, a one-dimensional index may fail to distinguish between various dimensions of institutional character, namely institutional quality and institutional stability. This raises the question as to why we do not construct two
separate indices for institutional quality and stability. The problem is that this would require a prior classification of available variables into ones that capture stability and ones that stand for quality. Having undertaken such a task in our preliminary exploration of the data, we are convinced that many of the available variables are not clear-cut measures of one dimension of institutional character as opposed to another. In light of these concerns, we perform an exploratory factor analysis (EFA). EFA is based on the premise that the variation in each of a set of observed variables is decomposed into a linear combination of a smaller set unobserved common factors, a specific factor (a constant), and a random error term. Since the common factors it identifies lend themselves to theoretical interpretation, the advantage of EFA lies in its ability to explore a theoretical structure underlying multivariate data. Further, by identifying common sources of variation in the observed variables, they are, by construction, relatively multicollinearity. Finally, the solution to EFA is only unique to a scaling constant. Thus, it is possible to normalize the factor loadings so that the predicted factors all have a mean of approximately zero and a variance of approximately one and simplifying the task of comparing the relative magnitudes of the factor variables’ coefficients in a regression equation. In obtaining the underlying latent factors, one faces the choice between several extraction methods, the most prominent being principle component extraction, principle factor extraction, iterated principle factor extraction, and maximum likelihood extraction. Of these, principal component extraction is inappropriate for our purpose since it seeks to explain as much of the observed variables’ variance as possible in a single factor. While free of this caveat, maximum likelihood extraction imposes the additional assumption of multivariate normality. As such, the EFA conducted here
employs the principle factor extraction method. It should be mentioned, however, that we have also replicated our analysis using the iterated principle factor extraction and maximum likelihood extraction methods and obtain virtually identical factors. With respect to the rotation procedure, one faces the choice between orthogonal and oblique methods. Orthogonal methods, such as orthomax or quartimax, require the additional assumption of orthogonality between the latent factors. Since this would lead to considerable loss of information if the factors are, in fact, correlated, we have followed the prescription of Costello and Osborne (2005) in choosing an oblique rotation procedure, specifically the promax method. Again, we have tested the robustness of our EFA by using alternative rotation methods, and have obtained nearly the exact same set of latent factors using the orthomax rotation method. The EFA reported in Table 2 reveals three common factors underlying the observed institutional variables that we interpret as Democracy, Transparency, and Credibility. We also identify two aspects of financial liberalization and we interpret these factors as Financial Freedom and Financial Robustness, respectively. The remainder of this section will be devoted to clarifying our interpretations of the factors. The variables with the greatest weights in the Democracy factor are the Legislative Index of Electoral Competition (0.824), the Executive Index of Electoral Competition (0.803), the Polity Index (0.801), the Political Fractionalization Index (0.724), and Checks (0.609). Note that the first two variables reflect the extent to which the political leadership of a country is determined by free and fair elections as opposed to being determined by dictate; the last two variables capture formal and informal constraints on the exercise of autocratic power; and the Polity Index combines both
dimensions. Hence, it is natural to interpret this factor as capturing the extent of democratization of a society. The factor Transparency is primarily composed of the Corruption Index (0.770), the Bureaucratic Quality Index (0.765), Regime Durability (0.630), and theDemocratic Accountability Index (0.603). The first two are clear indicators of the transparency of governance, while Regime Durability may be regarded as an indirect reflection of institutional transparency, since a regime may be durable precisely because it is perceived as operating a transparent administration with an independent and efficient bureaucracy and freedom from corruption. Democratic Accountability, which captures a regime’s responsiveness to the will of its people, fits with our interpretation of transparency in the sense that it measures the de facto responsiveness rather than merely responsiveness de jure through the presence of elections. It is also worth noting that other measures of democratic pluralism, such as Polity 2 (0.360), Electoral Fraud (0.391) and Political Polarization (0.304) factor prominently in the transparency factor. The factor Credibility is primarily determined by the Investment Profile Index (0.599) and the Government Stability Index (0.577). The former is a direct reflection of the credibility of a regime in terms enforcing property rights, while the latter reflects the credibility of policies in terms of their security against radical shifts in the government. In addition, the independence of banking regulation, as captured by Banking Supervision (0.339) plays an important role in the construction of the credibility factor. As such, it is natural to interpret this factor as capturing the perceived credibility of the government. The factor Financial Freedom is dominated by Directed Credit (0.955) and Credit Controls (0.947), while the other financial variables play a significant though less
