metadata: authors: 0: author: Docquier, Frederic
1: author: Ozden, Caglar
2: author: Peri, Giovanni
count: World
docna: 0: docna: The wage effects of immigration and emigration
docty: Policy Research Working Paper
subtopic: Indigenous Peoples,Indigenous Peoples Law,Human Migrations & Resettlements,Labor Markets,Educational Sciences,International Migration,Indigenous Communities,Rural Labor Markets,Migration and Development
teratopic: Social Protections and Labor,Health, Nutrition and Population,International Economics and Trade,Social Development,Communities and Human Settlements
abstracts: cdata!: Immigrants in Rome or Paris are more
            visible to the public eye than the Italian or French
            engineers in Silicon Valley, especially when it comes to the
            debate on the effects of immigration on the employment and
            wages of natives in high-income countries. This paper argues
            that such public fears, especially in European countries are
            misplaced; instead, more concern should be directed towards
            emigration. Using a new dataset on migration flows by
            education levels for the period 1990-2000, the results show
            the following: First, immigration had zero to small positive
            long-run effect on the average wages of natives, ranging
            from zero in Italy to +1.7 percent in Australia. Second,
            emigration had a mild to significant negative long-run
            effect ranging from zero for the US to -0.8 percent in the
            UK. Third, over the period 1990-2000, immigration generally
            improved the income distribution of European countries while
            emigration worsened it by increasing the wage gap between
            the high and low skilled natives. These patterns hold true
            using a range of parameters for the simulations, accounting
            for the estimates of undocumented immigrants, and correcting
            for the quality of schooling and/or labor-market downgrading
            of skills. All results go counter to the popular beliefs
            about migration, but they are due to the higher skill
            intensity of both emigration and immigration relative to non-migrants.
repnme: repnme: The wage effects of immigration and emigration
display_title: The wage effects of immigration and emigration
listing_relative_url: /research/2011/02/13713508/wage-effects-immigration-emigration
topicv3: Labor Markets m1326488 855,Indigenous Peoples m1326947 749,Education m1327048 470,Immigration m1504162 2837,Emigration m1504169 2844
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,International Economics and Trade,Social Development,Communities and Human Settlements
no_of_pages: 45
model_extractions: WPS5556 Policy Research Working Paper 5556 _Frédéric Docquier Çaglar Özden Giovanni Peri_ The World Bank Development Research Group Trade and Integration Team February 2011
Policy Research Working Paper 5556

# **Abstract**

Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the effects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration flows by education levels for the period 1990–2000, the results show the following: First, immigration had zero to small positive long-run effect on the average wages of natives, ranging from zero in Italy to +1.7 percent in Australia. Second, emigration had a mild to significant negative long-run effect ranging from zero for the US to −0.8 percent in the UK. Third, over the period 1990–2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labormarket downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cozden@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Wage E¤ects of Immigration and Emigration<sup>�</sup>

Frédéric Docquier<sup>a</sup> , Ça1⁄4glar Özden<sup>b</sup> , Giovanni Peri<sup>c</sup>

> a FNRS-IRES, Université Catholique de Louvain (frederic.docquier@uclouvain.be)

b The World Bank, Development Research Group (cozden@worldbank.org)

> c University of California, Davis and NBER (gperi@ucdavis.edu)

December 17th, 2010 Abstract Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the e¤ects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration ‡ows by education levels for the period 1990-2000, the results show the following: First, immigration had zero to small positive long-run e¤ect on the average wages of natives, ranging from zero in Italy to +1.7% in Australia. Second, emigration had a mild to signi...cant negative long-run e¤ect ranging from zero for the US to -0.8% in the UK. Third, over the period 1990-2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labor-market downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

JEL Codes: F22, J61, J31.

Keywords: Immigration, Emigration, Complementarity, Schooling Externalities, Average Wage, Wage inequality.

> �This article is part of a research project on "Brain drain, return migration and South-South migration: impact on labor markets and human capital" supported by the Austrian, German, Korean, and Norwegian governments through the Multi-donor Trust Fund on Labor Markets, Job Creation, and Economic Growth administered by the World Bank’s Social Protection and Labor unit. The ...rst author also acknowledges ...nancial support from the Belgian French-speaking Community (convention ARC 09/14-019 on "Geographical Mobility of Factors"). The ...ndings, conclusions and views expressed are entirely those of the authors and should not be attributed to the World Bank, its executive directors or the countries they represent.

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# 1 Introduction

Fear of immigration is once again manifesting itself strongly in Europe and other developed regions of the world.<sup>1</sup> Spurred by the recent crisis and giving voice to a large portion of the public, many politicians argue that immigration’s negative employment and wage e¤ects have become unbearable especially for the less skilled. Do the migration data and economic analysis bear out this pessimistic scenario? Might the populist outcry be ignoring a more important ‡ow of migrants? Does emigration, especially of highly educated workers, a¤ect the wages and employment of non-migrants? The loss of high skilled workers deprives their home countries of the scientists, entrepreneurs, educators and other professionals who drive their economies to higher levels of e¢ ciency and productivity.<sup>2</sup> Immigrants in Rome or Paris are more visible to the European public eye than the Italian or French engineers in Silicon Valley, but are they more harmful to the Italian and French labor markets? This asymmetric view leads to economic misconceptions of the economic e¤ects of migration. The goal of this paper is to assess the impact of recent global labor movements on the wages of those who do not migrate. We focus on the major European countries since they have experienced both emigration and immigration, especially when compared to the United States, Canada and Australia whose experiences have been primarily shaped by in‡ows of migrants. In addition, for comparison, we include several non-OECD countries (Argentina, South Africa and Singapore) with signi...cant migration ‡ows as well as Eastern European (Poland, Czech Republic, Hungary) and other developing countries (Mexico, Turkey) that have established important migration corridors with developed OECD countries. We use a newly available dataset to generate measures of migration ‡ows by education levels for all countries in our sample for the period 1990-2000. We analyze the wage e¤ects on the highly educated (college graduates) non-migrants and less educated (high school graduates or less) non-migrants separately to be able to assess the distributional impacts along with overall e¤ects.

In order to calculate these e¤ects at the national level, we adopt an aggregate production model which has been used in the evaluation of the impact of immigration at the national level (e.g. Borjas 2003, Manacorda et al., forthcoming) and in macroeconomic studies analyzing growth, productivity and skill premium in the US and other countries (e.g. Acemoglu and Zilibotti 2001, Caselli and Coleman 2006, Card and Lemieux 2001, Goldin and Katz 2008). We use this model to simulate the wage e¤ects of immigration and emigration, isolating this phenomenon from all other changes that happened in the same period. This simulation approach bypasses the issues of endogeneity and omitted variables encountered in the regression estimates of the wage e¤ects of immigration. The results, however, rely in important ways on the assumptions and on the parameter choice, which are therefore discussed and documented in detail. The model we use has four important components (with

> 1See, for example, the following recent articles from the Economist magazine on immigration to Spain ("Bad new Days", February 24 2010), to Italy ("Southern Misery" January 14th 2010) and to Europe in general ("No Boatloads but still troubles" August, 12th 2010).

> 2Even the academic literature has been mostly concerned with the impact of immigration on European Countries. Vis-a-vis the occasional study on the size of the "Brain drain" from Europe (such as Saint-Paul, 2008) there are scores of studies of the labor market impact of immigrants in Europe (see for instance Longhi et al 2005 for a summary).

2
associated parameters) that a¤ect the conclusions of our exercise. We start with a preferred (usually average) set of parameter values and we present the results as the benchmark case. Next, we consider for each parameter a range of values deemed reasonable by the literature and we discuss how robust our results are to these variations.

