Journal of Development Economics 95 (2011) 42–57

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Journal of Development Economics
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d eve c

Income maximization and the selection and sorting of international migrants
Jeffrey Grogger a, Gordon H. Hanson b,⁎ab

University of Chicago and NBER, United States
UCSD and NBER, United Statesa r t i c l e

i n f oArticle history:
Received 9 January 2010
Received in revised form 8 June 2010
Accepted 13 June 2010
JEL classiﬁcation:
F22
J61
Keywords:
International migration
Self selection
Wage differentials

a b s t r a c t
Two prominent features of international labor movements are that the more educated are more likely toemigrate (positive selection) and more educated migrants are more likely to settle in destination countries
with high rewards to skill (positive sorting). Using data on emigrant stocks by schooling level and source
country in OECD destinations, we ﬁnd that a simple model of income maximization can account for bothphenomena. Results on selection show that migrants for a source-destination pair are more educated relative
to non-migrants the larger is the absolute skill-related difference in earnings between the destination
country and the source. Results on sorting indicate that the relative stock of more educated migrants in a
destination is increasing in the absolute earnings difference between high and low-skilled workers. We use
our framework to compare alternative speciﬁcations of international migration, estimate the magnitude ofmigration costs by source-destination pair, and assess the contribution of wage differences to how migrants
sort themselves across destination countries.
© 2010 Elsevier B.V. All rights reserved.

1. Introduction
Most international migrants leave home bound for rich nations.
The United Nations (2006) estimates that in 2005, 40.9% of the global
emigrant population resided in just eight rich economies, with 20.2%
living in the US alone. In major destination countries, the number offoreign born is growing, reaching 12.5% of the total population in theUS, 11.2% in Germany, 10.5% in France, and 8.2% in the UK.1
Another striking feature of international labor ﬂows is that themore educated are those most likely to move abroad. Using data from
Docquier and Marfouk (2006), Fig. 1 plots the share of tertiaryeducated emigrants against the share of tertiary-educated nonemigrants by source country. Emigrants are generally positively
selected in terms of schooling, in that they are more educated thantheir non-migrant counterparts. This observation has renewedinterest in brain drain from developing economies.2
A second – and perhaps less appreciated – feature of internationalmigration is that countries with large absolute skill-related wage
differences attract a disproportionate share of more educated emigrants, a phenomenon we refer to as positive sorting. Table 1, also

⁎ Corresponding author.
E-mail address: gohanson@ucsd.edu (G.H. Hanson).
1
In 1990, these ﬁgures were 7.9% in the US, 7.3% in Germany, 6.3% in France, and 3.2%
in the UK, indicating that a substantial share of immigrants in these countries havearrived recently.
2
See Adams (2003), Beine, Docquier and Rapoport (2001, 2007, 2008), Docquier andRapoport (2008), Kapur and McHale (2005), and Docquier, Lohest, and Marfouk (2007).
0304-3878/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.jdeveco.2010.06.003

based on data from Docquier and Marfouk (2006), gives the shareof international migrants residing in OECD countries by major
destination region. The US and Canada, where skill-related earning
differences are relatively large, receive 51.4% of the OECD's immigrants, but 65.5% of its immigrants with tertiary schooling. Europe,
where skill-related earning differences are relatively small, receives
38.4% of the OECD's immigrants, but only 23.6% of its tertiaryschooled immigrants.
In this paper, we estimate a simple model of internationalmigration using the Roy (1951) income maximization framework.
The Roy model is the basis for a large body of work on migration(Borjas, 1999). In our application, an increase in the absolutedifference in earnings between high and low-skilled workers indestination countries causes migration from source countries to rise
and the mix of migrants to become more skilled. Using data from
Docquier et al. (2009) on the stock of migrants by education level andby source country residing in OECD destinations as of 2000, we ﬁndstrong support for the income maximization hypothesis.
A growing literature on global labor ﬂows considers the causes of
emigration from developing countries (Docquier et al., 2007), howaverage income and income inequality affect bilateral migration ﬂows
(Mayda, 2010; Clark et al., 2007; Ortega and Peri, 2009), and theimpact of labor productivity and log earnings inequality on skilledmigration (Rosenzweig, 2007; Brücker and Defoort, 2009; Belot andHatton, 2008). Much of this work is also based on the Roy model.
Our paper provides an integrated framework to examine the scaleof migration (the fraction of the population that emigrates), theselectivity of migrants in terms of schooling (the relative emigrationof high-skilled versus low-skilled individuals), and the sorting of

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

43Fig. 1. Share of emigrants and general population with 13+ years of schooling.

migrants by schooling level across destinations (the skill compositionof emigrants by destination).3 We use the framework to identify
sources of misspeciﬁcation in estimating the Roy model, address thenature of migrant selectivity, and evaluate the contribution of wage
differences to the destination choice of skilled migrants.
Our model delivers separate estimating equations for scale,selectivity, and sorting in migration. The model predicts that earnings
should have the same effect on each, but the speciﬁcations differ inthe data they require. The scale regression is the most data intensive,
calling for earnings by schooling level in the source and destinationand for data on ﬁxed migration costs. The selection regression, by
examining the difference between high and low-skilled migrationfrom a given source, eliminates ﬁxed migration costs from thespeciﬁcation. While results for both speciﬁcations support income
maximization, they reveal that by not controlling for unobserved
migration costs in the scale regression, we severely underestimate theimpact of earnings on migration.
The sorting regression, which uses ﬁxed effects to control for all
source-speciﬁc determinants of migration, examines the variation in
the skill mix of emigrants across destinations for a given source. Thisapproach means we can estimate the impact of earnings on migration
using destination-country wage data only. This is an advantage
because our source-country wage measures are coarser than thesurvey-based microdata that are available for destination-country
wages. Reassuringly, sorting regressions are similar for the two types
of wage measures. The sorting and selection regressions are similar aswell, as required by the theory.
The sorting regressions also allow us to examine the effects oftaxes. Post-tax earnings are a stronger correlate of migration than pretax earnings, consistent with migrants weighing tax treatment.
Previous studies of sorting focus on internal US migration (Borjas
et al., 1992; Dahl, 2002), and are silent on tax effects. Work on
international migration tends to focus on selection and ignore sorting.
A further contribution of our paper is to address conﬂicting resultson migrant selectivity. In seminal work, Borjas (1987) develops a
version of the Roy model which predicts that migrants who movefrom a source country with high returns to skill to a destination with
3
Other works use an alternative deﬁnition of selection based on the average
education level of migrants versus non-migrants. See Hanson (2010) for a discussion
and references in note 4 for recent examples.

low returns to skill should be negatively selected. Although the Borjas
framework performs well in explaining migration from Puerto Rico to
the US (Ramos, 1992; Borjas, 2008), it does less well in explaining
Mexico-to-US migration.4 Other work has tested the Roy model by
estimating the correlation between the skill composition of bilateralmigration and log income or log wage inequality. Those results are
sensitive to the sample of countries (Brücker and Defoort, 2009; Belotand Hatton, 2008).
To reconcile the Roy model with the strong positive selectivity
seen in Fig. 1, we compare two models. Our base speciﬁcation explains
migration with absolute wage differences, consistent with linear
utility. An alternative speciﬁcation explains migration with relative
wage differences, consistent with Borjas (1987) and with log utility.
The data strongly reject log utility, suggesting that migration responds
to absolute, not relative, rewards to skill. Since absolute skill-related
earning differences are much larger in destination than in source
countries, positive selection of migrants is a logical outcome. Of
course, other factors may also contribute to positive selection of
immigrants including liquidity constraints (McKenzie and Rapoport,
2010; Belot and Hatton, 2008).
In applications of our results, we use the selection regression toestimate bilateral migration costs between source and destination
countries, ﬁnding that these are often an order of magnitude larger
than source-country wages for low-skilled workers. We then use thesorting regression to decompose differences in immigrant skills acrossdestination countries into components due to wage differences,
language, distance, and other factors. Skill-related wage differences
are the dominant factor in explaining why the US and Canada receive
more skilled immigrants than other OECD destinations.
2. Theory and empirical speciﬁcation
2.1. Model of scale, selection and sorting in migration
Consider the stocks of migrants from many source countries
located in many destination countries. To be consistent with our data,

4
See Chiquiar and Hanson (2005), Orrenius and Zavodny (2005), McKenzie andRapoport (2010), Ibarraran and Lubotsky (2007), and Fernandez-Huertas (2011).

44

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

assume that workers fall into one of three skill groups, corresponding
to primary, secondary, or tertiary education. Let the wage for worker iwith skill level j from source-country s in destination-country h be5



j
2 2
3 3
Wish = exp μh + δh Dis + δh Dis ;ð1Þ

where exp(μh) is the wage for workers with primary education, δ2h(δ3h) is the return to secondary (tertiary) education, and Djis = 1 if
person i from source s has schooling level j.
Let Cjish be the cost of migrating from s to h for worker i with skill
level j, which we assume to have two components: a ﬁxed monetary
cost of moving from s to h, fsh; and a component that varies by skill, gjsh
(which may be positive or negative), such that
j

1

1

2

2

3

3

Cish = fsh + gsh Di + gsh Di + gsh Di :

ð2Þ

Migration costs are inﬂuenced by the linguistic and geographic
distance between the source and the destination and by destinationcountry immigration policies. The impacts of these characteristics may
depend on the migrant's skill due to time costs associated with
migration or skill-speciﬁc immigration policies in the destination.
Our primary interest is in a linear-utility model where the utility
associated with migrating from country s to country h is a linear
function of the difference between wages and migration costs as wellas an unobserved idiosyncratic term εjish such that


j
j
j
j
Uish = α Wih  Cish + εish ;ð3Þwhere α N 0. We think of Eq. (3) as a ﬁrst-order approximation tosome general utility function, with the marginal utility of income
given by α. One of the “destinations” is the source country itself, forwhich migration costs are zero.
Assuming that workers choose whether and where to emigrate so
as to maximize their utility, and assuming that εjish follows an i.i.d.
extreme value distribution, we can apply the results in McFadden(1974) to write the log odds of migrating to destination-country
h versus staying in the source-country s for members of skill group j
as6
Ej
ln shjEsjjj= αðWh  Ws Þ−αfsh −αgshð4Þwhere Ejsh is the population share of education group j in s that
migrates to h, Esj is the population share of education group j in s thatjremains in s, and Wjh = eμh + δh. Eq. (4) speaks to the scale of migration(in terms of the stock of individuals from s that choose to reside in h).
It says that income maximization, together with our assumptions
about utility and the error terms, implies that the skill group-speciﬁc
log odds of migrating to h from s should depend positively on the level
difference in skill-speciﬁc wages between h and s and negatively on
migration costs.

5
In Eq. (1), we do not allow for unobserved components of skill that may affectwages, which are of central concern in Borjas (1987, 1999). Since our data on migrant
stocks are aggregated by level of schooling and source country, it is not possible toaddress within education group heterogeneity in skill.
6
The speciﬁcation of the disturbance in Eq. (3) embodies the assumption that IIA
applies among destination countries. In the empirical analysis, the sample ofdestination countries is limited to OECD members. To use (4) as a basis for estimation,
we need only that IIA applies to the OECD countries in the sample. The analysis is thus
consistent with a more complicated nesting structure, in which we examine only theOECD branch of the decision tree. Alternatively, one might imagine that there are
multiple branches of the decision tree even among OECD destinations, such that IIA
fails. In the estimation, we test for this possibility, following the logic of Hausman and
McFadden (1984).

Table 1
Share of OECD immigrants by destination region and education, 2000.
Destination region

North America
EuropeAustralia and Oceania
All OECD

Education group
All

Primary

Secondary

Tertiary

0.514
0.384
0.102

0.352
0.560
0.088
0.355

0.540
0.349
0.111
0.292

0.655
0.236
0.109
0.353

This table shows the share of immigrants in OECD countries by schooling group anddestination region for North America (Canada, Mexico the US); Australia and Oceania(Australia, Japan, New Zealand, Korea), and Europe (other OECD members as of 2000).

