migration, self-selection and income inequality: an international analysis

8/8/2019 Migration, Self-Selection and Income Inequality: An International Analysis

1/22

KYKLOS, Vol. 57 2004 Fasc. 1, 125146

125

G4_HS:Auftrge:HEL002:15174_SB_Kyklos_2004-01:15174-A03:Kyklos_2004-01_S-003-146 29.11.96 28. Januar 2004 09:12

Migration, Self-Selection and Income Inequality:An International Analysis

Thomas Liebig and Alfonso Sousa-Poza*

I. INTRODUCTION

A well-established result in migration economics is the fact that migrants arenot a random sample of the population in the source countries. Processes of se-lection alter the sample on both sides of the market. Due to skills shortages inmany OECD countries, the importance of highly skilled migration is rising andmany countries have selectively opened their labour markets for highly skilledimmigrants1. Research on the determinants of skilled immigration, however, isstill largely lacking.

Among the migration theories, one that can clearly be used for the derivationof empirically testable hypotheses about the determinants of particular types of migration is self-selection theory. This theory has been adopted by Borjas(1987, 1994) to analyse the skills level of migrants. A key and much-disputed prediction of Borjas (1987) is that a more unequal income distribution in thesending country will have a negative impact on the skills mix of migrants in thehost country (i.e. , negative self-selection). Provided there is negative self-selec-tion, a second prediction of Borjas model is that a higher income inequality in

* Department of Economics and Research Institute for Labour Economics and Labour Law, Uni-versity of St. Gallen Guisanstrasse 92, CH-9010 St. Gallen, Switzerland. Corresponding author:

Thomas Liebig, e-mail: [email protected], tel. +41-71-224 2809. Earlier versions of this paper were presented at the Research Seminar of the University of St. Gallen, the 2003 AnnualCongress of the Swiss Society of Economics and Statistics (Berne), the VIIIth Spring Meetingof Young Economists (Leuven), the 15th Annual Meeting of the Society for the Advancement of Socio-Economics (Aix-en-Provence) and the 43rd European Congress of the Regional ScienceAssociation ( Jyvskyl). We would like to thank the participants of these conferences as well asSimon Gchter, Jakob de Haan, Fred Henneberger, Patrick Puhani, Paul Ryan, Hans Schmid, Al-exandre Ziegler and an anonymous referee for valuable comments. Financial support from theSwiss National Science Foundation (grant no. 5004-69464) and the German National MeritFoundations PhD scholarship programme is gratefully acknowledged.

1. These countries include Germany and the UK; see OECD (2001a) (ed.) for an overview of re-cent developments in highly skilled migration.


2/22

THOMAS LIEBIG/ALFONSO SOUSA-POZA

126


the origin country will lead to lower emigration rates. Chiswick (1999, 2000),however, argues that, generally, positive self-selection can be expected. In his

view, a higher income inequality in the origin country only attenuates positiveself-selection.Most studies that test for the self-selection of migrants rely on host-country

data (see the critique in Chiquiar and Hanson 2002 and the overview inChiswick 2000). Such an approach is problematic as specic host-countrycharacteristics such as migration policy, migration networks, historical links,geographical proximity, etc. are likely to bias immigration to these countries(see also Jasso et al. 2000). For example, if a countrys migration policy favoursimmigration of highly skilled labour, then testing for the selectivity of migrantswith immigration data from this country is not possible. These are severe lim-itations. Earlier studies on the selectivity of migrants have tried to counter theselimitations by either studying internal migration (e.g. Gabriel and Schmitz1995, Bailey 1993) or by focusing on the emigration of in-migrants (e.g.Constant and Massey 2002, Burda et al. 1998, Borjas and Bratsberg 1996,Beenstock 1996, Jasso and Rosenzweig 1988)2. The aim of this paper is toovercome the above-mentioned limitations by analysing multiple source-coun-try data on emigrationintentions . There are two main advantages of such an approach: rst, data on emigration intentions do not face the sample-selection problems mentioned above. Furthermore, empirical tests of a theory that deals

with migration incentives, such as self-selection theory, should rely on emigra-tion propensities instead of using immigration data3. Second, a multi-nationalanalysis based on microdata is possible. Although there are several studies based on source-country data, such as Chiquiar and Hanson (2002), these onlycover one source country and thus the generality of the ndings is limited. Our approach is valuable as it not only allows us to control for many important so-cio-demographic factors, but also enables us to test the generality of the results.We proceed in two steps. Firstly, we show that positive self-selection can gen-erally be expected in international migration. Secondly, we demonstrate that ahigher income inequality in the country of origin is generally associated with ahigher overall emigration propensity. Yet, positive self-selection even holds for

2. However, the economics that determine the out-migration decision differ substantially fromthose determining in-migration. Borjas and Bratsberg (1996) argued that out-migration intensi-es the original selection. Chiswick (2000) concludes from a survey of various studies that out-migrants are likely to be less favourably selected than the original migrants, whereas Jasso andRosenzweig (1988) argued that the most skilled are more likely to out-migrate.

