Does Brain Drain Lead to Institutional Gain?

A Cross Country Empirical Investigation

Xiaoyang Li∗

John McHale∗∗

Abstract

This paper presents a cross country empirical investigation of impacts of skilled labor emigration on

sending country’s institutional development. Based on Albert Hirschman’s “Exit and Voice” model, we

analyze different channels through which emigration can affect institutions; using Djankov et al.’s

“Institutional Possibility Frontier” framework, we assess emigration’s heterogeneous effects on

different aspects of institutions. In empirical analysis, we construct an instrumental variable for skilled

labor emigration rate using country’s geographical characteristics to identify the causal relationship.

Basic results show that skilled labor emigration has a positive effect on “political” institutions but a

negative effect on “economic” institutions for the sending country.

JEL No.: F22 N10 P16 P48

Key words: Emigration, Human Capital, Institutional Development

∗ Xiaoyang Li, Sauder School of Business, University of British Columbia, Canada. Xiaoyang.li@sauder.ubc.ca ∗∗ John McHale, Queen’s School of Business, Queen’s University, Kingston, ON, Canada. JmcHale@business.queensu.ca

1 Introduction

For the past thirty years, widespread international migration has been an enduring feature of social,

economic and political landscape all over the world. According to the United Nations (2002), the

number of international migrants increased from 154 to 175 million between 1990 and 2000. With this

accelerated pace of international migration, the consequence of emigration has become a more

concentrated academic focus. Yet there is a lack of consensus over the impact that emigration has on

home country development. Traditional development theory argues that emigration has an almost

exclusively negative impact on Third World countries as people who emigrate from developing

countries usually have high education attainments1. Therefore, this loss of intellectual and technical

resources has popularly been labeled “Brain Drain”. Recently, however, a few papers have found some

evidence of “Brain Gain”: Long run benefits from emigration mainly through the effects of returning

emigrants2 and through the creation of business networks.3 Before scholars can engage in a more

meaningful debate on this question, we find their analyses are incomplete: the focus of previous work

simply rested on the economic impacts of emigration. What seems to be missing is a focus on

emigration’s political impact, particularly on institutional development of the home country. This

academic void motivates our paper.

After the seminal works by North (1981, 1990), increasing attention was drawn to institution and

its relationship with economic development. It is now conventional wisdom to say that institution is the

critical determinant in development, and over the past few years a host of studies have provided

empirical support that this is so4. Though there is still much to be known on what is a good institution

and what determines institutional development, it is not surprising that domestic human capital

contribute a lot to a good institution. Glaeser et al. (2004) argue that human capital is a more basic

source of growth than institutions5 and suggest that countries that emerge from poverty accumulate

human and physical capital, and then, once they become richer, are increasingly likely to improve their

institutions. With some additional evidence confirming this relationship6, we go on to examine the

1 See Bhagwati and Hamada (1974), Kwok and Leland (1982) 2 See Stark and Helmenstein (1997), Domingues Dos Santos and Postel Vinay (2003) 3 See Dustmann and Kirchkamp (2002), Hunger (2002) 4 See, among others, Acemoglu et al. (2001), Rodrik et al. (2002) 5 Recently Acemoglu et al. (2005) provide some counter evidence. 6 We take advantage of the some limited available data on primary school enrollment in 1900 from Linder (2001) and GDP per capita

in 1900 from Madison (2003) and some other controls to check whether human capital 100 years ago can possibly explain

1

impact of the absence of domestic human capital, i.e. emigration, on home country’s institutional

development.

Little previous work has attempted to assess the impact of emigration on institutional or even

political development.7 Despite some anecdotal evidence8, the paucity of reliable and comparable data

on emigration and the intrusion of social, cultural, and political factors in institutional development

greatly hamper this study. Using new theoretical developments in institutional economics and a more

accurate dataset, this paper will enrich the literature by expanding the research scope of impacts of

emigration to include the institutional perspective, without which we believe such assessment can not

be complete.

The paper is organized as follows. Section 2 outlines the theoretical framework and mechanisms

through which human capital and emigration can exert an impact on institutional developments. Our

hypotheses are developed here. We test these hypotheses empirically, and do some robustness checks

in section 3. Section 4 concludes.

2 Theoretical Discussions

This section elaborates the theoretical framework underlining the discussion and outlines the relevant

hypotheses to test in section 3.

2.1 Exit and Voice Framework

Hirschman’s (1970) “Exit and Voice” model offers us a departure to examine the relationship between

domestic human capital, emigration and institutions. Hirschman posits that there are two alternative

responses to decline in organizations or states, namely exit and voice. Exit means leaving, while voice

means trying to reverse the decline by complaining, protesting or organizing internal opposition. Later,

Barry (1974) offers an augmented model by separating out the choice of “silence” from “exit” and

“voice”. He argues that “as well as silent exit and non-exiting voice, there is also silent non-exit, and

this may well be the rational course to follow if exit is unattractive, even if it is believed that things institutional quality now. The results support our hypothesis but we cannot take this result too far due to the simple specification and

limited sample size, however, these results, to some extent, can demonstrate human capital as the determinant of institutional

development, especially from a long term point of view. 7 Hirschman (1981), p. 260 8 See Hirschman (1981) and Papademetriou (1978)

2

3

could be done better by the firm, organization or state concerned”9. People tend to remain silent with

respect to institutional change as they will not individually feel directly responsible. Another possible

action after people exit is “return” which corresponds to the term “reversal phenomenon” by

Hirschman. Return emigration makes up for the loss of their pervious exit. In some countries,

returnees can have a dramatic impact on home country institution building. Moreover, emigration can

have an indirect effect on institutions by influencing the actions of people staying at home including the

authorities, those who intend to exit and those who do not have the option to leave. Based on Barry’s

framework, we link different mechanisms of effects on institutional quality to “stay and exit”. Figure 1

illustrates the framework and the following subsection offers a brief discussion.