To analyze emigrant selection, take the difference of Eq. (4)between tertiary- and primary-educated workers to yield:
3

3

E
E
3
3
3
1
1
1
ln sh
− ln s1 = α½ðWh  Ws  gsh Þ−ðWh  Ws  gsh Þ:
1
Esh
Es

ð5Þ

The ﬁrst term on the left of Eq. (5) is a measure of the skill mix ofemigrants from source s in destination h, which we refer to as the log
skill ratio (again, in terms of migrant stocks). The numerator is theshare of tertiary-schooled workers in s who migrate to h; thedenominator is the share of primary-schooled workers in s who
migrate to h. The second term on the left of Eq. (5) is the log skill ratio
for non-migrants in s, meaning the full expression on the left of Eq. (5)is the difference in skill mix between emigrants (from s to h) and nonmigrants for source-country s.
If the left side of Eq. (5) is negative, emigrants are negatively
selected; if it is positive, they are positively selected. Since α N 0,
Eq. (5) indicates that emigrants should be positively selected if thewage difference between the source and destination, net of skillvarying migration costs, is greater for high-skill workers. Note thatdifferencing between skill groups eliminates ﬁxed costs fsh from theselection Eq. (5).
To analyze the model's implications for how emigrants should sortthemselves across destinations, collect those terms in Eq. (5) that vary
only by source country to yield
E3
3
1
3
1
ln sh
= αðWh  Wh Þ−αðgsh  gsh Þ + τs
1
Esh

ð6Þ

where τs = ln(E3s / E1s ) − α(W3s − W1s ). Fixed costs do not appear in thesorting Eq. (6) because they are absent from the selection Eq. (5).
Eq. (6) states that emigrants from a given source country shouldsort themselves across destinations by skill according to the rewards
to skill in different destinations. If the (net) rewards to skill are higher
in destination h than in destination k, then destination h should
receive a higher-skilled mix of emigrants from source-country s thanshould destination-country k. Put differently, higher skill-related
wage differences should give destination countries an advantage incompeting for skilled immigrants.
2.2. Relationship to earlier research
The model summarized in Eqs. (4), (5), and (6) highlights the roleof ﬁxed costs and absolute wage differences in inﬂuencing the scale,selectivity, and sorting of migration ﬂows. In contrast, much of theliterature focuses on relative returns to skill and assumes migration
costs are proportional to income (see Borjas, 1991, 1999). To compare
these two frameworks, consider a model where wages and migration
costs are as before, but utility is logarithmic and the error term is
proportional rather than additive:j

Uish =


λ


j
j
j
Wih  Cishexp υishð7ÞJ. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57
j
We assume λ N 0 and υishfollows an i.i.d. extreme value distribution. The analogues to the scale, selection and sorting Eqs. (4)–(6)for this model are given by

j

E
j
j
j
ln shj = λðln Wh − ln Ws Þ−λmsh
Es
3

ð8Þ

3

E
E
3
3
3
1
− ln s1 = λðδh −δs Þ−λðmsh −msh Þ
ln sh
1
Esh
Es

ð9Þ

E3
3
3
1
ln sh
= λδh −λðmsh −msh Þ + ρs
1
Esh

ð10Þ

j

where mjsh = (fsh − gjsh) / Wh and ρs = ln(E3s / E1s ) − λδ3s .7 In the logutility model, the scale of migration is inﬂuenced by the relative wage
difference between the source and destination countries (see Eq. (8)),
and selectivity and sorting are functions of returns to skill, as givenby the δ terms, rather than skill-related level wage differences (seeEqs. (9) and (10)).
With log utility, differencing between skill groups does not ingeneral eliminate migration costs from the selection or sorting
Eqs. (9) and (10). Where skill-varying costs are proportional tojwages, such that gjsh = πshWh, differencing between skill groups
eliminates skill-varying costs, but not ﬁxed costs. Since much of theliterature focuses on models where skill-varying costs are assumed to
be proportional to wages and ﬁxed costs are assumed to be zero, it is acase of special interest.
Examining conditions for migrant selectivity provides a useful wayof comparing our linear-utility model with ﬁxed migration costs to themore standard log-utility model with proportional migration costs. To
analyze our linear-utility model, substitute Eq. (1) into Eq. (5),rearrange terms, and make use of the fact that eδ − 1 ≈ δ. Our linearutility model then predicts that emigrants should be negatively
selected in terms of skill if
"
δ3s
Wh1
N
δ3h
Ws1

!−1 #
3
gsh
1+
:
ðWs3  Ws1 Þ

ð11Þ

In the case where g3sh = 0 (i.e., ﬁxed migration costs are independent of skill), the condition for negative selection is δ3s / δ3h N W1h / W1s .
Under log utility, zero ﬁxed costs, and skill-varying costs that areproportional to wages, Eq. (9) shows that negative selection willobtain if δ3s / δ3h N 1, as in Borjas (1987).
With north-to-north migration similar productivity levels between the source and the destination imply that low-skill wages arealso similar, such that W1h ≈ W1s . In that case, both models predict thatemigrants who move from a source with high returns to skill to a
destination with low returns should be negatively selected. However,
the models make different predictions in the context of much southto-north migration, where differences in productivity imply thatW1h N N W1s . Here, our linear-utility model predicts negative selection
only when the relative return to skill in the source country (δ3s / δ3h)
exceeds the relative productivity advantage of the destination country(W1h / W1s ).87
In deriving Eq. (8), we use the approximation that ln(W − C) ≈ lnW − C / W for a
sufﬁciently small C / W. Eq. (9) follows from the fact that lnW3h − lnW1h = δ3h. Belot andHatton (2008) use the Roy model to motivate an empirical speciﬁcation similar toEq. (9).
8
Factoring in skill-speciﬁc migration costs makes predictions about selection evenmore ambiguous in the linear-utility/ﬁxed cost model. Recall that skill-speciﬁc costs in
Eq. (11), g3sh, may be positive or negative. If more skilled workers tend to have higher(lower) costs, the likelihood of negative selection would be higher (lower) than thebase case of no skill-speciﬁc costs.

45

To preview the data, Fig. 2 plots the log odds of having emigrated
for the tertiary-educated against that for the primary-educated bysource country, which is the dependent variable in Eqs. (5) and (9).
Nearly all points are above the 45 degree line, indicating positive
selection. Fig. 3a plots the log ratio of incomes at the 80th and 20th
percentiles against log income at the 20th percentile for the sample
countries.9 The series are negatively correlated, suggesting poor
countries have larger relative differences in income between the highand low-skilled. This is consistent with evidence that the returns toschooling tend to be higher in developing than in developed countries(Psacharopoulos et al., 2004; Hanushek and Zhang, 2006). Since mostdestination countries are clustered in the lower right of the graph, thelog-utility model predicts emigrants from developing countries
should be negatively selected, at odds with Fig. 2.
Fig. 3b plots the level difference in incomes between the 80th and20th percentiles against the level of income at the 20th percentile. The
strong positive relation indicates that rich countries tend to havelarger absolute income differences between the high and low-skilled.
With most destination countries in the upper right of the graph, linear
utility implies that emigrants should be positively selected, whichconcords with Fig. 2. These graphs are initial evidence the data favorlinear utility over log utility.
What is the intuition behind absolute wage differences being abetter predictor of migration patterns than relative wage differences?
Linear utility and log-linear utility are each simple frameworks that
abuse reality in different ways. Log-linear utility implies that thecurvature of the utility function is relevant for comparisons of all
possible destination pairs. Given the extreme income differences thatexist internationally, such an implication is problematic. Decreasing
marginal returns to income are unlikely to matter equally whencomparing incomes of $3000 a year in Ecuador to $15,000 a year in
Spain as when comparing $8000 a year in Turkey to $40,000 a year inGermany, yet log-linear requires that they do. The linear-utility modelis freed from this restriction. It has the unrealistic implication that
decreasing marginal returns to income are never an issue, to which
one could surely ﬁnd counterexamples. Yet, given the vast income
differences that exist between countries in our data this implication of
linear utility appears to abuse reality less that the strong curvature of
log-linear utility.
2.3. Estimation
The models we develop make it straightforward to derive
estimating equations which can be used to test for income
maximization. Let xsh be a vector of characteristics of the sourcedestination pair, such as geographic and linguistic distance, and let
skill-varying costs be given by gjsh = xshθ j. The empirical version of thescale equation is
j

Ê
j
j
3
j
ln shj = αðWh  Ws Þ + xsh β + Iðj = 3Þ⋅xsh β + ηsh
Ês

ð12Þ

where β3 = −αθ3; I(A) is the indicator function such that I(A) = 1 if A
is true and I(A) = 0 otherwise; hat notation denotes statisticaljaverages; ηsh= ln(Êjsh / Êjs) − ln(Ejsh / Ejs) is an error term reﬂecting
sampling error; and we have assumed that − αfsh = xshβ. The
empirical selection and sorting equations are given by
3

3

Ê
Ê
3
1
3
1
ln sh
− ln s1 = α½ðWh  Wh Þ  ðWs  Ws Þ + xsh γ + η′sh ;
1
Êsh
Ês

9

We discuss construction of income measures in Section 3.

ð13Þ

46

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

Fig. 2. Emigration odds (primary and tertiary-educated) by source country, 2000.

3

Ê
3
1
ln sh
= αðWh −Wh Þ + xsh γ + τs + ηsh ;
1
Êsh

ð14Þ

where γ = − α(θ3 − θ1), η′sh = η3sh − η1sh, and ηsh = ln(Ê3sh / Ê1sh) − ln
(E3sh / E1sh).10
The key hypothesis is that α N 0. If the models are properly
speciﬁed, all three equations should yield similar estimates of α. To
estimate the scale Eq. (12) we must assume ﬁxed costs are a functionof observable characteristics. If that assumption fails, the scaleequation may be misspeciﬁed. Fixed costs are differenced out of theselection and scale equations, so they should provide a more robust
basis for inference. The scale and selection equations require data on
both source and destination wages. This limits the sample, sincereliable wage data are not available for all source countries. The
sorting equation requires only destination wage data, increasing thenumber of countries that can be used in the estimation.
For comparison, we also estimate the log-utility model. In theimportant special case where ﬁxed costs are zero and skill-varyingjcosts are proportional to wages, such that λmjsh = − λgjsh / Wh =−λπsh, the empirical counterparts of Eqs. (8)–(10) are
j

ln

Êsh
j

Ês
3

j

j

j

= λðln Wh − ln Ws Þ + xsh θ + ηsh ;

ð15Þ

3

Ê
Ê
3
3
− ln s1 = λðδh −δs Þ + η′sh ;
ln sh
1
Êsh
Ês

ð16Þ

3

Ê
3
= λδh + ρs + ηsh ;
ln sh
1
Êsh

ð17Þ

10
Eqs. (15)–(17) are derived from (4)–(6), which imply bilateral migrant stocks are
positive, owing to the fact that for any source-destination pair at least a few
individuals will have sufﬁciently positive valuations of migration in Eq. (3) to justify
moving abroad. In ﬁnite populations, however, small probabilities of migration may
produce zero stocks. For this reason, in the estimation we will drop two very small
destination countries from the analysis, Iceland and Luxembourg. Among theremaining countries, zero stocks are common for only a single destination, Ireland,
which is the smallest destination in the sample.

where we assume that − λπsh = xshθ. The test for income maximization is that λ N 0. If the models are properly speciﬁed, all threeequations should yield similar estimates of λ.
3. Data and empirical setting
We base our regression analysis on data from Docquier et al.
(2009); hereafter, DLM), which update the data from Docquier andMarfouk (2006) discussed in Section 1. DLM tabulate data on stocks ofemigrants by source and destination country. In collaboration with
the national statistical ofﬁces of OECD countries, they estimate thepopulation in each OECD member of immigrants 25 years and older bysource country and education level. In some OECD destinations, thesecounts are based on census data, whereas in others they are based onregister data. DLM classify schooling levels into three categories:
primary (0–8 years), secondary (9–12 years), and tertiary (13 plus
years). Because education systems differ so much among countries, itis nearly impossible to categorize schooling at a ﬁner level of detail.11
3.1. Measurement of emigrant stocks
We focus on migration to the world's primary high income destinations. As a starting point, the OECD is a somewhat arbitrary group ofcountries. It consists of the world's rich economies (Australia, Canada,
EU 15, Iceland, Japan, New Zealand, Norway, Switzerland, US), as well as
an eclectic group of developing economies including some recent EU
members (Czech Republic, Hungary, Poland, Slovakia) and an assorted
mix of other middle income nations (Korea, Mexico, South Africa,
Turkey). To focus on migration to high income economies, we exclude
from the sample the eight developing nations among OECD members,
many of which are major source countries for migrants. As discussed
below, we also exclude Belgium and Italy, owing to these countries notenumerating immigrants by education level, and Greece, Japan,
Portugal, and Switzerland, owing to the absence of comparable
microdata on income for these countries.12 The resulting sample
11
DLM include separate counts of male and female emigrants. We use total
emigration, without a gender breakdown, owing to the difﬁculty of obtaining sex
speciﬁc wage measures, as would be necessary for empirical analysis.
12
We also exclude tiny Iceland and Luxembourg whose extremely small population
sizes (281,000 and 438,000 in 2000, respectively) complicate comparison with larger
destinations (see note 10).