3. Tidrick (1971) and Finifter (1976) were among the rst to test for selection processes usingquestionnaires on the inclination to emigrate from the US. A similar approach has been taken byBurda et al. (1998) for analysing on-migration in Germany.


3/22


4/22


128


tio of household income of the top 10 percent of the households to the incomeof the bottom 20 percent of the households. Borjas (1987) results support his

models predictions on the impact of income inequality on migration.

III. ALTERNATIVE MODELLING OF THESELF-SELECTION PROCESS

Borjas results have been challenged by other studies on immigrant self-selec-tion. Chiswick (1999) argues that larger relative wage differentials in the origincountries do only imply a less favourable selectivity,i.e. , negative self-selectionis not likely to occur. In a recent paper, Chiquiar and Hanson (2002) nd support for Chiswicks argument based on migration data from Mexico to the US.

The models proposed by both Chiswick (1999) and Chiquiar and Hanson(2002) can be interpreted as extensions of the human capital theory of migra-tion, which was rst proposed by Sjastaad (1962). In Chiswicks (1999) model,migrants tend to be positively self-selected due to the presence of out-of-pocketcosts to migration. These foster positive self-selection, as direct migration costshave a shorter time equivalent (i.e. , lower opportunity costs) for the highlyskilled. Furthermore, Chiswick (1999) argues that ability is likely to positivelyaffect efciency in migration, which reinforces positive self-selection. Borjas

model assumes instead that time-equivalent migration costs are equal for alltypes of migrants. In the presence of direct migration costs, a larger wage ine-quality in the country of origin thus does not necessarily imply negative self-selection, but rather only a less favourable (positive) self-selection.

We subsequently examine the dispute between Borjas, whose model predictsa negative self-selection of migrants from countries with a very large return toskills (i.e., earnings) differential, and Chiswick and others, who argue that largeincome differentials do not preclude positive self-selection.

IV. DATA AND METHODOLOGY

We test the two alternative hypotheses by analysing the relationship betweencountry-specic emigration propensities and each countrys score on the Gini-index on inequality. The 1995 International Social Survey Programme (ISSP)conducted a survey on national identity, which gathers the necessary data in aninternational microdata set. The survey covered 23 countries and has a samplesize of approximately 28000 individuals. It allows for the identication of so-cio-demographic characteristics of people with high emigration propensities,


5/22

MIGRATION, SELF-SELECTION AND INCOME INEQUALITY

129


especially of the skilled. This is particularly noteworthy as there are no interna-tional statistics on the relative share of skilled migrants. Furthermore, all tradi-

tional immigration countries (USA, Canada, Australia and New Zealand) par-ticipated in the survey as well as many EU countries, including Germany.A key question in the survey is whether the respondent would be willing to

move to another country. More specically, individuals were asked Would you be willing to move to another country to improve your work or living condi-tions?. Of course, this propensity is not necessarily associated with actualmigration4. However, it should be noted that self-selection theory is mainlyconcerned with incentives to migrate, which do not necessarily correspondwith actual migration (see also Chiquiar and Hanson 2002). Therefore, the mi-gration propensity might serve as a better proxy for an empirical test of the the-ory than immigration, which is likely to be biased due to the impact of host-country specics such as migration policy, network migration, country-specicmigration costs, and the like. These biases are likely to be at work in single-country analyses and in all studies based on host-country data i.e., in basicallyall empirical tests of self-selection theory5. Furthermore, in a reply to a Jassoand Rosenzweig (1990) critique on his 1987 paper, Borjas (1990) acknowl-edges that selective emigration of the foreign-born (i.e., on-migration) islikely to bias studies based on host-country data. Finally, it is generally as-sumed that intentions are a monotonic function of the underlying variables

motivating actual migration behaviour. Data on migration intentions have also been used in other studies on self-selection, such as in Burda et al. (1998)6.Using microdata allows us to control for socio-demographic characteristics.

We run ordered probit-regressions with three groups, one including all sur-veyed persons and two comprising the sub-sample of the highly skilled and thesub-sample of the respondents with medium and low education7.

Borjas (1987) tested for the selectivity of migrants by regressing entry-wagedifferentials and earnings growth of immigrants relative to natives on a set of variables, including the ratio of the income of the top ten per cent of the house-holds relative to that of the bottom 20 per cent. Chiswick criticises this ap-

4. For an overview of the discussion of the use of intention data, see Manski (1990).5. A noteworthy exception of the lack of empirical studies with source country data is provided by

Chiquiar and Hanson (2002). However, their study only covers Mexican emigration to the US.6. Note that self-selection theory is a supply-side theory. In a similar line of reasoning in the labour

supply literature, Kahn and Lang (1991) have argued that labour supply estimates could, due tohours constraints, be biased. They thus use data on desired working time instead.

7. Correctly, skilled refers to the ability of performing certain tasks, while qualied stands for educational attainment. As abilities are difcult to measure, most studies assume that thehighly skilled are also highly qualied and use qualication as a proxy for skills (see e.g. Win-kelmann 2001, Auriol and Sexton 2001). We will follow this approach in our analysis.