Figure 1: Exit and Voice Analytical Framework

2.2 Discussions

2.2.1 When staying at home, educated people contribute to institutional development

Belief in the possibility of improvement

Exit

Silence

Silence

Voice

Voice

Stay

Positive, small

Negative

Positive, big

Positive, small

Return Positive

?

Reasons may Cause Exit

Choice Behavior Effects on Institutions

1

4

3

2

9 Barry (1974), p. 91

Educated people remaining at home will contribute to the institutional development even if they opt to

stay silent. There are at least four ways to think so. First, skilled labor is an important input in GDP

growth accounting10, and better institutions become affordable when the scale of economic activity

expands11. Second, it is people who build and operate institutions. Educated people can thus better

design an institution and can better ensure its quality functioning. Third, according to Lipset (1960),

educated people are more likely to resolve their differences through negotiation and voting than

through violent disputes. Human capital leads to more benign politics, less violence, and more political

stability, all of which reflect better institutions. Finally, each community faces a set of institutional

opportunities, determined largely by the human and social capital of its population. The greater the

human capital, the more attractive are its institutional opportunities12. Our first hypothesis follows:

Domestic human capital contributes to institutional developments.

2.2.2 When educated people exit, their impact on institutional development is ambiguous

This subsection elaborates upon each mechanism of influence in Figure 1, as depicted by the options

associated with numbered circles:

(1) Educated people who exit and remain silent abroad have a negative effect on domestic

institutional development. Their exit simply means the absence of the above positive impact of

domestic human capital on institutions. Besides, exit-migration deprives the geographical unit that is

left behind of many of its more activist residents, including potential leaders, reformers, or

revolutionaries.

(2) Skilled emigration can have an indirect influence on institutions by affecting government’s

reaction. When educated people exit, they may provide a “safety valve” effect to domestic political

development.13 The socio-demographic profile of the migrants has consistently shown that the majority

of them are among those who are usually identified as the most mobilized of the developing polities’

citizens -- they are young, better trained and usually exposed to the sociopolitical and economic

mentality of large urban centers. Therefore, emigration may offer a safety valve by allowing some of the

more mobilized and impatient members of societies to opt out thereby facilitating the internal

10 Barro (1997) 11 There is extensive literature that relates economic development to democracy. See Lipset (1960), Barro (1999) 12 Djankov et al. (2003) 13 Papademetriou (1978)

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institutional reform.

(3) People who exit but voice from abroad have a positive but minor effect. Many of the Diasporas

feel a sense of kinship with the land of their ancestors and share an abiding interest in the politics of

their ancestral country or symbolic homeland14. Some American Diasporas are examples of groups who

exit but voice, for example. We have seen numerous events in which some U.S. based Diasporas played

a critical role in helping to attain their homeland political goals such as national self-determination and

the removal of dictatorial regimes.

(4) Returnees from emigration usually add to domestic labor supply with better educated and

trained human capital. More importantly, some returnees may be agent of institutional reform. For

example, the quality of top-level economic technocrats in Latin American has grown enormously over

the past generation, as a result of their schooling in North America and Europe. They bring with them

professional values regarding transparency and accountability that have spillover effects in their

countries.15

Out of the discussion follows our second hypothesis: Emigration can have both positive and

negative effects on home country’s institutional development, but the overall net impact is

ambiguous.

2.3 A Closer Look at Institutions

Douglass North defines institutions as “a set of rules, compliance procedures, and moral and ethical

behavioral norms….”16 As we can see, it is a rather broad and vague concept. For most countries,

skilled labor emigration only accounts a small fraction of its overall labor stock, therefore it is hard to

think that emigration can have a notable impact on a country’s institutions. This subsection breaks

down institutions to provide a meaningful framework in assessing the relationship between human

capital, emigration and institutions.

Djankov et al. (2003) argue that, to understand institutions, one need to understand the basic

tradeoff between the costs of disorder and those of dictatorship. Disorder refers to the risk to

individuals and their property of private expropriation in such forms as banditry, murder, theft,

violation of agreements, torts, or monopoly pricing. Dictatorship, on the other hand, refers to the risk 14 Shain (1994) 15 Fukuyama (2004) 16 North (1981), p. 201-202

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6

to individuals and their property of expropriation by the state in such form as murder, taxation, or

violation of property.

They develop an Institutional Possibility Frontier (IPF) faced by a society out of the trade-off

between disorder and dictatorship costs. The location of the IPF is determined by civic capital. The

level of human capital in the society also determines the location of the IPF because better educated

people are more likely to solve problems without violence. Emigration lowers the level of country’s

civic capital. On the one hand, skilled labor emigration itself represents a loss of human capital, on the

other hand, when individuals have a high probability of mobility, they are less likely to invest in social

capital.17 Figure 2 shows the IPF and how emigration can affect the IPF. Emigration can exert its effect

by either shifting the location of IPF entirely or by moving the efficient points along the IPF itself. Two

anecdotes, one from Ireland and the other from East Germany, illustrate different emigration’s effects

on institutions. Figure 3 depict these two distinct situations.

