Skilled migration and FDI:Countries’ characteristics or firms’ activity?

Ana Cuadros1,2, Joan Martın-Montaner1,2, and Jordi Paniagua∗3

1Universitat Jaume I2Institute of International Economics

3Catholic University of Valencia

29th February 2016

Abstract

We use bilateral data between 25 developed host countries and 92 homecountries to show the role played by migrants as a determinant of foreigndirect investment (FDI). We build a simple formal model that explains thedifferential effect of migrants on FDI as a result of the different skills requiredby the activity of the investor and the host country’s characteristics. Themodel predicts that the impact on FDI of skilled migrants is higher than lowskilled in countries with higher wage-migration elasticity (i.e., developed hostcountries) and for firms whose activities require high skilled labor. Gravityestimates reveal that skill level of migrants is relevant for both intensive andextensive margins of greenfield FDI. This paper is unique in showing differencesin the effect of migration skills within and across FDI activities (manufacturing,sales, construction and services).

Keywords: skilled migration, unskilled migration, FDIJEL Classification: F20, F21, F23

∗Corresponding author jordi.paniagua@ucv.es

1 Introduction

International migration has rapidly grown and changed in composition in terms

of educational attainment in the last few decades. According to Arslan et al. (2014),

emigration rates of highly educated are higher than total emigration rates in all

regions. In 2010/2011, there were about 35 million migrants with tertiary education

in the OECD. This level represents an unprecedented increase of 70% over the past

ten years. International trade and capital flows have also considerably increased

during the last decades. Most research has been concentrated on analyzing the

linkages between migration and trade in spite of the fact that capital flows and

particularly foreign direct investment (FDI) have grown faster.

Previous empirical studies highlight that migrants have an informational advant-

age and strong family and cultural ties to their homeland. Migrants can help poten-

tial investors by providing information about their origin country which may reduce

the transaction costs of investment. Migrants may also boost foreign investment by

sharing expertise on regulation, customs and procedures. It can also happen that

people living abroad demand product or services from their home country and com-

panies try to satisfy these needs by investing abroad. Companies may also bring their

executives from the home country to ensure that the subsidiary maintains quality

and product standards or it may employ fellow countrymen who are living or have

lived abroad (see Buch et al. (2006); Murat and Pistoresi (2009); Tong (2005); Bhat-

tacharya and Groznik (2008), among others).

The increase in the level of education of migrants seems to be a key factor for mul-

tinational investment decisions. Well-educated individuals from a certain ethnicity

1

possess specialized knowledge about how to conduct business associated with that

ethnicity. They are also likely to have language skills and cultural sensitivity that

would promote collaboration with business developers in host countries. Skilled mi-

grants are likely to have a strong understanding of customer behavior and to provide

insights about the type of products that would face higher level of demand (Foley

and Kerr, 2013). This type of migrants may be personally involved in investments

from their country of origin boosting capital flows.

The relevance of both migrants as well as their educational attainment as determ-

inants of FDI may however vary across sectors. On the one hand, the determinants

of foreign entry decisions seem to be different for service and manufacturing firms.

Foreign investment in the service sector can be more sensitive to institutional and

informational issues. Some services (as management or finance) may require higher

level of information or greater interaction with customers (see Kolstad and Villanger

2008). On the other hand, different types of FDI may require different types of skills

according to their main activity (extractive industries, manufactures or provision of

services (see Checchi et al., 2007).

Our paper builds further on the above premises and follow the mainstream of

the migration-FDI literature by focusing on both factors (migration and investment)

flowing in the same direction. That is, from the migrants’ homeland (which is also

the home country of investment) to the migrant’s country of residence (which is also

the host country of investment). However, we go deeper in the traditional analysis

as we control not only by the skill level of migrants but also by the activity of foreign

firms. By doing that we offer new insights about a crucial aspect omitted in most

2

previous analysis: the underlying mechanisms through which migrants may affect

FDI.

The contributions of this paper are threefold. Firstly, we provide additional

evidence regarding the effects of the changing composition of migrants in terms of

their educational attainment on the migration-FDI link. Secondly, we offer some

useful insights regarding the relevance of the activities of investment. This two

aspects are interconnected as different types of FDI may require different skill levels.

Finally, we go deeper into the analysis of the underlying mechanisms through which

the informational advantage of migrants may affect investments decisions: Migrants

living in the host countries mitigate the transaction costs of inputs or services. We

build on the idea of headquarter services (Helpman, 1984) to develop a model in

which migrant’s reduce the costs of inputs. The relative effect of high and low skilled

migrants depends on the country’s skill composition as well as the firm activity. The

model predicts that in northern countries and high-skilled activities the effect of

skilled migration is starker than low-skilled.

Our results confirm that migrants from country i (home land for both migrants

and investment) living in country j (host land for both migrants and investment) have

a positive and significant effect on bilateral FDI targeting their residence country (for

both intensive and extensive margins). This effect is stronger for high skilled migrants

and for the service activity.

The remainder of the paper is as follows. Section 2 summarizes the existing

literature. Section 3 presents the theoretical framework. Section 4 explains the data

and empirical methodology. Section 5 discusses results. Finally, section 6 concludes.

3

2 Background

As stated by recent approaches based on the theory of networks, migrants can

exert positive effects on both bilateral trade and FDI. Although the relationship

between migration and trade has been extensively analyzed (Gould, 1994), the evid-

ence about the migration-FDI link is relatively scarce. However, FDI implies a

long-term investment and requires a wider variety of information about the legal

framework and business structure in the host country. Migrants may provide in-

formation to foreign investors that may otherwise be difficult or costly to obtain.

Thus, migrant networks seem to matter more for bilateral FDI than for trade (Ja-

vorcik et al., 2011; Tong, 2005). In fact, as stated by Aubry et al. (2014), although

migration affects both trade and FDI (in terms of both the extensive and intensive

margins), the effect on FDI is greater.