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

47

Fig. 3. a. Log differences in income by country. b. Absolute differences in income by country (000 s USD).

includes 15 high income nations: Australia, Austria, Canada, Denmark,Finland, France, Germany, Ireland, the Netherlands, New Zealand,
Norway, Spain, Sweden, the UK, and the US. In 2000, these countries
accounted for 87.6% of immigrants residing among OECD members.
Aggregating data from multiple destination countries raises comparability issues. Some countries, such as Germany, deﬁne immigrants on
the basis of country of citizenship rather than country of birth. This
causes some of the foreign born to be excluded from DLM's immigrantcounts in these countries. We check the robustness of our regression
results by dropping such countries from some of the speciﬁcations. In
Belgium and Italy, the statistical ofﬁce reports aggregate immigrantcounts but does not disaggregate by education. DLM imputes the skill
distribution of immigrants in such cases using data from household
surveys. In light of the role that education plays in our analysis, we drop
Belgium and Italy from the sample of destinations.
National statistical ofﬁces differ in how they classify educational
attainment. Some countries' classiﬁcation systems have no attainment
category that distinguishes whether a person who lacks a secondaryschool qualiﬁcation (such as a high school diploma) acquired any

secondary education, or whether their schooling stopped at the
primary level (grade 8 or below). This could result in inconsistencies inthe share of primary-educated immigrants across destination
countries. In our regressions we control for whether the destination
country explicitly codes primary education.
Some immigrants may have acquired their tertiary schooling in thedestination country. By implication, they might have obtained lessschooling had they not migrated. Beine et al. (2007) provide evidence
on this point in the form of immigrant counts for those with tertiary
education that vary by the age at which migrants arrived in thedestination country (any age, 12 years or older, 18 years or older,
22 years or older). They ﬁnd that 68% of tertiary migrants arrive in thedestination country at age 22 or older and 10% arrive between ages 18
and 21, suggesting the large majority of tertiary emigrants departsending countries at an age at which they would typically haveacquired at least some post-secondary education. Reassuringly, thecorrelations in emigration rates by age at migration range from 0.97 to
0.99. In Section 4.2 we provide additional checks on the importance oftertiary schooling acquired in the destination.

48

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

Table 2
Share of emigrants to OECD by source country and destination region, 2000.
Destination region
Source country

All OECD

N America

EuropeAus. and Oceania

MexicoUK
Italy
Germany
TurkeyIndia
China
Philippines
Vietnam
Portugal
Korea
PolandMorocco
Cuba
Canada

0.113
0.053
0.042
0.038
0.035
0.030
0.030
0.030
0.022
0.022
0.021
0.020
0.019
0.015
0.015

0.219
0.041
0.027
0.028
0.003
0.038
0.039
0.046
0.032
0.011
0.025
0.019
0.002
0.028
0.025

0.001
0.027
0.062
0.049
0.085
0.023
0.009
0.007
0.008
0.040
0.002
0.024
0.048
0.002
0.004

0.000
0.206
0.038
0.045
0.005
0.018
0.066
0.030
0.026
0.002
0.075
0.010
0.000
0.000
0.006

This table shows the share of immigrants accounted for by the 15 largest source
countries for migrants to OECD destination countries.

Although our theoretical framework treats migration as a permanent
decision, many migrants do not remain abroad forever. There isconsiderable back-and-forth migration between neighboring countries(Durand et al., 2001), which we address by controlling for sourcedestination proximity. Furthermore, some migrants are students whowill return to their home countries after completing their education.
These migrants may have been motivated by educational opportunities
in destination countries, as well as wage differences (Rosenzweig,2007). DLM partially address this issue by restricting the foreign born tobe 25 years and older, a population that should have largely completed
its schooling. In Section 4.2 we attempt to control for differences in
educational opportunities between source and destination countries.
Tables 1 and 2 describe broad patterns of migration into OECD
countries. As noted in Section 1, Table 1 shows that North America
receives disproportionately high-skilled migrants, whereas Europe's
immigrants are disproportionately low-skilled. Table 2 shows theshare of OECD immigrants by country of origin for the 15 largest
source countries. Source countries tend to send emigrants to nearby
destinations, as is evident in Turkish migration to Europe, Korean
migration to Australia and Oceania, and Mexican and Cuban migration
to the United States. Yet, most of the source countries in Table 2 send
migrants to all three destination regions.
One important issue for the empirical analysis is the presence of zerobilateral migrant stocks. Based on the law of large numbers, theory
predicts that all bilateral stocks will be positive, though some may bevery small. In ﬁnite populations, however, zero migration stocks may
occur, if bilateral migration probabilities are small. Zero migration stocks
turn out to be a signiﬁcant problem for just one destination country,
Ireland, which has the smallest population among the 15 destinations.
We have income data for 102 origin countries. Out of the potential 1530
observations, there are 137 cases with zero migration stocks for either
high or low-skilled categories, which is 9.0% of the sample. Of thesezeros, 57 are for Ireland. Thus, ignoring Ireland, just 5.6% of theobservations involve zero stocks.13 In the estimation, we discuss
solutions to the presence of zero migration stocks in the data.
3.2. Wage measures
The key explanatory variables in our regression models arefunctions of skill group-speciﬁc wages in the source and destination

13
For some countries, migrant counts are based on subsamples of the population, in
which case zero values may be the result of sampling error rather than true zeromigration.

countries.14 Ideally, we would estimate wages by broad education
category from the same sources used by DLM. Since such data are notavailable to us, we turn to different sources.15
Our ﬁrst source is the Luxembourg Income Study (LIS (various years),
which collects microdata from the household surveys of 30 primarily
developed countries worldwide. Among the countries in the DLM data,
the LIS provides no data or incomplete data on Greece, Japan, Portugal,
and Switzerland, leading to their exclusion from the analysis. We use datafrom waves 4 and 5 of the LIS, which span the years 1994–2000. Although
the LIS attempts to “harmonize” the data from different countries, anumber of comparability issues arise. One limitation is that the LIS's
constituent household surveys sometimes classify educational attainment differently than the national statistical ofﬁce of the corresponding
country. This adds the problem of within-country comparability to thealready difﬁcult problem of between-country comparability. Ultimately,
it proved impossible for us to map education categories between the DLM
and the LIS data in a manner in which we had full conﬁdence.
Therefore, instead of using education-speciﬁc earnings to measureskill-related wages, we use quantiles of each country's earnings
distribution. We use the 20th percentile as our measure of low-skill
wages and the 80th percentile as our measure of high-skill wages.16
We average across 1994 to 2000 for each country in the LIS.
Although the cross-country comparability of the LIS is a desirable
feature, we can only use the LIS to estimate our sorting regressions.
The reason is that it provides wage data only for our destination
countries, whereas the scale and sorting regressions require comparable wage data for the source countries as well. To the best of ourknowledge, there is no study that provides micro-level wage data forthe 100+ source countries we examine. We rely on two sources ofaggregate data to construct the source-destination wage difference
measures needed to estimate the scale and sorting regressions.
One combines Gini coefﬁcients from the WIDER World Income
Inequality Database with per capita GDP from the World Development
Indicators (WDI). Under the assumption that income has a log normal
distribution, Gini coefﬁcients can be used to estimate the variance of log
income. Using per capita GDP to measure mean annual income, we canthen construct estimates of the 20th and 80th percentiles of income,which we are able to do for 102 source and 15 destination countries.
A second source uses data from Freeman and Oostendorp (2000);
hereafter FO), who have collected information on earnings by
occupation and industry from the International Labor Organization's
October Inquiry Survey. FO standardizes the ILO data to correct fordifferences in how countries report earnings. The resulting datacontain observations on earnings in up to 163 occupation-industries
per country in each year, from which FO constructs deciles forearnings by country and year. For each country, we take as low-skill
wages earnings corresponding to the 10th percentile and as high-skill
wages earnings corresponding to the 80th percentile. These deciles
give the highest correlations with 80th and 20th percentile wages inthe LIS. Since not all countries report data in all years, for each country
we take the mean across the period 1988 to 1997, creating a sample
with 101 source countries and 12 destinations.
14
An Appendix provides more detail on the construction of all wage measures. The
data are available for download at http://harrisschool.uchicago.edu/faculty/webpages/jeffrey-grogger-migration-ﬁles.asp.
15
Bilateral stocks of immigrants in 2000 are the cumulation of migration overprevious decades. Our wage measures are averages over the 1990s, which covers aperiod when there have been substantial contributions to the stocks of migrants indestination countries (see note 1 and Hanson, 2010).
16
In a previous version of the paper (Grogger and Hanson, 2007), we experimented
with alternative wage variables based on various measures of low-skill wages and thereturn to skill (the standard deviation of income, the Gini coefﬁcient). These
alternatives generated results similar to those we report in this paper. We also
experimented with using 90th and 95th percentile earnings to measure high-skilledwages in the source countries, considering that in many countries only 5 to 10% of the
population receives tertiary education. Those results were also similar to those wereport here.

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57
Table 3
Summary statistics for wage data.
A: Destination countries
Low-skill

High-skill

DifferenceN

Source

(1)

(2)

(3)

(4)

Pre-tax wage
LIS
WDI
FO

20.12
8.18
15.71

41.87
21.9
26.25

21.76
13.71
10.54

13
15
12

Post-tax wage
LIS
WDI
FO

13.02
5.22
10.03

23.41
12.15
14.56

10.39
6.94
4.54

13
15
12

2.42
3.97

6.99
7.67

4.57
3.71

102
101

B: Source countries
Pre-tax wage
SourceWDI
FO

See appendix for details on construction of wage variables.

