The Interdependence of Immigration Restrictions and Expropriation Risk

Lena Calahorrano; Philipp an de Meulen

doi:10.1515/bejeap-2013-0034

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The Interdependence of Immigration Restrictions and Expropriation Risk

Lena Calahorrano and Philipp an de Meulen

Published/Copyright: September 3, 2015

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From the journal The B.E. Journal of Economic Analysis & Policy Volume 15 Issue 4

Abstract

Factor price differences create economic incentives for migration to industrialized countries and for capital flows to developing countries. However, immigration restrictions and capital expropriation risks impede factor flows. Using a political-economy approach that takes into account different generations’ conflicting attitudes toward immigration and expropriation, we explore how these restrictions interact. Then, we run two separate country fixed regressions to explore the interdependence of policies empirically. The results from our theoretical model are borne out by the data: We find a positive relationship between emigration and foreign investors’ perceived security of property rights in developing countries and a negative relationship between the US foreign direct investment (FDI) outflows to developing countries and the share of US green cards granted to natives from the respective countries. Based on our analyses, we conclude that the key to lowering mobility barriers is not simply a quid pro quo.

Keywords: demographic change; foreign direct investment; migration; political economy

JEL Classification: D78; F21; F22; J10

1 Introduction

The importance of demographic structures for international migration and foreign direct investment (FDI) has aroused international interest. Factor movements are discussed not only as a means to realize efficiency gains but also as a driver for economic growth in developing countries and to secure pension systems in industrialized countries.^[1]

However, developing countries often do not offer the institutional framework for international investors to fully reap efficiency gains.^[2] In turn, governments of aging industrialized countries tend to be sensitive to native resentments toward the admission of immigrants. Observed international factor flows are indeed far too low to equalize the returns to capital and labor.^[3]

Even though research on the (political) impediments to international labor and capital flows is well established, the simultaneous consideration of migration and investment policies has so far been neglected. This is a gap in the literature which we aim to fill with our paper. Accounting for the interplay of policies provides new insights on the interdependence of restrictions to factor flows and on remedies to ease those restrictions.

We construct an integrated theoretical model to investigate this interplay of policies. We consider two open economies, each populated by two generations. While the majority of the population is young in the developing country, the reverse is true for the industrialized country. We assume policies to be determined by the respective median voter’s preferences in a one-period setting with sequential decisions. The government’s policy decision in the industrialized country is how many immigrants to admit, while in the developing country imported capital can either be expropriated or not.

We account for the fact that industrialized countries’ natives’ immigration preferences are driven by both income and non-income motives. In the static theoretical models by Benhabib (1996) and Mazza and van Winden (1996), capital owners support labor immigration while the working population does not. In dynamic settings, attitudes may be reversed if immigrants receive political rights, see, e.g., Dolmas and Huffman (2004) or Ortega (2010). In our model, old capital owners’ preferred level of immigration is limited because we account for the possibility that capital is invested abroad, but also because immigration entails a non-income disutility as in Calahorrano and Lorz (2011). For instance, natives may resent an increased heterogeneity of norms and customs, as in Hillman (2002), or immigration may reduce utility derived from public goods, as in Alesina and La Ferrara (2005). In fact, the empirical literature finds labor market, welfare state and cultural concerns to be the main determinants of immigration attitudes.^[4]

Concerning expropriation, we adhere to, e.g., Eaton and Gersovitz (1984), Azzimonti and Sarte (2007) or Harms and an de Meulen (2012) and assume that foreign investors withdraw their expertise after expropriation. While expropriated capital can still be used in production, this induces a drop in total factor productivity (TFP) and thus in the local wage level.^[5]

Our analysis is based on the notion that political decisions are influenced by heterogeneous interests within countries’ populations. Since the relative importance of labor and capital incomes changes over the life cycle, countries’ differential demographic structures do not only create incentives for factor flows but also affect political decisions on factor mobility constraints.

In our model, emigration paves the way to safer FDI: It makes expropriation less attractive for the developing country, since it increases the wage rate and therefore also the wage drop induced by expropriation, while reducing the returns to expropriated capital. However, the industrialized country admits less immigrants as FDI increases, since then, less labor is needed in the home economy. Due to this particular interdependence of policies, migration and FDI are both constrained in equilibrium.

Next, we investigate whether the suggested interdependence of policies stands up to empirical scrutiny. We first estimate the relationship between emigration from developing countries and the perceived security of property rights of foreign investors in those countries, based on the “investment profile” index from the International Country Risk Guide (ICRG) datasets. This analysis is closely related to Harms and an de Meulen (2013), who also use the “investment profile” index to investigate expropriation risk. Second, we focus on the United States as an industrialized country to investigate how FDI flows to various developing countries relate to the restrictions on immigration from those countries. Several recent papers analyze actual immigration policy decisions instead of voters’ attitudes, see, e.g., Miguet (2008), Facchini and Steinhardt (2011) or Hainmueller and Hangartner (2013). Our empirical approach is to use the share of US green cards issued to natives of different developing countries as a measure of immigration restrictions.

Our findings are in line with our theoretical model: the perceived security of property rights increases with emigration. Emigration thus eases restrictions on FDI. On the contrary, also in line with our theoretical model, the share of green cards issued to natives from any given developing country decreases as FDI increases in the respective country.

2 A Theoretical Model of Expropriation Risk and Immigration Restrictions

This section theoretically models factor flows between an industrialized country and a developing country in a political economy framework, where the respective median voters’ preferences determine policies. An extension to a setting with more than two countries^[6] would leave the main results unchanged. We explain the setup of the model in Section 2.1 and derive the equilibrium in Section 2.2.