Most previous studies have reported evidence showing that migrants may help

companies to identify business opportunities in the host county of investment as they

possess specific knowledge about how to conduct business in countries associated with

their ethnicity (Foley and Kerr, 2013).. They have the language, culture, values and

practices of their home country. Thus, by acting as an information-revealing network,

migrants may stimulate foreign investment (Federici and Giannetti, 2010). Recently,

Burchardi et al. (2016) highlight that migration, and the ethnic diversity resulting

from it, affects the pattern of international investment in a quantitatively important

way. These authors define the ancestry composition of US counties as the presence

in the United States of descendants of foreign migrants. According to their results,

a doubling of the number of residents with ancestry from a given foreign country

4

increases the probability that at least one local firm invests in that country. Cuadros

et al. (2015) find a link between migration, credit constraints and FDI. Their results

highlight that migrations aids small projects hedge financial constraints.

In principle, both skilled and unskilled migration can provide information to fa-

cilitate FDI. However, due to the complexity of investment operations abroad (when

we compare with trade transactions) skilled immigrants are expected to have a higher

impact than low skilled for both outward and inward FDI since they bring with them

greater information and influence. Also, a skilled labor force is a key determinant

of FDI inflows as it contributes to the capacity of host countries to adopt new tech-

nologies (Blomstrom et al., 2001; Borensztein et al., 1998). As recently highlighted

by De Smet (2013) the skilled immigration regime relevant for FDI across 93 eco-

nomies. Results obtained indicate that a less restrictive skilled immigration regime

is conducive in attracting FDI.

Some previous case-studies provide evidence in favor of the relevance of account-

ing by the skill level of migrants when analyzing the migration-FDI link. Javorcik

et al. (2011) focus on one of the most ethnically diverse nations on the planet (United

States) and conclude that the presence of migrants in the US increases the volume of

US outward FDI in the migrant’s country of origin. However, this effect appears to

be stronger for migrants with tertiary education. Foad (2012) looks at the regional

distribution of FDI and immigration within the United States confirming that growth

in the relative presence of an immigrant community leads to new FDI from those

immigrants’ native countries. This effect takes a few years and it is more important

for more educated migrant communities. The United Kingdom case, as one of the

5

most important destinations for migration, has also been analyzed by Gheasi et al.

(2013)). These authors conclude that UK FDI abroad is positively related with the

presence of migrants from a particular country in the UK. This study also reveals

that the more educated migrants from a certain country are, the stronger positive

effect they have on both inward and outward UK FDI.

A handful of multi-country studies also confirmed the relevance of the skill com-

position of migration. Docquier and Lodigiani (2010) report evidence on strong

networks externalities mainly associated with the skilled diaspora in both cross sec-

tional and panel frameworks for a sample of 114 countries. Instead of relying on

bilateral data, this analysis adopts a global perspective as it is based on the ag-

gregate stock of FDI-funded capital received by world countries. According to this

study, to the extent that skilled emigrants participate in business networks, they will

encourage future FDI flows towards their country of origin which will foster activity

and welfare in their homeland. Thus the size as well as the quality of the diaspora

matters. Flisi and Murat (2011) indicate that both inward and outward FDI in UK,

Germany and France are prompted by skilled immigrants, while those of Italy and

Spain are only influenced by their respective emigrant diasporas.

Unskilled migrants usually have an effect non-trade services (e.g. household care)

(Cortes, 2008). Unskilled wages are fixed at the host market independently of the

number of unskilled workers. Unskilled wages may be, for example, negotiated at

sectoral level by unions. The only effect of unskilled labor is through a cross effect

on high-skilled labor force at the host. Cortes and Tessada (2011) demonstrates that

un-killed immigration increase the labor force of high-skilled women.

6

The above evidence regarding the role played by the educational attainment of

migrants has however been challenged by recent empirical evidence. In the context

of the trade-migration link, Felbermayr and Jung (2009) report a lack of evidence

in favor on remarkable differences across educational groups. In similar terms, both

Aleksynska and Peri (2014) and Montaner et al. (2015) highlight that the distinction

by education level does not shed light about the way migrants affect bilateral trade

and conclude that the most relevant variable is the jobs migrants are occupying in

their destination country.

The sector composition of FDI seems to be also relevant for our analysis. Accord-

ing to previous studies, the determinants of foreign entry decisions may vary between

service and for manufacturing firms. Bouquet et al. (2004) and Kolstad and Villanger

(2008) emphasize that service multinationals face unique challenges when expanding

abroad. These companies need to cope with the challenge of transferring to foreign

subsidiaries the social assets, skills, and capabilities that have developed through

education and training of employees, as well as close contacts with end-customers.

As transferring such human capital entails substantial risks and difficulties, many

global service firms tend to rely on wholly owned subsidiaries and expatriate man-

agement staff when expanding internationally. Moreover, different types of FDI may

require different types of skills according to their main activity (extractive industries,

production of manufactures or provision of services) (see Checchi et al., 2007).

To the best of our knowledge, within the migration-FDI literature, just a handful

of studies have dealt with the sector composition of FDI. That is the case of Ku-

gler and Rapoport (2007). According to these authors, migrants residing in United

7

States may increase future US investment opportunities in their country of origin.

They find a dynamic complementarity between skilled migration and outward US

FDI in services, and contemporaneous substitutability between unskilled migration

and manufacturing outward US FDI. Javorcik et al. (2011) examine if the positive

relationship between migration and US FDI abroad that they obtain at the aggreg-

ate level is also present at the industry level. According to their results, there is a

positive relationship between US outward FDI in a given sector in a given partner

country and the stock of migrants from the partner country employed in this sector

in the US. This result is stronger for migrants with tertiary education.