6/22


130


proach on two grounds. Firstly, he contends that this method does not test for the effect of income inequality on positive or negative selectivity in interna-

tional migration, but only for whether inequality in income is associated witha greater or lesser degree of selectivity (Chiswick 1999, p. 184). In our analy-sis, we control for the impact of the education level on migration inclination.By doing so, we can identify the type of selectivity.

Secondly, Chiswick (1999) argues that relative skills differentials and Bor- jas measure of income inequality may only be poorly related. In Borjas (1987)empirical test, this problem is particularly pronounced, as he also proxies for the selectivity of immigrants by the entry wage differential between immi-grants and natives. The analysis presented here avoids this latter problem as wehave a direct measure of selectivity (i.e., a combination of emigration inclina-tion and educational level). The general problem of proxying relative returns toskills by income inequality is more difcult to tackle. Ideally, one would not useincome inequality, but wage inequality net of taxes and transfer payments. In-ternationally comparable data are available only for gross wages and only for asubset of countries (Austria, Canada, Czech Republic, Germany, Great Britain,Ireland, Italy, Japan, Netherlands, Poland, Spain, Sweden and the UnitedStates)8. Using only this subset would heavily bias our results, as it excludes allless developed countries that participated in the 1995 ISSP. The correlation be-tween the wage inequality in the OECD data and the income-inequality mea-

sures in World Bank (2001) is remarkably high, with a Pearson statistic of 57 per cent. Excluding New Zealand, where the difference between the two con-cepts is particularly pronounced, even yields a correlation of 77 per cent9.

A nal important point is that we test a theory that is relative in nature withabsolute data. More specically, according to Borjas theory, negative self-se-lection occurs when therelative income distribution is more unequal in thesource than in the host country. The ISSP data set, however, captures migrationintentions and has no information on the host country which each respondentimplicitly refers to. Thus, it is conceivable that a large proportion of e.g. Filipi-

8. These data are from the OECD and are partly published and discussed in OECD (1996). One of the reasons for the better availability of income data as compared to wage data is that the former are generally collected for tax purposes, whereas the latter can often only be derived from labour force surveys, which many countries do not have. Although the Luxembourg Income Study(LIS) covers a broader range of countries than the OECD (including countries from Eastern Eu-rope), it should be noted that the LIS wage data are not fully comparable: for some countries,only net wages are available, for other countries there are only data on gross wages. Even moreimportantly, it is not possible to consistently restrict the data to fully employed individuals,whereas the OECD data only covers persons in full employment.

9. For an overview of the issues relating to the sources and measurement of income inequality, seeAtkinson and Bourguignon (eds.) (2000).


7/22


131


nos have the United States in mind, whereas Polish respondents implicitly refer to Germany. Testing such a relative theory with absolute data is only possible

when our set of countries in principle comprises a reasonably closed system,i.e. , the emigration intentions of most respondents refer to another country of the sample. As we have included all traditional immigration countries and mostWestern European countries, we believe this to be a fair assumption. Note thateven if a kind of clustering in migration takes place, e.g. the Eastern Europeancountries refer to Germany and the Western European nations to the US, our estimates will not be biased.

V. EMPIRICAL ANALYSIS

The country-specic Gini-values are shown in the last column of Table 1 . Ascan be seen, the Anglo-Saxon countries have a much higher inequality thancountries in Western Europe. Eastern European countries have fairly divergingGini-values, ranging from below 20 in Slovakia to almost 40 in Poland.

Table 1 also depicts the desired emigration rates, i.e., the percentage of re-spondents that stated they would be very willing to move to another countryin the 1995 ISSP.

No clear pattern seems to exist. Surprisingly, many eastern European coun-

tries have very low desired emigration rates. This result corresponds well withthe relatively low actual emigration rates from these countries that have beenreported elsewhere10. Averaging the possible responses to the question regard-ing the willingness to emigrate on the Lickerts scale from 0 (very unwilling)to 4 (very willing) alters the picture somewhat. The results are shown in

Figure 1 . Still, there is no obvious pattern. Now, however, the Philippines standout as the country with the highest emigration propensity, followed by Sweden.At rst sight, Swedens high emigration inclination appears puzzling. However,it should be noted that Sweden joined the European Union in the year the sur-vey was conducted and Sweden was among the countries with the highest in-crease in unemployment rates between 1992 and 1996. Thus, there might have been an increased awareness of new opportunities abroad at that particular date.

10. See, for example, the recent analysis of the International Organization for Migration (2002) andFidrmuc (2001).


8/22


9/22


133


Figure 1Ranking of emigration inclination

Note: average is based on the scale ranging from 0 (very unwilling) to 4 (very willing).

Table 2 depicts sample-mean tests (t-tests) in order to establish the signicanceof country differences in emigration propensities. There are large inter-countrydifferences in the emigration inclination, even between neighbouring countriessuch as, for example, Sweden and Norway.