2.1 Setup

We consider an industrialized country and a developing country, both populated by young and old individuals. Each young individual supplies one unit of labor, potentially in either country, while the old individuals are out of the labor force.^[7] Each old individual in the industrialized country owns a given amount of capital kˉ. The old in the developing country do not own any productive capital, only an endowment e∗ which they can consume, as in Cole and English (1991). This is a plausible assumption since financial institutions are rudimentary in many developing countries, and savings often take the form of tangible assets. It does not drive our results as we discuss below.

The size of the total population is normalized to one in both countries:

Ny+No=1andNy∗+No∗=1,

where the asterisk denotes the developing country’s variables. The old are in the majority in the industrialized country, while the opposite holds for the developing country, that is No>0.5 and No∗<0.5.

In both countries a homogeneous good is produced with a Cobb–Douglas production function:

Y=AKαL1−αandY∗=A˜(K∗)α(L∗)1−α.

The size of the capital stock owned by the old generation in the industrialized country is kˉ⋅No. Production in the developing country hinges on capital inflows from the industrialized country (K∗=kˉ⋅No−K) since the developing country’s inhabitants own no productive capital. FDI is administered by a mutual fund, which coordinates the individual investment decisions. We set the depreciation rate to zero for simplicity.^[8]

TFP in the industrialized country (A) exceeds TFP in the developing country. This results from a less favorable business climate (due to an inferior infrastructure, a higher regulatory burden, etc.). However, productivity in the developing country depends not only on these initial conditions but also on foreign investors’ capacity to cope with these conditions and on their technological expertise. Köse et al. (2010) argue that FDI goes along with transfers of technological expertise. Foreign takeovers have also been found to have positive effects on wage levels; see te Velde and Morrissey (2003) among others. We therefore assume that TFP A˜ exceeds the level the developing country would achieve without the foreign expertise (A∗):

A˜=1θA∗with0 < θ < 1.

Defining M as labor migration from the developing to the industrialized country, factor prices are given by

[1]w=(1−α)A(KNy+M)α , r=αA(KNy+M)α−1,w*=(1−α)A˜(K*Ny*−M)α and r*=αA˜(K*Ny*−M)α−1

in the industrialized country and the developing country, respectively.

Individuals’ utility is linear in consumption:

Ui=ci−d⋅MandUi∗=ci∗,i=y,o.

Whereas both FDI and migration affect consumption, immigration also causes a disutility d to all of the industrialized country’s citizens, proportional to the share of immigrants M. This disutility parameter captures potential welfare effects of immigration not accounted for in individual incomes in a tractable way. In the absence of such an assumption, immigration would not be constrained in equilibrium. However, despite large potential efficiency gains from liberal immigration policies, unrestricted migration flows are hardly observed.

Each country’s government sets policy to maximize the respective median voter’s utility. The developing country government decides whether to expropriate foreign capital. Expropriation refers to the withdrawal of the foreign capital stock, and, for simplicity, it is assumed to be always total. The developing country is thus subject to a classical time-inconsistency problem and would always expropriate if this did not entail any costs for its inhabitants. Then, no capital would flow there.

Since foreign investors lose control over invested capital as a consequence of expropriation, they will no longer provide their expertise. Hence, expropriated capital may still be used in production, but output is reduced since TFP drops to A∗. This enables us to model the capital levy problem, which is central to the political economy of expropriation, in a one-period setting.

The benefit from expropriation (the gross return to capital) is distributed equally among the developing country’s old and those young who have not emigrated. Each inhabitant of the developing country thus receives a transfer:

[2]t=T1−M=(1+θr∗)K∗1−M.

The developing country’s costs of expropriation do not affect both generations equally. Whereas the decline in output reduces the young generation’s wages, the old generation does not incur any cost from expropriation. Expropriation thus induces a distributional conflict along demographic lines.^[9]

The government of the industrialized country decides on the number of admitted immigrants. Immigration to the industrialized country affects its citizens’ welfare in two ways. First, it alters factor prices. The young generation clearly suffers since wages decline. The old generation benefits from increasing capital returns on the part of capital invested at home k and suffers from decreasing returns on that part invested in the foreign developing country k∗. Note that k and k∗ do not denote the capital intensities in production (K/L and K∗/L∗) but rather the capital used in home and foreign production per investor (K/No and K∗/No). Second, immigration causes a nonmonetary disutility as argued above.

Consumption levels are thus given by

cy=w,

co=k(1+r)+k∗(1+r∗)

and

cy∗={wincaseofemigrationw∗incaseofnon−expropriationθw∗+tincaseofexpropriation,

co∗={e∗incaseofnon−expropriatione∗+tincaseofexpropriation.

We assume the following sequence of events. First, the industrialized country’s old allocate their capital to both countries, and at the same time, the industrialized country’s government determines the immigration quota.^[10] Second, the developing country’s young migrate before third, the developing country’s government decides whether to expropriate the foreign-owned capital stock. Fourth, production and consumption take place. We solve the model by backward induction, starting with the expropriation decision.

2.2 Equilibrium Policy

We now determine equilibrium migration and FDI. We solve for the four equations determining the volume of individually optimal and politically determined factor flows, starting with the expropriation decision.

Note that high emigration from the developing country may change the identity of the median voter there from a young to an old individual, whereas the median voter in the industrialized country is always old. We therefore use the term young-median-voter equilibrium to refer to a situation where the majority in the developing country is young and the term old-median-voter equilibrium to refer to a situation where, due to high emigration, the majority in the developing country is old.

2.2.1 Non-expropriation Constraint

When deciding whether to expropriate, the developing country’s government faces given levels of capital inflows K∗ and migration M. We define the non-expropriation constraintK∗max as the level of FDI for which the median voter in the developing country is indifferent between expropriation and non-expropriation. If the median voter is an old individual (because of high emigration) any foreign capital will always be expropriated: Old individuals benefit from being transferred parts of the gross capital returns. Meanwhile, they are not affected negatively by the drop in TFP.