3 The model

3.1 Setup

The model follows closely standard trade and FDI setups like Melitz (2003)

andHelpman et al. (2008). The basic setup is a world of J countries with the assump-

tion of a Cobb-Douglas utility function Uj = XµLjX

1−µHj , for a two sector economy with

goods L (non traded) and H (traded).Sector L uses primarily non-skilled labor and

sector H skilled labor. The parameter µ controls the relative preference for goods

produced by low and high skilled labor.

The aggregate consumption of a good in the traded sector is Xj =[xjz

ιdz]1/ι

,

where σ ≡ (1− ι)−1 > 1 and z is a firm. The demand is xjz =p−σzj (1−µ)Yj

P 1−σj

, where price

index is a CES function Pj =[zpijz

1−σdz]1/(1−σ)

.

8

3.2 Foreign production

Let firm a firm z from country i in the traded sector produce a variety of goods

in country j. The aggregate consumption in this sector is the sum of all goods

produced . The firm uses three inputs capital K , skilled inputs or services H (which

are provided by high-skilled labor) and low-skilled inputs or L services (which are

provided by high-skilled labor) in the production of the goods xijz. The production

is a standard Cobb-Douglas function:

xijz = θ(K)k(H)h(L)l, (1)

where θ > 1 is a technology parameter. The constants k, l and l measure the intensity

by which the inputs are used in production. For simplicity, these constants are the

same than the country’s preferences for financial, traded and non-traded goods. That

is, the intensity by which each factor is used in production depends on the relative

factor endowment in the country. Equation (1) exhibits decreasing returns since

k + h+ l < 1.

The firms seeks capital locally by negotiating with domestic banks at country i.

Each unit of capital comes at a cost of ri that reflects the capital and interest costs.

Headquarter services are captured with whj and wlj, which are related to the cost of

providing headquarter services in the host country j. These costs increase with the

wage level of skilled and unskilled workforce, which are a strictly concave function

of the skilled and unskilled workforce.

Upon entry, the firm discovers its productivity 1/α, where α is the number of

9

input units per input bundle used by the firm to produce one unit of output. We

follow the standard assumption that the distribution of α across firms is continuous

Pareto c.d.f. G(α) with [αFDI , αExp], where 0 < αFDI < αExp. The density of G(α)

is denoted by g(α)and the distribution is the same across countries.

To produce a good in destination j, an i-country firm incurs in a marginal cost

of:

ωFDIij (α) ≡ τijα(rjK + whjH + wljL) (2)

where transaction costs τij > 1 are proportional to the distance between the countries

and τii = 1. The firm has a fixed cost of production f and sells its product at prices

pj. Thus, the problem maximization of the firm is:

maxK,H,L

πFDIiz = maxpjθ(K)k(H)h(L)l − ωij(α)− fj. (3)

In equilibrium the market clears and the firms determines the optimal level of

capital investment and labor according the first order condition of:

pjθk(K)k−1(H)h(L)l = τijαrj (4a)

pjθh(K)k(H)h−1(L)l = τijαwhj (4b)

pjθl(K)k(H)h(L)l−1 = τijαwlj. (4c)

10

The optimal equilibrium for capital is:

K∗ijz =

(pjθl

lhhk1−h−l

ατij (rj)1−l−h (whj)

h (wlj)l

) 11−µ

(5)

Equation (5) is effectively a gravity equation for foreign capital and shows that

foreign capital investment is governed both by capital and skilled and unskilled ser-

vice costs. Foreign investment decreases with transaction costs τij, capital costs rj,

the cost of high and low skilled services and the sectoral parameter µ. In country

with no skilled goods the capital (µ = 1), the capital invested is at its minimum.

The firm gauges these costs to determine the productivity level at which it enters

the foreign market.

3.3 Migration

We let two types of exogenous shocks on skilled and unskilled headquarter ser-

vice costs. We assume that migrants from country i reduce the cost of skilled and

unskilled inputs. The primary mechanism through wages. With a higher number

of migrants from home, the firm faces lower transaction costs to contract labor at

the host. Furthermore, migrants increase the labor force at the host, which pulls

downs wages. In both case, we expect that w′j = ∂w∗j/∂Mij < 0. The relative impact

depends on the sector and the relative endowments of the host’s economy and the

wage-migrant elasticity.

Proposition 1. Skilled and unskilled migration affects bilateral foreign capital in-

vestment. The relative effect of skilled and unskilled migration is governed by the

11

country’s wage-migration elasticity and the firm’s skilled to unskilled labor ratio.

Proof. Let the headquarter costs at country j be a strictly decreasing concave func-

tion of firm’s ability to negotiate with the host’s country labor force. Skilled (un-

skilled) migration from country i reduces the costs skilled (unskilled) headquarter

services. All other things considered, the change in foreign capital invested by our

firm z response to skilled and unskilled migration is:

∂K∗ijz/∂Mhij =h

µ− 1

w′

hj

whj

(pjθl

lhhk1−h−l

ατij (rj)1−l−h (whj)

h (wlj)l

) 11−µ

=h

µ− 1εhK

∗ijz (6a)

∂K∗ijz/∂Mlij =−l

1− µw′

lj

wlj

(pjθl

lhhk1−h−l

ατij (rj)1−l−h (whj)

h (wlj)l

) 11−µ

=l

µ− 1εlK

∗ijz, (6b)

where ε = −w′j

wj> 0 is the migration wage elasticity. The effect of migration increases

in the relative intensity of the firm’s use of skilled labor with respect to the sector’s.

The effect of skilled migration is higher in those firms with a higher use of skilled

migration:∂K∗ijz/∂Mhij

∂K∗ijz/∂Mlij

= εhh/εll. (7)

What equation 7 reveals is that the differential impact of migration depends on

the firm’s activity rather than the sectoral composition. Assuming similar wage-

migration elasticities, the impact of skilled migration is higher than unskilled migra-

12

tion firms which require a higher proportion of skilled migration for their production

activity.