The ...rst component of our model assumes that aggregate labor is combined with physical capital to produce output. While capital may take some time to adjust to changes in the labor supply, we assume that it adjusts in the long run to maintain constant the capitaloutput ratio (and hence its rate of return). Such property may be derived from a classic or neoclassic growth model (such as Solow 1956, Ramsey 1928) in the case of a closed economy or alternatively from the assumption of an open economy. Most of our simulated wage e¤ects should be understood as long-run e¤ects<sup>3</sup> .

In the second building block of the model, we combine labor of highly educated and less educated workers in a function with constant elasticity of substitution. This representation is common in labor markets studies (such as Katz and Murphy, 1992, Card and Lemieux, 2001) and in cross-country analysis of relative productivity (Caselli and Coleman, 2006). Following the literature, we choose college graduates as the highly educated portion of the labor force and we pick a range between 1.3 and 2 for the elasticity of substitution which spans most labor market studies including Angrist (1995), Borjas and Katz (2007) and Katz and Murphy (1992).

The third ingredient of our model is that immigrants and natives within the same skill/education category are allowed to be imperfect substitutes within a CES structure. There is debate in the literature on the estimates of the elasticity of substitution between natives and immigrants. Borjas et al (2008) put it essentially at in...nity, Ottaviano and Peri (forthcoming) and Card (2009) put it around 20 and Manacorda et al. (forthcoming) put it around 6. We will analyze the wage impact of immigration and emigration on natives using each one of these parameter values. We describe the sensitivity of simulated average wages and the distributional e¤ects for non-migrant natives with di¤erent education levels to di¤erent assumptions about this elasticity.

Finally, the fourth ingredient of the model is to allow human capital (skill) intensity to have a productivity externality as immigration and emigration alter the skill composition in an economy (i.e. the ratio of highly educated to the less educated). There is some debate in the literature on this issue as well. Moretti (2004a and 2004b), based on data from the US cities, puts the elasticity of productivity to the share of college educated between 0.75 and 1. On the opposite end of the spectrum, Acemoglu and Angrist (2001) estimate essentially no productivity e¤ect of increased schooling in US states. Iranzo and Peri (2009) estimate an externality around 0.44 using US state level data. Again, we analyze the impact of immigration, emigration and net migration under each of these parameter values.

While the speci...c quantitative details of the simulations vary with the choice of parameter values, some general results emerge from this exercise. First, in general, over the period 1990-2000 immigration had zero to small positive long-run e¤ect on the average wages of non-migrant natives in the rich OECD countries (Western Europe plus the US, Canada,

> 3In section 5.5 by making assumptions on how the total migration ‡ows are distributed over the years, and on the short-run speed of adjustment of capital we can calculate the short-run average wage e¤ects accounting, that is, for the sluggish adjustment of capital.

3
Australia). Using the estimates for the average values of the parameters, this positive effect ranges from zero in Italy to +1.7% in Australia. Second, over the period 1990-2000, emigration had a mild to signi...cant negative long-run e¤ect on the wages of non-migrants. Still focusing on rich OECD countries, the e¤ects range from 0 for the US (due to the near absence of emigration) to -0.8% in the UK and -0.7% in Portugal. Third, over the period 1990-2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives.

All three results go counter to the popular beliefs about migration, but they are a result of the nature of migrant ‡ows from 1990 to 2000. European countries (along with the US, Canada, Australia) have experienced both immigration and emigration that were usually more skill intensive than (or as skill intensive as) their domestic labor forces. Under these conditions, in the long run, immigration is associated with average wage gains and emigration with average wage losses for non-migrant natives unless there is no externality of human capital and migrants are perfectly substitutable with natives. In this latter case, both immigration and emigration have no e¤ect on the average wages of non-migrant natives.

The skill composition of migrants relative to non-migrants is crucial in determining our average wage results. We attempt to correct the "e¤ective" skills of migrants to account for certain important phenomena which might not be fully re‡ected in our aggregate statistics. First, we use estimates of the extent of illegal immigration into the main Western European countries to correct for the in‡ows of migrants into Europe. Second, we account for the potential lower quality of schooling for migrants who completed their education in their home countries or for the "downgrading" of their skills in the host countries’labor market. Both corrections reduce the e¤ective percentage of highly skilled immigrants. While the corrections make some di¤erence, the general picture described above remains unchanged.

We consider two other extensions. First, we introduce the possibility of crowding e¤ects of immigrants on productivity due to the presence of a ...xed factor or the presence of a positive density externality, such as in Ciccone and Hall (1996), on productivity. While their presence either attenuates or increases slightly the e¤ects but leaves the basic results unchanged. Second, we account for the short-run impact of immigrants by including in the simulations sluggish capital adjustment. We distribute net immigration into yearly ‡ows of immigrants and assume a speed of adjustment of capital that is consistent with estimates from the macro literature. While such sluggish adjustment generates a small negative contribution to average wages in the short run, the overall short-run e¤ects of immigration are still positives in four of the ten considered European countries. When negative, these short-run e¤ects are very small, with the largest negative impact achieved in Spain in the order of -0.35%. Emigration turns out still to exert a negative wage e¤ect on non-movers in the short run and usually larger, in absolute value, than the e¤ect of immigration.

The rest of the paper is organized as follows. Section 2 presents the simple aggregate production framework from which we derive wages as marginal productivity of di¤erent types of workers. Section 3 describes the data, their construction and their sources and shows some simple summary statistics about the educational structure of labor force data and migrant data. Section 4 presents the basic results of the simulated wage e¤ects of immigrants using our model and the range of parameters available from the literature. Section 5 considers the wage e¤ect when accounting for undocumented workers, for schooling quality di¤erences, 4
for downgrading of skills, for density e¤ects and adjusting for employment rates and for the short-run capital adjustment. Section 6 concludes the paper.

# 2 Model

We construct an aggregate model of the economy to examine the long-run wage e¤ects using data from 1990 and 2000 national censuses on migration and native non-migrant labor force by education level. The analysis builds on two di¤erent strands of the literature. The ...rst strand aims at identifying the impact of immigration on national labor markets while the second analyzes the external e¤ects of individual schooling on overall productivity. In this context, international migrations have two essential long-run e¤ects. The ...rst is to change the schooling composition of the sending and receiving economy. The second is to introduce in the host countries workers with a di¤erent skill sets from natives and, hence, even for given education, not perfect substitutes with them. These two aspects imply that the size and the educational composition of immigrants and emigrants relative to the non-migrants are the crucial factors in determining the long-run domestic wage e¤ects of international migration patterns.

## 2.1 Aggregate production function

The prevalent model adopted in this literature is based on a production function where the labor aggregate is represented as a nested constant elasticity of substitution (CES) function of di¤erent types of workers. In the production function (we omit country subscripts for simplicity), we assume that at time t output (Yt) is produced in a country according to a constant-returns-to-scale Cobb-Douglas production function with two factors, physical capital (Kt), and labor in e¢ ciency units (Qt): The term A<sup>e</sup> t represents the total factor productivity (TFP), and � is the income share of labor.

Assuming that physical capital is internationally mobile and that each single country is too small to a¤ect the global capital markets, the returns to physical capital are equalized across countries. If R<sup>�</sup> denotes the international net rate of return to capital, the following arbitrage condition implicitly de...nes the equilibrium capital-to-labor ratio in the economy: The above condition holds in the short and in the long run in a small open economy. However, even in a closed economy as in Ramsey (1926) (or Solow 1951) condition (2) holds in the long-run balanced growth path, with R<sup>�</sup> being a function of the inter-temporal discount rate of individuals (or of the savings rate)<sup>4</sup> . Hence in the long-run we can substitute this

> 4As long as immigration does not change the saving rate of an economy the pre- and post- migration R� are identical.

5
arbitrage condition into (1) to obtain an expression of aggregate output as linear function of the aggregate labor Qt: where At � A<sup>e1</sup> t<sup>=�</sup> [(1 � �)=R<sup>�</sup> ]<sup>(1��)=�</sup> is an increasing function of TFP and is referred to as modi...ed TFP.