Table 3 presents summary statistics of these wage measures. The
top two panels provide data for the destination countries. The top
panel shows that the LIS produces higher wages and larger skillrelated wage differences than the other sources. Despite thedifferences in scale, the correlation between skill-related wage
differences in the LIS and the WDI data is 0.86; between the LIS andthe FO data it is 0.78.
The second panel reports summary statistics for after-tax
measures of destination-country wages. We consider such measuressince pre-tax wage differences overstate the return to skill enjoyed byworkers and since tax policy varies within the OECD (Alesina andAngeletos, 2005). To construct post-tax wage differences we employ
average tax rates by income level published by the OECD since 1996
(OECD, 2007).17 To 20th percentile earnings we apply the tax rateapplicable to single workers with no dependents whose earnings
equal 67% of the average production worker's earnings. To 80th
percentile earnings we apply the tax rate applicable to a comparable
worker with earnings equal to 167% of the average production
worker's earnings. After-tax wage differences are only about half aslarge as pre-tax differences.18
The third panel provides data for the source countries. Only WDI
and FO data are shown, since the LIS provides no source-country data.
Source-country wages vary less than destination-country wages
between the two sources; the correlation between skill-related
wage differences is 0.91. Unfortunately, we have no tax data formost of our source countries. Thus the scale and selection regressions
below are estimated only from pre-tax wage data, whereas we reportsorting regressions for pre- and post-tax wages.
3.3. Other variables in the regression model
Differences in language between source and destination countries
may be relatively more important for more educated workers, sincecommunication and information processing are likely to be salient
aspects of their occupations. We control for whether the source anddestination country share a common ofﬁcial language based on datafrom CEPII (http://www.cepii.fr/). Similarly, English-speaking countries
17
Prior to averaging income across years, we match to each year and income groupthat year's corresponding tax rate. Since the tax data only go back to 1996, we use tax
rates for that year to calculate post-tax income values in 1994 and 1995.
18
We assume natives and immigrants face the same tax rates. Recently, somecountries have begun to offer tax breaks to high-skilled immigrants to encourageskilled immigration (e.g., the Netherlands). This practice appears to have been
uncommon during the 1990s, which is the period for our income data.

49

may attract skilled emigrants because English is widely taught in school
as a second language. To avoid confounding destination-country
skilled–unskilled wage differences with the attraction of being in an
English-speaking country, we control for whether a destination country
has English as its primary language.
Migration costs are likely to be increasing in distance between
countries. Proximity may make illegal immigration less costly,
thereby increasing the relative migration of the less-skilled. We
include as regressors great circle distance, the absolute difference inlongitude, and an indicator for source-destination contiguity. Migration networks may lower migration costs (Munshi, 2003), possibly
beneﬁting lower-income individuals more (Orrenius and Zavodny,
2005; McKenzie and Rapoport, 2010). As networks may be stronger
between countries that share a common colonial heritage, we include
CEPII's indicators of whether a pair of countries have short or longcolonial histories. We also control for migrant networks using lagged
migration, measured as the total stock of emigrants from a source
country in a destination as of 1990.19
Destination countries impose conditions in deciding which
immigrants to admit, many of which involve education. One indicatorof skill bias in a country's admission policies is the fraction of visas it
reserves for refugees and asylees. Less-educated individuals may be
more likely to end up as refugees, making countries that favor
refugees in their admissions likely to receive more less-educatedimmigrants. We control for the share of immigrant inﬂows composedof refugees and asylees averaged over the 1992–1999 period (OECD,2005).20 European signatories of the Schengen Agreement havecommitted to abolish all border barriers, including temporary
migration restrictions, on participating countries. We control forwhether a source-destination pair were both signatories of Schengenas of 1999. Similarly, some countries do not require visas for particularsource countries, with the set of visa waiver countries varying acrossdestinations. While visa waivers strictly affect only tourist andbusiness travelers, they may indicate a source-country bias that also
applies to other immigrant admissions. We control for whether adestination country grants a visa waiver to individuals from a source
country as of 1999. Clearly, other aspects of policy may inﬂuence
migration as well. Unfortunately, existing data give little indication of
how immigration policies vary across destinations. As important as
immigration policy may be, existing data simply do not permit a moredetailed characterization of the policy environment.21
Finally, note that the regressors used in the analysis vary either bydestination or source-destination pair. Other source-country-speciﬁccharacteristics could also affect migration. Some, such as the state of
the credit market or the poverty rate, are observable and could becontrolled for explicitly. Others, however, are unobservable. Rather
than controlling for a limited set of observable source-country
characteristics explicitly, we provide implicit controls for bothobservable and unobservable source-country characteristics via thesource-country ﬁxed effects in the sorting regression.22
19
Because we are missing lagged migration for many observations in the sample, we
add the variable only in later speciﬁcations. All results are robust to its inclusion.
20
Countries also differ in the share of visas that they reserve for skilled labor.
Unfortunately, we could only obtain this measure for a subset of destination countries.
Over time, the share of visas awarded to asylees/refugees and the share awarded toskill workers are strongly negative correlated (OECD, 2005), suggesting policies onasylees/refugees may be a sufﬁcient statistic for a country's immigration priorities.
21
Ortega and Peri (2009) use measures of immigration policy from Mayda (2010) in
their analysis of immigration in the OECD. As the Mayda variables measure changes inimmigration policy, with no information on initial policy levels, it is suitable forstudying migration ﬂows but not migration stocks.
22Docquier et al. (2007) examine the source-country correlates of the scale ofmigration. In unreported results, we experimented with two source-speciﬁc variables.
Private credit to the private sector as a share of GDP is a measure of the ﬁnancial
development of the source country (Aghion et al. 2009), which may affect constraints
on ﬁnancing migration. The incidence of poverty in the source country, as proxied by
the share of agriculture in GDP (Belot and Hatton, 2008), may also affect credit
constraints. The inclusion of neither variable affected our core results.

50

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

Our main regression analyses are based on the scale, selection, andsorting regressions derived from the linear-utility model, as shown in
Eqs. (12)–(14), respectively. Our main results are based on wage
measures constructed from the WDI and LIS data. Estimates arereported in Table 4.
In the scale equation reported in column (1), the unit ofobservation is the source-destination-skill group cell, with one
observation for the primary-educated (j = 1) and one observation
for the tertiary-educated (j = 3) for each source-destination pair. The
dependent variable is the log odds of emigrating from source s todestination h for members of skill group j, and the wage measure is
the skill-speciﬁc difference in pre-tax wages between the destinationjand source countries, Wh − Wjs. In the selection equation reported incolumn (2), the unit of observation is the source-destination pair.23
The dependent variable is the difference between the log skill ratioof emigrants from s to h and the log skill ratio of non-migrants insource s.24 The wage measure is the difference between thedestination and the source in skill-related pre-tax wage differences,(W3h − W1h) − (W3s − W1s ). In the sorting equations reported in columns (3) through (6), the unit of observation is again the sourcedestination pair, but the dependent variable is the log skill ratio of
emigrants from s to h. The key independent variable is the skillrelated wage difference of the destination country, (W3h − W1h). Like
the scale and selection regressions, the sorting regressions in columns(3) and (4) are based on the WDI data; column (3) is based on pre-tax
data, whereas column (4) is based on post-tax data. Columns (5) and(6) are based on pre- and post-tax data from the LIS.
Because the dependent variables have a log-odds metric, themagnitude of the regression coefﬁcients does not have a particularly
useful interpretation. We focus in this section on the signs andsigniﬁcance levels of the coefﬁcients. Below, we discuss applications
that provide information about the quantitative effects of key
variables. In addition to the variables shown, all of the regressions
include a dummy variable equal to one if the destination-country
statistical ofﬁce explicitly codes a primary education category. This
controls for systematic differences in our dependent variable that arise
from different coding schemes, as discussed in Section 3. The scale
regression includes a dummy variable equal to one for observations
corresponding to the tertiary-educated skill group, denoted I(j = 3),
and interactions between that dummy and all other regressors (notshown to save space). The sorting regressions include a full set ofsource-country dummies. Standard errors, reported in parentheses,
are clustered by destination country.
The wage coefﬁcients in columns (1) through (3) are directlycomparable because they are all based on pre-tax data from WDI. In
the context of our model, they each provide estimates of the same
parameter α, where income maximization implies α N 0. Furthermore,
if the regression models are properly speciﬁed, the coefﬁcients fromscale, selection, and sorting regressions should be the same.
In Table 4, all three wage coefﬁcients are positive, as predicted bytheory. The coefﬁcients in the selection and sorting regressions are
similar in value and statistically signiﬁcant. However, the coefﬁcientin the scale equation is smaller and insigniﬁcant. This may indicatethat omitted ﬁxed costs result in a misspeciﬁed scale equation. In thescale equation we assume ﬁxed costs are a function of observablecharacteristics of the source-destination pair, while the selection andsorting regressions difference out ﬁxed costs. The difference in wage
coefﬁcients between the scale and selection regressions suggests thescale equation omits ﬁxed costs that are negatively correlated withthe difference in skill-speciﬁc wage differences between destinationand source countries.
The wage coefﬁcient in column (4) suggests that migrants sort
more strongly on post-tax wages than pre-tax wages, as one might
expect. The estimates in columns (5) and (6), based on wage datafrom the LIS, show a similar pattern. Both coefﬁcients are positive andsigniﬁcant, and the coefﬁcient on post-tax wages in column (6) islarger than the coefﬁcient on pre-tax wages in column (5). Among thedestination countries in the sample, the U.S and Canada haverelatively large pre-tax skill-related wage differences. Since thesecountries also have less progressive tax systems, their relative
attractiveness to skilled migrants is enhanced by accounting for taxes.
The regressions also include variables reﬂecting geographic,
linguistic, social, and political relationships between countries. The
positive coefﬁcient on the Anglophone-destination dummy in column(1) shows that English-speaking countries receive more immigrants
than other countries, all else equal. The coefﬁcient in the selection
regression (column (2)) shows that emigrants bound for English-

23
In the WDI data, there are 15 destinations and 102 source countries. Since source
countries do not send emigrants to every destination country, the number ofobservations is less than 1 × 102 = 1530.

24
Equivalently, the dependent variable can be seen as the difference in the log odds
of migrating from source s to destination h between the tertiary-educated and theprimary-educated.

Table 4
Regression results from linear-utility model.
Equation

Scale

Selection

Sorting

Sorting

Sorting

Sorting

Wage data source

WDI

WDI

WDI

WDI

LIS

LIS

(1)

(2)

(3)

(4)

(5)

(6)

VariablejWh − Wjs

0.018(0.029)(W3h − W1h) −(Ws3 − Ws1)
(W3h − W1h), pre-tax

0.072(0.013)0.060(0.026)(W3h − W1h), post-tax
Anglophone dest.
Common language
ContiguousLongitude diff.
Log distanceLT colonial rel.
ST colonial rel.
Visa waiver
Schengen sig.
Asylee share
Observations
R-squared
Clusters

1.451(0.873)0.648(0.293)0.880(0.401)
0.003(0.004)− 1.152(0.171)
2.159(0.411)2.641(0.601)
0.589(0.314)0.058(0.337)− 1.221(3.698)
2786
0.44
15

0.567
(0.183)1.268(0.248)− 0.384(0.373)− 0.009(0.003)0.676(0.131)− 0.711(0.193)− 0.395(0.431)− 0.299(0.135)0.402(0.166)− 2.512(0.818)
1393
0.47
15

0.838
(0.183)0.355(0.137)− 1.005(0.229)0.004(0.002)− 0.245(0.092)− 0.391(0.176)− 0.129(0.256)
0.335(0.164)0.430(0.250)− 3.590(0.901)
1393
0.61
15

0.026
(0.013)0.103(0.045)0.636(0.256)0.352(0.139)− 1.007(0.237)0.004(0.002)− 0.259(0.097)− 0.445(0.161)− 0.187(0.257)0.364(0.172)0.403(0.252)− 3.635(0.709)
1393
0.61
15

0.817
(0.193)0.331(0.125)− 1.108(0.230)0.005(0.003)− 0.273(0.107)− 0.505(0.150)− 0.195(0.276)0.440(0.200)0.528(0.295)− 3.998(0.929)
1214
0.63
13

0.048
(0.022)0.678(0.241)
0.332(0.124)− 1.097(0.240)0.005(0.003)− 0.279(0.111)− 0.550(0.137)− 0.224(0.276)0.471(0.203)0.507(0.304)− 4.007(0.810)
1214
0.63
13

Dependent variables: (i) scale: log ratio of emigrants in the destination to the populationin the source for the tertiary or primary skill group; (ii) selection and sorting: log ratio of
emigrants in the destination to the population in the source for the tertiary skill groupminus the corresponding log ratio for the primary skill group. Robust standard errors inparentheses. Regressions include a dummy variable for whether the destinationcountry statistical ofﬁce explicitly codes a primary education category. Scale regressions
include a dummy for the tertiary skill group and interactions between that dummy andvariables shown. Sorting regressions include source-country dummies.