3.4 Extensive margin

The firm gauges production costs to determine the productivity level at which it

enters the foreign market (Melitz, 2003) . Exporting firms combine inputs at home

and ships the products abroad. The firm faces the following problem:

maxK,E,L

πExpiz = maxpjθ(K)k(H)h(L)l − τijα(rjK + whiH + wliL)− fi. (8)

As in Helpman et al. (2004) the firms setups a foreign production plant if πFDIijz >

πExpiz . Therefore, the cut-off productivity is:

α∗ =(fi − fj)/τij

(rj − ri)K + (whj − whi)H + (wlj − wli)L. (9)

Only a fraction G(α∗) of the active firms invest in country j. With similar capital

costs, the cut-off productivity which determines the number of investors (extensive

margin) depends on the wage differentials between host and home countries. Migra-

tion reduces wage differentials and therefore reduces the threshold, increasing the

number of cross-border projects.

13

3.5 Multiple firms

Aggregating across firms, we obtain the aggregate capital investment:

˜FDI ij = Nj

α∗

αL

Mhµ−1

εh

hij Ml

µ−1εh

lij K∗ijz

g(α)

G(α∗)dα =

= NjMhµ−1

εh

hij Ml

µ−1εl

lij K∗ijz

α∗

αL

α1

µ−1g(α)

G(α∗)dα, (10)

where Mhµ−1

εh

hij Ml

µ−1εh

lij captures the effect of skilled and unskilled migrants on the

capital invested by the firm.

We can re-write equation (10) as follows:

˜FDI = NjMhµ−1

εh

hij Ml

µ−1εl

lij K(αL)Vij, (11)

where

Vij ≡ α∗

αL

(α

αL

) 1µ−1 g(α)

G(α∗)dα., (12)

and K(αL) is the capital invested by the most productive firm.

We assume that 1/a follows a Pareto distribution. We define G(α) =ακ−ακLακ−ακL

, with

κ > 11−µ . Therefore, we can re-write Vij in (12) as:

Vij =κ

κ− 11−µ

Ωij, (13)

whereΩij ≡ max

(α∗αL

)κ− 11−µ−1(

α∗αL

)κ−1

, 0

selects which firms engage in FDI. Ωij, is con-

trolled by the cut-off variable α∗ in (9). Using this expression, we can obtain a

14

log-linear and estimable equation from (11):

fdiij = θ0 + ni +1

1− µpj −

1− l − h1− µ

ln rj −h

1− µlnwhj −

l

1− µlnwlj −

1

1− µln τij+

+h

µ− 1εhmhij +

l

µ− 1εhmlij + vij, (14)

where lowercase variables are the natural log of the uppercase. θ0 is a constant that

bundles the rest of parameters. Using a standard parametrization for the transfer

cost:

1

1− µln τij = ζdij − uij, (15)

where dij is the log of bilateral distance between countries, and uij ∼ N(0, σ2u) is an

unobserved i.i.d investment friction. Recurring to standard notation we can obtain

an empirical gravity-like equation:

fdiij = θ0 + ni + sj − ζdij +h

µ− 1εhmhij +

l

µ− 1εhmlij + vij + uij, (16)

where ni and sj ≡ 11−µpj −

1−l−h1−µ ln rj − h

1−µ lnwhj − l1−µ lnwlj are the fixed country

supply and demand effects.

In sum, we have derived gravity equation for multiple heterogamous firms where

high-skilled an low skilled have different and positive effect on both the extensive

and intensive margins. The model leads to three testable predictions:

1. Migration is positive for both extensive and intensive margins.

2. The effect of skilled migration will be greater than low-skilled migration in

countries with higher wage-migration elasticity (e.g., developed countries).

15

3. The effect of skilled migration will be higher than low-skilled migration for

firms with higher intensity of skilled labor. (h/l > 1).

In the next section we describe the estimation procedure to quantify these effects.

4 Empirical methodology and data

Our baseline specification is the following augmented gravity equation:

lnFDIit = β1 ln (Yi ∗ Yj) + β2 ln (Dij) + β3borderij + β4colonyij + β5langij+

+ β6relij + β7lockedij + β8BITij + β9FTAij+

ρhmhij + ρlmlij + λi + λj + eij, (17)

where FDIijt is the aggregate investment, and total number of investments, between

home country i and host j in year t. The equation measures market demand through

a number of variables; Y denotes the domestic gross product (GDP); D is the dis-

tance in kilometers between countries; border takes a value of one when the countries

share a common border and zero otherwise; colony is set at 1 if the two countries

have ever had a colonial link; lang (Common language) takes a value of 1 if both

countries share the same official language; rel (Religion) is a composite index which

measures the religious affinity between country pairs with values ranging from zero

to one; and locked is the number of landlocked countries (0,1 or 2). BIT (Bilateral

Investment Treaty) is a dummy that takes a value of one if the country pair has a

bilateral investment treaty in force; FTA (Free Trade Agreement) is a dummy that

16

indicates whether both countries have a free trade agreement in force. mij is the log

of migrants. The baseline specification includes a full set of country fixed effects (λ).

Lastly eijt represents a stochastic error term.

The baseline gravity equation (17) suffers from several biases. The log version of

the gravity equation has a self-selection bias, which stems from the omission of zeros.

Additionally, the estimation of FDI capital expenditure flows suffers a potential over-

aggregation bias. We adopt different empirical strategies to hedge these empirical

caveats.