Figure 2 Institutional Possibility Frontier (IPF) Diagram18 and Emigration’s Effects

Disorder

Dictatorship

Institutional Possibility Frontier (IPF)

Total loss minimization

·

• Emigration shifts the entire IPF out Emigration changes the efficient location

17 Edward L. Glaeser “The Formation of Social Capital”, mimeo, Harvard University. 18 The horizontal axis captures the social losses from dictatorship, which grow moving out along the axis. Moving towards the origin

indicates declining social losses and improving property rights – the origin represents perfect property rights and minimal loss from

dictatorship. The vertical axis reflects social losses from disorder, also measured relative to perfect property rights. The downward

sloping 45 degree line holds the total social costs of dictatorship and disorder constant. The tangency point is the efficient

institutional choice for a given society.

7

In the 1950s, Irish emigrants from rural districts surged and reached the highest level in a century, (15

per mile per year in 1956-1961). This outflow aroused deep concern and became an important public

and political issue that led to a decisive turn in economic policy. The increasing concerns that “Ireland

was a dying country . . . led to calls for new economic policies, the adoption of various plans by the

different political parties, the appearance of emigration as an issue in a parliamentary election for the

first time, and finally the unopposed acceptance (in 1958) of a national economic plan designed to

develop Ireland and prevent emigration”19. This reform package reduces the social losses from mass

emigration pulling down the IPF originally “pushed out” by mass emigration.

Figure 3 Ireland and East Germany’s IPF changes due to emigration

A distinct situation arose in East Germany which in the 1950s experienced a flood of (illegal)

emigration toward West Germany. At first, the East Germany power holders thought it advantageous

to be rid of disgruntled and irreconcilable “class enemies”; but soon, the flow of people among them

many highly skilled members of the labor force was so large that the authorities simply closed its

frontier by building the Berlin Wall in 1961 to stop the outflow. After they had substantially reduced

exit by building the Wall, the authorities realized that they could weaken internal opposition by a

selective policy of either permitting certain people to exit or outright expelling critical voices

Disorder

Dictatorship

• • East Germany

Ireland

19 Hirschman (1981)

considered to be dangerous or obnoxious, besides, they resorted to overt acts of heightened domestic

repression to consolidate dictation. In the IPF framework, we can see that emigration induced more

dictatorship but less disorder.

3 Empirical Investigation

Previous part provides us with clear predictions on the impacts domestic human capital might have on

institutions, but not so clear on the impacts of emigration. We want to use empirical assessment to

entangle this impact. The first challenge is to set up an appropriate specification to explain institutional

quality.

Economists, sociologists, and political scientists have developed many theories of institutional

development20. But each strand of literature tends to focus on one aspect while overlooking others. To

prevent the specification from omission of relevant variables, we follow the way put forth by North to

divide theories into three broad categories of economic, political, and cultural factors. And we need to

find reasonably exogenous sources of variation in economic, political, and cultural characteristics of

countries.

First, we use GDP per capita to proxy for economic factors, although income is not exogenous.

Consequently, it is necessary to control for a country’s wealth since human capital can affect

institutions indirectly by making a country richer. Second, we follow Easterly and Levine (1997) by

using a country’s Ethnolinguistic Fractionalization as a proxy for political factor. They predict that, as

ethnic heterogeneity increases, social capital and trust fall, then the governments become more

interventionist and the political freedom declines. Third, we use the percentage of population in each

country of different religious affiliation for a cultural proxy. Following Landes (1998), we mainly focus

on Muslim and Catholic religions due to their detrimental effects to development. Fourth, a country’s

legal origin crudely represents the political orientation of government and institutions. Legal origin can

determine political allocation of power thereby influence the institution development. But the predicted

effects of legal origins on institutions are not straightforward.

3.1 Baseline specification

20 See, among others, North (1981, 1990), Putnam (1993), Fukuyama (1995), and Landes (1998)

8

Our baseline specification is:

2 0 0 0 1 9 0 2 9 0 3 9 0 4 5 6D FI H H Y E F C A M Uα β β β β β β= + + + + + + + e

Where 2000I denotes institutional quality in year 2000;

90DH Domestic human capital denotes percentage of domestic people with tertiary education out of

all working aged (25 and above) residents at home country in 1990;

Foreign human capital denotes percentage of overseas compatriots with tertiary education out

of all working aged residents at home country in 1990;

denotes logarithm of GDP per capita for 1990;

EF denotes ethno-linguistic fractionalization;

CA denotes percentage of people with Catholic affiliation;

MU denotes percentage of people with Muslim affiliation.

In the regression, we use ex ante data on domestic and foreign human capital to examine their

impacts on ex post institution quality. From the construction of

90FH

90Y

Dt sH − and (they share the same

denominator), we can see that 2

Ft sH −

1β β− is the net institutional gain (loss) of moving educated people

from abroad back home and test for this difference can indicate whether it is good to let people stay or

let them go. In the result tables, we report the difference of domestic human capital coefficient and

foreign human capital coefficient and the related P value test.

3.2 Data

Until recently, there have been only a few empirical assessments of the economic impact of

emigration21. Despite many case studies and anecdotal evidences, the main reason for this is the lack of

harmonized and accurate data on migration. Recently, this bottleneck has been broken through by

Docquier and Marfouk (2005). They use the stock of adult (age 25 and plus) immigrants to OECD

countries to proxy for the sending country’s overall adult emigrants number and sort them by three

educational attainments (primary, secondary and tertiary) to compile harmonized data for emigration

rates for 174 origin countries and territories in 1990. We use this dataset to compute our domestic and

21 See, among others, Docquier and Rapoport (2004)

9

foreign human capital for tertiary education level.