Following the labor (Katz and Murphy 1992, Card and Lemieux 2001) and growth (Caselli and Coleman 2006) literature, we assume that labor in e¢ ciency unit (Qt) is a nested CES function of highly educated (Qh;t) and less educated workers (Ql;t): where �q is the relative productivity level of highly educated workers (with tertiary education) and is set to 0.6 for the rest of the paper. The second parameter, �q, is the elasticity of substitution between the two groups of workers.

We distinguish between natives and immigrants within each labor aggregate Qh;t and Ql;t. If native and immigrant workers of education level s were perfectly substitutable, the aggregate Qs;twould simply be equal to the sum of natives’and immigrants’labor supplies. However, there are various reasons to believe that native and immigrant workers may di¤er in several respects which are relevant to the labor market. First, immigrants have skills, motivations and tastes that may set them apart from natives. Second, in manual and intellectual work, they may have culture-speci...c skills and limitations (e.g., limited knowledge of the language or culture of the host country), which create comparative advantages in some tasks and disadvantages in others. Third, even in the absence of comparative advantage, immigrants tend to concentrate in di¤erent occupations than natives due to migration networks or historical accidents. In particular, new immigrants tend to disproportionately cluster in those sectors or occupations where previous migrant cohorts are already over-represented. Finally several studies (such as Card 2009, Ottaviano and Peri, forthcoming, Manacorda et al., forthcoming) ...nd imperfect degrees of substitution between natives and immigrants. Hence, we assume that the quantities of high-educated (Qh;t) and low-educated labor (Ql;t) are both nested CES functions of native and immigrant labor stocks with he respective education levels. This is given by, where Ns;t is the number of type-s native workers and Is;t is the number of type-s immigrant workers who are present in the country. �I is the elasticity of substitution between natives and immigrants in group s. The parameter �s captures the relative productivity level of natives and is set at 0.6 for the rest of the paper as it was the case with �q. This choice provides reasonable skill premia and wage di¤erentials between natives and immigrants. Our results, however, are insensitive to the value of these parameters.

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# 2.2 Wages

We consider each country as a single labor market since workers are free to move within it to arbitrage away wage di¤erences. Then we derive the wage rates for native workers of both education levels (wh;t and wl;t) by substituting (4) and (5) into (3) and taking the derivative with respect to the quantity of labor. This yields the following: These expressions allow us to evaluate the e¤ects of immigration/emigration on nonmigrant natives. The change in the average wages of non-migrant natives due to 1990-2000 immigration ‡ows of immigrants (de...ned as new gross immigration minus return migration of foreigners to their home countries) is de...ned as where wh;2000 and wl;2000 are the wages of more and less educated natives, respectively, as de...ned by (6) and (7) and calculated using aggregates Qt+1; Qh;t+1 and Ql;t+1 inclusive of immigrants observed in 2000. Moreover, wh;<sup>IMMI</sup> 2000<sup>andw</sup> l;<sup>IMMI</sup> 2000 are the wages calculated for year 2000 keeping the stock of immigrants as observed in 1990 (i.e. excluding the 1990-2000 immigration ‡ows).

The change in average wage of non-migrant natives due to 1990-2000 emigration ‡ows of natives (de...ned as new gross emigration minus return migration of natives) is de...ned as the following where wh;t<sup>EMI</sup> +1<sup>and w</sup> l;t<sup>EMI</sup> +1<sup>are the wages of highly and less educated natives calculated for 2000</sup> using the stock of emigrants observed in 1990 (i.e. excluding 1990-2000 emigrant ‡ows), but keeping immigrants constant at their 2000 values. Note that to compute changes in natives’ average wage, we keep Nh;t and Nl;t at their 2000 values. Indeed, the e¤ects on average wage of non-migrants are weighted at the observed composition of natives. This isolates only the wage e¤ects of emigration on non-migrants and not those e¤ects due to changing composition of the domestic labor force. Adding the two e¤ects, we obtain the average wage e¤ect of net international migration.

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## 2.3 Schooling externalities

We also consider the possibility of a positive externality from highly educated workers, in the spirit of the recent literature (Acemoglu and Angrist 2000, Ciccone and Peri 2006, Moretti 2004a, 2004b and Iranzo and Peri 2009). There is a large body of growth literature (beginning with Lucas 1988, and extending to Azariadis and Drazen 1990, Benhabib and Spiegel 2005, Cohen and Soto 2007 and Vandennbussche et al 2009) that emphasizes the role of human capital (schooling) on technological progress, innovation and growth of GDP per capita. More recently, however, the empirical literature has pointed out that while it is sometimes hard to ...nd an e¤ect of human capital on growth of income per capita (Benhabib and Spiegel 2005), there seems to be evidence that human capital contributes to the level of income per person beyond its private returns. This implies that TFP is an increasing function of the schooling intensity in the domestic labor force. Such formulation is particularly appropriate to be included in our model and, based on the expressions used in Moretti (2004a, 2004b), the TFP can be expressed as follows , where A0 captures the part of TFP independent of the human capital externality, and � is the semi-elasticity of the modi...ed TFP to the share of highly skilled in the economy, <u>Qh;t</u> 5 Qt . Acemoglu and Angrist (2000) and Iranzo and Peri (2009) use a similar formulation to express schooling externalities and we use their estimates of the parameter �. Relying on this structure and using migration data from national Censuses, we can simulate the e¤ects of immigration and emigration on wages of non-migrants in a range of countries for a range of values obtained from the literature for the three key parameters �q; �I and �.

# 3 Data description

Assessing the national wage e¤ects of immigration to and emigration from diverse set countries across the globe requires country-level international migration and labor force data by skill level. The detailed description of the migration data is in the Data Appendix. Here we describe brie‡y the main sources and features of the migration and labor force data used.

## 3.1 International migration data

The relevant migration ‡ows to be used in our exercise are immigration and emigration ‡ows (namely gross ‡ows of immigrants and emigrants net of returnees and re-migrants). They capture the change in actual supply of migrants in a country.

There are several sources for migration ‡ows by receiving country (e.g. OECD International Migration database, UN migration statistics) but those only include gross in‡ow of people in a country and they almost never correct for migrants who leave or go back to their country of origin. Moreover they never record undocumented migrants and they often

> 5The externality is expressed in terms of the ratio between labor composite. Such a ratio, however, is almost identical to the share of workers with high schooling in the labor force.

8
record immigrants when they achieve their resident status rather than when they ...rst enter the country. Finally, de...nition of migrants - by nationality or birth - vary across countries. Most importantly for our purposes, those data are not available by education level. The ‡ows of immigrants to a country can only be recovered by measuring the stock of foreign born people in a destination country (from a certain origin country) at di¤erent points in time and then taking the di¤erence. The other advantage of starting with data on stocks of migrants is that they are usually from national censuses which tend to be more representative and complete than other data sources. Plus censuses often account for (i) undocumented immigrants at least in some countries like the US, (ii) they categorize immigrants by place of birth, rather than nationality which can change over time and across countries due to naturalization laws and (iii) report their education levels.

Our database is described in Docquier et al. (2010) who construct bilateral measures of immigrant and emigrant stocks for 195 countries in 1990 and 2000. The starting point for the new data is Docquier and Marfouk (2005) which collected the stock of foreign-born in OECD destination countries in 1990 and 2000, by country of origin and level of schooling (primary, secondary and tertiary). These data are supplemented with original data from the censuses of a large number of non-OECD countries. Finally, for many destination countries with no data on immigration, bilateral migrant stocks were predicted using a gravity framework as described in greater detail in Docquier et al. (2010). Their own census data would su¢ ce to measure immigration into OECD countries. However, evaluation of emigration also requires data from all the possible destination countries, at least the most relevant ones. In other words, emigrant stocks from a certain origin can only be measured by aggregating all migrants recorded in the censuses of all destination countries. As some important destination countries (such as Russia, South Africa, Brazil, Argentina, and Singapore) are outside the OECD, this new database ensures the coverage of essentially all emigrants from all countries in our sample. Table A2 in the appendix show that the majority of emigrants from Western Europe are in destination countries for which we have actual census data.<sup>6</sup> For most OECD countries, less than 10% of their emigrants are in countries with imputed (rather than actual) migration data. The only European country relying on imputed data for a large fraction of its emigrants is France at about 30%.