Silva and Tenreyro (2015) show that Helpman et al. (2008) two-stage estimation

imposes too strict homecedastic restrictions on the error term, which are hardly

present in FDI or trade data. Alternatively, the authors show that the simpler PPML

method yields similar results as the two-step procedure. To overcome this caveat, we

use a non-linear variant of the gravity equation in line with that proposed by Silva

and Tenreyro (2006), which does not require a log-linearization of the variables:

FDIijt = exp

β1 ln (Yit ∗ Yjt) + β2 ln (Dij) + β3borderij + β4colonyij+

β5langij + β6smctryij + β7relij + β8lockedij+

β9BITijt + β10FTAijt + ρXmXij + λi + λj

+ εijt

(18)

We apply Pseudo-Poisson Maximum likelihood (PPML) to estimate (18). PPML

offers additional advantages to the log-linear specification. First, it is robust to

heteroskedascity in the error term (Silva and Tenreyro, 2010). Second, it ensures the

convergence of the maximum likelihood estimation by prior inspection of the data

17

Silva and Tenreyro (2011). Additionally, Baltagi et al. (2014) claim that the PPML

estimator is appropriate for gravity data.

We follow similar studies (Paniagua and Sapena, 2014) and substitute the left-

hand side variable of (18) for the number of foreign investments between country

pairs. The estimation of the extensive margin reduces an over aggregation bias of

capital flows in the estimation of the gravity equation Hillberry (2002). Addition-

ally, the extensive margin reveals information about the creation of new partners

Felbermayr and Kohler (2006).

4.1 Data

The Financial Times Ltd. cross-border investment monitor (FDIMarkets, 2013) is

the source of the FDI dataset. The extensive margin is measured in firm-level projects

counts, while the intensive margin is measured in capital flows in constant 2005 USD.

The dataset covers bilateral firm-level greenfield investments from 2003 to 2012,

aggregated between 190 countries. Greenfield projects initiate foreign production

from scratch and are prone to to energy costs constrains. Consequently, greenfield

investments are optimal for measuring the influence of EMI on FDI.

Overall, the database is heavily unbalanced with 70% of zero observations, mean-

ing that not all countries received investment in all years. The World Bank (2013) is

the source of GDP data, measured in constant 2005 US dollars. Distance, common

language, colony and border come from the CEPII (2011) database and control for

freight, information, cultural, historic and administrative transaction costs between

country pairs.

18

Religion is calculated with data from the CIA World Factbook (2011) according

to the following formula for country each country pair: %Christiani∗%Christianj+

%Muslimi ∗%Muslimj + %Hindui ∗+%Jewishi ∗%Jewishj. Institutional agree-

ments such as Free Trade Agreements and Bilateral Investment Treaties reduce the

foreign institutional uncertainty (Bergstrand and Egger, 2013). BITs are manually

constructed with data from UNCTAD (2013). The source of FTA is Head et al.

(2010) in conjunction with UNCTAD (2013). For a detailed description of the vari-

ables, countries and descriptive statistics, see Paniagua and Sapena (2013, 2014).

We use information on the foreign-born population by country of birth. These

data come from the OECD International Migration Database (OECD, 2014). Table

1 lists the countries included in this study. The host countries are northern developed

and home countries are mixed.

5 Results

5.1 Country estimates

Table 2 displays the results for the PPML estimation using 2004 data. We estim-

ate separate equations for each attained educational level indicator as explanatory

variable. Results for both the intensive and the extensive margins in FDI are shown.

These results are aggregated by country and therefore we test the effect migrant’s

skill for developed countries. The results are in line with our theoretical exceptions.

Overall, the estimation results indicate that skilled migrants have a higher effect

than unskilled in developed host countries.

19

Table 1: List of CountriesHost Countries

Australia, Austria, Belgium, Canada, Chile, Czech Republic,Denmark, Finland, France, Germany, Greece, Hungary, Ireland,

Italy, Luxembourg, Netherlands, New Zealand, Poland, Portugal,Slovakia, Spain, Sweden, Switzerland, UK, United States.

Home CountriesAlgeria, Angola, Argentina, Armenia, Australia, Austria,

Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Bermuda,Brazil, Bulgaria, Canada, Cayman Islands, Chile, China,Colombia, Costa Rica, Croatia, Cyprus, Czech Republic,

Denmark, Dominican Repub.., Ecuador, Egypt, Estonia, Finland,France, Germany, Greece, Hong Kong, Hungary, Iceland, India,Indonesia, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan,

Kazakhstan, Kuwait, Latvia, Lebanon, Lithuania, Luxembourg,Macedonia FYR, Malaysia, Malta, Mexico, Moldova, Morocco,Netherlands, New Zealand, Nigeria, Norway, Pakistan, Panama,Papua New Guinea, Peru, Philippines, Poland, Portugal, Qatar,

Russia, Saudi Arabia, Singapore, Slovakia, Slovenia, South Africa,South Korea, Spain, Sri Lanka, Sweden, Switzerland, Taiwan,

Thailand, Togo, Trinidad & Toba.., Tunisia, Turkey, UAE, UK,Ukraine, United States, Uruguay, Venezuela, Vietnam.

20

The first evidence to be highlighted is that outcomes are not homogeneous across

margins. Thus, the estimator for GDP is significant (with the expected positive sign)

only for the extensive margin, the same as the common language effect. The FTA

dummy only has a significant (and negative) effect on the intensive margin. Distance,

however, has the usual negative impact on FDI for the two types of margins. With

regard to our variables of interest, the education acquired has a positive effect for

every level attained; this result holds for both margins.

The fact that all the coefficients are highly significant suggests that the mere

presence of migrants stimulates investments from their homeland towards their host

country. As expected in a pool of developed host countries, the estimates display

higher values as the educational level attained increases; in other words, migrants

enjoying higher qualification levels are more effective in attracting investments from

their countries of origin (extensive margin) and these investments tend to be higher

(intensive margin).