important role. Note that both of the dominant variables reflect the absence of policies that curtail the freedom of privately-owned banks to maximize profits. This is also true of the variable Privatization (0.396), which ranks third in terms of weight. Thus, we interpret this factor as capturing the freedom of private enterprise in the banking sector. The relevance of this interpretation is highlighted by the fact that our measure of property rights in the form of the Investment Profile Index (0.328) and our measure of policy stability, measured by the Government Stability Index (0.301) contribute to this factor. The last factor, Financial Robustness, is primarily determined by Security Markets (0.625), Capital Controls (0.601), Interest Rate Controls (0.526), and Banking Supervision (0.499). The Security Markets and Banking Supervision variables explicitly aim to capture policies designed to improve the efficiency of the financial sector. A similar case could be made about Capital Controls since restrictions on the international flow of capital isolate the domestic financial sector and compel domestic investors to hold portfolios comprised mainly of domestic securities. This may expose them to a greater degree of risk from domestic shocks. Compensation for the greater degree of risk takes the form of higher expected rates of return on investment, which in turn leads to a higher cost of capital for firms. As such, the absence of such isolating policies improves the efficiency of the financial sector. Interest Rate Controls lends itself to a similar interpretation. Intervention in the market determination of interest rates causes a divergence between expected and actual returns on private investment, which may lead to an adverse selection of investment projects. As such, the absence of such forms of intervention contributes to a more efficient financial sector and should be expected to contribute to Financial Robustness.
5. Results Table 3 reports the initial results for our estimation of the impact of Financial Freedom and Financial Robustness on the log selection rates of women, men, and the ratio of the female to male selection rates. Columns (1) through (3) report the results of our model for the log of female selection (column 1), the log of male selection (column 2), and the log of the ratio of female selection to male selection (column 3), in which we include the institutional factors (democracy, transparency and credibility). Beginning with the distance and contiguity measures standard to the gravity model, we generally see that distance tends to reduce the rates of brain drain, and that measures of cultural and political similarity (colonial history and common language) tend to increase it. In other words, educated men and women who migrate internationally will tend to have a stronger preference for closer and more culturally similar destinations than will less educated men and women. There may be two reasons for this. First, low-skill emigrants with strong preferences for closeness and cultural similarity in their choice of destination may receive a higher benefit from internal (rural-urban) migration (or migration to a high- or middle-income non-OECD country) than might high-skill emigrants. Examples of this may include low-skill workers from South Asia who migrate to oil-abundant countries in the Middle East. Low-skill workers who are willing and able to make the trip to an OECD country, may have weaker preferences for migrating to closer or more culturally similar countries. Also notice that distance impacts female selection more that of men. One reason for this may come from the fact that secondary wage-earners (which women are more often than men) tend to be more “tied” to their spouse and to their families than primary wage earners (typically men). This effect may to arise due to the fact that the
returns from choosing the primary wage-earner’s optimal destination for family migration are higher than choosing the optimal destination for the secondary wageearner (Mincer, 1977). Another reason for female “tied-ness” comes from social pressure from a woman’s elders in traditional cultures to be available to respond to the needs of her children as well as those of aging parents and parents-in-law. Hence, those women who do migrate internationally will either (a) move to the same destination as their spouse; and (b) choose destinations that will make it possible to return home quickly should her children or elders need care. Our results also reveal that size also influences brain drain. The population of both the home and the host country positively impact the brain drain, with the effect being slightly higher on the male brain drain than that of women. Land area of the host country also increases brain drain, with a slightly (but statistically significantly) higher impact for women. Institutions in the home and host country also tend to magnify the brain drain, especially the level of democracy in the host country. A roughly one standard deviation improvement in the democracy factor increases the selection of skilled workers relative to unskilled workers by about 16 percent for women and by about 24 percent for men. Interestingly, the credibility of the host country has the opposite impact, especially for men, with a one standard deviation improvement in this factor being associated with about a 10 percent reduction in the proportion of male emigrants who are skilled. None of these institutional factors, however, significantly impacts the relative selection of men to women. The signs and significance levels for our control variables are roughly the same when we omit the institutional controls. However, with the exception of the log of the