We distinguish two skill types s, denoted by s = h for college graduates (referred to as highly educated) and s = l for individuals with secondary education completed and less (referred to as less educated). The database covers the years 1990 and 2000 and the di¤erences in stocks by country of origin and destination provides the measures of the ‡ows. It focuses on individuals aged 25 and over as a proxy of the working-age labor force which is one of the main di¤erences with other migration databases (such as Ozden et al. 2010). This choice maximizes comparability between data on migration and on labor force per education attainment. Furthermore, it excludes a large number of students who emigrate temporarily to complete their education or children who migrate with their families.<sup>7</sup> The data description and some summary statistics follow.

> 6This pattern is also con...rmed in Ozden et.al. (2010) which presents global bilateral migration stocks but does not disaggregate by education levels.

> 7The dataset contains 195 source countries: 190 UN member states (after excluding North Korea), the Holy See, Taiwan, Hong Kong, Macao, and the Palestinian Territories. We consider the same set of countries in 1990 and 2000, although some of them had no legal existence in 1990.

9
# 3.2 Labor force data per education level

It is relatively easier to identify the number and average education level of workers in each country of the world. Several data sources can be used to assess the size and skill structure of the labor force of each country. The size of the working-age labor force (i.e. population aged 25 and over) is provided by the United Nations. Data is missing for a few countries but can be estimated using the CIA world factbook.<sup>8</sup> Labor force data is then split across skill groups using international indicators of education attainment. Here, we follow Docquier and Marfouk (2006) and Docquier, Lowell and Marfouk (2009) in combining di¤erent data sets documenting the proportion of postsecondary educated workers in the population aged 25 and over. They use De La Fuente and Domenech (2006) for OECD countries and Barro and Lee (2001) for non-OECD countries. For countries where Barro and Lee’s measures are missing, they estimate the proportions educated using Cohen and Soto’s measures (see Cohen and Soto, 2007). In the remaining countries where both Barro–Lee and Cohen–Soto data are missing (about 70 countries in 2000), they apply the educational proportions of the neighboring country having the closest enrollment rate in secondary/tertiary education, or the closest GDP per capita.

# 3.3 Description and summary statistics for our sample

Table 1 shows the 1990-2000 ‡ows of immigrants, emigrants and their di¤erence (net migration) for a set of ten large Western European countries and four other groups: (i) three Anglo-Saxon non-European countries (US, Canada and Australia) traditionally attracting large numbers of immigrants, (ii) three large Eastern European countries (Czech Republic, Hungary and Poland) with a range of emigration rates, mostly to Western Europe, (iii) two middle income countries with large emigration rates (Mexico and Turkey) and (iv) three non-OECD countries with large immigration rates (Argentina, South Africa and Singapore). We focus on the 1990-2000 ‡ows for several reasons. First, they are the most recent ‡ows that one can construct for both immigration and emigration (based on censuses) hence their skill composition and size is more relevant and possibly closer to later migration ‡ows during the 2000’s.<sup>9</sup> One could also assess with our method the labor market impact of the total stock of immigrants and emigrants across countries (reported for 1990 and 2000 in Table A3 of the Table Appendix). However the stock is accumulated over many decades and re‡ects migration that took place in the far past. Hence the migrant stock is less relevant to establish recent labor market e¤ects of immigration and emigration.

Several features of migration ‡ows are worth emphasizing. First, the US, Canada and Australia have much larger immigration (between 4.4 and 10.6%) than emigration (between 0.2 and 1.3%) rates among the highly educated portion of the labor force. The US is the only country with a comparable rate of immigration of less educated (5.8%) while both the immigration and emigration of low skilled is small in Canada and Australia. In Western Europe, high skilled immigration rates range from 0.2% (Greece) to 8.5% (the UK) while the emigration rates range between 1.1% (France and Germany) and 8.1% (Portugal). Em-

> 8See http://www.cia.gov/cia/publications/factbook.

> 9Clearly it would be best to have the ‡ow of migrants between 2000 and 2010 but this will be available only in a few years as the Censuses from the 2010 round are collected, processed and made public.

10
igration rates of the highly educated in Eastern Europe and in middle income countries can be very high while the rate of immigration and emigration of the less educated is comparable. Mexico is the main exception with signi...cant emigration of less educated (7.8%) and minimal immigration. Finally the non-OECD immigration-receiving countries have comparable immigration and emigration rates, except for Singapore that has a very large in‡ow of highly educated workers.<sup>10</sup> The picture emerging from a ...rst glance at the data is that both the recent in‡ow of immigrants and out‡ow of emigrants has a high-skill concentration greater than those of native non-migrants for Western European countries as well as for the other rich countries included as comparison. This con...rms the data-based observation of Grogger and Hanson (forthcoming) and of Docquier and Marfouk (2005) but is in sharp contrast with the anecdotal image of unskilled immigrants ‡ooding Western European labor markets. This con‡icting perception is mainly due to the fact that less educated migrants formed a smaller share of the migrant ‡ow than the corresponding group for the native labor force, but they still outnumber the highly skilled migrants in absolute numbers. Hence the large number of less educated immigrants stand out and attract the public attention in most European countries. Germany is a good example to illustrate this point. Looking at the composition of the migrant ‡ow in the 1990s, the highly educated immigrants were 3.1% and the less educated immigrants 2.2% of their respective groups in the domestic labor market. However as 78% of the domestic labor force was less educated (and only 22% highly educated in 1990) there were still twice as many less educated immigrants relative to highly educated ones.

Another important observation is that the recent ‡ows are usually more educated when compared with the stock of immigrants already present as of 1990 (see table A4). Hence, if the impression on immigrants is based on the stock, rather than recent ‡ows, there may be a perception bias towards less educated migrants who possibly migrated long ago. Third, in spite of the fact that census data are better than o¢ cial immigration data, they may miss some undocumented migrants, especially in Western Europe. If those undocumented migrants that are missing from the census data are mainly less educated, the actual numbers of the less educated are understated by our data. We address this issue in section 5.1 by using estimates on the extent of undocumented migration in di¤erent destination countries.

Finally the perception of the skills of immigrants may be based on the occupations and the labor market performance of immigrants (rather than their formal education) as analyzed by Mattoo et.al. (2008) in the US labor market. In many cases, college educated immigrants are less productive and take less skilled occupations than college educated natives since their education quality, mostly obtained at home, might be less easily transferable or lower than those of the natives in the destination. The lower quality/downgrading of education levels for immigrants matter for our exercise as well. We address quality adjustment in the extensions of our simulation exercise in section 5.2.

In terms of the overall picture of migration across countries in the world, the nonEuropean Anglo-Saxon countries (US, Canada, Australia) attract highly educated immigrants, from all over the world and their native-born citizens tend not to emigrate. Western European countries seem to attract highly educated immigrants (from other European and

> 10 See our discussion below on undocumented migration, which tends to be less skill intensive, and how we try to account for this in our analysis.

11
less developed countries) but also lose highly educated emigrants (to other OECD countries and between each other). This is more similar to what happens to countries of intermediate income level that attract immigrants from poorer countries and send migrants to richer countries. With these overall patterns in mind, we turn to the simulation exercise.