We subsequently ran a set of ordered probit regressions, controlling for so-cio-demographic characteristics. As Borjas migrant self-selection theory pre-dicts fundamentally different migration incentives for highly skilledvis--vislow-skilled persons, the overall sample was divided in two groups. The rstgroup included only people with a university degree and the second was com-

prised of the rest of the sample, i.e., people with medium or low education.Standard migration theory predicts that young, highly qualied, singlemales should be the most mobile group11. In the base regression (model 1), wecontrolled for age, education, marital status and gender. All of these factors hadthe predicted sign and were highly signicant. The positive impact of high ed-ucation on the migration propensity is particularly noteworthy. Unless other factors, such as income inequality, counterweight the impact of education, one

11. See, for example, the overviews provided by Massey et al. (1993) and Ghatak et al. (1996).


10/22

1 3 4

G4

_H

S : A

uf t r

g e: H E L 0

0 2 : 1

5 1 7 4

_ S B

_K

y k l o

s _2

0 0 4 - 0 1 : 1

5 1 7 4 -A

0 3 : K y k l o

s _2

0 0 4 - 0 1

_ S - 0

0 3 -1 4

6

2 9 .1 1 . 9

6

2 8 . J

an u ar 2

0 0 4

0 9 : 1 2

Table 2

Probabilities of equal means (t-tests)

RP S BG CDN NL NZ GB DW E SK PL N I USA IRL SLO DE LV R

RP 0.77 0.45 0.07 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0S 0.65 0.17 0.02 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.

BG 0.41 0.08 0.11 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0

CDN 0.30 0.34 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0

NL 0.93 0.29 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NZ 0.39 0.01 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

GB 0.14 0.15 0.09 0.06 0.03 0.00 0.00 0.00 0.00 0.00 0.00

DW 0.99 0.84 0.68 0.48 0.00 0.00 0.00 0.00 0.00 0.00

E 0.85 0.70 0.51 0.00 0.00 0.00 0.00 0.00 0.00

SK 0.83 0.62 0.00 0.00 0.00 0.00 0.00 0.00

PL 0.77 0.01 0.00 0.00 0.00 0.00 0.00

N 0.01 0.00 0.00 0.00 0.00

I 0.55 0.14 0.01 0.00 0.0

USA 0.32 0.02 0.00 0.0

IRL 0.28 0.02 0.

SLO 0.13 0.

DE 0

LV

RUS

CZ A

H

J


11/22

1 3 5

G4

_H

S : A

uf t r

g e: H E L

0 0 2 : 1

5 1 7 4

_ S B

_K

y k l o

s _2

0 0 4 - 0 1 : 1

5 1 7 4 -A 0

3 : K

y k l o

s _2

0 0 4 - 0 1

_ S - 0

0 3 -1 4

6

2 9 .1 1 . 9

6

2 8 . J an

u ar 2

0 0 4

0 9 : 1 2

Table 3

Determinants of desired emigration ordered probits

model 1 model 2 all high edu-

cationmedium and

low educationall high edu-

cationmedium and

low education

constant 0.814**(0.107)

1.339**(0.343)

0.794**(0.113)

0.560**(0.113)

1.003**(0.360)

0.528**(0.120)

0.(0.

male a 0.128**(0.016)

0.190**(0.041)

0.116**(0.017)

0.111**(0.017)

0.181**(0.044)

0.092**(0.019)

0.(0.

age 0.022**(0.005)

0.019(0.017)

0.021**(0.006)

0.015**(0.025)

0.008(0.018)

0.014*(0.006)

0.0(0.

age 2 10 3 0.048(0.069)

0.007(0.208)

0.005(0.073)

0.025(0.073)

0.139(0.217)

0.051(0.077)

0.0(0.

high education a,b 0.292**(0.024)

0.287**(0.024)

0.(0.

low education a,b 0.257**(0.023)

0.233**(0.023)

0.(0.

married a 0.158**(0.018)

0.142**(0.047)

0.167**(0.020)

0.138**(0.019)

0.112*(0.049)

0.146**(0.020)

0.(0.

full-time employed a,c 0.083**(0.025)

0.063(0.082)

0.115**(0.026)

0.(0.

part-time employed a,c 0.082*(0.032)

0.115(0.093)

0.102**(0.035)

0.(0.

studenta,c

0.376**(0.046) 0.352**(0.134) 0.427**(0.049) 0.(0.

unemployed a,c 0.128**(0.036)

0.334**(0.129)

0.112**(0.038)

0.(0.


12/22

1 3 6

G4

_H

S : A

uf t r

g e: H E L 0

0 2 : 1

5 1 7 4

_ S B

_K

y k l o

s _2

0 0 4 - 0 1 : 1

5 1 7 4 -A

0 3 : K y k l o

s _2

0 0 4 - 0 1

_ S - 0

0 3 -1 4

6

2 9 .1 1 . 9

6

2 8 . J

an u ar 2

0 0 4

0 9 : 1 2

Table 3 (continued)

model 1 model 2

all high edu-cation

medium and low education

all high edu-cation

medium and low education

foreign parents a 0.(0.

has lived abroad a 0.(0.

speaks English d 0.(0.

N 20 165 2 828 17 337 19 527 2 770 16 757 13 1

log likelihood 25 918 4 096 22 821 26 122 4 009 22 096 17 227

pseudo R2 0.050 e 0.052 e 0.045 e 0.051 e 0.053 e 0.046 e 0.