For institutional quality, we mainly use the World Bank governance data by Kaufman, Kraay and

Mastruzzi (2005). This dataset has six aggregate governance indicators: Voice and Accountability (VA),

Political Stability (PS), Government Effectiveness (GE), Regulatory Quality (RQ), Rule of Law (RL)

and Control of Corruption (CC) covering about 200 countries and territories for five years: 1996, 1998,

2000, 2002 and 2004. These indicators are based on several hundred individual variables measuring

perceptions of governance and virtually all the scores are between -2.5 to 2.5, with a higher score

denoting a better situation. A close look at what each variable measures reveals that they can give us a

good empirical proxies of the “dictatorship” and “disorder” in the IPF framework.

The first two governance clusters are intended to capture “the process by which those in

authorities are selected and replaced”. VA measure the extent to which citizens of a country are able to

participate in the selection of governments including in it a number of indicators measuring various

aspects of the political process, civil liberties and political rights. PS combines several indicators which

measure perceptions of the likelihood that the government in power will be destabilized or overthrown

by possibly unconstitutional and/or violent means. We term these two measures political institutions.22

To some extent, this cluster corresponds to the “dictatorship” in IPF. Dictatorship usually controls

people’s human rights and civil freedom by repressing democracies and independent media. Here we

expect the coefficients on these two variables of the foreign human capital to be positive. According to

Hirschman, people who chose emigration were obviously dissatisfied in some way with the country and

society they were leaving. With exit available as an outlet for the disaffected, they were less likely to

resort to voice: The ships carrying the migrants contained many actual or potential anarchists and

socialists, reformers and revolutionaries. Therefore, for political institutions, we expect emigration’s

impacts to be positive.

The next two clusters summarize various indicators of the ability of government to formulate and

implement sound policies. GE combines into single grouping responses on the quality of public service

provision, the quality of bureaucracy, the competence of civil servants. RQ focuses on the policies

themselves. It includes measures of the incidence of market-unfriendly policies such as price controls

or inadequate bank supervision. We term these two measures economic institutions.

22 Acemoglu et al. (2004) decomposes institutions into political and economic institutions.

10

What these two variables measure crudely corresponds to the “disorder” on IPF. Disorder

measures the social losses due to private expropriation, and a better regulatory quality and effective

governance will alleviate the loss of disorder. The main focus of GE is on “inputs” required for the

government to be able to produce and implement good policies and deliver public goods. As the

effective implementation and delivery of key public services require “thousands of face-to-face

discretionary transactions by service providers23, higher quality human capital will generate better policy

outcomes. Therefore, skilled labor emigration that deprives the country of quality civil servants will

have a negative effect on economic institutions.

The last two measures summarize the respect of citizens and the states of institutions which

govern their interactions. RL includes several indicators which measure the extent to which agents have

confidence in and abide by the rules of society. CC measures perceptions of corruption, conventionally

defined as the exercise of public power for private gain. From their definitions, we can see that these

two variables fall in between what political and economic institutions measure. And we have no clear

predictions as to emigration’s effects on these two variables.

We obtain the legal origin, the percentages of people with Catholic, Muslim and Protestant

affiliations and Ethnolinguistic Fractionalization data from La Porta et al. (1998). GDP per capita data

in 1990 is from World Development Indicators.

3.3 Instrumental Variable

A big concern of the result is that the specification suffers from reverse causality problem, i.e. the

direction of causation can go from institutions to emigration rate24. To address this problem, one

solution is to find an instrumental variable.

Our search for instrumental variable follows the method suggested by Frankel and Romer (1999).

They used a country’s geographical suitability for international trade to construct an instrument for

trade share. Though people’s migration is constrained by some economic and political factors, we

believe that geographic considerations matter people’s migration pattern. In line with that, we use a

country’s geographical features to instrument the emigration rate of tertiary-educated people. The first

variable we use is the country’s land area. As a country becomes bigger, people intending to migrate can

23 Pritchett, Lant and Woolcock Michael (2002) 24 Papademetriou (1978)

11

choose to migrate internally from one place to another. Another variable is a dummy whether a country

is an island or not. Island countries are generally very small and open to migration. Yet another variable

we use is the country’s distance to the United States25. To some extent, distance matters when people

choose the destination of emigration possibly due to close economic ties and similar cultural

background. The United States and its neighbor Canada are two of the largest skilled immigrants host

countries. We create two interaction terms using the product of island dummy and land area and the

product of island dummy and distance to the United States because data show that Caribbean and

Central America countries exhibit extremely high rates of skill emigration.

Our first stage regression is:

1990 0 1 2 3 4 5* *FH Lnarea Island LndistUS Island Lnarea Island LndistUSφ φ φ φ φ φ= + + + + + + ε

Where Lnarea denotes the logarithm of a country’s land mass; Island is a dummy variable for an island

country and LndistUS is the logarithm of surface distance of one country’s capital to Washington D.C.

From Table 1 we can see that the signs on the coefficient are consistent with what we expect, and

all the coefficients are statistically significant26 except land area. The high adjusted R square (.475)

shows that the instrument fits the data well. Figure 4 depicts the scatterplot of the fitted value of

foreign tertiary educated percentage against the actual value.