# 4 Simulated wage e¤ects: basic speci...cation

## 4.1 Parameterization

Our model allows us to calculate the wage e¤ects of migration depending on the values of three fundamental parameters �q; �I and �: We take a range of values from the literature. Table 2 summarizes the values of the parameters chosen and the respective sources. We would like to remind the reader that parameters �q and �s are set to 0:6 in all simulations. This choice generates reasonable skill premia and wage di¤erentials between natives and immigrants when immigrants represent around 10 percent of the skill-speci...c labor force.<sup>11</sup> For the parameter �q there are several estimates in the literature. A group of in‡uential papers propose speci...c estimated values for low, intermediate and high levels of substitution. For instance Johnson (1970) and Murphy et al (1998) estimate values for �q around 1.30 (respectively 1.34 and 1.36); Ciccone and Peri (2005) and Krusell et al. (2000) estimate values around 1.50 (respectively 1.50 and 1.66) and Ottaviano and Peri (forthcoming) estimate a value close to 2.

The parameter �I has been the subject of several recent papers and has generated a certain level of debate. This parameter is particularly relevant to determine the e¤ect of immigrants on wages of natives and, as we will see, the choice of this parameter makes some di¤erence in evaluating the e¤ects of migration in certain countries. Borjas et al (2008) and Ottaviano and Peri (forthcoming) use US data and Manacorda et al (forthcoming) use UK data in their estimation.

Finally the parameter �, whose magnitude has been estimated using data from US cities (Moretti 2004a, 2004b) or US states (Acemoglu and Angrist 2000 and Iranzo and Peri 2009) is also subject to a certain level of disagreement between those who ...nd substantial schooling externalities and those who do not.

To preview the main features of the dependence of the simulated e¤ects on parameter values, �q has very little bearing on the impact of immigration and emigration on average wages, but it is critical for the e¤ects on the wage distribution between more and less educated natives. The parameter �I in‡uences the average wage impact of immigration on natives but has no bearings on the impact of emigration. Finally � matters for the impact of immigration and emigration on average wages with no e¤ect on the wage distribution.

## 4.2 Simulation with the basic speci...cation

Table 3 and the three panels of Figure 1 show the long-run impact of immigration (dotted line), emigration (dashed line)) and net migration (solid line) between 1990-2000 on average

> 11Those preference parameters play a minor role in our wage simulations. They enter both the numerator and denominator in the expressions for percentage changes in wages and hence they canel out.

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wages (Panel 1a) as well as on the wages of highly educated (1b) and of less educated (1c) non-migrants. All ...gures are presented as percentage of the 2000 value of the respective wage level. We have arranged countries starting with the non-European Anglo-Saxon on the left, followed by the Western European, Eastern European countries and then, the countries of emigration (Turkey and Mexico) and ...nally the non-OECD countries. The graph provide a clear visual impression that immigration has a positive average wage e¤ect on non-migrants (except for Argentina, the dotted line is always above zero) while emigration has a negative average wage e¤ect (the dashed line is always below zero). The e¤ect of net migration (combining immigrants and emigrants) on average wages is clearly positive for Canada, Australia and Singapore, clearly negative for Portugal and Poland and usually negative but not too large for the other Western European countries. Figure 1b shows that immigration has usually a negative e¤ect on wages of highly educated (except for the US), while emigration has a positive e¤ect on those wages. Finally Figure 1c shows the positive and sometimes very large (in the case of Singapore and Australia) e¤ect of immigration on wage of less educated and the negative and also sometimes large (e.g. for Portugal, Greece and Poland) e¤ect of emigration on the wage of less educated workers.

Focusing on European countries, some patterns emerge clearly. First immigration has either a null (Italy and Greece) or a positive e¤ect on the average wage of natives, particularly sizeable for Sweden (+0.5%), the Netherlands (+0.5%) and the UK (+1.0%). At the same time emigration has negative average wage e¤ects for all European countries and those are particularly large for Greece (-0.4%), the UK (-0.8%) and Portugal (-0.7%). As a consequence, in countries where emigration is greater than immigration (such as Portugal and Greece, but also in Italy during the considered period), non-migrants su¤er net wage losses. On the other hand, in countries of larger immigration (UK and France) non-migrants bene...t of the positive wage externalities from the arrival of highly educated immigrants.

Very interesting is also the e¤ect of emigration on wage inequality. Emigration from European countries has a much stronger e¤ect on the less educated non-migrants whose wages, for example, decline by 2.5%, 2.3% and 1.3% in the UK, Portugal and Greece, respectively. As far as immigration is concerned, Table 3 shows that recent immigration ‡ows are usually more education-intensive in many countries. Hence, immigration substantially helps the wages of low-educated British workers (+2.8%), so that in net, they gain from international mobility. Low-skilled workers in Sweden and Netherlands also experience signi...cant gains from immigration. However, the gains from immigration in Portugal and Greece do not compensate the losses from emigration and the low-skilled workers su¤er considerable net losses of -2.1 and -1.3%, respectively.

We report the e¤ects of immigration, emigration and net migration also for the aggregate EU15, considered as one country, in Table 1 and Figure 1. These ...gures ignore intra-EU15 mobility and only consider the overall e¤ect of immigration from and emigration to the rest of the world on the aggregate EU15 economy. As a whole, EU15 is much less open to labor movements than some of its countries. Immigration levels for both the high-skilled (2.6%) and the low-skilled (1.4%) are higher when compared to the emigration levels (0.9 and - 0.4% respectively) but the net migration levels are almost identical across the education levels. Emigration, which exhibits strong positive selection, has a negative e¤ect of 0.2% on average wages while immigration, also positively selected relative to non-migrants, has a positive e¤ect of equal magnitude. Even more remarkably, less educated European workers 13
experience a 0.5% wage increase due to immigration into EU15 and a 0.6% wage decrease due to emigration out of EU15 to the rest of the world.

What is the channel through which less educated natives lose as a result of emigration? The emigration of engineers, teachers and scientists (highly educated) implies that fewer high-tech companies, schools and research laboratories operate, leading to lower demand for construction workers, assistants and lab technicians (less educated). The supply of highly educated creates demand for the complementary less educated workers and loss of the ...rst group decrease the demand and, in the long-run, the wages of the latter group. Interestingly, the pattern in European countries is similar to what happens in Poland, Mexico and South Africa. The reverse takes place in Canada and Australia where immigration strongly helps the wages of less educated (+3.3 and +4.5% respectively). In the US, we see very small e¤ects on wages of less educated (-0.4%) and highly educated (+0.3%).

# 4.3 Robustness checks

Figure 2, 3 and 4 show various sensitivity analyses for a range of parameter values. Following the speci...c values presented in Table 2, Figures 2, 3 and 4 show the variation in our results when we vary the values of �q, �I and �, respectively. For purposes of clarity, we only represent the wage e¤ect of immigration and emigration leaving aside the net e¤ect (which is approximately the sum of the two).