4. Regression analysis
4.1. Main results

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

speaking destinations are more highly educated in relation to their
non-migrant countrymen than emigrants bound elsewhere. The
coefﬁcients in the sorting regressions (columns (3) through (6))
show that English-speaking destinations tend to attract higher-skilled
immigrants. Emigration is also greater toward destinations that share
a common language with the source, and such emigrants are moreskilled than either their non-migrant counterparts or emigrants fromthe same source bound to other destinations. This suggests that
migrants perceive higher rewards to skill in destinations where they
can speak a language they know.
The next three variables capture the effects of geography.
Contiguity raises the scale of migration. However, it reduces the skillsof emigrants, all else equal, in relation both to non-migrants (as seenin the selection regression) and to migrants to non-contiguous
destinations (as seen in the sorting regression), perhaps reﬂecting
the ease of illegal migration between neighboring countries. In thescale equation, the longitude-difference coefﬁcient is insigniﬁcant, but
the log-distance coefﬁcient is negative and signiﬁcant. One interpretation is that migration is lower, the greater the distance between thesource and the destination, but controlling for distance, the need tocross an ocean (which follows from large longitudinal distances) has
no independent effect.
Colonial relationships increase the scale of migration, all else
equal. At the same time, emigrants to the former colonial power are
less-skilled than non-migrants and less-skilled than emigrants toother destinations. Recent literature suggests that economic andsocial networks between industrialized countries and their former
colonies contribute to bilateral migration ﬂows, much in the way suchnetworks also appear to contribute to bilateral trade (Pedersen et al.,
2004; Mayda, 2010). Our results are consistent with these linkages
disproportionately affecting migration of the less-skilled.
There is also an important role for our measures of immigration
policy.25 The effect of asylum policy on the scale of immigration is
insigniﬁcant, but generous asylum policies reduce immigrant skills
with relation to both non-migrants and migrants to other destinations. This ﬁnding suggests destinations that allocate a higher share ofvisas to asylees and refugees may limit opportunities for more skilledmigrants to gain entry, producing a less-skilled migrant inﬂow.26 Visa
waivers are associated with higher migration rates, although theeffect is marginally signiﬁcant. Visa waivers signiﬁcantly reduce theskills of emigrants in relation to non-migrants, but increase skill in
relation to emigrants who move to a destination with which thesource country has no visa waiver. The Schengen accord has had little
effect on the scale of migration among signatory countries, but it isassociated with positive selection and positive sorting of migrants.
4.2. Results for the log-utility model
Table 5 reports results based on the scale, selection, and sorting
regressions derived from the log-utility model in Eqs. (15)–(17). The
dependent variables in Table 5 are the same as those in thecorresponding columns of Table 4. The wage measures differ between
the linear and log-utility models. In the scale equation of the logutility model, reported in column (1), the wage measure is the skillspeciﬁc difference in pre-tax log wages between the destination andjsource countries, lnWh − lnWjs. In the selection equation reported incolumn (2), the measure is the difference between the destinationand the source in the return to skill, (δ3h − δ1s ), where the return to skill
in a country is the log ratio of high-skill to low-skill wages. In thesorting equations reported in columns (3) through (6), the wage
variable is the return to skill in the destination country, δ3h. Columns(1)–(4) are based on the WDI data, whereas columns (5) and (6) are25
On migration policy impacts, see Mayda (2010), Clark et al. (2007), and Ortega andPeri (2009).
26
On asylee and refugee policy in Europe, see Hatton and Williamson (2006).

51

Table 5Regression results from log-utility model.
Equation

Scale

Selection

Sorting

Sorting

Sorting

Sorting

Wage data source

WDI

WDI

WDI

WDI

LIS

LIS

(1)

(2)

(3)

(4)

(5)

(6)

Variablejln Wh − ln Wjs− 0.435(0.087)(δ3h − δ1s )− 1.307(0.186)

δ3h, pre-tax

3.929(0.767)δ3h, post-tax
Anglophone dest.
Common language
ContiguousLongitude diff.
Log distanceLT colonial rel.
ST colonial rel.
Visa waiver
Schengen sig.
Asylee share
Observations
R-squared
Clusters

5.338(0.886)3.342(0.761)1.466(0.857)1.315(0.213)0.656(0.301)− 0.008(0.003)− 0.530(0.180)1.912(0.485)2.185(0.413)− 0.793(0.259)− 0.523(0.350)− 2.065(3.257)
2786
0.29
15

1393
0.17
15

1393
0.40
15

1393
0.38
15

4.146
(1.297)

1214
0.43
13

1214
0.38
13

Dependent variables: (i) scale: log ratio of emigrants in the destination to the populationin the source for the tertiary or primary skill group; (ii) selection and sorting: log ratio of
emigrants in the destination to the population in the source for the tertiary skill groupminus the corresponding log ratio for the primary skill group. Robust standard errors inparentheses. Regressions include a dummy variable for whether the destinationcountry statistical ofﬁce explicitly codes a primary education category. Scale regressions
include a dummy for the tertiary skill group and interactions between that dummy andvariables shown. Sorting regressions include source-country dummies.

based on LIS data. Returns to skill are based on pre-tax data incolumns (1)–(3), and (5) and on post-tax data in columns (4) and (6).
To focus on a case of special importance in the literature, we impose
the assumptions that ﬁxed migration costs are zero and skill-varying
costs are proportionate to wages. This implies that in the scale
regression the regressors control for proportional migration costs (seeEq. (.(15)) and surrounding discussion). It also means that the only
regressor in the selection regression is (δ3h − δ1s ), since proportionalcosts are differenced out. Likewise it implies that the only regressors
in the sorting regressions are δ3h and the source-country dummies.
As in the linear-utility model, utility maximization implies that all of
the coefﬁcients on log wages and returns to skill should be positive.
Furthermore, if the model is properly speciﬁed, the coefﬁcients incolumns (1) through (3) should be similar. In fact, the wage coefﬁcientsin the scale and selection regressions are negative and signiﬁcant,whereas the sorting coefﬁcients are both positive and signiﬁcant.
The assumptions that ﬁxed costs are zero and skill-varying costs
are proportional to wages result in rather sparsely parameterized
regressions.27 When we relax these restrictions by assuming both

27
Belot and Hatton (2008) ﬁnd that the correlation between skilled migration rates
and the skill-speciﬁc difference in log wages between source and destination countries
is sensitive to whether controls for poverty rates in the source are included in theestimation. In unreported results, we ﬁnd that the negative coefﬁcient on the returns
to skill we estimate in the log utility selection regression obtains whether or notcontrols for poverty rates are included in the estimation (see note 20).

52

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

ﬁxed and skill-varying costs to be functions of observed country-paircharacteristics, the wage coefﬁcients in the scale and selection
regressions remain negative and signiﬁcant and the wage coefﬁcientsin the sorting regressions remain positive and signiﬁcant.28 Thus, thesign pattern of the coefﬁcients in Table 5 holds whether or not other
regressors are included in the estimation.
We see two potential explanations for the difference between thelinear-utility and log-utility regressions. One concerns omitted
variable bias due to weak controls for ﬁxed costs. Differencing thescale equation between skill groups eliminates ﬁxed costs from theselection and sorting regressions in the case of linear utility, but not in
the case of log utility. Fixed costs that were strongly negatively
correlated with source-destination differences in log wages andreturns to skill could explain the negative coefﬁcients in the scale andselection regressions in Table 5.
Perhaps more important is the lack of negative selectivity in thedata, as seen in Fig. 1. Log-utility maximization requires λ N 0. It also
requires that for destination-source pairs where δ 3h − δ 3s b 0,
migrants be negatively selected. In the data, there are many cases
where δ3h − δ3s b 0, but no negative selection, as seen by comparing
Figs. 2 and 3. Inspection of Eq. (9) shows that such negative
correlation between δ3h − δ3s and ln(E3sh / E1sh) − ln(E3s / E1s ) will tend toresult in a negative estimate of λ, contrary to the requirements of thetheory. In other words, the lack of negative selection in the data appears
at odds with migrants maximizing the log utility of net wages.29 One
caveat is that the version of the log-utility model we consider does notallow for liquidity constraints, which could restrict emigration for lowskilled workers. McKenzie and Rapoport (2010) and Belot and Hatton(2008) consider this possibility explicitly.
A remaining question is why the wage coefﬁcients in the log-utility
sorting regressions are positive, like their counterparts in the linearutility sorting regressions. Why do the sorting regressions fail todistinguish between linear and log utility, when the selection
regressions draw the distinction so clearly? The reason is that thewage measure only varies among the 15 destination countries, andamong countries with similar labor productivity sorting on log
differences in wages looks similar to sorting on level differences inwages. The rank correlation between the log wage difference and thelevel wage difference across destinations is 0.68. In order todistinguish between linear and log utility on the basis of the sorting
regressions, one would need a sample that included destinations withmore widely differing levels of productivity.30

4.3. Robustness checks
Tables 6–8 report speciﬁcations designed to check the robustnessof our results. We restrict attention to the linear-utility model in lightof its superior performance relative to the log-utility model. We
further restrict attention to the selection and sorting regressions, since
they are more robust in the presence of ﬁxed migration costs. For thesorting regressions, we focus on speciﬁcations with post-tax wage
differences. All regressions include the variables in our baseline28
In the log utility model, if we assume that ﬁxed migration costs are a function of
the same variables as in Table 4, allowing for ﬁxed costs means including thesevariables as regressors, divided by the destination country wage, as shown in thederivations of Eqs. (8)–(10). Alternatively, one might imagine including these
regressors uninteracted with the destination wage. Under either speciﬁcation, thelog wage variable enters with a negative sign in the scale and selection regressions.
29
Strictly speaking, it is at odds with the joint assumptions that migrants maximize
log utility and the extreme value disturbances are multiplicative. Since we continue toestimate a negative coefﬁcient on log wages when other regressors are added to theselection regression, the assumption of proportional migration costs does not appear
to account for the failure of the log utility model.
30
The similarity of productivity levels among US states may explain why log-utility
models have yielded evidence in favor of sorting among US domestic migrants (Borjas,
Bronars, and Trejo 1992; Dahl, 2002).