Table 1 Here

Figure 4 Scatter plot of instrument vs. actual foreign human capital

25 The distance is computed as great circle distance from each country or territory’s capital to Washington DC. US’s internal distance

is computed by the formula by Nitsch. 26 Throughout this paper, statistically significant means significant at 5% confidence level.

1/ 2( / )area π

12

-.05

0.0

5.1

.15

Geo

grap

hica

l Ins

trum

ent

0 .1 .2 .3Emigration of Tertiary Educated

3.4 Regression Results

To test hypothesis two, we run the regression using the baseline specification both with and without EF,

CA and MU using both Ordinary Least Squares (OLS) and Instrumental Variable (IV) methods. As we

can see from table 2, coefficients on domestic human capital are positive and statistically significant for

VA, RQ, RL and CC; for PS and GE, the signs are positive, but not significant27. The patterns from

OLS and IV estimation are generally the same. These results generally confirm our proposition that

domestic human capital contributes to institutional development.

The pattern of the coefficients on foreign human capital is revealing: For VA and PS, the

coefficients are positive; for all other measures, the signs are negative. VA and PS are political

institutions that can be seen as measures of “dictatorship” on IPF. The data shows that emigration has

brought political institutional gains for home country. For the economic institutions, GE and RQ or

“disorder”, emigration’s effects are negative. Reflected on the IPF, emigration will shift the efficient

institutional choice upward bringing more disorder but less dictatorship.

For RL and CC, results indicate that foreign human capital’s impacts are negative. These two

measures are in between political and economic institutions, but the components of them include lots

27 The reason why for some measures the coefficients on domestic human capital are not significant is that we control for the GDP per capita as they are highly correlated with each other. If we omit GDP per capita control, all the coefficients become much bigger and significant.

13

of economic-wise data such as “enforceability of contracts”, “the frequency of additional payments to

get things done”, therefore, the coefficients show that they lean towards a country’s economic

institutions.

The difference between domestic and foreign human capital’s effect and its related significance

tests are clear. The difference is negative for VA and PS but positive for all other measures. As to why

emigration can better home country’s Voice and Accountability, Hirschman (1961) provided a potential

explanation: Exit and voice seem to interact with each other: the availability and threat of exit on the

part of an important customer or group of members may powerfully reinforce their voice.

The positive difference for other dependent variables shows that when skill labor emigrate, they

bring a net loss on home country’s economic-wise institutions. Emigration can exacerbate home

country’s economy since economics institutions determine economic performance28. The coefficients

on logarithm of GDP per capita are always positive and significant and this means either that

development improves institutions or good institutions promote development. We do not intend to

show any causal relationship, but adding it as a control can make our estimation more convincible.

Table 2 Here

Table 3 reports our baseline regression results. Compared with Table 2, adding these religion and

legal origin variables does not change our results much. The pattern of the coefficients and difference

tests stays the same. Foreign human capital has positive impact on political institutions but negative

impact on economics institutions. However, we can observe several differences. First, the coefficients

on domestic human capital generally diminish and most of them become insignificant. Second, the

coefficients on foreign human capital fall except for IV regression of PS, but for GE, RQ, RL and CC,

the OLS regression coefficients become statistically significant. Thirdly, the P value of the test for

difference between domestic and foreign human capital’s effect on Voice and Accountability using IV

regression increases from 0.0343 to 0.1480, i.e. the difference turns insignificant. The difference tests

seem to show the net effect of skilled emigration on political institutions is trivial, but the net effect on

economic institutions is significantly negative.

Table 3 Here

3.5 Robustness Checks

28 Acemoglu et al. (2004)

14

Our dependent variables, the World Bank governance data are constructed mainly from survey or poll

data, so they are highly influenced by some policy outcomes or political and economic environment of

that particular year. When we use the average values from 1996 to 2004 as our dependent variables to

minimize this selection bias, the results are generally the same. Table 4 reports the results.

Table 4 Here

An interesting and somewhat surprising finding is that emigration brings positive effect on home

country’s political institutions. To check whether this finding is due to some idiosyncratic way of

collecting the data, we use a country’s democracy score and executive constraint score in 1990s from

Polity IV Project to check its robustness. The variable “Executive Constraint” refers to “the extent of

institutionalized constraints on the decision-making powers of chief executives, whether individuals or

collectivities” The variable “Democracy” reflects the extent to which “the three essential,

interdependent elements” are actually adhered to. From these definitions, we can see that these two

variables measure a country’s political institution. We use both the country’s average scores from 1991

to 2000 and the score of 2000 as our dependent variables in our baseline specification. Table 5 reports

the regression results. And the positive effects on political institutions hold as well.

Table 5 Here

We also check the results by adding additional independent variables such German Legal Origin,

Scandinavia Legal Origin, and the results show that the above pattern is fairly consistent. Domestic

human capital has a positive effect on institutional quality. Foreign human capital indeed has a positive

impact on home country’s political institutions, but a negative impact on economics institutions. The

net effect of moving a portion of tertiary educated people abroad generally has no significant influence

on a country’s political institutions, but significant damage to a country’s economic institutions.