We call the threshold value of migration for which there remain as many old as young individuals in the developing country Mcrit, with Mcrit=Ny∗−No∗. If M≤Mcrit, the median voter in the developing country is young. The young who have not emigrated benefit from the transfer like the old, but additionally suffer from a reduced wage rate due to the drop in TFP. A young median voter weakly prefers non-expropriation if the transfer does not compensate for the wage loss:

(1−θ)w∗≥t.

Using eq. [2], this can be written as

[3](1−θ)w∗≥K∗1−M+K∗⋅θr∗1−M.

In eq. [3] we can identify three effects of capital inflows on the attractiveness of expropriation. The first one is a wage effect (1−θ)w∗: Larger capital inflows increase wages. This increases the wage loss from a TFP drop in the wake of expropriation. The effect on the transfer can be decomposed into an effect on the seizable capital stock K∗/(1−M) and an effect on the gross capital return K∗⋅θr∗/(1−M). Although the return rate declines, larger capital inflows raise the sum of gross capital returns.

Subtracting the return effect on both sides and inserting eq. [1] yield

1−θθA∗(1−α)−αA∗(Ny∗−M)/(1−M)Ny∗−Mα(K∗)α≥K∗1−M.

Note that the sign of the term in squared brackets on the left-hand side is independent of the level of FDI, K∗. As a necessary condition for positive FDI in any young-median-voter equilibrium, the term in squared brackets has to be positive. This is the case for sufficiently low θ:

[4]θ≤1−α(1−α)+αNy∗.

A low θ is equivalent to high TFP losses in case of expropriation. The wage effect then exceeds the return effect. Intuitively, if TFP in the capital receiving country were similar to that in the industrialized country, the expropriation-induced withdrawal of foreign expertise would not matter much. Then, expropriation would always pay, not only for the old but also for the young individuals there, and therefore no FDI would flow in at all. To rule out such a corner solution, we restrain our analysis to the case where eq. [4] holds.

Solving for K∗ yields

K∗≤(A∗)11−α1−θθ(1−α)(1−M)−α(Ny∗−M)Ny∗−Mα11−α.

Consequently, we can write the upper bound for capital inflows, the non-expropriation constraint, as

[5]K∗max={0ifM>Mcrit(A∗)11−α1−θθ(1−α)(1−M)−α(Ny∗−M)Ny∗−Mα11−αifM≤Mcrit,

for the case of an old and a young median voter, respectively. We can calculate the derivative of the young median voter’s non-expropriation constraint with respect to emigration as

[6]dK∗maxdM=K∗maxNy∗−Mα1−θθA∗α(1−M)Ny∗−M−1+αA∗.

The sign of this derivative is ambiguous. Emigration has three effects on the non-expropriation constraint. First, wages increase and so does the wage effect from expropriation. Second, capital returns decrease. Both make expropriation less attractive. However, third, the number or recipients of a possible transfer decreases, making expropriation more attractive:

d K ∗ m a x / d M > 0 holds for all M between 0 and Mcrit if

[7]θ>Ny∗−α(1+α)Ny∗−α.

Recall that a larger θ goes along with a higher risk of expropriation, thus lowering FDI inflows. Furthermore, the lower the FDI, the lower the transfer to be distributed in case of expropriation and thus, the lower the effect of emigration on the transfer. We assume eq. [7] to be fulfilled. Then the effect of emigration on the transfer is sufficiently small for emigration to go along with lower risk of expropriation, paving the way for additional FDI inflows. The non-expropriation constraint is represented by the red line in Figure 1.

As we outline below, the suggested positive effect of emigration on the critical level of FDI is sufficient for a unique equilibrium to exist in the case of a young median voter in the developing country. It is an empirical question whether emigration actually eases expropriation risk and how this effect depends on the relative development level of the capital receiving country. Our empirical investigation in Section 3.1 suggests that emigration does in fact ease expropriation risk in developing countries. Moreover, once we restrict our sample to the least developed countries, the positive relationship disappears, as suggested by eq. [7].^[11]

2.2.2 Emigration Constraint

Before the developing country government decides on expropriation, the developing country’s young take their migration decision for a given level of FDI and for a given immigration policy in the industrialized country. In the absence of any immigration restrictions they would migrate until wages in both countries are equal. This yields the emigration constraint:

[8]Mopt=(θA/A∗)1/α(kˉNo−K∗)Ny∗−K∗Ny(θA/A∗)1/α(kˉNo−K∗)+K∗.

Mopt is declining in the level of FDI since FDI reduces wage differences between both countries:

dMoptdK∗=−(θA/A∗)1/α(kˉNo−K∗)(Ny+Ny∗)(θA/A∗)1/α(kˉNo−K∗)+K∗2<0.

The emigration constraint is represented by the blue line in Figure 1. However, potential migrants have to obey the limit on immigration set by the industrialized country’s government, the immigration policy constraintMmax, which we derive in the next section. Whereas potential migrants know the level of FDI, the industrialized country’s government does not at the time of its immigration policy decision.

2.2.3 Immigration Policy Constraint

Immigration policy is set simultaneously to the investors’ allocation of capital. Immigration from the developing country raises the capital return on the part of capital invested in the industrialized country and decreases the capital return on the part invested in the developing country. Note that the foreign capital returns only accrue to the industrialized country’s investors if K∗≤K∗max. For any volume of FDI exceeding the non-expropriation compatible level, the impact of migration on foreign capital returns would therefore not be taken into account.

Maximizing the indirect utility function of the median voter, who is always an old individual, while assuming non-expropriation yields the following first-order condition for immigration policy for every value of K∗ between zero and kˉNo:

[9]kdrdM+k∗dr∗dM=d,

with

drdM=1−αNy+Mranddr∗dM=−1−αNy∗−Mr∗,

and d denoting the nonmonetary disutility related to a marginal increase in immigration. Equation [9] illustrates that immigrants are admitted as long as the marginal gain from immigration, k(dr/dM), outweighs the marginal cost, −k∗(dr∗/dM)+d.