relative GDP per capita of the host country, omitting these controls attenuates the magnitudes of the effects. This suggests that there is likely to be considerable omitted variable bias introduced to our specification by omitting these variables. To ensure that the source of these differences is not from an endogeneity problem in the impact of the relative standard of living between the two countries, we have re-estimated the model in columns (1) through (3) using the life expectancies and inflation rates in the home and host countries as instruments for GDP per capita. Numerous studies have documented these instruments as important factors in determining GDP per person, and the Hansen test for over-identification fails to reject the null hypothesis that the variables are not overidentified. Yet, tests of endogeneity fail to reject the null that the log of the ratio of GDP per capita is exogenous. Turning now to our variables of interest – financial freedom and financial robustness – our results show that policies that enhance both freedom and robustness of the financial sector in both the home and the host countries tend to increase the proportion of all migrants between two countries who are highly skilled. This increases is especially strong when it comes to financial robustness in the host country. A one standard deviation increase in the host country’s financial robustness factor increases the proportion of female migrants with a college degree by about 29 percent and increases the proportion of male migrants with a college degree by about 26 percent. The effect of host country robustness on the female-male selection ratio is about 3.7 percent, but this effect is not statistically significant in the model that includes institutions.6 This variable is indeed significant in its impact on the relative brain drain of women compared to men, but as we have previously mentioned, we believe that omitting these institutional controls may introduce considerable bias. 6
One reason that the financial liberalization variables significantly impact selection in general but do not impact the relative rate of female selection compared to that of men may lie in the channels through which financial liberalization operates to affect the relative costs (and benefits) of migration. For example, one reason that women may be more attracted to host countries with institutions that promote a more robust financial sector is that women may be more impacted by the opportunity costs of time lost from work that are bound to accompany any migration decision (Sjaastad, 1962; Chiswick, 1999). In particular, women may have a greater need for physical security while they are in search of work in the host country. Therefore, to the extent that robust financial markets reduce real interest rates, as well as facilitate the transfer of reverse remittances to provide financial assistance to meet a female immigrant’s greater need for safe quarters and other basic needs. Moreover, educated women may have a greater preference for formal institutions to meet these needs than men for reasons similar to the ones that increase her need for physical security in the first place. To examine this possibility, we consider in Table 4 the addition of the real interest rate in both the home and host country to the specification, both as a simple intercept dummy (columns 1 through 3) and interacted with the effects of each of the financial liberalization factors (columns 4 through 6). The first thing that we find is that the real interest rate itself has a relatively small impact on the overall selection of women and their rate of selection relative to that of men. This effect becomes smaller still when we consider the interaction effects of interest rates with financial liberalization, with none of the coefficients on the interest rate achieving statistical significance at a level higher than 0.10.
Including the impacts of interest rates does, however, sharpen the picture our results paint with respect to financial liberalization, especially when we consider the interaction of interest rates with financial liberalization. From columns (4) and (5), the direct effect of financial liberalization generally follows the same positive impact as before with respect to the impact on the selection rates of women and men individually. However, the interaction effect of financial robustness with the real interest rate significantly reduces the skill selection for women, but not that of men. We see from column (6) that interacting the real interest rate with financial liberalization teases out an effect for financial robustness in the host country that is consistent with our hypothesis about the impacts of security. First, the non-interacted effects of financial liberalization show a positive impact of robustness in the host country, with smaller (and statistically insignificant) effects for freedom in both countries and for robustness in the home country. This indicates that highly educated women are more likely to choose destination countries that have formal policies in place that strengthen and transparently regulate the security of financial markets relative to both less skilled women and highly skilled men. Moreover, we see from the interaction effect that this preference for robust financial systems is amplified when the country is able to sustain a relatively lower real interest rate. On the net, we see that the impact of financial reforms that promote the robustness of the financial sector increase the rate of female brain drain relative to that of males for countries with interest rates lower than about 7.818% (about the lower 75 percent of OECD host countries); reforms that enhance robustness decrease the rate of female brain drain relative to males for countries with interest rates higher than
7.818%.7 For a host country at the median level of interest rate, a one standard deviation improvement in robustness-enhancing policies increases the quality of female immigrants it receives by about 3 percentage points relative to that of their male counterparts. None of the other institutional or financial reform variables in our model show a statistically significant impact on the female-male brain drain gap.