4 Conclusions

Exploring the causal relationship between human capital and institutions is difficult. This paper jumps

this step to examine what impact skilled labor emigration has on institutions. This question is new and

intriguing since emigration is usually associated with a negative effect on source country’s economic

and political development. Hirschman’s “Exit and Voice” model provides us a departure in studying the

effect of emigration on home country’s institutional development. Djankov et al. put forward a

15

framework for us to break down the institutions into political and economic institutions corresponding

to “dictatorship” and “disorder” on the Institution Possibility Frontier (IPF). We are fortunate to find

that World Bank Governance data in some sense measure the political and economic institutions

respectively.

With the updated international emigration data sorted by education attainment, we regress ex post

institutional quality on ex ante skilled labor emigration, controlling for domestic human capital, GDP

per capita and some other factors. An immediate problem arises when trying to decide which direction

the relationship goes. To cope with it, we use country’s geographical characteristics to construct an

instrument. Our results show that domestic human capital contributes to institutional development.

Emigration’s effect differs with respect to different aspect of institutions: it has a positive effect on

home country’s political institutions but a negative effect on economics institutions. Represented in the

IPF diagram, emigration of human capital tends to shift the IPF outward and move the efficiency point

upward.

From another point of view, our results show that domestic human capital has a positive impact on

home country institutional building as when these people leave, they bring a net loss to home country’s

economics institutions. Though some migration economists find some positive evidence on the

economic effect of emigration, they did not take into account of the fact that emigration can bring

economic losses by retarding home country’s economic institutions. Our paper therefore provides

another perspective to comprehensively assess the economic impact of emigration on the sending

countries.

16

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19

Table 1 First stage regression result using geography components

Foreign Tertiary Educated 1990

Ln Area -0.0026c

(0.0014)

Island Dummy 0.4334a

(0.0980)

Ln Distance to US -0.0187a

(0.0066)

Island * Ln Distance to US -0.0366a

(0.0109)

Island * Ln Area -0.0067b

(0.0027)

Constant 0.2123a

(0.0617)

Sample Size 166

Adj. R-square 0.4753

Note: Standard deviation is in the parenthesis. Superscripts a, b and c denote the significance level of 1%, 5% and 10%,

respectively.

20

Table 2 Effect of Emigration on Institutions

VA2000 PS2000 GE2000 RQ2000 RL2000 CC2000

OLS IV OLS IV OLS IV OLS IV OLS IV OLS IV

Domestic Tertiary

Educated 1990

2.3539b

(1.0989) 3.1084a

(1.1296)0.8335

(1.2060) 0.8918

(1.2501)1.9052c

(1.0214)1.5900

(1.0571)2.3574b

(0.9420)2.2722b

(0.98) 2.0108b

(0.8517) 1.7991b

(0.8824) 2.4021b

(0.9639)2.2436b

(1.0013)

Foreign Tertiary

Educated 1990

3.8654a

(1.0982) 6.7405a

(1.6893)1.6243

(2.0492) 1.3825

(2.9258)-1.0344(1.0578)

-2.4378(1.6879)

-0.6951(0.9591)

-0.9878(1.5177)

-0.6140 (0.8662)

-1.5264 (1.3665)

-0.9593(0.9983)

-1.5826(1.5989)

Ln GDP per capita

1990

0.2910a

(0.0548) 0.2494a

(0.0572)0.3974a

(0.0598) 0.3955a

(0.0642)0.4273a

(0.0510)0.4454a

(0.0535)0.3211a

(0.0470)0.3253a

(0.0496)0.4755a

(0.0424) 0.4879a

(0.0447) 0.4529a

(0.0481)0.4613a

(0.0507)

Constant -2.3565a

(0.3521) -2.1701a

(0.3578)-2.9990a

(0.3808) -2.9847a

(0.3967)-3.2093a

(0.3279)-3.2878a

(0.3347)-2.3828a

(0.3020)-2.4015a

(0.3102)-3.5839a

(0.2728) -3.6385a

(0.2793) -3.4181a

(0.3094)-3.4547a

(0.3179)

Sample Size

148 148 128 128 146 146 147 147 147 147 146 146

Adj. R-square

0.4715 0.4832 0.4817 0.48 0.5854 0.5886 0.5302 0.5298 0.7146 0.7161 0.6548 0.6549

Coefficient difference

-1.5115 -3.6321 -0.7908 -0.4908 2.9396 4.0278 3.0524 3.2600 2.6248 3.3254 3.3614 3.8261

P Value of difference

0.2832 0.0343 0.7268 0.8632 0.0303 0.0182 0.0134 0.0333 0.0185 0.0162 0.0089 0.0179

21

Table 3 Further test of Effect of Emigration on Institutions

VA2000 PS2000 GE2000 RQ2000 RL2000 CC2000

OLS IV OLS IV OLS IV OLS IV OLS IV OLS IV

Domestic Tertiary Educated

1990

1.6838 (1.0302)

2.1486b

(1.0671) 0.5805

(1.2208)0.7202

(1.2739)1.2163

(1.0117)1.0791

(1.0607)1.5610c

(0.9336)1.4511

(0.9802) 1.5045c

(0.7946) 1.4782c

(0.8376)1.5979c

(0.9359)1.6515c

(0.9876)

Foreign Tertiary Educated

1990

2.7158b

(1.1556) 4.6740a

(1.7764) 1.5230

(2.1580)2.0331

(3.1071)-2.3496b

(1.1575)-2.7906(1.8775)

-2.1688a

(1.0577)-2.4649 (1.6883)

-1.9645 b

(0.9004) -1.8598(1.4385)

-2.2806b

(1.0708)-1.7076(1.7481)