Table 6
Key wage coefﬁcients based on alternative wage measures.
A. WDI wages, PPP-adjusted
Equation

Selection

Sorting

Variable(1)(2)(W3h − W1h) − (W3s − W1s )0.108(0.016)(W3h − W1h), post-tax
Observations
R-squared
Clusters

1379
0.50
15

0.082
(0.054)
1379
0.60
15

B. LIS wages, PPP-adjusted(W3h − W1h), post-tax

0.047(0.024)
1202
0.63
13

Observations
R-squared
Clusters
C. Freeman–Oostendorp wages(W3h − W1h) − (W3s − W1s )0.082(0.016)(W3h − W1h), post-tax
Observations
R-squared
Clusters

1093
0.49
12

0.072
(0.045)
1093
0.63
12

D. Freeman–Oostendorp wages, PPP-adjusted(W3h − W1h) − (W3s − W1s )0.064(0.011)(W3h − W1h), post-tax
Observations
R-squared
Clusters

1059
0.49
12

0.091
(0.039)
1059
0.64
12

The dependent variable is the log ratio of emigrants in the destination to the populationin the source for the tertiary skill group minus the corresponding log ratio for the
primary skill group Robust standard errors in parentheses. In addition to variables
shown, all regressions include all variable shown or discussed in the note to Table 4.

speciﬁcations, shown in columns (2), (4) and (6) of Table 4. We
present only the wage coefﬁcients in order to conserve space.
Table 6 presents estimates based on alternative wage measures.
The top panel reports results based on WDI wages in which source
wages are adjusted by source-country PPP and destination wages areadjusted by destination PPP, to account for differences in the cost ofliving across countries. Adjusting for PPP makes the coefﬁcient in theselection regression slightly larger and the coefﬁcient in the sorting
regression slightly smaller and insigniﬁcant. In the second panel, we
see that adjusting for PPP using LIS wages yields wage coefﬁcients thatare positive and signiﬁcant, as in Table 4.
The bottom two panels of Table 6 presents results based on theFreeman–Oostendorp wage data described in Section 3. Without
adjusting for PPP, the wage coefﬁcients in both the selection andsorting regressions are positive. The selection coefﬁcient is signiﬁcant,whereas the sorting coefﬁcient has a t-statistic of 1.6. Adjusting theFreeman–Oostendorp wages for PPP reduces the selection coefﬁcientand raises both the sorting coefﬁcient and its signiﬁcance. The key
results from the linear-utility model thus appear to be fairly robust toalternative wage measures.
In Table 7 we return to our original unadjusted, WDI and LIS-based
wage measures and report results obtained from alternative speciﬁcations. Columns (1) through (3) address the problem that someemigrants may have obtained their tertiary education in the destination
country rather than the source country. If the cost of acquiring tertiary
education across destination countries were negatively correlated with

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

53

Table 7
Additional selection and sorting regressions.
Equation

Selection

Sorting

Sorting

Selection

Sorting

Sorting

Selection

Sorting

Sorting

Sorting

Sorting

Wage data source

WDI

WDI

LIS

WDI

WDI

LIS

WDI

WDI

LIS

WDI

LIS

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(W3h − W1h) − (W3s − W1s )0.065(0.013)0.121(0.047)0.068(0.019)0.106(0.039)0.050(0.018)− 0.039(0.055)
963
0.64
15

− 0.157(0.058)
823
0.69
13

2338
0.59
15

2044
0.62
13

(W3h − W1h), post-tax

0.078(0.012)0.121(0.054)0.082(0.012)0.052(0.025)

Relative university quality

0.127(0.053)− 0.002(0.002)0.000(0.001)0.058(0.015)− 0.004(0.002)− 0.148(0.031)
963
0.59
15

Log emigrant stock 1990
Observations
R-squared
Clusters

1393
0.40
15

1393
0.57
15

1214
0.57
13

1348
0.49
14

1348
0.63
14

1169
0.65
12

The dependent variable is the log ratio of emigrants in the destination to the population in the source for the tertiary skill group minus the corresponding log ratio for the primary
skill group. Robust standard errors in parentheses. In addition to variables shown, all regressions include all variables shown or discussed in the note to Table 4. Columns (1)–(3)report regressions in which we redeﬁne skilled migrants to be migrants with either secondary or tertiary education; columns (4)–(9) differ from the corresponding regressions inTable 4 only by in the inclusion of the indicated regressor; and columns (10) and (11) re-estimate the sorting regression using the full sample of source-destination pairs for which
destination wage data are available.

wide), interacted with a dummy variable equal to one if the source
country has no ranked universities. We intend this as a proxy for theeducation-related beneﬁt of migrating relative to remaining in thehome country. Relative university quality has no effect on emigrant
selectivity, as seen in column (4). The coefﬁcients in the sorting
regressions (columns (5) and (6)) are negative, as one might expect(higher ranked institutions have ranks closer to one), and signiﬁcant.
Higher ranked universities appear to act as a draw for higher-skilled
immigrants from countries with low-quality education systems,
consistent with Rosenzweig (2006). The wage coefﬁcients in all
three regressions are similar to those from our baseline speciﬁcations.
Columns (7) through (9) add the log total stock of emigrants fromthe source in the destination as of 1990. We are missing this variable
for about 30% of our sample, which causes the number of observations
to drop. Nevertheless, the wage variables have similar magnitudes
and patterns of signiﬁcance as in Table 4. In the selection regression,
the lagged migrant stock enters with a negative sign and is precisely

destination-country wage differences, then the effect on immigrant skill
that we attribute to wage differences could instead be due to differencesin educational costs. To deal with this issue we redeﬁne the numeratorof the skill ratios in the dependent variables to be the sum of tertiaryand secondary-educated immigrants. This addresses the problem if we
can assume all tertiary-educated immigrants would have obtained atleast a secondary education in their source country. The coefﬁcients incolumns (1) through (3), where the dependent variables are based on
this alternative deﬁnition of the log skill ratio, are all positive andsigniﬁcant and differ little from estimates in our baseline speciﬁcations.
Columns (4) through (9) report the results of adding to ourbaseline speciﬁcations two variables designed to capture otherpotential costs or beneﬁts of migration that vary by skill. Columns(4)–(6) add a relative university quality measure based on theworldwide ranking of universities by Shanghai Jiao Tong University(http://ed.sjtu.edu.cn). It is equal to the average rank of universities
within the destination country (among top 250 universities world-

Table 8Wage coefﬁcients from selection and sorting regressions from samples that omit one destination.
A. Selection regressions; WDI wage measures
Omitted destination

AUS

AUT

CAN

DEU

DNK

ESP

FIN

FRA

GBR

IRL

NLD

NOR

NZL

SWE

USA

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(15)

(16)

(W3h − W1h) − (W3s − W1s )0.066(0.013)
1296
0.47
14

0.071
(0.013)
1294
0.47
14

0.068
(0.012)
1294
0.46
14

0.071
(0.013)
1303
0.47
14

0.078
(0.012)
1299
0.47
14

0.078
(0.012)
1298
0.48
14

0.072
(0.013)
1306
0.46
14

0.075
(0.013)
1293
0.48
14

0.072
(0.013)
1293
0.47
14

0.079
(0.012)
1348
0.49
14

0.074
(0.013)
1293
0.47
14

0.066
(0.013)
1295
0.48
14

0.067
(0.013)
1301
0.48
14

0.074
(0.013)
1292
0.48
14

0.073
(0.015)
1297
0.44
14

0.111
(0.038)
2206
0.39
14

0.122
(0.036)
2180
0.39
14

0.112
(0.039)
2176
0.41
14

0.113
(0.039)
2204
0.39
14

0.103
(0.040)
2161
0.40
14

0.108
(0.039)
2152
0.40
14

0.141
(0.037)
2287
0.41
14

0.112
(0.039)
2156
0.39
14

0.080
(0.040)
2177
0.41
14

0.095
(0.040)
2178
0.42
14

0.134
(0.039)
2160
0.41
14

0.122
(0.054)
2184
0.38
14

Observations
R-squared
Clusters

B. Sorting regressions; WDI post-tax wage measures(W3h − W1h), post-tax
Observations
R-squared
Clusters

0.094(0.043)
2166
0.40
14

0.110
(0.040)
2175
0.39
14

0.112
(0.035)
2170
0.39
14

C. Sorting regressions; LIS post-tax wage measures
Omitted destination

AUS

AUT

CAN

DEU

DNK

ESP

FRA

GBR

IRL

NLD

NOR

SWE

USA

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(W3h − W1h), post-tax

0.031(0.020)
1872
0.43
12

0.051
(0.019)
1881
0.41
12

0.053
(0.016)
1876
0.41
12

0.051
(0.017)
1912
0.41
12

0.055
(0.018)
1886
0.41
12

0.051
(0.018)
1882
0.43
12

0.047
(0.018)
1867
0.42
12

0.052
(0.018)
1858
0.42
12

0.067
(0.015)
1993
0.43
12

0.051
(0.018)
1862
0.41
12

0.037
(0.014)
1883
0.43
12

0.055
(0.018)
1866
0.43
12

0.071
(0.033)
1890
0.40
12

Observations
R-squared
Clusters

Robust standard errors in parentheses. In addition to variables shown, all regressions include all variable shown or discussed in the note to Table 4.

54

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

estimated. Larger past bilateral migration is associated with lesseducated current migration, consistent with migrant networks
lowering migration costs disproportionately for the less-skilled(Orrenius and Zavodny, 2005; McKenzie and Rapoport, 2010;
Clark et al., 2007). In the sorting regressions, lagged migration also
enters negatively, indicating that the pull of an existing migrant stock
in a destination is stronger for less-skilled migrants, but the coefﬁcientis precisely estimated only in one of the regressions.
In columns (10) and (11), we present sorting regressions based ondata from all the available source countries, irrespective of whether
we have wage data for them. This highlights the advantage of thesorting regression, for which only destination-country wage data isnecessary. The results are similar to those for the smaller sample.
Table 8 addresses the independence of irrelevant alternatives (IIA)
assumption implicit in the conditional logit framework. IIA arises fromthe assumption that the error terms in Eq. (3) are i.i.d. acrossalternative destinations. IIA may be violated if two or more of ourdestinations are perceived as close substitutes by potential migrants.
Hausman and McFadden (1984) note that if IIA is satisﬁed, then theestimated regression coefﬁcients should be stable across choice sets.
In the context of our application, this means that the regression
coefﬁcients should be similar when we drop destinations from thesample. To check for violations of IIA, we re-estimated our models(15) times, each time dropping one of the 15 destinations. The
resulting coefﬁcients on the key wage variables are reported inTable 8. In general, they are quite similar across samples, suggestingthat the IIA property is not violated in our data.31
The results in Table 8 are also useful for evaluating the importanceof zero migration cells in the estimation. Because we log thedependent variable, zeros, which total 9% of the sample, are dropped
from the analysis. Ireland, the smallest destination country, accounts
for 42% of the zeros. In Table 8, we see that excluding Ireland has no
impact on the results. Finland, the next smallest destination country,
accounts for 11% of the zeros, the second most of any destination.
Excluding Finland also has no impact on the results. These ﬁndings do
not constitute a rigorous analysis of zero migration cells (see note 10),
but they do suggest that in our sample the issue may not be grave.

Table 9Fixed migration costs and migrant stocks for selected source and destination countries.
Source\destination

Australia

Canada59.94
21,37580.28
117,17089.88
3,083
133.45
38
50.52
6,314
34.69
102,219105.82
157
19.66
45,36527.4
216,316
76.79
680
66.14
25,160117.03870
65.41
78,10545.63
52,88746.13
13,32966.39
11,972
63.17
26,16012.39
966,13939.56
128,666

China
Colombia
Dominican
Republic
El Salvador
Germany
GuatemalaIreland
Italy
Jamaica
Korea, Rep.
MexicoPhilippines
PolandPortugal
Spain
TurkeyUnited Kingdom
VietnamCanadaFranceGermany

UK

US

56.8
287,820
69.4
11,725
62.58
3,225
26.82
27,78019.94
163,880
45.01
8,880
22.87
24,52014.95
312,185− 1.73103,265
48.94
50,86059.6
24,795
46.64
191,61526.38
154,525
9.32
143,145
63.67
9,695
68.03
13,04511.87
580,250
31.02
127,590

53.84
8,910
90.83
24,547
72.84
6,136
90.62
238
83.06
328
14.8
109,425
83.78
346
34.63
3,845
13.37
371,714
67.18
299
73.86
6,164
90.46
3,064
83.42
4,767
27.95
91,122− 2.55538,106
15.77
308,500
40.1
133,89023.02
61,31738.45
58,570

48.66
7,998
88.57
26,069
71.72
5,895
59.85
3,188
82.21
332

33.7
49,95487.73
33,380
71.43
8,928
95.37
350
89.85
302
13.76
164,165
96.95
212− 28.28
420,10231.34
86,876− 9.31
124,313
72.08
7,434
90.62
3,558
67.99
34,78242.89
33,661
31.08
26,00642.88
40,59253.27
36,754

10.6
715,82555.6
841,69934.67
402,935
6.62
527,520− 0.16619,18521.83
646,815
8.86
341,590
16.55
148,68023.3
461,085− 2.98449,795
29.06
676,640
06,374,825
29.58
1,163,555
29.83
399,165
19.23
187,64550.85
73,83560.36
64,78036.99
613,93021.01
807,305

80.14
448
13.76
13,28410.91
456,000
51.03
872
60.2
12,226
82.5
4,029
68.31
12,5395.21
198,000
8.81
113,216
27.11
109,613
7.95
1,272,000
17.29
90,00045.46
43,10561.75
19,137

Top ﬁgure in each cell is ﬁxed migration cost in 000s of annual 2000 USD; bottom ﬁgure
is emigrant stock.