6. Conclusion We have demonstrated that different types of financial liberalization in the home and host countries have different impacts on the bilateral rates of brain drain. While both the financial robustness and financial freedom of both the home and the host countries increase the rate of skilled migration, it is policies in the host country that promote financial robustness that have the greatest effect. We also find that hostcountry policies that enhance robustness by supporting securities markets and transparently enforcing clear regulations that ensure greater stability in the financial system affect women more than they affect men. In fact, we find that greater robustness in the financial system widens the “brain drain gap” by between about 4 percent once we control for other forms of institutional quality. These results have some important policy implications. First, they provide a slight caveat for developing countries looking to pursue financial reform in that reform may exacerbate the problem of the brain drain. Second, in a time enhancing the quality of immigrants to developed countries has become a priority, our results suggest that one way for host countries to attract more highly-educated immigrants would be to take
7
We calculate this cutoff by noting that the total impact of financial robustness is: dSelect/dRobusthost = 0.129 – 0.0165(Interest Ratehost).
steps to solidify the robustness of their financial markets. However, to the extent that these workers represent an important asset to the developing countries from which they emigrate, this may be counterproductive to global development if it reduces the incentive for policy makers in the immigrants’ home countries to invest in public education. One way to overcome this challenge would be to coordinate financial liberalization between home and host countries so that the benefits of skilled migration can be partially or even fully recuperated by the home country. For example, an emerging literature has proposed that instead of a brain drain, skilled migration may in fact lead to a brain gain. This brain gain might be achieved by financial liberalization through two channels. First, skilled immigrant networks facilitate foreign direct investment (Kugler and Rapoport, 2007), which can be directly enhanced by greater financial freedom. Second, immigrants who earn more tend to send higher levels of remittances home (Faini, 2007; Bollard et al., 2011), and financial liberalization may further increase the rate of remittances (Bang et al., 2013). More work needs to be done to look at the push- and pull- factors that affect female migration and the role of financial liberalization in shaping the pattern of immigration and immigration policy. This analysis adds to the growing literature on gender issues in international migration and is by no means the final word in a very important discussion.
References Abiad, A., Detragiache, E. and T. Tressel, (2010), A new database of financial reforms, IMF Staff Papers 57(2), 281-302. Alesina, A. and R. Perotti, (1996), Income distribution, political instability, and investment, European Economic Review 40(5), 1203-1228. Ang, J.B. (2011), Financial development, liberalization and technological deepening, European Economic Review 55(5), 688-701. Ang, J.B., (2010), Finance and Inequality: The Case of India, Southern Economic Journal 76(3), 738-761. Bang, James T. and Aniruddha Mitra, (2011), Brain Drain and Institutions of Governance: Educational Attainment of Immigrants to the US 1988-1998, Economic Systems, 35(3), 335-354. Bang, James T. and Aniruddha Mitra. 