Ln GDP per capita

1990

0.3101a

(0.0565) 0.2851a

(0.0583) 0.3408a

(0.0680)0.3332a

(0.0716)0.4531a

(0.0556)0.4611a

(0.0579)0.3346a

(0.0512)0.3412a

(0.0535) 0.4839a

(0.0436) 0.4863a

(0.0457)0.4882a

(0.0514)0.4868a

(0.0539)

Ethnolinguistic

Fractionalization

0.0483 (0.2261)

0.0497 (0.2232)

-0.4395 (0.2767)

-0.4331(0.2788)

-0.2988(0.2222)

-0.2545(0.2218)

-0.3763(0.2052)

-0.3331 (0.2050)

-0.2309 (0.1746)

-0.1804(0.1759)

-0.2159(0.2056)

-0.1446(0.2065)

Catholic 0.0024

(0.0023) 0.0018

(0.0023) -0.0021 (0.0029)

-0.0021(0.0029)

-0.0038c

(0.0023)-0.0033(0.0023)

0.0000(0.0021)

0.0004 (0.0021)

-0.0032c

(0.0018) -0.0030(0.0018)

-0.0051b

(0.0021)-0.0049b

(0.0022)

Muslim

-0.0036 (0.0025)

-0.0038 (0.0025)

-0.0021 (0.0030)

-0.0022(0.0030)

0.0001(0.0025)

0.0004(0.0025)

0.0013(0.0023)

0.0016 (0.0023)

0.0003 (0.0019)

0.0006(0.0020)

-0.0023(0.0023)

-0.0020(0.0023)

UK Legal Origin

-0.1484 (0.2013)

-0.1395 (0.1967)

-0.3077 (0.2362)

-0.2961(0.0233)

0.1421(0.1983)

0.0873(0.1957)

0.2089(0.1828)

0.1564 (0.1807)

0.1076 (0.1556)

0.0484(0.1544)

0.0930(0.1834)

0.0116(0.1822)

French Legal Origin

-0.3151 (0.2208)

-0.3041 (0.2190)

-0.3964 (0.2629)

-0.3960(0.2631)

-0.1353(0.2178)

-0.1613(0.2187)

-0.0418(0.2005)

-0.0653 (0.2015)

-0.2734 (0.1707)

-0.2975(0.1721)

-0.1206(0.2015)

-0.1509(0.2036)

Constant

-2.2199a

(0.4395) -2.0908a

(0.4434) -1.9784a

(0.5274)-1.9431a

(0.5377)-3.0269a

(0.4318)-3.0763a

(0.4405)-2.2902a

(0.3983)-2.3328a

(0.4070) -3.2612a

(0.3390) -3.2833a

(0.3478)-3.2192a

(0.3995)-3.2266a

(0.4101)

Sample Size

139 139 123 123 137 137 138 138 138 138 137 137

Adj. R-square 0.5656 0.5701 0.5024 0.5021 0.6263 0.6208 0.5584 0.5515 0.7671 0.7616 0.7049 0.6967

Coefficient Difference

-1.0321 -2.5254 -0.9425 -1.3129 3.5658 3.8697 3.7298 3.9159 3.4690 3.3380 3.8785 3.3591

P Value of difference

0.4577 0.1480 0.6855 0.6584 0.0113 0.0360 0.0038 0.0182 0.0016 0.0185 0.0030 0.0503

22

Table 4 Robustness Check with average values from 1996 to 2004 as dependent variable

Average VA 1996 - 2004

Average PS 1996 - 2004

Average GE 1996 - 2004

Average RQ 1996 - 2004

Average RL 1996 - 2004

Average CC 1996 - 2004

OLS IV OLS IV OLS IV OLS IV OLS IV OLS IV

Domestic Tertiary

Educated 1990

1.4818 (0.9309)

1.9248b

(0.9618) 0.1250

(1.1320) 0.1833

(1.1847)1.4630

(0.8507)1.2421

(0.8918)1.2827

(0.7918)1.1327

(0.8294)1.1452

(0.8073) 1.0386

(0.8456) 1.6584c

(0.8512)1.4255

(0.8873)

Foreign Tertiary Educated

1990

2.2679b

(1.0442) 4.1726a

(1.6011) 2.1126

(2.0011) 1.7749

(2.8896)-2.4653b

(0.9732)-3.3401b

(1.5786)-1.8518b

(0.8971)-2.3905c

(1.4244)-1.8215 (1.3568)

-1.9058 (1.8444)

-1.2221(1.6310)

-2.4490(2.1707)

Ln GDP per capita

1990

0.3324a

(0.0510) 0.3087a

(0.0525) 0.3432a

(0.0630) 0.3414a

(0.0666)0.4807a

(0.0467)0.4928a

(0.0487)0.3864a

(0.0434)0.3948a

(0.0453)0.4957a

(0.0450) 0.5002a

(0.0473) 0.5320a

(0.0490)0.5461a

(0.0513)

Ethnolinguistic Fractionalization

0.0568 (0.2043)

0.0670 (0.2012)

-0.1896 (0.2566)

-0.1974(0.2593)

-0.1629(0.1869)

-0.1280(0.1865)

-0.2380(0.1740)

-0.2099(0.1734)

-0.1546 (0.1831)

-0.1528 (0.1849)

0.0123(0.2135)

-0.0029(0.2122)

Catholic

0.0022 (0.0021)

0.0016 (0.0021)

-0.0022 (0.0027)

-0.0023(0.0028)

-0.0037(0.0019)

-0.0032(0.0021)