The first-order condition can also be written as

[10]α(w−w∗)1−Ny=d.

For unrestricted migration Mopt, the wage rates in both countries are equal, and the left-hand side is zero. This would also be a solution for the immigration policy constraint if d=0, i.e., if there were no costs of integrating immigrants. For positive levels of d we can thus abstract from the emigration constraint, as equilibrium migration is always determined by the industrialized country’s policy.

Even though we cannot solve explicitly for Mmax (except for the case K∗=0), using the implicit function theorem, we can show that the derivative of the industrialized median voter’s preferred level of migration to FDI is negative:

[11]dMmaxdK∗=−rNy+M+r∗Ny∗−MKNy+M⋅rNy+M+K∗Ny∗−M⋅r∗Ny∗−M<0.

With larger FDI, investors place a higher weight on foreign capital returns. These become large for low levels of migration. Therefore, chosen immigration is a declining function of FDI, given by the black line in Figure 1.

Again, whether this relationship is valid is an empirical question, which we investigate in the next section. For the United States at least, our empirical analysis supports this theoretical finding.

2.2.4 Investment Constraint

At the same time with the immigration policy decision, the industrialized country’s old allocate their capital to both countries. In the absence of the possibility of expropriation, the industrialized country’s investors would export the share of capital necessary to equalize capital returns in both countries. The level of FDI for which this investment constraint binds, K∗opt, is given by

[12]K∗opt=(A/A˜)1α−1⋅kˉNo⋅(Ny∗−M)(Ny+M)+(A/A˜)1α−1⋅(Ny∗−M).

The difference in capital returns and thus the optimal level of FDI flows is lower the higher immigration:

dK∗optdM=−(A/A˜)1α−1⋅kˉNo⋅(Ny+M)(Ny+M)+(A/A˜)1α−1⋅(Ny∗−M)2<0.

The investment constraint is represented by the green line in Figure 1.

Actual FDI is given by the minimum of K∗opt and K∗max. This is straightforward if K∗opt<K∗max. If K∗max<K∗opt, in turn, no FDI exceeding the non-expropriation compatible level is an optimal choice since expropriated FDI yields no return to foreign investors. Nor is it optimal to reduce FDI below K∗max, foregoing high capital returns in the developing country. The assumption that investors’ capital is administered by a mutual fund solves the coordination problem between investors to ensure K∗<K∗max.

If the median voter in the developing country is old, the non-expropriation constraintK∗max equals zero and thus always binds. However, in case of a young median voter, K∗opt is not necessarily higher than K∗max since in the interval M<Mcrit the former is a decreasing and the latter an increasing function of migration, see Figure 1. In fact, the non-expropriation constraint binds for any M<Mcrit if we assume K∗max(Mcrit)<K∗opt(Mcrit).^[12] Our equilibrium is thus characterized by the two eqs. [5] and [9], the black line and the red line in Figure 1.

Figure 1 shows the two policy equations and the equations for individually optimal migration and FDI for α=0.3, A=1, A∗=0.6, θ=0.75, Ny=0.45, Ny∗=0.55 and d=0.12. In choosing these benchmark parameter values, we adhere to common assumptions in the literature.^[13] The disutility parameter d is, of course, rather arbitrary since we have not explicitly modeled immigration-related disutility. Since Mmax decreases with d, the two policy functions intersect in the range 0≤M≤Mcrit if d>dcrit, where dcrit is defined as the level of d such that K∗max and Mmax intersect exactly at Mcrit. While Figure 1 shows the case d>dcrit, we also discuss the case where d≤dcrit.

2.2.5 Equilibrium

Our political economy model can be summarized as a game between the industrialized country’s investors and the industrialized country’s government, subject to the non-expropriation constraint. As K∗opt>K∗max for all M, K∗max(M) is the investors’ best response to the government’s choice of immigration M. Given no expropriation, the government’s best response to any choice of FDI is given by the immigration policy constraintMmax|K∗. The intersection of best responses then determines a Nash equilibrium.

In an old-median-voter equilibrium, FDI is restricted to zero for any level of migration, and migration is thus determined by Mmax|K∗=0. Note that for an old-median-voter equilibrium to exist, Mmax|K∗=0 must not be smaller or equal to Mcrit. This is fulfilled if d is not too large, i.e., d<(1−α)αAkˉα/(2Ny∗+Ny−1)α⋅(1−Ny)1−α.

Being based on d>dcrit, Figure 1 illustrates the young-median-voter equilibrium, which is given by the intersection of Mmax and K∗max,young, the non-expropriation constraint for the young median voter. Given that dK∗max/dM>0, the two policy functions have one unique intersection point, which is the young-median-voter equilibrium if d>dcrit. For decreasing disutility levels d, the number of admitted migrants increases. If d becomes smaller than dcrit, the immigration policy constraint and the non-expropriation constraint intersect at some migration level larger than Mcrit. In this case, the area defined by M≤Mcrit and K∗≤K∗max(M) contains all feasible young-median-voter equilibria. Within this area, (Mcrit,K∗max(Mcrit)) is the point closest to utility maximizing K∗opt and Mmax. While investors would like to export more, the median voter favors admitting more immigrants. However, increasing FDI and admitting more immigrants would both lead to expropriation.

$Figure 1: Migration and FDI in equilibrium (d>dcrit$d \gt {d^{{\rm{crit}}}}$).$

Figure 1:

Migration and FDI in equilibrium (d>dcrit).

In summary, different equilibria are possible, depending on the disutility parameter d. For any d>0, the policy decisions determine equilibrium migration and FDI. If d is sufficiently large, the only feasible young-median-voter equilibrium is located at the intersection of the migration policy constraint and the developing country’s young median voter’s non-expropriation constraint. In such an equilibrium, FDI and migration are both positive but restricted by policy. While a key to lower expropriation risk lies in relaxing immigration barriers in industrialized countries, lowering expropriation risk does not relax immigration restrictions.