4.4. Fixed costs and the sorting of migrants by skill level
In this section we use our parameter estimates to shed light on
different dimensions of international migration. The ﬁrst issue we
address concerns ﬁxed costs, which play a role in determining thescale of migration. Our framework allows us to estimate migration
costs that are speciﬁc to each source-destination pair.
The estimates stem from the scale Eq. (4). If we include a dummy
variable for each source-destination pair in our sample, assuming as
before that skill-varying costs are given by gjsh = xshθ j, we obtain
numerically identical estimates to those obtained by estimating theselection Eq. (13). However, as a by-product, we obtain estimatesof −αfsh from the coefﬁcients on the source-destination dummy
variables. To recover an estimate of ﬁxed costs fsh, we divide those
coefﬁcients by our estimate of −α, where α is the coefﬁcient on
wages. This provides estimates of ﬁxed migration costs relative to an
omitted source-destination base pair, in thousands of 2000 US dollars
per year (the units in which wages are measured). We choose the
Mexico–US pair as the base since it involves the largest migration
ﬂow. Of course, these estimates reﬂect not only direct monetary costs,
but also the monetary value of psychic costs and source-speciﬁc
immigration policies imposed by the destination countries.

31
We attempted to compute asymptotic chi-square statistics along the lines ofHausman and McFadden (1984) to test for stability across choice sets in all theregression coefﬁcients. For the most part, the asymptotic covariance matrices were
singular, a ﬁnite-sample problem that often arises in Hausman tests.

Estimates for each source-destination pair in our sample areshown in an online appendix.32 Table 9 presents estimates for thesubset of source and destination countries that appear in the 25
source-destination pairs with the largest stocks of migrants. Withineach source-destination cell, the ﬁrst entry is the estimated ﬁxed
migration cost. The second entry is the number of emigrants from thesource to the destination.
The US is the low-cost destination for all the Western Hemispheresource countries except Jamaica, and it receives more emigrants fromthose countries than any other destination. At the same time,
migration costs are only part of the story. For Chinese emigrants,
the cost of migrating to Canada and the US is about the same. Yet
many more go the US, presumably due to the higher wages there. The
situation is similar for German emigrants. Canada, France, and the UK
are all lower-cost destinations than the US, yet the US has more
German immigrants than those three destinations combined.
Several entries highlight the role of history. Germany is by far thelowest-cost destination for Turkish emigrants, despite Turkey's similar
proximity to the other European countries. The US is the low-cost
destination for Vietnam, despite the country's proximity to Australia
and colonial ties to France. Presumably, these estimates reﬂect
Germany's labor-recruitment strategy from the 1960s, America's post-

32
Available at http://harrisschool.uchicago.edu/faculty/web-pages/jeffrey-groggermigration-ﬁles.asp.

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

55

Table 10Decomposition of the immigrant skills gap.
Share of immigrant skills gap explained byMean
immigrant
skillsImmigrant
skills gap

Wage
Englishdifference dest.

Commonoff. lang.

Contiguous Long.
diff.

Log
Colony, Colony, Visa
Schengen Sharedistance LT
STwaiverasylees

Share
explained

Destination

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

Australia
Austria
CanadaDenmark
FinlandFranceGermany
IrelandNetherlands
N. Zealand
NorwaySpain
SwedenUK
US
Mean bootstrap
standard error
Mean shareexplained

1.29− 0.541.30− 0.51− 0.75− 0.10− 0.311.39− 0.540.71
1.15− 0.03
0.28
0.40
2.01

0.72
2.55
0.71
2.52
2.76
2.11
2.32
0.62
2.55
1.30
0.86
2.04
1.73
1.61
0.00

0.84
0.36
0.99
0.28
0.38
0.44
0.44
1.52
0.32
0.64
0.53
0.50
0.55
0.35

0.00
0.25
0.00
0.25
0.23
0.30
0.27
0.00
0.25
0.00
0.74
0.31
0.37
0.00

− 0.010.03− 0.06
0.04
0.03
0.02
0.03
0.00
0.03
0.00
0.10
0.02
0.05
0.00

− 0.030.02− 0.02
0.00
0.00
0.02
0.03
0.00
0.00
− 0.02
0.01
0.00
0.00
− 0.01− 0.16
0.07
0.00
0.07
0.07
0.08
0.08
0.27
0.07
− 0.13
0.21
0.08
0.11
0.10

0.22
− 0.080.02− 0.07− 0.06− 0.08− 0.09− 0.37− 0.070.14− 0.17− 0.06− 0.09− 0.09− 0.010.00− 0.01
0.00
0.00
0.02
0.00
−0.01
0.00
0.00
− 0.01
0.03
0.00
0.07

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

− 0.01− 0.04− 0.04− 0.04− 0.04− 0.05− 0.05− 0.31− 0.04− 0.04− 0.12− 0.05− 0.06− 0.060.00− 0.020.00− 0.02− 0.02− 0.02− 0.030.00− 0.020.00− 0.06− 0.03− 0.030.00− 0.070.39− 0.13
0.36
0.09
0.22
0.05
0.83
0.55
− 0.260.40− 0.06
0.36
0.22

0.78
0.98
0.76
0.87
0.68
0.96
0.76
1.95
1.10
0.32
1.64
0.74
1.27
0.58

0.52

0.20

0.02

0.01

0.05

0.05

0.02

0.04

0.02

0.11

0.20

0.58

0.21

0.02

0.00

0.07 − 0.06

0.01

− 0.07− 0.02

0.21

0.96

0.38

1.63

0.00

Results are based on the model reported in column (4) of Table 4.

war asylum policy in the 1980s, and the immigrant networks that havedeveloped in their wake.
We next ask how wage differences and skill-varying migration
costs explain differences in mean immigrant skills among the destination countries. The ﬁrst column of Table 10 presents our measure of
immigrant skills, which is the mean log skill ratio among immigrants
in each destination country. Based on this measure, the US has themost highly skilled immigrants on average, followed by Ireland andCanada.33 We seek to explain the immigrant skill gap, deﬁned as thedifference between the mean log skill ratio among immigrants in the
US and the mean log skill ratio among immigrants in other destination
countries. The immigrant skill gap is reported in column (2).
We use the sorting regression reported in column (4) of Table 4 tocarry out the decomposition, which explains the immigrant skills gap
as a linear combination of the differences in mean values of theregressors, using the regression coefﬁcients as weights. To aidinterpretation, we report results in the form of the share of theimmigrant skill gap explained by each variable in the regression.34
Results are reported in columns (3) through (14). Column (3)shows that on average the wage difference explains 58% of theimmigrant skill gap; in all destination countries it explains at least
25%. The next two columns show the importance of language. English
explains at least 20% of the immigrant skill gap for each nonAnglophone-destination country. The role of common languages issmaller overall, but nevertheless important for some of thosedestination countries whose languages are not widely spoken
elsewhere. Among the policy variables, visa waivers and the Schengentreaty explain relatively little of the immigrant wage gap. Asylum
policy, in contrast, has important effects. In seven of the destination
countries, asylum policy explains at least 20% of the immigrant skill
disadvantage. In Canada and New Zealand, in contrast, the skills gap
would be over 10% larger were it not for their relatively restrictive
33
Other skill measures give somewhat different rankings. For example, Canada ranks
ﬁrst in the share of immigrants with tertiary education. The reason for the difference isthat the US has a lower share of primary-educated immigrants than Canada. We focus
on the log skill ratio because that is the skill measure that follows from our model andthe measure for which our regressions can provide a decomposition.
34
Nothing constrains the share explained by any subset of components to be lessthan one.

admissions of asylum seekers. The next-to-last row of the table
reports bootstrap standard errors for the estimates in each column,
averaged over all the destination countries. These are based on 100
bootstrap replications where destination countries were taken as theunit of observation. Since the standard errors tended to be similar,
with the exception of Ireland with its many fewer source countries,
we report average standard errors to reduce clutter. The standard
errors show that there is considerable variablity in the share of theimmigrant skills gap explained by each of our explanatory variables.
5. Conclusions
Two dominant features of international labor movements are
positive selection of individuals into migration and positive sorting of
migrants across destinations. We show that a simple model of income
maximization can account for both phenomena.
In our selection regression, we ﬁnd that migrants for a sourcedestination pair are more educated relative to non-migrants, thelarger is the skill-related difference in earnings between the destination country and the source. That is, positive selectivity is stronger
where the reward to skill in the destination is relatively large. This
result obtains for wage differences expressed in levels, but not in logs.
Log wage differences, which capture cross-country differences in
returns to skill, fail to account for bilateral migration patterns because
cross-country differences in returns to skill are dwarfed by crosscountry differences in labor productivity. On their own, cross-country
differences in returns to skill would predict negative selection of
migrants, which occurs rarely in the data.
Positive sorting is a general prediction of income maximization. In
our sorting regression, the relative stock of more educated migrants ina destination is increasing in the level earnings difference between
high and low-skilled workers. This correlation is stronger when wage
differences are adjusted for taxes, implying that migrants weigh posttax earnings when choosing a destination. The US and Canada enjoy
relatively large post-tax skill-related wage differences, which largely
account for their ability to attract more educated migrants relative toother OECD countries.
In the sorting regression, we obtain qualitatively similar results when
we use wages constructed from microdata as when we approximate

56

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57

wages using aggregate income data and impose the assumption of log
normality. As a practical matter, this means that one can obtain
empirically meaningful estimates of skill-related wage differencesfrom commonly available data sources. The sorting regression allows
one to test income maximization even without source-country wage
data, which makes our approach applicable to a wide variety of settings.
Destinations with liberal refugee and asylum policies draw
relatively low-skilled immigrants, all else equal. Unfortunately, ourability to say more about policy is limited by the sparseness of data
allowing one to compare the regimes of different destination
countries. Our model provides a framework in which comparative
analysis of immigration policies could be undertaken, but with current
limitations in data we are limited in the analyses we can carry out.

limited attention to studies of income, as opposed to earnings or
consumption, since income studies were much more numerous.
We then transformed each Gini coefﬁcient into the standard
deviation of log income, assuming that income is lognormal. If income
X is log normally distributed, so lnX ∼ N(μ, σ2), then the relationshipbetweenp
σﬃﬃﬃand the Gini coefﬁcient for income, denoted by G, is given
1by σ = 2Φ−1 G +, where Φ− 1 is the inverse of the standard
2
normal cdf (Bendel et al., 1989, p. 399). We averaged over all suchobservations for each country to obtain country-speciﬁc standard
deviations, denoted by σ.
Under log normality, quantiles of income xα such that P(X b xα) = α
are given byxα = exp ðμ + zα σ Þ;ðA1ÞAcknowledgements
We thank for comments Eli Berman, George Borjas, Gordon Dahl,
Frederic Docquier, Larry Katz, Hillel Rapoport, Dean Yang, and seminar
participants at the University of Chicago, the Federal Reserve Bank ofAtlanta, Harvard, LSE, Princeton, UCSD, UC Irvine, UC Berkeley, UCL,
Yale, the University of Virginia, the University of Colorado, theUniversity of Lille, Bar Ilan University, the AEA meetings, and the NBER
Summer Institute. Any errors are ours alone.
Appendix A. Wage data
A.1. LIS wage measures
We use the LIS to measure the 20th and 80th percentiles of theannual wage distribution for destination countries. Our wage measureis gross cash wage and salary income. To limit quantity variation inmeasuring the price of labor, we restrict the samples to male
household heads between the ages of 25 and 64 who reported
working at least 39 weeks per year and at least 30 h per week.
Percentiles were tabulated separately for each LIS ﬁle from waves 4
and 5.35 This provided country- and year-speciﬁc percentiles in units
of nominal national currency. These were then converted to US dollars
using exchange rates from the Penn World Tables.36 These were
deﬂated using the US Consumer Price Index to provide wages inconstant 2000 US dollars. The after-tax measures were constructed bymultiplying these estimates by one minus the relevant yearly tax rate
(see below). Finally, we averaged over years to create the countryspeciﬁc measures we used in the regressions.
A.2. WDI/WIDER estimates
We use GDP data from the World Development Indicators (WDI)
database, and Gini coefﬁcients from the World Income Inequality
Database, v. 2.0 (WIID), to estimate 20th and 80th income percentiles
for all countries.
In the WIID database, the unit of observation is an income
inequality study that produced a Gini coefﬁcient. Other variables
indicate the country for which the Gini coefﬁcient was computed; theyear in which the underlying income survey was ﬁelded; the coverageof the underlying income survey, in terms of the target population,
target geography, and target age range; and the deﬁnition of theincome measure under study. We included only studies of income
data collected between 1990 and 2000 which targeted the full
population, geography, and age distribution of the country. We also
35
The countries and years for which LIS ﬁles were available during waves 4 and 5
were: Australia (1995, 2001), Austria (1994, 1995, 1997, 2000), Canada (1994, 1997,
1998, 2000), Denmark (1995, 2000), France (1994, 2000), Germany (1994, 2000), theNetherlands (1994, 1999), Norway (1995, 2000), Spain (1995, 2000), Sweden (1995,
2000), the UK (1994, 1995, 2000), and the US (1994, 1997, 2000).
36
For the PPP-adjusted wage measures, we applied PPP indexes from the Penn
World Tables.