2013. "Civil War, Ethnicity, and the Migration of Skilled Labor." Eastern Economic Journal, 39(3): 387-401. Bang, James T., Aniruddha Mitra, and Phanidgra V. Wunnava. (2013). Financial Liberalization and Remittances: Recent Longitudinal Evidence. IZA Discussion Paper No 7497. Basu, Alaka Malwade. “Why Does Education Lead to Lower Fertility? A Critical Review of Some of the Possibilities.” World Development 30, no. 10 (October 2002): 1779-90. Beck, T., Demirgüç-Kunt, A., and R. Levine, (2007), Finance, inequality, and the poor. Journal of Economic Growth 12 (1), 27–49. Beine, M., Docquier, F., and H. Rapoport, (2008a), Brain drain and human capital formation in developing countries: winners and losers, Economic Journal 118 (528), 631–652. Beine, Michel, Frederic Docquier, and Hillel Rapoport. “Brain Drain and Economic Growth: Theory and Evidence.” Journal of Development Economics 64, no. 1 (February 2001): 275-89. Bekaert, G., Harvey, C.R. and C. Lundblad, (2005), Does financial liberalization spur economic growth? Journal of Financial Economics 77(1), 3 – 55. Bekaert, G., Harvey, C.R. and C. Lundblad, (2011), Financial Openness and Productivity, World Development 39(1), 1-19. Bhagwati, Jagdish N, and Carlos Alfredo Rodriguez. “Welfare-Theoretical Analyses of the Brain Drain.” Journal of Development Economics 2, no. 3 (September 1975):
195-221. Blackden, C Mark, and Chitra Bhanu. Gender, growth and poverty reduction: Special Program of Assistance for Africa, 1998 status report on poverty in sub-Saharan Africa. In collaboration with the Poverty and Social Policy Working Group of the Special Program of Assistance for Africa. Technical Paper, no. 428. Washington, D.C.: World Bank, 1999. Blackden, Mark, Sudharshan Canagarajah, Stephan Klasen, and David Lawson. “Gender and Growth in Sub-Saharan Africa: Issues and Evidence.” World Institute for Development Economic Research (UNU-WIDER), no. 2006 (2006): 28. Bollard, A., McKenzie, D., Morten, M., & Rapoport, H. (2011). Remittances and the brain drain revisited: The microdata show that more educated migrants remit more. The World Bank Economic Review, 25(1), 132-156. Cattaneo, Cristina. “International Migration, the Brain Drain and Poverty: A CrossCountry Analysis.” World Economy 32, no. 8 (August 2009): 1180-1202. Cerrutti, Marcela, and Douglas S Massey. “On the Auspices of Female Migration from Mexico to the United States.” Demography 38, no. 2 (May 2001): 187-200. Chattopadhyay, Arpita. "Gender, Migration, and Career Trajectories in Malaysia." Demography 25, no. 3 (1998): 335-44. Chinn, M.D. and H. Ito, (2006), What matters for financial development? Capital controls, institutions, and interactions, Journal of Development Economics 81(1), 163-92. Chiswick, Barry R, and Paul W Miller. “Language Skills and Earnings among Legalized Aliens.” Journal of Population Economics 12, no. 1 (February 1999): 63-89. Claessens, S. And E. Perotti, (2007), Finance and Inequality: Channels and Evidence, Journal of Comparative Economics 35(4), 748-773. Clarke, G.R.G., Xu, L.C., and H-f. Zou, (2006), Finance and Income Inequality: What Do the Data Tell Us? Southern Economic Journal 72(3), 578-596. Commander, Simon, Mari Kangasniemi, and L Alan Winters. “The Brain Drain: A Review of Theory and Facts.” Brussels Economic Review 47, no. 2004 (2004): 29-44. Costello, A.B. and J.W. Osborne, (2005), Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most from Your Analysis, Practical Assessment, Research & Evaluation 10(7), 1-9. Crozet, M. (2004). Do migrants follow market potentials? An estimation of a new economic geography model. Journal of Economic Geography, 4(4), 439-458.