3 Empirical Evidence

Our theoretical model assesses the interdependence of policies that restrain capital flows to developing countries and migration to industrialized countries. For a given set of sensible parameter values, the model predicts a negative effect of migration on expropriation risk (that is a positive effect on FDI) but a positive effect of FDI on immigration restrictions (a negative effect on migration). This section therefore investigates whether the effects predicted by our model are actually in line with empirically observed patterns of politically determined migration and FDI. To do this, we have to run two different regressions, one for some measure of expropriation risk and one for some measure of immigration restrictions.

3.1 Emigration and the Risk of Expropriation

This section investigates how the “investment profile” index from ICRG by the Political Risk Services Group (2008) is related to emigration. The “investment profile” index reflects ratings of the security of foreign investments. It consists of three subcomponents, namely risk of expropriation or contract viability, payment delays and barriers on the repatriation of profits, each with a minimum score of zero points (very high risk) and a maximum score of four points (very low risk). Among developing countries there is a high negative correlation between the actual number of outright expropriations and the “investment profile” index, as Harms and an de Meulen (2013) show.

We include information on 68 developing countries and 5 periods, 1984–85, 1986–90, 1991–95, 1996–2000 and 2001–05. Countries are selected according to the World Bank Income Classification. In each period, the sample includes only countries that do not belong to the group of high-income countries. Our panel is thus unbalanced.

As our key explanatory variable we use emigration rates, defined as emigrants to the six major Organisation for Economic Cooperation and Development (OECD) countries, that is Australia, Canada, France, Germany, the United Kingdom and the United States,^[14] divided by the sum of residents and emigrants aged above 25 years from Defoort and Rogers (2008). Emigration rates are available every 5 years. To mitigate potential problems of reverse causality we run our regressions for the “investment profile” indices in 1984–85, 1986–90, etc. on the emigration rates in 1980, 1985, etc.

We further include variables measuring institutional quality as well as educational, economic and demographic variables. All of these are likely both to have an impact on expropriation risk and to be correlated with emigration. Summary statistics for all variables can be found in the Appendix. The control variables are measured in 5-year averages, except for our demographic variable, which, similarly to emigration rates, is measured in initial values.

Regarding institutional quality, we use another variable from the ICRG dataset, namely the “law and order” index, which measures the strength of the legal system. The law subcomponent is an assessment of the strength and impartiality of the legal system, while the order subcomponent is an assessment of popular observance of the law. Both subcomponents comprise zero (low quality) to three points (high quality). We expect this index to be positively correlated with the “investment profile” index since a strong and independent legal system can protect foreign investors’ property rights even if the executive is willing to expropriate. In a robustness check, we also include a measure of political repression, the “political rights” index by Freedom House (2009). It measures the degree of freedom in the electoral process, political pluralism and participation, and the functioning of government is rated on a scale of 1 (most free) to 7 (least free). Political repression reduces a government’s accountability and may thereby increase expropriation risk, see, e.g., Li and Resnick (2003) or Jensen (2003). However, among democracies, a re-election seeking government may find it worthwhile to enact redistributive expropriation, if this is in the voters’ interest, see, e.g., Wells (1998).

At the same time, bad institutional quality is likely to enhance emigration, and emigration may also have a repercussion on institutional quality: Emigration facilitates the spillover of foreign norms and values, and emigrants from countries with bad institutional quality may become politically active in order to change institutions in their home countries. Finally, Docquier et al. (2010) argue that the threat of skilled emigration may set elites under pressure to reform.

To measure education, we recur to the average years of schooling of individuals above 25 years from the Barro and Lee (2001) dataset. Skilled workers in developing countries are more likely to be employed by foreign firms, and, since foreign firms tend to pay higher wages to skilled workers, see, for instance, te Velde and Morrissey (2003) or Görg and Girma (2007), these are more likely to oppose emigration. At the same time, higher education levels facilitate labor emigration.

We further add several economic variables. In our baseline specification, we use the logarithm of real gross domestic product (GDP) per capita taken from the Penn World Table (2009). Economic well-being may affect expropriation risk in several ways. As Picht and Stüven (1991) argue, it may protect foreign firms from expropriation out of public discontent or even desperation. In turn, the state of the economy may affect profits in the private sector and, as in our theoretical model, thereby affect incentives for expropriation, see also Tomz and Wright (2008). At the same time, workers’ emigration decisions are clearly affected by the overall economic situation in their home country, whereas emigration and emigrants’ remittances (see Rapoport and Docquier 2006) have repercussions on home countries, too.

In a robustness check, we add countries’ real GDP growth, also taken from the Penn World Table (2009), as well as the logarithm of countries’ consumer price inflation rates, taken from the World Bank (2009). Since insecure property rights hamper growth and development, see, e.g., Hall and Jones (1999) or Rodrik, Subramanian, and Trebbi (2004), we use lagged values of GDP per capita and of GDP growth to mitigate reverse causality problems.

Finally, we include countries’ integration into international trade into our baseline specification. It is measured by the World Bank (2009) as the sum of exports and imports of goods and services relative to GDP,^[15] again lagged to account for possible reverse causality. More open economies suffer higher losses from expropriating foreign investors if this triggers an economic isolation as in Cole and English (1991), Thomas and Worrall (1994), Aguiar, Amador, and Gopinath (2009) and Aguiar and Amador (2011). Meanwhile, emigration promotes trade, see, e.g., Felbermayr and Toubal (2012): Immigrants may build a bridge when it comes to cultural and linguistic boundaries between their home country and the country they emigrated to. Moreover, if migrants have home-biased preferences, this may promote imports from their home countries.