Figure 2a shows that the sensitivity of the average wage e¤ect to changes in �q is quite minimal. The simulated lines are almost completely overlapping, indicating that the average wage e¤ects of immigration and emigration do not critically depend on the elasticity of substitution between more and less educated workers in the labor force. What depends on this parameter, however, as shown by ...gure 2b and 2c are the distributional e¤ects - wage e¤ects for non-migrants with di¤erent education levels. Higher values of �q imply closer substitutability between more and less educated workers and this reduces the negative (positive) e¤ect of immigration (emigration) on the wages of highly educated. At �q = 2; the positive e¤ect of emigration on highly educated wages is almost eliminated and the negative e¤ect of immigration is turned into a small positive e¤ect due to the imperfect substitution and positive externality of immigrants. For the wages of less educated non-migrants, on the other hand, higher substitutability of more and less educated workers reduces both the positive e¤ect of immigration and (in absolute value) the negative e¤ect of emigration. However, even at �q = 2; there are clear wage gains from immigration and clear wage losses from emigration for less educated non-migrants. This is due to the fact that for less educated workers, the wage e¤ects (operating through skill-complementarities, schooling externalities and imperfect substitution with immigrants) go in the same direction - they are positive for immigration and negative for emigration. Hence even when we reduce the strength of the skill-complementarity channel via increasing �q, the other two channels remain strong and of opposite direction for immigration and emigration on less educated wages. For the more educated workers, on the other hand, reducing the schooling-complementarity channel increases the relative importance of schooling externalities and of the imperfect substitutability between native and immigrants. These last two e¤ects are positive and, for high values of �q, may prevail generating null or positive overall e¤ects of immigration on highly educated. With these mechanisms in mind, we can also easily understand and interpret the sensi14
tivity analysis of the parameter �I performed in Figure 3. First, we should note that this parameter, as expected, has no impact on how emigration a¤ects average or skill-speci...c wages. All of the lines in all three panels for emigration are perfectly overlapping. As the substitutability between natives and immigrants decreases, on the other hand, the average wage e¤ects of immigration become stronger and more positive for every country in the sample. Intuitively this occurs because the in‡ow of immigrants is more bene...cial to natives’ wages when the two groups are more complementary with each other. Interestingly when �I = 6 (based on Manacorda et al., forthcoming) immigration implies wage bene...ts for both more and less educated in most of the countries. In some countries, such as Canada, Australia, the UK and even more Singapore, the positive average wage impact of immigrants is quite large, in the order of 2, 3 and even 5%.<sup>12</sup> Finally, Figure 4 shows the sensitivity to the schooling externality and reveals some interesting e¤ects. First, when we completely eliminate this channel (� = 0), the average wage e¤ects of emigration become essentially zero while the e¤ects of immigration on non-migrant wages stay positive, driven by imperfect substitution as discussed above. If we increase the value and e¤ect of �, the average wage e¤ect of immigration also increases with signi...cant positive e¤ect on the less-educated and lower negative e¤ect on the highly educated workers, o¤-setting the negative impact on highly educated due to the skill-complementarity channel.

The robustness checks show that essentially for the whole parameter range, immigration has a positive e¤ect on average wages of non-migrants for most considered countries. For most European countries, it is positive or, in some cases, zero for the extreme parameter values. On the other hand, emigration has an e¤ect that ranges from 0 to negative depending on parameter values. For all the Western European countries considered the e¤ect of emigration are always negative. The winners from immigration are, for Europe, the less educated native workers while the losers from immigration are the more educated. For this group, however, the wage losses are signi...cantly reduced and also turned into gain if (i) the elasticity between more and less educated is at the high end of the spectrum, (ii) the elasticity between natives and immigrants is at the low end or (iii) if the schooling externality is at the high end of the spectrum. Average wages and wages of he less educated seem to bene...t from immigration and su¤er from emigration in all simulations for all European countries.

# 4.4 Best-case and worst-case scenarios

Previous sections presented the wage e¤ect of immigration and emigration for a wide range of critical parameter values (�q; �I; �) as identi...ed in the literature (see Table 2). Figure 5 shows the estimated average wage e¤ect of immigration (panel 5a) and emigration (panel 5b) for non-migrants considering the con...gurations of the parameters that produce the most and the least bene...cial wage e¤ect on natives. In particular, the combination of parameters producing the most bene...cial wage e¤ects of immigration on non-migrants is: �q = 1:3; �I = 6; � = 0:75; while the con...guration producing the least bene...cial e¤ect is �q = 2; �I = 1; � = 0: For emigration, the worst case scenario is reached when �q = 1:3; � = 0:75 and the best case scenario when �q = 2; � = 0 independently from the value of �I:

> 12With imperfect substitution between natives and immigrants, new immigrants bene...t native workers in the host country. They compete, however, more directly with previous immigrants. However their wages are not included in the simulated e¤ects for national non-migrants.

15
As far as immigration is concerned, the ’Worst-case’e¤ect is essentially equal to zero for all countries whereas the ’Best-case’e¤ect is positive everywhere. The largest gains are obtained for Singapore (+3.7%), Australia (+2.6%), Canada (+1.9%) and the UK (+1.7%). The opposite picture emerges with regard to emigration. The ’Best-case’e¤ect is essentially equal to zero for all countries whereas the ’Worst-case’e¤ect is negative everywhere. The largest losses are obtained for the UK (-1.3%), Portugal (-1.1%), Poland (-0.9%), Singapore and South Africa (-0.8%), Greece and Canada (-0.7%). For EU15 as a whole the best case scenario for immigration implies a gain around 0.5% of non movers’wages. On the other hand the worst-case scenario implies a loss from emigration also around -0.5% of non movers’ wage.

# 5 Simulated e¤ects: extensions wage

The parametrization of our model re‡ects di¤erent results on the impact of immigration, schooling externalities, and the substitutability between highly educated and less educated workers. However our analysis of the wage e¤ects of immigration can be seen as somewhat optimistic. First, our database may not capture illegal immigration to the OECD countries, which is widely believed to be low-skill intensive. Second, it assumes that highly educated immigrants are homogeneous across origin countries and contribute the same skills as native highly educated workers to the destination labor markets. Finally, we did not include yet the potential congestion e¤ects of immigration on the labor market of receiving countries, nor did we examine the short-run e¤ects accounting for sluggish adjustment of physical capital to immigrants. This section deals with those issues and extends our robustness analysis.

## 5.1 Accounting for undocumented immigrants

By focusing on census data, our database fails to record undocumented immigrants in most countries, for whom systematic statistics by education level and country of birth are not available. An exception is the United States where demographic evidence indicates that most residents, regardless of legal status, are identi...ed in the census. However, other host countries provide no accurate data about the size and educational status of undocumented migrants. In their recent report, Kovacheva and Vogel (2009) estimate the size of irregular migrant populations in the European Union and in selected member states. Annex 1 of their paper provides lower and upper bounds for the proportion of undocumented in the immigrant population in 2002.<sup>13</sup> Data are available the for the 10 European countries in our sample - Belgium (11-18%), France (9-15%), Germany (14-20%), Greece (42-63%), Italy (53-75%), Netherlands (11-26%), Portugal (18-89%), Spain (8-29%), Sweden (1.7-2.5%), and the U.K. (11-21%).

Figure 6 gives the e¤ect of immigration on average wages when illegal immigration is taken into account where the ’Lower Bound’and ’Upper Bound’scenarios use the proportions listed above. Although there may be some instances of highly skilled undocumented migrants (such as those overstaying their H-1B visas in the United States), the majority of undocumented migrants are believed to be low-skilled. Hence, we make the two extreme assumptions that

> 13See http://irregular-migration.hwwi.net/Stock_estimates.6170.0.html.

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(i) all undocumented immigrants as measured in the early 2000’s are less educated and that (ii) they migrated to the destination country between 1990 and 2000. Our simulation uses the baseline set of elasticities of Table 3. Hence the solid line in Figure 6 corresponds to our baseline simulation depicted on Figure 1 Panel 1a. In the original scenario, the e¤ect of immigration is positive or nil in all EU countries. Adding illegal immigration (see dashed and dotted lines) slightly modi...es our conclusions only in countries where the proportion of illegal immigrants is very large (above 50 percent). Hence, Greece and Italy now su¤er small immigration costs (0.2 and 0.1%, respectively), while the e¤ect for the Netherlands turns to zero in the ’Upper Bound’scenario. The change is obviously driven by the adverse e¤ect on less educated non-migrants. Most of the countries however (such as UK, Belgium, France, Netherlands, Portugal, Spain and Sweden) maintain a positive wage e¤ect of immigration even under the most pessimistic scenario which is likely to largely overestimate the undocumented. In summary, only Italy and Greece su¤ered a (small) negative wage e¤ect from immigration even after adding the highest estimates of undocumented immigrants and counting all of them as less educated.