Curran, Sara R, and Estela Rivero-Fuentes. “Engendering Migrant Networks: The Case of Mexican Migration.” Demography 40, no. 2 (May 2003): 289-307. Docquier, Frederic and Hilel Rapoport. "Endogenous Discrimination, Migration Prospects, and the Protection of Ethnic Minorities." Annales d'Economie et de Statistique 71-72 (2003b): 79-85. Docquier, Frederic and Hilel Rapoport. "Ethnic Discrimination and the Migration of Skilled Labor." Journal of Development Economics 70 (2003a): 159-72. Docquier, Frederic, B Lindsay Lowell, and Abdeslam Marfouk. “A Gendered Assessment of Highly Skilled Emigration.” Population and Development Review 35, no. 2009 (2009): 297-321. Dumont, Jean-Christophe, John P. Martin and Gilles Spielvogel. "Women on the Move: The Neglected Gender Dimension of the Brain Drain." IZA Discussion Paper No. 29020 (2007): 24. Ehrenreich, Barbara, and Arlie Russell Hochschild. Global Woman: Nannies, Maids, and Sex Workers in the New Economy. 1st ed. Holt Paperbacks, 2004. Elson, Diane. “Gender Equality and Economic Growth in the World Bank World Development Report 2006.” Feminist Economics 15, no. 3 (July 2009): 35-59. Faini, Riccardo. “Remittances and the Brain Drain: Do More Skilled Migrants Remit More?.” World Bank Economic Review 21, no. 2 (2007): 177-91. Goyette, Kimberly, and Yu Xie. “The Intersection of Immigration and Gender: Labor Force Outcomes of Immigrant Women Scientists.” Social Science Quarterly 80, no. 1999 (1999): 395-408. Haddad, Lawrence. “Women's Status: Levels, Determinants, Consequences for Malnutrition, Interventions, and Policy.” Asian Development Review 17, no. 1 (1999): 96-131. Hayfron, John E. “Panel Estimates of the Earnings Gap in Norway: Do Female Immigrants Experience a Double Earnings Penalty?” Applied Economics 34, no. 11 (July 2002): 1441-52. Jong-A-Pin, R., (2009), On the measurement of political instability and its impact on economic growth, European Journal of Political Economy 25, 15-29. Jutting, Johannes P, Christian Morrisson, Jeff Dayton-Johnson, and Denis Drechsler. “Measuring Gender (In)equality: The OECD Gender, Institutions and Development Data Base.” Journal of Human Development 9, no. 1 (March 2008): 65-86. Klasen, Stephan. "Does Gender Inequality Reduce Growth and Development? Evidence from Cross-Country Regressions." Policy Research Report on Gender and
Development, Working Paper Series no. 7 (1999): World Bank. Knowles, Stephen, Paula K Lorgelly, and P Dorian Owen. “Are Educational Gender Gaps a Brake on Economic Development? Some Cross-Country Empirical Evidence.” Oxford Economic Papers 54, no. 2002 (2002): 118-49. Kugler, M. and H. Rapoport, (2007), International labor and capital flows: complements or substitutes?, Economics Letters 94 (2), 155–162. Kugler, Maurice, and Emanuela Lotti. “Migrant Remittances, Human Capital Formation and Job Creation Externalities in Central America.” Integration and Trade 11, no. 27 (December 2007): 105-34. Lagerlof, Nils-Petter. “Gender Equality and Long-Run Growth.” Journal of Economic Growth 8, no. 4 (December 2003): 403-26. Levine, R., (2005), Finance and Growth: Theory and Evidence, in P. Aghion and S. H. Durlauf (eds.) Handbook of Economic Growth Vol. 1, Amsterdam: Elsevier, 865934. Li, X. and J. McHale, (2006), Does brain drain lead to institutional gain? a cross country empirical investigation, mimeo, University of British Columbia. Lien, Donald, and Yan Wang. “Brain Drain or Brain Gain: A Revisit.” Journal of Population Economics 18, no. 1 (March 2005): 153-63. Massey, Douglas, Mary J. Fischer and Chiara Capoferro. "International Migration