Notes: the dependent variable ranges from 0 (very unwilling) to 4 (very willing) (see Table 1 ). The coefcients foare depicted in Figure 1 . Regressions conducted for individuals aged 2060. Standard error in parenthesis.a dummy variables.

b reference category; middle education.c reference category; nonemployed.d all English-speaking countries were omitted (USA, Canada, Ireland, New Zealand, Great Britain). In addition, due to mis

Russia were excluded.e the pseudo- R2 measure is that of McFadden (1973)./ signicant at the 5%/1% level, respectively.


13/22


137


would generally expect positive self-selection. The results of the base regres-sion are, to our knowledge, the rst empirical evidence for the positive selec-

tivity-hypothesis of migrants based on a multi-country analysis of microsource-country data. In the highly qualied group, only gender and marital sta-tus had a signicant impact.

In model 2, we additionally controlled for employment variables. Comparedto the non-employed, both students and unemployed had a signicantly higher migration propensity.

In model 3, we added control variables for foreign parents, knowledge of English, and whether the respondent has lived abroad. Theoretically, these fac-tors should enhance migration propensity by lowering both psychic and actualmigration costs, particularly regarding information costs. Indeed, knowledge of English and prior migration have a positive and signicant impact on the mi-gration propensity. Nevertheless, the three characteristics mentioned abovehave to be interpreted with caution as they are highly correlated. In this regres-sion, we also had to exclude all English-speaking countries as well as Spain andRussia. In the latter two countries, the corresponding question had not been posed, while inclusion of the former would have obviously biased our analysis.

Table 4 depicts the results of a second set of ordered probit regressions. Thecountry dummies have now been replaced by each countrys respective PPP-adjusted GNP per capita value. Furthermore, several country-specic measure-

ments of income inequality were added. We used the standard Gini-coefcient,as well as the income share of the top ten per cent of the population, and theincome share of the upper ten per cent relative to the lower 20 per cent as alter-native measures. Furthermore, we ran three separate ordered probit regres-sions. Model 4 only included education variables as controls; model 5 also en-compassed gender, age and civil status. Model 6 included all of the former aswell as employment variables. The results in all settings were very similar.

Even though we have been controlling for GNP per capita (which had theexpected signicant negative inuence), the Gini-coefcient always had a pos-itive and highly signicant impact12. A higher income inequality thus leadsce-teris paribus to higher incentives to migrate. In Borjas (1987) analysis basedon US immigration data, however, countries with more income inequality hadlower migration rates. Such a negative coefcient is implied by the assumptionof wealth maximisation if there is negative selection.Vice versa , a positive co-efcient can be expected if there is positive self-selection: the migration incen-tives for the low-skilled increase while the highly skilled will still migrate. Yet,

12. This still applied after we additionally controlled for unemployment rates in 1995, which alsohad a signicant positive impact on migration inclination.


14/22


138


their migration incentive will be attenuated. This appears to be the case in our analysis, as the interaction term between the income inequality measurements

and the high education dummy variable was always negative, whereas the latter variable itself always remained positive.

Table 4

Desired emigration and income inequality ordered probits

Although the coefcients of ordered probit models do not have a simple inter- pretation, we can get an idea of the magnitude of the effect of income inequalityon migration inclinations by estimating imputed values of the dependent vari-able (see Wooldridge 2002, p. 507). According to standard migration theory,

model 4 model 5 model 6

gini 1: standard gini a 0.006**(0.001)

0.007**(0.001)

0.007**(0.001)

gini 1 high education 0.015**(0.003)

0.017**(0.003)

0.018**(0.003)

high education 0.750**(0.108)

0.846**(0.108)

0.873**(0.109)

gini 2: income shareof top 10% a

0.006**(0.002)

0.006**(0.002)

0.007**(0.002)


0.033**(0.005)

0.034**(0.005)


1.115**(0.127)

1.149**(0.129)

gini 3: income share of upper10% relative to lower 20% a

0.014**(0.004)

0.017(0.004)

0.018**(0.004)


0.047**(0.010)

0.050**(0.010)


0.474**(0.044)

0.481**(0.049)

Notes: the dependent variable ranges from 0 (very unwilling) to 4 (very willing) (seeTable 1 ).Model 4 only includes the education variables as controls; model 5 encompassed also gender, ageand civil status; model 6 comprises all of the former as well as employment variables (full-time, part-time, student, unemployed). All regressions included a PPP-adjusted GNP per capita variable.Regressions conducted for individuals aged 2060. Standard error in parenthesis.a values are taken from World Bank (2001). Note that for some countries, consumption data

were used instead of income data. For details, see the corresponding World Bank publication.*/** signicant at the 5%/1% level, respectively.