# 5.2 Accounting for the quality of education and skill downgrading

Simulations presented in Section 4 account for potential complementarity between natives and foreign workers in each skill category. Such imperfect substitutability emerge because immigrants have di¤erent motivations, culture-speci...c skills and limits compared to natives. Another source of heterogeneity between immigrants and natives arises from the absence of equivalence between national and foreign degrees both in terms of quality and subject coverage (such as the case in law degrees). In particular, highly educated immigrants trained in developing countries could be less productive in high-skill jobs than natives with similar educational degrees.

Evidence of such heterogeneity in the quality of education is provided by Coulombe and Tremblay (2009), who compare the skill intensity and schooling levels of Canadian immigrants and natives who were both submitted to standardized tests in literacy, math, and problem solving. These tests provide measures of pro...ciency that are comparable across countries and over time. On this basis, Coulombe and Tremblay estimate a ’skill-schooling gap’for each origin country, de...ned as the di¤erence between the mean years of schooling of the immigrant subgroup and the typical native with the same pro...ciency level. A positive skill-schooling gap of n years means that Canadian nationals with y years of schooling are as productive as immigrants with y+n years of schooling. The larger the skill-schooling gap, the lower is the quality of education in the country of origin. Simple bivariate OLS regressions show that the skill-schooling gap is a decreasing function of per capita income of the origin country. Their -0.10 point estimate of the slope coe¢ cient indicates that the skill-schooling gap is one year smaller when per capita income increases by US$10 000 in the origin country. Using this estimate and cross-country data on per capita income, we construct an indicator of skill-schooling gap for each origin country. Then, assuming that one year of schooling generates a productivity gain of 8 percent, we estimate the relative productivity of educated immigrants and natives in each country, with a benchmark value of one for workers trained in Canada (as well as workers trained in richer origin countries, i.e. the upper bound of this index is one). For example, college graduate immigrants from Angola and Portugal are 17
equivalent to 0.73 and 0.85 Canadian college graduates, respectively.

We revisit the wage e¤ect of immigration on average wages when the skill levels of immigrants and nationals are quality-adjusted. Di¤erences in the quality of education is likely to be a source of ’brain waste’(see Matoo et al, 2008). Hence, our adjustment consists in multiplying the number of college graduates originating from a given country by the relative productivity index computed for that country, and consider the remaining fraction as less educated workers. In the previous example a college graduate from Angola is considered as a combination of 0.73 college graduates and 0.27 non-college graduates in the rich country. This method has two main limitations. First, as our adjustment factor is based on Canadian data, it su¤ers from a selection bias. Indeed, Moroccan migrants to Canada are more than likely to have higher skills than Moroccan migrants to France. For this reason, we also provide a second simulation with a correction based on the squared of the Canadian index. Coming back to our example above, one college graduate immigrant from Angola or Portugal accounts for 0.51 or 0.72 units of highly skilled workers, respectively. Second, while our benchmark non-adjusted measure implies that immigrants’human capital is equivalent to that of natives (as if all migrants were trained in the host country), our adjusted measure implies that all immigrants were trained in their birth country. Reality is obviously somewhere in between. However our only objective here is to explore whether a correction for education quality can modify our predictions, hence an extremely negative assumption implies simply that our estimate is a lower bound of the wage e¤ect.

Our third correction in the accounting of foreign-born highly educated is based on the US wage of immigrants from each country of origin (in year 2000). We calculate the weekly wage of college educated immigrants from country j as a linear combination of the wages of US-born college educated and non college educated workers. We then use those weights to distribute a highly educated foreign-born between more and less educated US equivalents. With this method, for instance, a highly educated Vietnamese is considered as equivalent to 0.73 units of US college graduate and 0.27 units of US less educated. If foreign-born college educated migrants from a certain country are paid more than US born they are considered pure college equivalent.

Our fourth and ...nal correction is based on Mattoo et.al (2008) who calculate the probability that an immigrant from country j with tertiary education (completed at home prior to migration) obtains a skilled job in the US labor market. Using the data from 1990 and 2000 censuses and controlling for individual factors (such as age, experience and years since migration), they calculate these probabilities for hypothetical individuals identical in every respect except their origin country. They ...nd that migrants from South Asia perform very well while those from Eastern Europe and Latin America are placed in relatively unskilled jobs.

The results from all of the four corrections are reported in Figure 7. Some corrections imply smaller gains for host countries (especially for the US, Canada, Australia and Singapore) The largest change, relative to the baseline, is for the US where we now observe a loss of around 0.2% in average wages due to ten year of immigration, which is still a small percentage. The corrections do not change the overall picture and the a positive impact of immigration in most European countries is maintained. The limit of all corrections is that, for data limitations, they are all based on schooling quality and labor market assessment of immigrants in Canada or the US and not in European countries.

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# 5.3 Accounting for density/crowding externalities

An additional aggregate e¤ect of immigration may stem from its impact on the aggregate scale of production. On one hand, the existence of a ...xed factor in production (such as land) would cause aggregate decreasing returns. On the other hand, the e¢ ciency of production may be increased by an increase in employment density due to "agglomeration externalities" as in Ciccone and Hall (1986). In general agglomeration externalities/crowding e¤ects can be modeled by assuming that the TFP is also as function of the aggregate scale of production (Qt). An extension of expression 10 above can be stated as follows: In this expression, the crowding e¤ect of labor force size on land, assuming a share of land in production of 0:03 in rich countries (see Ciccone and Hall 1986) would give � = �0:03 . On the other hand, incorporation of the positive density externalities estimated by Ciccone and Hall (1986) would lead to a positive value of � = 0:06:<sup>14</sup> We explore the implication of such e¤ects for the impact of immigration, keeping the values of the other parameters at the basic speci...cation.

Figure 8 illustrates the estimates in the benchmark case and compares with either type of externality. Since the values of the estimates for the parameter � and the e¤ect of emigration in changing total employment (and its density) are small, the e¤ects of introducing this externality (either positive or negative) are also minimal. The presence of an agglomeration externality would marginally increase the positive average wage e¤ects of immigrants, while the crowding externality would marginally reduce it but the di¤erences are on the order of a small fraction of a percentage point.

# 5.4 Accounting for employment rates

While the model is based on employment the empirical analysis is based on working-age population data. As reduced labor market participation of immigrants and/or natives may alter the wage e¤ect of immigration (see Angrist and Kugler 2003, or D’Amuri et al 2010) it is important to check whether excluding from the analysis non-employed natives and immigrants a¤ects our predictions.

Using the European Labor Force Survey, we correct the size of each skill group (highly and less educated natives and immigrants) for their employment/population ratio as of year 2000. If these employment rates are identical across the four groups, the results would not change at all. If they are very di¤erent between natives and immigrants, they may imply di¤erences in relative employment e¤ects for given population sizes. While employment/population ratios may di¤er across countries what matters for our wage e¤ects is their within country di¤erence across the four skill groups described above. Figure 9 shows the simulated wage

> 14Notice that we simply model the density e¤ect as depending on average density: total population divided by area. The fact that immigrants move mainly into cities that are already dense should increase even further the e¤ect, if one uses the exact Ciccone and Hall (1996) index that counts density in cities (where most population lives) as more relevant.

19
e¤ects using employment data (obtained applying group-speci...c employment-population ratios) relative to the baseline case calculated with population data. The new estimates are hardly distinguishable from the baseline case. This is because while the employment rate is somewhat di¤erent between highly and less educated, they are very similar for native and immigrants for the same education levels in most countries. In some countries immigrants have in fact slightly higher employment rates than natives. In most countries less educated immigrants have a somewhat smaller employment rate than corresponding natives. This is true in particular for Spain and Sweden where the low employment rate of highly skilled immigrants (relative to natives) reduces the positive wage e¤ects of immigration but only slightly.