21

Table 2: PPML Education 2004(1) (2) (3) (4) (5) (6)FDI Extensive Margin FDI Extensive Margin FDI Extensive Margin

GDP -0.303 0.467∗∗∗ -0.205 0.444∗∗∗ -0.346 0.383∗∗∗

(0.30) (0.13) (0.30) (0.12) (0.32) (0.11)

Distance -1.379∗∗∗ -0.258∗∗ -1.363∗∗∗ -0.264∗∗ -1.271∗∗∗ -0.275∗∗

(0.23) (0.12) (0.22) (0.11) (0.22) (0.11)

Border -0.463 0.026 -0.548 -0.059 -0.416 -0.073(0.45) (0.21) (0.45) (0.21) (0.42) (0.20)

Common language 0.270 0.750∗∗∗ 0.123 0.681∗∗∗ -0.041 0.505∗∗∗

(0.39) (0.14) (0.38) (0.14) (0.39) (0.14)

Colony 0.582 -0.016 0.524 -0.013 0.661 0.059(0.42) (0.14) (0.41) (0.14) (0.42) (0.14)

Landlocked -0.239 -0.044 -0.260 0.007 -0.360∗ -0.065(0.21) (0.08) (0.22) (0.08) (0.21) (0.08)

BIT -0.458 0.132 -0.285 0.147 -0.682∗ 0.078(0.28) (0.15) (0.25) (0.14) (0.37) (0.15)

FTA -1.335∗∗ -0.183 -1.435∗∗∗ -0.163 -1.287∗∗ -0.181(0.56) (0.25) (0.55) (0.25) (0.53) (0.23)

No education 0.175∗∗∗ 0.107∗∗∗

(0.06) (0.03)

Secondary education 0.318∗∗∗ 0.152∗∗∗

(0.08) (0.03)

Tertiary education 0.346∗∗∗ 0.242∗∗∗

(0.11) (0.04)

Observations 651 651 687 687 708 708R2 0.848 0.901 0.856 0.902 0.847 0.904

Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

In Table 3 we replicate the estimations above using cross-section data referred to

2008. Several differences appear, the non-significance of GDP in most cases being

the first. The education indicators are still significant and increase with the level

achieved by migrants, but only in the extensive margin. Distance is still significant

and with the expected sign, whereas common language and having been a colony of

22

the investing country display a positive effect on both the extensive and the intensive

margin. Globally considered, the outcome for the 2008 estimates (and the differences

with regard to those obtained with 2004 data) seems to respond to the different pool

of countries used in the estimation.

Table 3: PPML education year 2008(1) (2) (3) (4) (5) (6)FDI Extensive Margin FDI Extensive Margin FDI Extensive Margin

GDP -0.310 -0.171 -0.159 0.210∗ -0.408 0.224∗∗

(0.26) (0.12) (0.27) (0.12) (0.26) (0.11)

Distance -0.418∗∗ -0.286∗∗∗ -0.363∗∗ -0.243∗∗∗ -0.317∗ -0.173∗∗

(0.19) (0.08) (0.17) (0.07) (0.17) (0.07)

Border 0.443 0.145 0.314 0.079 0.399 0.162(0.43) (0.17) (0.36) (0.14) (0.36) (0.14)

Common language 0.794∗∗ 0.403∗∗∗ 0.693∗ 0.465∗∗∗ 0.653∗ 0.428∗∗∗

(0.37) (0.15) (0.36) (0.15) (0.35) (0.14)

Colony 0.960∗∗∗ 0.269∗ 1.149∗∗∗ 0.202 1.114∗∗∗ 0.192∗

(0.34) (0.14) (0.38) (0.12) (0.38) (0.11)

Landlocked -0.179 -0.315∗∗∗ -0.254 -0.281∗∗∗ -0.258 -0.316∗∗∗

(0.19) (0.11) (0.18) (0.10) (0.18) (0.10)

BIT 0.042 -0.512∗∗ -0.066 -0.179 -0.288 0.012(0.35) (0.20) (0.35) (0.17) (0.38) (0.18)

FTA 0.012 -0.168 0.289 -0.140 0.169 -0.072(0.45) (0.16) (0.46) (0.14) (0.38) (0.13)

No education 0.028 0.123∗∗∗

(0.08) (0.04)

Secondary education 0.029 0.161∗∗∗

(0.09) (0.04)

Tertiary education -0.022 0.217∗∗∗

(0.11) (0.05)

Observations 286 286 315 315 319 319R2 0.830 0.975 0.815 0.975 0.797 0.980

Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

23

5.2 Estimates by firm’s activity

Our database allows us to check whether the results above (and specifically those

referred to the educational attainment) vary across investment activities. Thus, we

split our dependent variable in four possible activities of the investment project:

Manufacturing, Sales, Construction and Services. We present the different estimates

for our 2004 and 2008 datasets in Tables 4 and 5, respectively. We expect the effect

of skilled migrants to be higher in those activities which require higher education

(e.g., sales and services).

Taking into consideration different activities allows clarifying the role played by

the explanatory variables. In our 2004 estimates, and focusing first just in the

control variables, GDP is significant in all sectors except in the case of Services.

In fact, investments in services appear to be more related to other issues than the

bilateral market size or geographical or cultural elements: only distance seem to be a

significant explanatory variable for these investment flows. The only significant effect

in the case of Sales comes from the estimates of GDP. Distance and contiguity have

a significant effect on investments in Manufacturing and Construction, whereas the

cultural indicator (common language) is only relevant in the case of Manufacturing.

Thus, geography and cultural ties only stimulate FDI in sectors where the physical

dimension of the productive activity is more relevant.

Notwithstanding all these differences across activities in the significance of the

effects of explanatory variables, we observe interesting traits with regard to FDI’s

activity. Table 4 reports the effect of educational level on manufacturing, sales,

construction and services. The educational attainment indicators are remarkably

24

significant in most cases (the non-significance of highly educated migrants in the

case of Construction being the only exception). Thus, education matters for the

link between migration and foreign investment, also when we analyze each activity

separately.