A last variable that we include is countries’ population share of persons aged 15–39 from the United Nations Population Division (2008). As in our theoretical model, younger individuals in developing countries are more likely to benefit from the presence of foreign-owned firms. This is because they may find work there and also gain higher wages than in domestically owned firms. The share of young workers also affects the local wage level and foreign investors’ capital returns and thereby the costs and benefits of expropriation. At the same time, younger individuals are more likely to migrate than older individuals.

Based on this set of control variables, we estimate the following equation:

[13]investmentprofileit=β0+β1⋅emigrationrateit+∑j=2kβj⋅xj,it+ξt+εit,

where the indices i and t denote the country and the time period, respectively and the xj denote the set of control variables, presented above. Equation [13] is estimated by pooled ordinary least squares (OLS) as well as using country fixed effects to account for unobserved heterogeneity. Standard errors are calculated based on a robust covariance matrix of the error term to control for heteroskedasticity and serial correlation at the country level.

Table 1 shows our regression results. The first two columns show the results of the OLS and fixed effects estimations using our baseline specification. The estimated effect of the emigration rate from the respective developing country is insignificant according to the baseline OLS regression, but it turns significant when country fixed effects are included to control for unobserved heterogeneity between countries. Ceteris paribus, the “investment profile” index is thus higher (and emigration risk is lower) in times of high emigration.

Table 1:

Regression results for the “investment profile” index.

OLS, fixed effects, IV and dynamic panel estimation
Investment profile	Baseline specification		Demographic structure		Additional controls		Alternative estimators
	OLS	Fixed effects	OLS	Fixed effects	OLS	Fixed effects	Fixed effects IV	A r e l l a n o − B o n d GMM
Emigration rate	2.305	17.160***	2.304*	17.848***	1.017	18.039**	21.470***	40.498***
	[1.384]	[5.743]	[1.382]	[5.528]	[1.388]	[6.896]	[6.793]	[13.631]
Law and order	0.397***	0.291**	0.396***	0.323**	0.223***	0.166	0.323***	0.500
	[0.099]	[0.123]	[0.098]	[0.128]	[0.084]	[0.117]	[0.114]	[0.301]
Schooling	0.124*	–0.067	0.122*	0.122	0.128**	0.032	0.137	0.850
	[0.069]	[0.209]	[0.067]	[0.237]	[0.060]	[0.160]	[0.218]	[0.943]
GDP per capita (–1)	0.110	–0.478	0.098	–0.521	–0.083	–0.066	–0.483	–2.994**
	[0.211]	[0.552]	[0.219]	[0.582]	[0.177]	[0.526]	[0.400]	[1.272]
Openness (–1)	0.298	0.853	0.294	0.796	0.183	0.162	0.770	3.935**
	[0.284]	[0.733]	[0.281]	[0.771]	[0.313]	[0.611]	[0.620]	[1.768]
Young population share			1.318	–13.935			–14.195*	–1.021
			[3.749]	[9.975]			[7.451]	[30.518]
Political repression					–0.217***	–0.238***
					[0.059]	[0.084]
GDP growth (–1)					0.104***	0.074**
					[0.024]	[0.029]
Inflation					–0.320***	–0.416***
					[0.082]	[0.121]
Investment profile (–1)								0.218
								[0.168]
Constant	2.978*	10.174**	2.584	15.143**	7.545***	8.575**	14.726***
	[1.527]	[4.459]	[1.745]	[5.899]	[1.274]	[4.179]	[4.156]
Hansen J test								[0.566]
AR(2) test								[0.400]
R ²	0.447	0.530	0.445	0.535	0.563	0.613
Observations	318	318	318	318	305	305	314	180

^[*]

This result is in line with the prediction from our theoretical model, where the mechanism driving this effect was the following: If foreign capital is expropriated, the gross returns to capital are distributed among the country’s inhabitants. With higher emigration, there are fewer recipients, making expropriation more attractive. As a counter-effect, however, the distributed capital returns decline. Moreover, the withdrawal of foreign expertise triggers a wage drop in the face of expropriation, which is stronger the larger the wage rate and thus the larger the outflow of labor.

The significance of the estimated effect is unchanged by the inclusion of our indicator of developing countries’ demographic structure and of additional controls in the next four columns.

Since we cannot rule out that expropriation risk actually causes emigration and not vice versa, we carry out two additional robustness checks. First, we apply an instrumental variable (IV) estimator, using countries’ lagged emigration rates as an instrument for the contemporaneous ones. Second, to take account of the possibility that the “investment profile” index is persistent over time, we add its lagged values to our set of explanatory variables. In such a dynamic panel model, the fixed effects estimator is inconsistent, however. Therefore, we make use of the dynamic panel estimator by Arellano and Bond (1991), which first differences the left- and the right-hand sides of eq. [13], eliminating the country fixed effects. This generalized method of moments (GMM) estimator then uses lagged levels of the (potentially endogenous) explanatory variables as instruments for the first differenced ones.

The penultimate column shows the result of the static fixed effects IV estimation, while the last column presents the results of the dynamic panel estimation. In both estimations we include our main explanatory variables, that is the baseline set augmented by countries’ population share of persons aged 15–39. The relationship between emigration and expropriation risk predicted by our model also holds under this more sophisticated approach.

3.2 FDI and the Granting of Green Cards in the United States

This section investigates the second prediction of our theoretical model that industrialized countries tighten the restrictions on immigration from developing countries in the face of higher FDI in those countries. An (inverse) measure of immigration restrictions by country of origin is available in the United States: The US Department of Homeland Security (2010) provides information on the number of granted green cards by immigrants’ country of birth for the years 2001–2010. We use the share of total green cards granted to immigrants from the respective country as our dependent variable. Again, we adhere to the World Bank Income Classification to select the sample of countries. In each year, the sample includes only non-high-income countries, 60 in total.

Our key explanatory variable is the size of the US FDI positions in the 60 developing countries. It is taken from the US Bureau of Economic Analysis (2014) and measured in US dollars on a historical cost basis.