15/22


139


the typical migrant is a young, single male. Using this benchmark (assumingan age of 30), we calculated the imputed values of the propensity to migrate for

highlyvis--vis low-qualied persons (see Figure 2 ).Within the range of the Gini-measures in our sample, these highly qualiedindividuals always have a greater propensity to migrate. Thus, although thereis an attenuating effect of income inequality, it is not very large. To get a further

Figure 2

Imputed values for the emigration propensity

Note: the gure depicts values for a 30 year old, single male.

indication of the size of this effect, we also estimated the (marginal) effect of achange in the Gini-value on the propensity of being very willing to emigrate(score = 4).Ceteris paribus , a 10-point increase in the Gini-value decreases the probability of being very willing to emigrate by only about 1.5 per cent for thehighly qualied. The corresponding value for all other individuals is an in-crease in the probability to migrate by about 1 per cent. Importantly, and due to


16/22


140


the small size of the effect, it does not lead to a shift in the self-selection processfrom positive to negative self-selection13.

VI. CONCLUSIONS

Positive self-selection can be generally expected in international migration,even in the presence of high income inequality in the country of origin. The empirical analysis has shown that human capital has a persistently positive effecton migration propensity. Whereas this result is in contrast to Borjas (1987) prediction, his model is correct in as far as it shows that income inequality hasan effect on the selectivity of migrants: there is a clear attenuating effect of in-come inequality, but this does not lead to negative self-selection.

This conclusion is in line with that of Chiswicks (1999) and Chiquiar andHansons (2002) models of self-selection processes, which are based on a hu-man capital model. They predict that positive self-selection will even occur incountries with high inequality if migration costs are lower for the highlyskilled, although positive self-selection may be weakened. Our ndings supportthese models. Firstly, we found that a higher income inequality tends to foster emigration. In Borjas (1987) model, this is also expected if positive self-selec-tion is present: whereas the highly skilled still have an incentive to migrate, it

becomes increasingly attractive for the low-skilled to migrate as well. Sec-ondly, we observed that although positive self-selection is attenuated by higher income inequality, highly skilled individuals are still much more inclined to mi-grate. The inuence of income inequality on the selectivity of migrants is onlyof a second order. Thus, even when income inequality in the origin country ishigh, positive self-selection can be expected14.

These results also have important implications for policies seeking to attractskilled migrants using nancial incentives, particularly since a competition for skilled migrants seems to be currently emerging among OECD countries. Interalia , tax incentives have been introduced to attract highly skilled workers. In

13. These basic results also hold if we use sample means for the regressors instead of taking the presumably typical migrant as an example. We also ran OLS estimates, where the coefcientscan be directly interpreted. This also leads to the same conclusion.

14. Though our ndings are in accordance with the predictions of Chiswicks (1999) modelling of migration with xed migration costs and similar models of the self-selection process, theycould also be triggered by some alternative explanations. Positive self-selection might be dueto some unobserved variables. For example, there might be more discontent among the highlyskilled relative to the rest of the population. They might be more willing to migrate not becauseof stronger incentives, but rather because of an unease with the situation in their country, for which they may have a more pessimistic view than other groups of the population.


17/22


141


the Netherlands, for example, highly skilled foreigners benet from a 30 per cent income tax discount for ten years, which was specically initiated to be a

magnet for highly skilled migrants. Even egalitarian Sweden has introducedtax incentives for highly skilled foreign workers15. In France, stock option tax-ation has been relaxed to make the country more attractive for mobile workersin the ICT sector 16. In the US, highly skilled temporary immigrants are exemptfrom income tax for the rst three years (see OECD 2001b, p. 190). In this con-text, it is of particular interest to determine which factors inuence the locationchoice and the supply of highly skilled migrants, or, more generally, which per-sons nd it worthwhile to migrate to a certain host country. If Borjas predic-tion of negative self-selection in the case of high earnings inequality in the or-igin were right, countries with an equal income distribution would particularlyattract low-skilled people regardless of the level of earnings (which, of course, still matters for the overall size of the migration ow). This has impor-tant implications for zones of free migration, such as in the wake of Eastern EUenlargement: countries with a very equal income distribution, such as Germanyand Sweden, would then attract the low-skilled, whereas highly skilled wouldmigrate to countries like the UK 17. In light of our ndings, however, countrieswith a rather equal income distribution need not per se be concerned about ex- periencing adverse selection and attracting only low-skilled people. Even coun-tries with a rather equal income distribution can expect relatively skilled immi-

grants, though the positive selectivity will be more pronounced in hostcountries with a higher inequality18.

15. See Mahroum (2001), who also provides other examples.16. For a discussion of the role of stock option taxation in the competition for mobile highly skilled

labour, see Liebig (2001).17. For a comparative analysis of Germanys immigration policy, see Liebig (2003).18. This latter fact might explain why the UK (which has a relatively pronounced inequality) will

as the only major EU country immediately introduce full freedom of movement for the ac-cession countries once they have joined the union. Our results also cast some doubt on calls for tax incentives in order to remain competitive for highly skilled migrants.


18/22


19/22


143


Table A2

Summary statistics

REFERENCES

Atkinson, A. and F. Bourguignon (eds.) (2000). Handbook of Income Distribution . Amsterdam et al.:Elsevier.