and Gender in Latin America: A Comparative Analysis." International Migration 44, no. 5: 63-91. Mayda, A. M. (2010). International migration: A panel data analysis of the determinants of bilateral flows. Journal of Population Economics, 23(4), 1249-1274. Mitra, Aniruddha, James T. Bang and Phanindra V. Wunnava, (Forthcoming), "Financial Liberalization and the Selection of Emigrants: A Cross-national Analysis" Empirical Economics DOI 10.1007/s00181-013-0735-0. Morrison, Andrew R, Maurice Schiff, and Mirja Sjöblom. The International Migration of Women. Washington, D.C.: World Bank; Houndmills, 2008. Naufal, George and Carlos Vargas-Silva. "Changing Fertility Preferences One Migrant at a Time: The Impact of Remittances on the Fertility Rate" IZA Discussion Paper Series no. 4066 (2009): 29. Nejad, M. N. (2013). “Institutionalized inequality and brain drain: an empirical study of the effects of women's rights on the gender gap in high-skilled migration” IZA Working Paper No. 7864. Perez-Moreno, S., (2011), Financial development and poverty in developing countries: a
causal analysis, Empirical Economics 41, 57-80. Perotti, R., (1996), Growth, income distribution, and democracy: what the data say, Journal of Economic Growth 1, 149–187. Petropoulos, Nicholas P, and Dionysios Balourdos. “Fertility among Greek Migrant Women Returnees.” Spoudai 52, no. 3 (September 2002): 3-30. Rajan, R.G. and L. Zingales, (2003), The great reversals: the politics of financial development in the twentieth century, Journal of Financial Economics 69(1), 5– 50. Ramos, R., & Suriñach, J. (2013). A Gravity Model of Migration between ENC and EU. IZA Discussion Paper No. 7700. Rodriguez, Carlos Alfredo. “Brain Drain and Economic Growth: A Dynamic Model.” Journal of Development Economics 2, no. 3 (September 1975): 223-47. Roig Vila, Marta, and Teresa Castro Martin. “Childbearing Patterns of Foreign Women in a New Immigration Country: The Case of Spain.” Population 62, no. 3 (2007): 351-80. Stark, Oded. “Rethinking the Brain Drain.” World Development 32, no. 2004 (2004): 15-22. World Bank. Global Monitoring Report 2007: Millennium Development Goals: Confronting the Challenges of Gender Equality and Fragile States. Washington, D.C.: World Bank, 2007. World Bank. World Development Report 2006: Equity and Development. Washington, D.C.: World Bank, 2006. Zhang, Junsen, Jie Zhang, and Tianyou Li. “Gender Bias and Economic Development in an Endogenous Growth Model.” Journal of Development Economics 59, no. 2 (August 1999): 497-525. Zlotnik, Hania. "International Migration Policies and the Status of Female Migrants." International Migration Review 24, no. 2 (1990): 372-81. Zlotnik, Hania. "The South-to-North Migration of Women." International Migration Review. 29, no. 1 (1995): 229-54.
Tables Table 1: Summary Statistics
Table 2: Factor Analysis Method: Principle Factor Observations 335 Rotation: Promax Retained Factors 8 Number of Parameters 140 Rotated factor loadings (pattern matrix) and unique variances Variable Democracy Freedom Transparency Robustness Directed Credit 0.098 0.955 0.134 0.106 Credit Controls 0.125 0.947 0.096 0.183 Interest Rate Controls 0.377 0.380 0.067 0.526 Entry Barriers 0.219 0.369 -0.012 0.469 Privatization 0.175 0.396 0.141 0.340 Capital Controls 0.321 0.340 0.236 0.601 Banking Supervision 0.166 0.381 0.193 0.499 Security Markets 0.257 0.339 0.356 0.625 Government Stability -0.048 0.301 -0.012 0.216 Investment Profile 0.125 0.328 0.245 0.282 Corruption 0.162 0.118 0.770 0.001 Bureaucratic Quality 0.179 0.151 0.765 0.252 Dem. Accountability 0.523 0.177 0.603 0.186 Polity 2 0.801 0.068 0.360 0.181 Durability 0.061 0.231 0.630 0.086 Leg. Electoral Comp. 0.824 0.171 0.018 0.104 Exec. Electoral Comp. 0.803 0.108 0.116 0.113 Electoral Fraud -0.101 0.111 -0.391 -0.170 Pol. Fractionalization 0.724 0.133 0.019 0.134 Political Polarization 0.430 0.107 0.304 0.101 Checks 0.609 0.065 0.263 0.126