To rule out endogeneity problems that go along with omitting variables which are both related to US immigration restrictions and US FDI activity we add several controls. Immigration policies are primarily driven by voters’ attitudes, see, for instance, Facchini and Mayda (2010). Attitudes, in turn, are contingent both on voters’ and on immigrants’ characteristics. We fully control for any kind of US-specific variables in general and US-voter-specific variables in particular by using time dummies. Furthermore, we include developing country fixed effects to control for unobserved differences between developing countries or developing countries’ populations that are time invariant.

As controls, we include the US exports to and imports from the developing countries, taken from the US Department of Commerce, Bureau of the Census, Foreign Trade Division (2014), as a share of US GDP. Mundell (1957) and the models by Heckscher, Ohlin and Samuelson as well as Stolper and Samuelson suggest substitutability between trade and factor mobility, while the works by, e.g., Markusen (1983) and Wong (1986) extend the Heckscher–Ohlin–Samuelson framework to show that there can be complementarity between trade and factor flows.

We also use the number of workers with tertiary education in the various developing countries, recurring to the World Bank (2014). In the previous section, we already argued that education levels in developing countries are likely to be related to both FDI and migration. This clearly holds for the granting of green cards as well: Although there are several ways to obtain a green card, workers with advanced degrees are generally preferred.

Further characteristics of the developing countries, relating to their demographics, their economic well-being and the quality of their institutions, certainly determine the supply of migrants from those countries, but are less likely to determine demand, reflected by the number of granted green cards. Therefore, we only include such indicators in a robustness check.

There we use the size of the total population in the country of origin as a measure of the potential supply of green card applicants. Further, we use the share of the working-age population (aged 15–64). A larger relative number of young workers affect foreign investors’ capital returns and thus their investment decisions. At the same time, it increases labor market competition, which may lead young people to emigrate. As a macroeconomic variable we include real GDP per capita in constant 2005 dollars. Emigration is often the result of gloomy economic prospects. At the same time a country’s development affects capital returns of foreign investors. All those variables are taken from the World Bank (2014).

Furthermore, we include two variables from Freedom House (2009), the “political rights” index, see Section 3.1, and an (inverse) index of civil liberties. The latter measures the degree of freedom of expression and belief, associational and organizational rights and rule of law. A lack of political and civil freedom may lead people to leave their home countries. At the same time it affects the business climate foreign investors face. Descriptive statistics for all variables can be found in the Appendix.

We estimate the following regression equation:

[14]Greencardsit=γ0+γ1⋅USFDIit+∑j=2kγj⋅xj,it+τt+μit,

where, again, the indices i and t denote immigrants’ country of origin and the time period, respectively, and the xj denote the set of control variables. Equation [14] is estimated by pooled OLS as well as using developing country fixed effects. Again, standard errors are calculated based on a robust covariance matrix of the error term.

Table 2 shows the regression results. The first two columns include only US exports and imports as a share of GDP and the number of workers with tertiary education in addition to US FDI in the respective country as explanatory variables. The third and fourth columns include the full set of developing country controls. We first estimate both the baseline and the augmented regression equation using OLS, then using country fixed effects.

Table 2:

Regression results for the granting of US green cards.

OLS, fixed effects and dynamic panel estimation
Share of green cards	Baseline specification		Additional controls		Dynamic panel estimation
	OLS	Fixed effects	OLS	Fixed effects	Arellano–Bond GMM
US FDI1	–0.639**	–1.243***	–0.643**	–1.257***	–1.835**
	[0.308]	[0.366]	[0.311]	[0.366]	[0.743]
US exports/GDP	24.067***	18.902***	25.660***	19.150***	31.026***
	[5.512]	[4.564]	[5.775]	[5.220]	[5.917]
US imports/GDP	–1.101	–3.676	–2.217	–3.095	–7.694
	[3.009]	[2.887]	[3.414]	[2.968]	[6.330]
Educated workers¹	0.001	0.182*	–0.217	0.189*	0.365
	[0.042]	[0.096]	[0.397]	[0.099]	[0.249]
Population (total)²			0.041	–0.013
			[0.072]	[0.109]
Working age population share			–0.008	0.015
			[0.035]	[0.033]
GDP per capita¹			–0.028	0.318
			[0.106]	[0.424]
Political repression³			–0.073	–0.043
			[0.127]	[0.045]
Lack of civil liberties³			0.249	–0.135
			[0.203]	[0.092]
Share of green cards (–1)					–0.205
					[0.270]
Constant	0.003	0.010***	0.002	0.003
	[0.002]	[0.002]	[0.024]	[0.024]
Hansen J test					[0.357]
AR(2) test					[0.313]
R ²	0.875	0.500	0.880	0.503
Observations	266	266	265	265	179

^[*] ^[1]

The estimated effect of the US direct investment in any developing country on the share of green cards issued to natives from that country is significant and negative even in the OLS regressions. Its statistical significance increases when unobserved heterogeneity between countries is accounted for. Ceteris paribus, the United States thus grants fewer green cards to natives from any particular country as it invests more in that country.

This is in line with the prediction of our theoretical model, where the mechanism driving this effect was the following: In capital-rich and relatively “old” host countries like the United States, immigration has a positive effect on domestic capital returns, while it negatively affects capital returns in the migration source country. The more capital from the United States is invested in that country, the more important becomes the latter effect, and the stronger the opposition to immigration from there.

This finding is robust to the inclusion of additional controls. The fact that the developing country variables are hardly significant suggests that the shares of green cards granted are actually demand and not supply driven.