Auriol, L. and J. Sexton (2001). Human Resources in Science and Technology: Measurement Issuesand International Mobility, in: OECD (ed.): International Mobility of the Highly Skilled . Paris:OECD: 13 38.

Bailey, A. (1993). A Migration History, Migration Behavior and Selectivity, Annals of Regional Science . 27: 315326.

Beenstock, M. (1996). Failure to Absorb: Remigration by Immigrants Into Israel, International Migration Review . 30: 950978.

Borjas, G. (1994). The Economics of Immigration, Journal of Economic Literature . 32 (4): 1667 1717.

Borjas, G. (1990). Self-Selection and the Earnings of Immigrants: Reply, American Economic Re-view. 80 (1): 305308.

Borjas, G. (1987). Self-Selection and the Earnings of Immigrants, American Economic Review . 77(4): 531553.

mean standard error

desired emigrationa 0.081 0.272

malea 0.475 0.499

age 39.108 11.263

high educationa 0.139 0.346

low educationa 0.190 0.392

marrieda 0.661 0.473

full-time employeda 0.592 0.491

part-time employeda 0.106 0.307

studenta 0.046 0.208

unemployeda 0.074 0.261

foreign parentsa 0.073 0.260

has lived abroada 0.182 0.385

speaks Englisha 0.449 0.497

N 17 399

Notes: due to the listwise deletion of variables the number of observations does not correspond to

that of the regressions in tables 3 and 4. Sample restricted to individuals aged 2060.a dummy variables


20/22


144


Borjas, G. and B. Bratsberg (1996). Who leaves? The Outmigration of the Foreign-born, Review of Economics and Statistics . 78 (1): 165176.

Burda, M., W. Hrdle, M. Mller and A. Werwatz (1998). Semiparametric Analysis of German East-

West Migration Intentions: Facts and Theory, Journal of Applied Econometrics. 13 (5): 525542.Chiquiar, D. and G. Hanson (2002). International Migration, Self-Selection, And the Distribution of Wages: Evidence from Mexico and the United States . Working Paper No. 9242, NBER.

Chiswick, B. (2000). Are Immigrants Favorably Self-Selected? An Economic Analysis, in: Brettell,C. and J. Hollield (eds.), Migration Theory: Talking Across Disciplines . New York: Routledge:6176.

Chiswick, B. (1999). Are Immigrants Favorably Self-Selected?, American Economic Review . 89 (2):181185.

Constant, A. and D. Massey (2002).Self-Selection, Earnings, and Out-Migration: A Longitudinal Study of Immigrants to Germany . Discussion Paper No. 672, IZA, Bonn.

Fidrmuc, J. (2001). Migration and Adjustment to Shocks in Transition Economies . Working Paper No.B23, Center for European Integration Studies, University of Bonn.

Finifter, A. (1976). American Emigration,Society. 13: 30 36.Gabriel, P. and S. Schmitz (1995). Favorable Self-Selection and the Internal Migration of Young

White Males in the United States, Journal of Human Resources . 30: 460471.Ghatak, S., P. Levine and S. Wheatley Price (1996). Migration Theories and Evidence: An Assess-

ment, Journal of Economic Surveys . 10 (2): 159198.International Organization for Migration (2002). New Challenges for Migration Policy in Central

and Eastern Europe . Geneva: International Organization for Migration.Jasso, G. and M. Rosenzweig (1990). Self-Selection and the Earnings of Immigrants: Comment,

American Economic Review . 80 (1): 298304.Jasso, G. and M. Rosenzweig (1988). How Well Do U.S. Immigrants Do? Vintage Effects, Emigra-

tion Selectivity, and Occupational Mobility, Research in Human Capital and Development . 6:229253.

Jasso, G., M. Rosenzweig and J. Smith (2000). The Changing Skill of New Immigrants to the United

States: Recent Trends and Their Determinants, in: Borjas, G. (ed.), Issues in the Economics of Immigration, NBER Conference Report. Chicago and London: University of Chicago Press,185255.

Kahn, S. and K. Lang (1991). The Effect of Hours Constraints on Labor Supply Estimates, Reviewof Economics and Statistics . 73: 605611.

Liebig, T. (2003). Recruitment of Foreign Labour in Germany and Switzerland, forthcoming in:OECD (ed.), Bilateral Labour Agreements and Other Forms of Recruitment of Foreign Workers .Paris: OECD.

Liebig, T. (2001). Is There a Tax Penalty on Stock Options in Germany?, Discussion Paper No. 73,Research Institute for Labour Economics and Labour Law, University of St. Gallen.

Massey, D., J. Arango, G. Hugo, A. Kouaouci, A. Pellegrino and J. Taylor (1993). Theories of Inter-national Migration: A Review and Appraisal, Population and Development Review . 19 (3): 431 466.

Mahroum, S. (2001). Europe and the Immigration of Highly Skilled Labour, International Migration.39 (5): 2743.

Manski, C. (1990). The Use of Intentions Data to Predict Behavior: A Best-Case Analysis, Journal of the Americal Statistical Association. 85 (412): 934940.