Credibility 0.071 0.109 0.086 0.179 0.212 0.149 0.339 0.177 0.570 0.599 -0.040 0.137 0.103 0.007 0.083 0.025 0.064 -0.078 0.015 0.046 0.059
Factor6 0.008 0.039 0.014 0.026 0.015 -0.046 0.149 0.134 0.028 0.010 0.068 0.060 0.048 0.054 0.013 -0.068 -0.038 -0.090 0.328 0.492 0.384
Factor7 Factor8 Uniquenes 0.010 0.001 0.044 0.007 0.003 0.032 0.045 0.074 0.417 0.001 0.266 0.492 0.208 0.064 0.586 -0.016 0.010 0.340 0.187 0.050 0.366 -0.095 -0.120 0.228 -0.043 -0.003 0.532 0.024 0.008 0.378 -0.088 0.093 0.345 -0.011 -0.074 0.268 0.091 0.115 0.263 -0.054 0.100 0.175 0.173 -0.156 0.477 0.050 -0.003 0.274 0.029 -0.056 0.309 0.283 -0.027 0.701 -0.056 0.030 0.329 -0.004 0.055 0.455 0.013 -0.147 0.367
Table 3: Financial Liberalization and Brain Drain by Gender (1) (2) (3) Female Selection Male Selection Selection Ratio -0.146*** -0.147*** 0.00105 (0.0103) (0.0104) (0.00651) ln(Distance) -0.0818*** -0.0513*** -0.0305*** (0.0187) (0.0173) (0.0112) Border -0.364*** -0.385*** 0.0206 (0.0908) (0.0912) (0.0486) Colonial History 0.105** 0.133*** -0.0274 (0.0531) (0.0505) (0.0304) Common Language 0.270*** 0.267*** 0.00358 (0.0316) (0.0323) (0.0169) Same Country -0.108 0.0994 -0.207** (0.146) (0.174) (0.0809) ln(Host GDP p.c./Home GDP p.c.) -0.00277 -0.0114 0.00866 (0.0216) (0.0226) (0.0150) ln(Home Population in Millions) 0.0972*** 0.104*** -0.00728 (0.0122) (0.0130) (0.00804) ln(Host Population in Millions) 0.0867*** 0.131*** -0.0444*** (0.0141) (0.0135) (0.00882) Land area (Home) 6.12e-09 -1.77e-09 7.89e-09** (6.33e-09) (6.43e-09) (3.83e-09) Land area (Host) 4.70e-08*** 3.45e-08*** 1.25e-08*** (6.82e-09) (6.71e-09) (4.51e-09) Financial Freedom (Home) 0.119*** 0.116*** 0.00272 (0.0142) (0.0140) (0.0103) Financial Freedom (Host) 0.0846*** 0.0949*** -0.0103 (0.0256) (0.0263) (0.0196) Financial Robustness (Home) 0.106*** 0.0954*** 0.0106 (0.0174) (0.0163) (0.0123) Financial Robustness (Host) 0.294*** 0.257*** 0.0372 (0.0320) (0.0327) (0.0228) VARIABLES ln(Total Bilateral Migrant Stock)
(4) (5) (6) Female Selection Male Selection Selection Ratio -0.0457*** -0.0457*** -0.00143 (0.00279) (0.00268) (0.00620) -0.0216*** -0.0153** -0.0320*** (0.00617) (0.00633) (0.0112) -0.0527*** -0.0687*** 0.0228 (0.0204) (0.0207) (0.0489) 0.0190 0.0321* -0.0241 (0.0161) (0.0166) (0.0303) 0.0923*** 0.0943*** 0.00648 (0.0109) (0.0114) (0.0169) -0.0910* -0.0454 -0.206*** (0.0488) (0.0544) (0.0793) -0.0273*** -0.0276*** 0.00109 (0.00431) (0.00445) (0.00906) 0.0312*** 0.0319*** -0.00341 (0.00352) (0.00371) (0.00798) 0.0236*** 0.0358*** -0.0390*** (0.00398) (0.00401) (0.00821) 1.19e-09 -1.11e-09 7.31e-09** (1.94e-09) (2.05e-09) (3.52e-09) 1.90e-08*** 1.62e-08*** 9.41e-09** (2.43e-09) (2.47e-09) (4.18e-09) 0.0234*** 0.0264*** 0.00363 (0.00394) (0.00411) (0.00913) 0.0220*** 0.0232*** 0.00342 (0.00513) (0.00567) (0.0156) 0.0158*** 0.0132*** 0.0122 (0.00472) (0.00492) (0.0117) 0.0771*** 0.0544*** 0.0709*** (0.00762) (0.00828) (0.0194)
Table 3: Financial Liberalization and Brain Drain by Gender (Continued) VARIABLES Democracy (Home) Democracy (Host) Transparency (Home) Transparency (Host) Credibility (Home) Credibility (Host) Constant Observations R-squared Home Region Dummies Host Region Dummies F-statistic Robust standard errors in parentheses *** p