where zα is the α quantile of a unit normal random variable (Johnson
and Kotz, 1970, p. 117). Since E(X) = exp(μ + σ2 / 2) under log
normality, we can re-write (A1) as
2xα = Eð X Þ exp σzα  σ = 2 :ðA2Þ

To estimate the 20th and 80th percentiles of income using (A2),
we used per capita GDP in 1990 and 2000 to estimate E(X) and σ toestimate σ. The after-tax measures for destination countries whichwere used in the sorting regressions were constructed by multiplying
these estimates by one minus the relevant average tax rate, where
averages were taken over the period 1996 to 2000 (see below).
A.3. Freeman–Oostendorp data
Freeman and Oostendorp (2000) provide estimates of averageannual earnings by occupation and industry for a large cross section ofcountries, covering the years 1983 to 2003. For most countries, data
are available for a just a few years within this span. We restrict ourattention to the period 1988 to 1997. For each country, we convert
earnings into 2000 US dollars and then take mean earnings acrosstime by occupation and industry. For each country, we take earnings
at the 10th percentile to correspond to low-skill wages and earnings
at the 80th percentile to correspond to high-skill wages (thesepercentiles give skill-related wage differences that have the highest
correlation with the LIS, relative to other percentiles). FO estimates
several different versions of their earnings series. We use the series
that corresponds to “country-speciﬁc calibrations with imputation,”as this was the most highly correlated with the LIS.
A.4. Tax data
Tax data are drawn from OECD (various years). These documents
provide country-speciﬁc annual average tax rates by income level,marital status, and family size for the period 1996 to 2000. We
imputed 1996 values to 1994 and 1995 to merge with the annual
country-level LIS data. Income levels are deﬁned as a share of theaverage production worker wage (APW). To 20th percentile wages weapplied the tax rate facing workers with income equal to 67% of the
APW. To 80th percentile wages we applied the tax rate applicable toworkers with income equal to 167% of the APW. Since the tax
treatment of marriage and children varies widely among countries,
but we had no data on family status, we applied the tax rate facing
single workers with no children. All wage data used in the analysis
are available at: http://harrisschool.uchicago.edu/faculty/web-pages/
jeffrey-grogger-migration-ﬁles.asp.
References
Adams, Richard, 2003. “International Migration, Remittances, and Brain Drain: A Study of
24 Labor Exporting Countries.” World Bank Policy Research Working Paper no. 2972.

J. Grogger, G.H. Hanson / Journal of Development Economics 95 (2011) 42–57
Aghion, Philippe, Bacchetta, Philippe, Ranciere, Romain, Rogoff, Kenneth, 2009.
Exchange rate volatility and productivity growth: the role of ﬁnancial development. Journal of Monetary Economics Elsevier 56 (4), 494–513.
Alesina, Alberto, Angeletos, George-Marios, 2005. Fairness and redistribution: US
versus Europe. The American Economic Review 95 (4), 960–980.
Beine, Michel, Docquier, Frédéric, Rapoport, Hillel, 2001. Brain drain and economic
growth: theory and evidence. Journal of Development Economics 64 (1), 275–289.
Beine, Michel, Docquier, Frédéric, Rapoport, Hillel, 2007. Measuring international
skilled migration: a new database controlling for age of entry. World Bank
Economic Review 21, 249–254 June 2.
Beine, Michel, Docquier, Frédéric, Rapoport, Hillel, 2008. Brain drain and human capitalformation in developing countries: winners and losers. The Economic Journal 118,
631–652.
Belot, Michele, Hatton, Timothy, 2008. Immigrant Selection in the OECD. CEPR Working
Paper No. 6675.
Bendel, R.B., Higgins, S.S., Teberg, J.E., Pyke, D.A., 1989. Comparison of skewness
coefﬁcient, coefﬁcient of variation, and Gini coefﬁcient as inequality measures
within populations. Oecologia 78, 394–400.
Borjas, George J., 1987. Self-selection and the earnings of immigrants. The American
Economic Review 77 (4), 531–553.
Borjas, George J., 1999. The economic analysis of immigration. In: Ashenfelter, Orley
C., Card, David (Eds.), Handbook of Labor Economics. North-Holland, Amsterdam,
pp. 1697–1760.
Borjas, George J., 2008. Labor outﬂows and labor inﬂows in Puerto Rico. Journal of
Human Capital 2, 32–68.
Borjas, George J., Bronars, Steven G., Trejo, Stephen, 1992. Self-selection and internalmigration in the United States. Journal of Urban Economics 32, 159–185.
Brücker, Herbert, Defoort, Cécily, 2009. Inequality and the self-selection of internationalmigrants: theory and new evidence. International Journal of Manpower 30 (7),742–764.
Chiquiar, Daniel, Hanson, Gordon, 2005. International migration, self-selection, and the
distribution of wages: evidence from mexico and the United States. Journal of
Political Economy 113 (2), 239–281 April.
Clark, Ximena, Hatton, Timothy, Williamson, Jeffrey, 2007. Explaining US immigration,
1971–1998. The Review of Economics and Statistics 89 (2), 359–373.
Dahl, Gordon, 2002. Mobility and the return to education: testing a Roy model withmultiple markets. Econometrica 70 (6), 2367–2420.
Docquier, Frédéric, Marfouk, Abdeslam, 2006. International migration by educational
attainment, 1990–2000. In: Ozden, Caglar, Schiff, Maurice (Eds.), International
Migration, Remittances, and the Brain Drain. The World Bank and Palgrave
McMillan, Washington, DC, pp. 151–200.
Docquier, Frédéric, Lohest, Olivier, Marfouk, Abdeslam, 2007. Brain drain in developing
countries. World Bank Economic Review 21 (2), 193–218.
Docquier, Frédéric, Lindsay Lowell, B., Marfouk, Abdeslam, 2009. A gendered assessment ofhighly skilled emigration. Population and Development Review 35 (2), 297–321.
Docquier, Frédéric, Rapoport, Hillel, 2008. Skilled migration. The perspective ofdeveloping countries. In: Bhagwati, Jagdish, Hanson, Gordon (Eds.), Skilled
Migration: Prospects, Problems and Policies. Oxford University Press, Oxford.
Durand, Jorge, Massey, Douglas S., Zenteno, Rene M., 2001. Mexican immigration in the
United States. Latin American Research Review 36 (1), 107–127.
Fernandez-Huertas, Jesus, 2011. “New Evidence on Emigration Selection.”. Review of
Economics and Statistics 93 (1), 72–96.
Freeman, Richard B., Oostendorp, Remco H., 2000. Wages around the World: Pay acrossOccupations and Countries. NBER Working Paper No. 8058.
Grogger, Jeffrey, Hanson, Gordon H., 2007. Income Maximization and the Sorting of
Emigrants across Destination Countries. Mimeo, UCSD.

57

Hanson, Gordon H. 2010. “International Migration and the Developing World.” In Dani
Rodrik and Mark Rosenzweig, eds., Handbook of Development Economics, Vol. III,
North Holland, forthcoming.
Hatton, Timothy J., Williamson, Jeffrey G., 2006. Refugees, asylum seekers, and policy inEurope. In: Foders, F., Langhammer, R.J. (Eds.), Labor Mobility and the World
Economy. Springer.
Hanushek, Eric A., Zhang, Lei, 2006. Quality-Consistent Estimates of International
Returns to Skill. NBER Working Paper No. 12664.
Hausman, Jerry, McFadden, Daniel, 1984. Speciﬁcation tests for the multinomial logit
model. Econometrica 52 (5), 1219–1240.
Ibarraran, Pablo, Lubotsky, Darren, 2007. The socioeconomic status of Mexican migrantfamilies: new evidence from the 2000 Mexican Census. In: Borjas, George J. (Ed.),
Mexican Immigration. University of Chicago and National Bureau of Economic
Research, Chicago.
Johnson, Norman, Kotz, Samuel, 1970. Continuous Univariate Distributions-1. John
Wiley and Sons, New York.
Kapur, Devesh, McHale, John, 2005. Give Us Your Best and Brightest: The Global Huntfor Talent and its Impact on the Developing World. Center for Global Development
and the Brookings Institution, Washington, DC.
Luxembourg Income Study (LIS) Micro database, (various years); harmonization oforiginal surveys conducted by the Luxembourg Income Study, Asbl. http://www.
lisproject.org/.
Mayda, Anna Maria, 2010. International migration: a panel data analysis of thedeterminants of bilateral ﬂows. Journal of Population Economics 23, 1249–1274.
McFadden, Daniel, 1974. The measurement of urban travel demand. Journal of Public
Economics 3, 303–328.
McKenzie, David, Rapoport, Hillel, 2010. Self-Selection Patterns in Mexico US
Migration: The Role of Migration Networks. Review of Economics and Statistics
92 (4), 811-821.
Munshi, Kaivan, 2003. Networks in the modern economy: Mexican migrants in the US
labor market. Quarterly Journal of Economics 118, 549–597 May.
OECD, 2005. Trends in International Migration. OECD, Paris.
OECD, 2007. Taxing Wages. OECD, Paris.
Orrenius, Pia M., Zavodny, Madeline, 2005. Self-selection among undocumented
immigrants from Mexico. Journal of Development Economics 78 (1), 215–240
October.
Ortega, Francesc, Peri, Giovanni, 2009. The Causes and Effects of International
Migrations: Evidence from OECD Countries, 1980–2005.” NBER Working Paper
No. 14833.
Pedersen, Peder J., Pytlikova, Mariola, Smith, Nina, 2004. Selection or Network
Effects? Migration Flows into 27 OECD Countries, 1990–2000,”IZA Discussion
paper 1104.
Psacharoupolos, George, Patrinos, Harry Anthony, 2004. Returns to investment ineducation: a further update. Economics of Education 12 (2), 111–134.
Ramos, Fernando, 1992. Out-migration and return migration of Puerto Ricans. In: Borjas,
George, Freeman, Richard (Eds.), Immigration and the Work Force. University of
Chicago Press and NBER, Chicago, pp. 49–66.
Rosenzweig, Mark, 2006. Global Wage Differences and International Student Flows.
Brookings Trade Forum.
Rosenzweig, Mark, 2007. Education and Migration: A Global Perspective. Mimeo, Yale.
Roy, A.D., 1951. Some thoughts on the distribution of earnings. Oxford Economic Papers
3, 135–146 June.
United Nations. 2006. “World Migrant Stock: The 2005 Revision Population Database”
http://esa.un.org/migration/.