McFadden, D. (1973). Conditional Logit Analysis of Qualitative Choice Behavior, in: Zarembka, P.(ed.), Frontiers in Econometrics . New York: Academic Press: 105142.

OECD (2001a) (ed.). International Mobility of the Highly Skilled . Paris: OECD.OECD (2001b).OECD Employment Outlook . Paris: OECD.OECD (1996).OECD Employment Outlook . Paris: OECD.Roy, A. (1951). Some Thoughts on the Distribution of Earnings,Oxford Economic Papers . 3: 135

146.


21/22


145


Sjaastad, L. (1962). The Costs and Returns of Human Migration, Journal of Political Economy. 70(Supplement): 8093.

Tidrick, K. (1971). Need for Achievement, Social Class and Intention to Emigrate in Jamaican Stu-

dents,Social and Economic Studies. 20: 5260.Winkelmann, R. (2001). Why Do Firms Recruit Internationally? Results from the IZA InternationalEmployer Survey 2000, in: OECD (ed.), International Mobility of the Highly Skilled . Paris:OECD: 133150.

Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data . Cambridge/London:MIT Press.

World Bank (2001).World Development Report 2000/2001 . New York: Oxford University Press.World Bank (1998).World Development Indicators. Washington: The World Bank.

SUMMARY

In the context of an emerging focus on highly skilled migration throughout the OECD area, the ques-tion under which circumstances migrants can be expected to be relatively skilled is of particular importance. Borjas has analysed the relation between the income distribution and the skills of migrants.His self-selection model predicts that immigrants from countries with a higher income inequalitytend to be negatively selected (i.e., less skilled than the average worker in both host and source coun-tries). According to other models based on the human capital theory of migration, however, migrantscan be expected to be relatively skilled. Empirical tests of Borjas much-disputed negative self-se-lection hypothesis generally rely on immigration data, particularly to the US, and may therefore be biased due to host-country specics such as network migration and the impact of migration policy.This paper analyses the relationship between country-specic emigration propensities and eachcountrys score on various indices of income inequality with a rich international microdata set. The

main result is that highly-skilled persons are more inclined to migrate, though a higher income ine-quality attenuates the positive selectivity.

ZUSAMMENFASSUNG

Vor dem Hintergrund eines wachsenden Wettbewerbs um hochqualizierte Arbeitskrfte kommt der Frage, unter welchen Voraussetzungen Migranten ein hohes Qualikationsniveau aufweisen, beson-dere Bedeutung zu. Borjas hat den Zusammenhang zwischen Einkommensungleichheit und demQualikationsniveau der Migranten untersucht. Resultat seines Selbstselektionsmodells ist die Hy- pothese, dass Migranten aus Lndern mit einer hheren Einkommensungleichheit negativ selektiertsind (d. h., sie weisen ein niedrigeres Qualikationsniveau auf als der durchschnittliche Arbeitneh-mer in Sende- und Empfngerland). Andere Migrationsmodelle, die auf der Humankapitaltheorie ba-

sieren, fhren hingegen zur Hypothese, dass Migranten generell berdurchschnittlich qualiziertsind. Empirische Tests von Borjas umstrittener Selbstselektionstheorie basieren in der Regel auf Einwanderungsdaten, insbesondere aus den USA. Es besteht daher die Gefahr einer Verzerrungdurch den Einuss spezischer Charakteristika des Empfngerlandes, beispielsweise durch Netz-werkmigrationseffekte und Einwanderungspolitik. Dieser Aufsatz analysiert den Zusammenhangzwischen lnderspezischer Emigrationsneigung und Einkommensungleichheit anhand eines inter-nationalen Mikrodatensatzes. Zentrales Ergebnis ist, dass hochqualizierte Personen eine hhere Mi-grationsneigung haben. Dieser Effekt wird jedoch durch hhere Einkommensungleichheit abge-schwcht.


22/22


146

RSUM

Vu lattention croissante que suscite la migration des personnes hautement qualies dans toute la

zone de lOCDE, la question de savoir dans quelles circonstances on peut sattendre ce que les im-migrants soient relativement qualis revt une importance particulire. Borjas a analys la relationentre la distribution des revenus et les comptences des travailleurs immigrs. Son modle de propreslection prdit que les immigrants en provenance de pays ayant une ingalit des revenus leve onttendance tre ngativement slectionns (cest--dire, moins qualis que le travailleur moyen dansles deux pays). En revanche, dautres modles bass sur la thorie du capital humain prdisent queles immigrants devraient tre relativement qualis. Les tests empiriques de cette hypothse contro-verse de slection ngative se basent gnralement sur des donnes dimmigration, surtout vers lesEtats-Unis, et peuvent donc tre fausss en raison des particularits du pays daccueil, comme la mi-gration en rseau et la politique dimmigration. Cet article analyse le lien entre la propension mi-grer de diffrents pays et leur ingalit des revenus (mesure avec toute une srie dindices) laidedune base de donnes microconomiques internationale. Le rsultat principal est que les personneshautement qualies sont plus enclines migrer. Nanmoins, lingalit des revenus tend attnuer cette slection positive.

migration, self-selection and income inequality: an international analysis

Documents