According to Hainmueller and Hangartner (2013) and Markaki and Longhi (2013), immigration restrictions exhibit potentially negative autocorrelation as a result of saturation. Meanwhile, due to network effects (see, e.g., Docquier et al. 2014), the supply of green card applicants from a certain country may exhibit positive autocorrelation. Hence, we include the lagged share of green cards as an additional independent variable and employ the dynamic panel estimator introduced by Arellano and Bond (1991). As argued in the last section, by first differencing eq. [14], the fixed effects disappear and the lagged levels of the explanatory variables are taken as instruments for the first differenced ones. Results for this robustness checks are shown in the last column of Table 2. The US FDI is still estimated to have a significant effect on the share of green cards granted. The second prediction of our theoretical model, a tightening of immigration restrictions concerning developing countries in the face of higher FDI in developing countries, is thus also borne out by the data.

4 Conclusion

This contribution has shed light on the interdependence of politically induced barriers to factor flows, both empirically and theoretically. The novel feature of our theoretical approach is the modeling of the interplay of policies in limiting factor flows between industrialized and developing countries.

We have set up a one-period model of two countries with heterogeneous agents, young and old. Accounting for international demographic differences, we have assumed an old median voter in the industrialized country but a young median voter in the developing country.

In equilibrium, factor flows are politically restricted, leaving room for efficiency gains from removing mobility barriers. Relaxing immigration restrictions may also relax restrictions on FDI in the form of expropriation risk. On the contrary, improving foreign investors’ property rights in developing countries does not help developing countries’ natives in gaining access to industrialized countries’ labor markets. The latter finding may also be interpreted differently: Improving property rights does help developing countries in preventing a brain drain.

We subjected both these findings to empirical scrutiny. We used an index of the perceived security of foreign investors’ property rights as an inverse measure for expropriation risk in developing countries and investigated how this index is related to emigration rates. We found emigration to have a robust positive effect on perceived property rights, as expected from our theoretical model. For developing country policymakers (who may decide to expropriate foreign capital) emigration and FDI inflows thus seem to be complements.

To investigate the effect of FDI outflows on industrialized country immigration restrictions, we used the share of granted US green cards differentiated by nationality. We found FDI outflows to have a robust negative effect on the share of green cards issued to natives of the FDI destination countries. FDI and immigration thus seem to be substitutes for US policymakers, as in our theoretical model and as would be expected from a purely economic model.

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Appendix

Table 3:

Emigration and the risk of expropriation: country list.

Algeria, Argentina, Bangladesh, Bolivia, Botswana, Brazil, Bulgaria, Cameroon, Chile, Colombia, Costa Rica, Czech Republic, Democratic Republic of Congo, Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia, Gambia, Ghana, Greece, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Islamic Republic of Iran, Ireland, Israel, Jamaica, Jordan, Kenya, Liberia, Malawi, Malaysia, Mali, Mexico, Mozambique, Nicaragua, Niger, Pakistan, Panama, Papua New Guinea, Paraguay, People’s Republic of China, Peru, Philippines, Poland, Portugal, Republic of Congo, Romania, Russia, Senegal, Sierra Leone, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sudan, Syrian Arab Republic, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Uganda, Uruguay, Venezuela, Zambia, Zimbabwe

Table 4:

Emigration and the risk of expropriation: summary statistics.

Variable	Mean	Overall std. dev.	Min.	Max.	Between-country std. dev.	No. of observations
Emigration rate¹	2.91	5.69	0.02	43.68	5.95	473
GDP growth (–1)¹	1.09	4.45	–30.60	24.95	3.11	463
GDP per capita (–1)	7.95	0.90	5.56	9.69	0.91	472
Inflation	2.33	1.31	–0.84	7.99	0.96	450
Investment profile	6.29	2.00	1.33	11.81	1.40	417
Law and order	3.09	1.20	0.57	6	1.04	417
Openness (–1)	0.66	0.34	0.11	2.05	0.35	481
Political repression	4.19	1.93	1	7	1.79	485
Schooling	4.22	2.34	0.37	10.52	2.50	365
Young population share¹	39.07	2.74	34.16	49.01	2.55	496

^[1]

Table 5:

FDI and the granting of green cards: country list.

Albania, Algeria, Argentina, Armenia, Azerbaijan, Belarus, Bolivia, Bosnia-Herzegovina, Brazil, Bulgaria, Cambodia, Chile, Colombia, Costa Rica, Croatia, Cuba, Czech Republic, Dominican Republic, Ecuador, Egypt, Estonia, Georgia, Guatemala, Guyana, India, Indonesia, Islamic Republic of Iran, Jordan, Kazakhstan, Latvia, Lebanon, Macedonia, Madagascar, Mauritius, Mexico, Montenegro, Morocco, Namibia, Nicaragua, Niger, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russia, Saudi Arabia, Serbia and Montenegro, Slovakia, South Africa, Sri Lanka, Syrian Arab Republic, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uruguay, Venezuela

Table 6:

FDI and the granting of green cards: summary statistics.

Variable	Mean	Overall std. dev.	Min.	Max.	Between-country std. dev.	No. of observations
GDP per capita	4,661.05	8,460.48	118.64	51,001.54	8,792.01	1,088
Lack of civil liberties	3.96	1.60	1	7	1.60	1,137
Political repression	4.16	1.99	1	7	1.96	1,137
Educated workers³	7.02	23.99	0	234	28.80	290
Population (total)¹	39.58	126.77	1.02	1337.71	124.50	1,127
Share of green cards²	0.72	1.86	0	20.66	1.80	1,131
US FDI	3,259.38	9,899.26	–1,344	91,046	8,443.55	916
US exports/GDP⁴	214.90	967.60	0.02	10,941.40	936.90	1,126
US imports/GDP⁴	468.30	2,160.20	0	24,398	2,065.80	1,126
Working age population share2	60.26	6.66	47.59	73.51	6.64	1,107

^[1]

Published Online: 2015-09-03

Published in Print: 2015-10-01

Articles in the same Issue

https://doi.org/10.1515/bejeap-2013-0034

Keywords for this article

demographic change; foreign direct investment; migration; political economy