However, we observe two important differences. First, the impact of migrants

is different between activities. Focusing on tertiary education, the coefficient for

services is 0.667 (and significant to the 1% level), showing a significantly lower coef-

ficient for manufacturing (0.211 and significant to the 1% level), sales (0.239 and

significant to the 1% level), and not significant for construction. The results high-

light the heterogeneous effect of migrant’s education across activities, which remain

hidden in the aggregated measures of FDI. The results confirm our model, which

predicted a higher impact in those activities required skilled labor.

Second, the impact of migrants’ education is different within activities. For ex-

ample, in manufacturing and sales activities, secondary and tertiary education have

a similar impact and higher than primary education. Conversely, in construction the

higher impact is for secondary education followed by primary and tertiary education

(which has no significant impact). For services, the highest value corresponds to

tertiary, which nearly doubles the impact of secondary education.

Results for 2008 in Table 5 indicate that the impact of migration is significant only

for services and especially for those migrants with education higher than primary.

Again, we must interpret these results with caution, as the country set is not strictly

comparable with the results of 2004. Nonetheless, the results show a similar pattern

between sectors; services are still the activity where education is more relevant.

25

Tab

le4:

Act

ivit

ies

2004

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Man

ufa

cturi

ng

Sal

esC

onst

ruct

ion

Ser

vic

es

GD

P0.

987∗

∗∗0.

884∗

∗1.

287∗

∗∗0.

743∗

∗0.

720∗

0.68

4∗∗

1.65

7∗∗

1.84

2∗∗

1.50

7∗∗

0.04

00.

092

-0.1

23(0

.38)

(0.3

7)(0

.36)

(0.3

5)(0

.38)

(0.3

4)(0

.70)

(0.7

2)(0

.72)

(0.4

3)(0

.43)

(0.4

1)

Dis

tance

-0.7

04∗

-0.7

50∗

-0.5

880.

269

0.30

30.

181

-1.7

83∗∗

∗-1

.752

∗∗∗

-1.7

13∗∗

∗-0

.608

∗∗-0

.553

∗-0

.378

(0.4

0)(0

.39)

(0.3

8)(0

.31)

(0.3

1)(0

.29)

(0.5

5)(0

.56)

(0.6

4)(0

.29)

(0.3

0)(0

.30)

Bor

der

-1.4

35∗∗

-1.6

80∗∗

∗-1

.196

∗∗-0

.143

-0.1

52-0

.102

-1.4

73∗

-1.4

77∗

-1.1

99-0

.160

-0.3

31-0

.131

(0.6

4)(0

.65)

(0.5

9)(0

.53)

(0.5

1)(0

.49)

(0.8

3)(0

.82)

(0.7

6)(0

.50)

(0.5

0)(0

.46)

Com

mon

langu

age

1.62

4∗∗∗

1.44

6∗∗∗

1.51

2∗∗∗

0.23

50.

161

0.07

8-1

.463

-1.5

36-1

.713

0.64

20.

495

-0.0

93(0

.32)

(0.3

1)(0

.32)

(0.3

7)(0

.37)

(0.4

0)(1

.19)

(1.2

3)(1

.26)

(0.4

2)(0

.42)

(0.4

5)

Col

ony

0.16

30.

225

0.17

40.

320

0.28

00.

333

0.52

70.

388

0.83

20.

203

0.16

50.

085

(0.4

6)(0

.45)

(0.4

7)(0

.53)

(0.5

4)(0

.56)

(1.2

1)(1

.29)

(1.1

0)(0

.36)

(0.3

8)(0

.39)

Lan

dlo

cked

0.20

80.

201

0.06

30.

073

0.17

00.

118

0.06

40.

018

-0.0

300.

066

0.12

40.

002

(0.4

5)(0

.42)

(0.4

2)(0

.24)

(0.2

3)(0

.24)

(0.3

7)(0

.40)

(0.4

0)(0

.21)

(0.2

1)(0

.22)

BIT

-0.5

53-0

.536

-1.3

33∗∗

∗-0

.199

-0.1

24-0

.069

-0.5

30-0

.005

-0.1

370.

004

0.00

60.

086

(0.5

0)(0

.37)

(0.4

2)(0

.38)

(0.3

6)(0

.35)

(1.1

6)(1

.02)

(1.0

1)(0

.36)

(0.3

5)(0

.33)

FT

A-0

.201

-0.1

26-0

.095

1.03

11.

060

0.91

00.

628

0.38

00.

764

-0.4

42-0

.321

-0.3

66(0

.52)

(0.5

3)(0

.58)

(0.7

1)(0

.71)

(0.6

9)(1

.60)

(1.6

1)(1

.75)

(0.5

8)(0

.58)

(0.5

9)

No

educa

tion

0.17

4∗∗

0.15

3∗∗

0.24

9∗0.

248∗

∗

(0.0

7)(0

.07)

(0.1

3)(0

.10)

Sec

ondar

yed

uca

tion

0.26

5∗∗∗

0.21

4∗∗

0.43

9∗∗

0.37

9∗∗∗

(0.0

7)(0

.09)

(0.2

1)(0

.13)

Ter

tiar

yed

uca

tion

0.21

1∗∗

0.23

9∗∗

0.23

80.

667∗

∗∗

(0.1

0)(0

.11)

(0.2

5)(0

.17)

Obse

rvat

ions

465

482

495

496

525

550

290

299

318

451

467

474

R2

0.85

10.

845

0.83

40.

688

0.69

20.

685

0.99

50.

995

0.99

50.

614

0.63

40.

655

Rob

ust

stan

dar

der

rors

inp

aren

thes

es

∗p<

0.1

0,∗∗

p<

0.05

,∗∗

∗p<

0.01

26

Tab

le5:

Act

ivit

ies

2008

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Man

ufa

cturi

ng

Sal

esC

onst

ruct

ion

Ser

vic

es

GD

P0.