-0.0006(0.0018)

-0.0002(0.0018)

-0.0030 (0.0019)

-0.0029 (0.0019)

-0.0054a

(0.0021)-0.0052b

(0.0021)

Muslim

-0.0040c

(0.0023) -0.0043c

(0.0022) -0.0026 (0.0028)

-0.0028(0.0028)

-0.0001(0.0021)

0.0003(0.0021)

-0.0006(0.0019)

-0.0003(0.0019)

0.0002 (0.0020)

0.0003 (0.0170)

-0.0009(0.0023)

-0.0008(0.0022)

UK Legal Origin

-0.0891 (0.1819)

-0.0910 (0.1773)

-0.4360b

(0.2190) -0.4044c

(0.2170)0.0353

(0.1667)-0.0105(0.1646)

0.1519(0.1550)

0.1160(0.1529)

0.0394 (0.1562)

0.0162 (0.1543)

-0.0076(0.1703)

-0.0020(0.1669)

French Legal Origin

-0.2850 (0.1995)

-0.2786 (0.1974)

-0.4949b

(0.2438) -0.4856b

(0.2447)-0.2024(0.1832)

-0.2277(0.1839)

-0.0495(0.1701)

-0.0673(0.1705)

-0.2596 (0.1738)

-0.2659 (0.1743)

-0.1779(0.1879)

-0.1756(0.1863)

Constant -2.3826a

(0.3971) -2.2622a

(0.3996) -2.0027a

(0.4891) -1.9946a

(0.5001)-3.2388a

(0.3631)-3.3074a

(0.3704)-2.6938a

(0.3378)-2.7429a

(0.3444)-3.3686a

(0.3495) -3.3886a

(0.3563) -3.6702a

(0.3583)-3.7398a

(0.3932)

Sample Size

139 139 123 123 137 137 138 138 125 125 112 112

Adj. R-square 0.6259 0.6316 0.5128 0.5097 0.7269 0.7229 0.6774 0.6739 0.7775 0.7761 0.7848 0.7863

Coefficient Difference

-0.7860 -2.2478 -1.9876 -1.5915 3.9282 4.5822 3.1345 3.5232 2.9667 2.9444 2.8805 3.8745

P Value of difference

0.5312 0.1531 0.3578 0.5645 0.0010 0.0034 0.0042 0.0122 0.0442 0.0965 0.0986 0.0631

23

Table 5 Using Polity IV data as dependent variables

Executive Constraint 1990s

Democracy 1990s Executive

Constraint 2000Democracy 2000

OLS IV OLS IV OLS IV OLS IV

Domestic Tertiary Educated

1990

4.2849c

(2.5267) 4.3191c

(2.5617) 8.6508

(5.2113)8.5347

(5.2041) 4.4803c

(2.6201)4.4133

(2.6591)8.7408c

(5.0120)8.5347c

(5.0927)

Foreign Tertiary Educated

1990

6.0911 (5.1358)

6.9468 (7.5673)

14.0562(10.5686)

14.8267 (15.2853)

8.3408(5.3789)

7.8879(3.8034)

16.8554(10.2862)

14.8267 (14.9453)

Ln GDP per capita

1990

0.3621b

(0.1508) 0.3488b

(0.1547) 1.0260a

(0.3109)0.9736a

(0.3121) 0.4244a

(0.1459)0.4196a

(0.1498)0.9781a

(0.2791)0.9736a

(0.2869)

Ethnolinguistic

Fractionalization

-0.5943 (0.6685)

-0.6763 (0.6616)

-0.8969(1.3798)

0.2725 (0.3406)

0.0746(0.5939)

-0.0445(0.5892)

0.5290(1.1358)

0.2725 (1.1285)

Catholic 0.0067

(0.0060) 0.0064

(0.0060) 0.0182

(0.0124)0.0022

(0.0016) 0.0011

(0.0060)0.0007

(0.0011)0.0029

(0.0115)0.0022

(0.0117)

Muslim

-0.0138b

(0.0063) -0.0143b

(0.0063) -0.0275b

(0.1297)-0.0292b

(0.0132) -0.0135b

(0.0061)-0.0141b

(0.0061)-0.0278b

(0.0117)-0.0292b

(0.0117)

UK Legal Origin

-0.0882 (0.4861)

-0.0078 (0.4759)

0.1468 (0.9787)

-0.1242 (0.3549)

-0.3675(0.5013)

-0.2443(0.4934)

-0.3869(0.9586)

-0.1242 (0.9449)

French Legal Origin

-0.3984 (0.5383)

-0.4014 (0.5418)

-0.6140(0.1164)

-0.1288 (0.2342)

-0.2272(0.5423)

-0.2164(0.5471)

-0.1588(1.0371)

-0.1288 (1.0478)

Constant

2.3807b

(1.1630) 2.4848b

(1.1756) -1.7451(2.3905)

-1.6702 (2.6511)

1.9287c

(1.1039)1.9921c

(1.1192)-1.7780(2.1110)

-1.6702 (2.1435)

Sample Size

94 94 94 94 112 112 112 112

Adj. R-square 0.4853 0.4819 0.5482 0.5439 0.4282 0.4206 0.4747 0.4661

Coefficient Difference

-1.8061 -2.6277 -5.4054 -6.2921 -3.8606 -3.4746 -8.1146 -6.2921

P Value of difference

0.7374 0.7258 0.6269 0.6729 0.4955 0.6553 0.4539 0.6729

24