407

0.54

10.

520

0.40

20.

594

0.63

30.

115

-0.6

86-0

.244

1.08

7∗∗∗

1.30

0∗∗∗

1.31

9∗∗∗

(0.7

6)(0

.79)

(0.7

4)(0

.61)

(0.6

6)(0

.61)

(1.7

3)(0

.68)

(0.8

4)(0

.33)

(0.3

5)(0

.31)

Dis

tance

1.59

8∗∗

1.63

1∗∗

1.46

5∗∗

0.67

9∗∗∗

0.74

6∗∗∗

0.79

6∗∗∗

-1.6

29∗∗

∗-1

.756

∗∗∗

-1.5

51∗∗

∗-0

.871

∗∗∗

-0.6

84∗∗

∗-0

.531

∗∗

(0.7

3)(0

.67)

(0.6

3)(0

.24)

(0.2

3)(0

.23)

(0.4

3)(0

.51)

(0.4

7)(0

.22)

(0.2

1)(0

.21)

Bor

der

2.86

4∗∗

2.46

5∗∗

2.55

0∗∗

2.36

4∗∗∗

1.71

5∗∗∗

1.66

7∗∗∗

-1.6

37-1

.662

∗∗-1

.713

∗∗∗

-0.2

48-0

.670

∗-0

.191

(1.4

3)(1

.25)

(1.2

4)(0

.47)

(0.4

6)(0

.43)

(1.6

4)(0

.82)

(0.6

2)(0

.45)

(0.3

7)(0

.41)

Com

mon

langu

age

-0.9

86-0

.666

-0.4

52-0

.688

∗∗-0

.199

-0.2

240.

696

0.74

30.

367

0.75

1∗∗

0.60

1∗∗

0.56

6∗∗

(1.0

7)(0

.96)

(1.0

3)(0

.28)

(0.3

8)(0

.37)

(1.9

7)(0

.69)

(0.7

5)(0

.30)

(0.2

9)(0

.27)

Col

ony

1.47

32.

081∗

∗2.

138∗

∗-0

.072

-0.0

730.

160

1.69

0∗∗∗

1.02

1∗∗

0.84

5∗∗∗

0.75

2∗∗

0.53

9∗0.

544

(0.9

2)(0

.92)

(1.0

7)(0

.37)

(0.4

0)(0

.37)

(0.5

5)(0

.43)

(0.3

0)(0

.35)

(0.3

2)(0

.33)

Lan

dlo

cked

-0.3

12-0

.244

-0.2

630.

150

0.00

40.

105

-2.9

16-1

.310

-1.5

88-0

.362

-0.4

44∗

-0.4

46∗

(0.4

9)(0

.50)

(0.4

8)(0

.42)

(0.3

7)(0

.32)

(3.5

9)(1

.05)

(1.1

9)(0

.27)

(0.2

5)(0

.24)

BIT

-0.5

840.

657

-0.1

38-2

.125

∗-0

.210

0.94

0∗-2

.162

-2.0

36-1

.847

∗∗∗

-0.9

24∗

-0.8

16(0

.97)

(0.8

2)(1

.01)

(1.1

0)(0

.81)

(0.5

4)(1

.80)

(1.8

9)(0

.49)

(0.5

1)(0

.52)

FT

A1.

984∗

2.17

7∗∗

2.14

50.

799∗

0.84

7∗∗

1.24

4∗∗∗

-0.9

83-0

.639

-0.5

75-1

.243

∗∗∗

-1.0

17∗∗

∗-0

.794

∗∗

(1.1

2)(1

.06)

(1.3

4)(0

.43)

(0.4

0)(0

.41)

(1.0

6)(0

.80)

(0.7

5)(0

.36)

(0.3

9)(0

.35)

No

educa

tion

0.01

7-0

.100

0.67

30.

073

(0.1

8)(0

.10)

(0.5

8)(0

.08)

Sec

ondar

yed

uca

tion

0.14

60.

002

0.24

50.

378∗

∗∗

(0.1

9)(0

.12)

(0.1

5)(0

.10)

Ter

tiar

yed

uca

tion

0.07

8-0

.017

0.01

80.

276∗

∗

(0.2

5)(0

.15)

(0.2

1)(0

.13)

Obse

rvat

ions

171

188

190

231

252

256

105

139

146

232

250

251

R2

0.85

40.

826

0.82

40.

808

0.72

90.

790

0.94

20.

926

0.96

70.

940

0.93

30.

932

Rob

ust

stan

dar

der

rors

inp

aren

thes

es∗p<

0.1

0,∗∗

p<

0.05

,∗∗

∗p<

0.01

27

6 Conclusions

Rapid growth as well as changes in composition in international migration has

lead policy-makers to devote greater attention to its main determinants and impact.

Establishing activities abroad requires a wide variety of general information about the

host market. Migrants may help companies to overcome informational barriers when

entering a new market. This paper offers new insights into the FDI-Migration link by

acknowledging that education levels of migrants have remarkably improved during

the last decades. As migrant effects vary with skill level and between activities, we

also account for the type of activity where investment took place.

Our findings indicate that migration favors FDI from the homeward of migrants

to the host country of investment. This effect is stronger when migrants have a high

level of education and when the investment is located in the services. According to

our results, migration has a positive effect on FDI which should be considered when

analyzing the costs and benefits of labor mobility. WE have shown that the effect of

skilled migrants on FDI is higher than low skilled in a group of northern countries.

This relative effect is also higher for firm’s which activities are tied to skilled labor.

As governments increasingly seek to attract foreign direct investment as a driver of

long term development, our results may inform migration policies helping to identify

those migrants and activities particularly relevant for establishing linkages that may

enhance international investments.

28

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