# 5.5 Accounting for physical capital sluggishness (short-run e¤ects)

While the model has, so far, evaluated the long-run e¤ects of immigrants and emigrants, by assuming full capital adjustment, we can also calculate the short-run e¤ects by accounting for imperfect capital adjustment. From the production function in (3) we can obtain the compensation to the composite factor Q, wt<sup>Q;whichcorrespondstotheaveragewage,as:</sup> where �t = Kt=Qt approximates the capital-labor ratio. In balanced growth path (or in an <u>1</u> e <u>1</u> open economy) the capital-labor ratio is given by the following expression �<sup>�</sup> t<sup>=</sup> � <u>1R�</u><sup>�</sup> <u>�</u> � � At�<sup>:</sup> Hence the average wage in the balanced growth path (aggregating immigrants and natives) depends only on total factor productivity, A<sup>e</sup> and on the rate of return of capital and not on the total labor supply Qt: In the short run, however, the percentage change in average wages may depend on the percentage response of �t to immigration. Taking the di¤erential of the logarithm of (12) relative to immigration, and approximating logarithmic with percentage changes, we have: where (��t=�t)immigration is the percentage deviation of the capital-labor ratio from �<sup>�</sup> t due to immigration, while �A<sup>e</sup> t=A<sup>e</sup> t<sup>theproductivitye¤ect(ifany)fromim-</sup> � �immigration<sup>is</sup> migrants. With full capital adjustment and the economy on the balanced growth path, (��t=�t)immigration equals 0 and the only average wage e¤ect of immigrants is through A<sup>e</sup> t<sup>15</sup> . At the opposite extreme, with ...xed total capital, Kt = K; then (��t=�t)immigration equals the negative percentage change of labor supply due to immigration: �<sup><u>�</u></sup> Q<sup><u>I</u></sup> t<sup><u>t</u>,where�Itisthe</sup> net change in foreign-born workers and Qt is, approximately, the aggregate labor supply at the beginning of the period. While for short periods or for sudden shocks the short run

> 15Obviously immigration has also an e¤ect on relative wages of native-immigrants and highly-less educated as shown above. Physical capital, however, only a¤ects the average wage and hence we can simply add this e¤ect to the group-speci...c wage e¤ect.

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e¤ect may be evaluated considering capital as ...xed in the case of ten years of immigration, which is a slow and continued phenomenon, we need to account explicitly for the dynamics of capital.

A popular way to analyze the deviation of ln(�t) from its balanced growth path trend, used in the growth and business cycle literature, is to represent its time-dynamics in the following way: where the term �2(trend) captures the balanced growth path trajectory of ln(�t); equal to <u>1 1��</u> �<sup>ln</sup> R<sup>�Aet</sup> ; and the term �1 ln(�t�1) captures the sluggishness of yearly capital adjust� � ment. The parameter (1 � �1) is commonly called the “speed of adjustment ” since it is the share of the deviation from the balanced growth path (trend) eliminated each year. Finally,<sup><u>�</u></sup> Q<sup><u>I</u></sup> t<sup><u>t</u>represents the yearly immigration ‡ows as share of the labor force and "tare other</sup> mean-zero uncorrelated shocks. Once we know �1, � and the sequence of yearly immigration ‡ows,<sup><u>�</u></sup> Q<sup><u>I</u></sup> t<sup><u>t</u>;wecanuse(14)toobtainanimpulseresponseofln(�t)asof2000indeviation</sup> from its trend (short run). This in turn allows us to correct the average wage e¤ect (currently determined only by the productivity e¤ect of immigration and emigration) for this sluggish capital adjustment and to obtain the short-run e¤ect on the average wage of nonmigrants. The empirical growth literature (Islam, 1995; Caselli et al., 1996) and the business cycle literature (Romer, 2006, Chapter 4), provide model-based and empirical estimates of �1. The recent growth literature usually estimates a 10% speed of convergence of capital to the own balanced growth path for advanced (OECD) economies (Islam, 1995; Caselli et al., 1996), implying �1 = 0:9: Similarly, the business cycle literature calculates the speed of convergence of capital to be between 10% and 20% in each year (Romer, 2006, Chapter 4) for closed economies, and even faster rates for open economies. Hence �1 = 0:9 seems a reasonable estimate (if anything on the sluggish side).

We use the immigration and emigration rates over 1990-2000 for the considered countries, and assume a uniform distribution of migration ‡ows over the ten years. We adopt as parameters of capital adjustment �1 = 0:9 and � = �0:9 (assuming that capital adjustment begins the same year as immigrants are received or emigrant lost). Hence the recursive equation (14) allows us to calculate (��1990�2000=�1990)migration as of year 2000. Using formula (13) we can calculate the e¤ect of �� on the average wage and by adding this to the baseline average wage estimate for non-movers we obtain the short-run e¤ects of immigration and emigration. We report the simulated short-run e¤ect (together with the long-run e¤ects from the baseline case) in Figure 10. Such a short-run correction, driven by sluggish capital adjustment, is usually negative for immigrants and positive for emigrants. The largest value of the short-run correction for immigration is -0.9% for the US average wage, as this country has by far the largest immigration rate over the period. Several other countries (such as Mexico, Hungary and Czech Republic) have a correction term for immigrants close to 0<sup>16</sup> . For Europe the largest short-run adjustments in average wages due to capital sluggishness are for Sweden (-0.48%) and for Spain (-0.55%). The smallest are for France (-0.14%) and for

> 16In terms of net ‡ows of immigrants, during the 90’s the largest yearly in‡ows were in Spain, at 0.2% of the labor force in each year and the smallest were in Greece at 0.02% of the labor force per year.

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Greece (-0.04%). For emigration, except for Mexico that has a very large and positive shortrun correction, due to a very large emigration rate (1.2% per year in the period considered), the other corrections are very small. Figure 10 shows the simulation of short-run e¤ects visa-vis the long run e¤ects of immigration and emigration on average wages for the baseline scenario. Combining imperfect capital adjustment with the long-run e¤ects, we still have positive short-run average wage e¤ects of immigration on non-migrants in four European countries. In the UK, France, Netherlands and Sweden immigration had a positive e¤ects on average native wages already in the short-run. For Greece, Italy, Portugal and Belgium the short-run e¤ects on average wages are negative and very small (less than -0.2% in absolute value). For Germany and Spain they are negative and between -0.27 and -0 35%. The same countries experienced a long-run increase in their average wage due to immigrants equal to 0.20%. Hence, even in the short run, accounting for sluggish capital adjustment we do not ...nd much of a negative average wage e¤ect of immigration. As for emigration the shortrun e¤ects are smaller in absolute value but still negative for seven European countries and for the EU15 as a whole. Even in the short run in most European countries the e¤ect of emigration is negative and larger in absolute value than the e¤ect of immigration. Both of them, however, are rather small in the order of few tenths of a percentage points. Even in the short run and accounting for the slow adjustment of capital immigration has a very moderate e¤ect, usually close to zero and, if negative, smaller (for seven countries out of ten) than the e¤ect of emigration. Not even in a short-run perspective did immigration have a signi...cant negative impact on native wages for European countries. To the contrary emigration did not produce any short-run gains for native wages that may reduce the welfare cost of the long-run losses.

# 6 Conclusions

Immigrants are highly visible to the governments and public in destination countries especially in times of economic anxiety and high unemployment levels. They are also relatively easy to measure by statistical agencies of the host countries through censuses, population and labor force surveys. On the other hand emigrants are much less visible to governments of their birth countries and less known to the general public. There is no mandatory registration for those who leave a country, they are dispersed in many receiving countries and no statistical agency keeps detailed records of all its emigrants and returnees. There is rarely a policy instrument that can a¤ect emigration since freedom of movement allows people to leave any democratic country.

During the last decades emigration has been considered mostly in connection with poor countries and usually in the context of brain drain. There have been studies on the labor market impact of immigration in the destination since the 1970s. However, only very recently and only in connection with the debate on brain drain and brain gain, some economists have began to measure empirically the aggregate income e¤ects of emigration (Beine et.al. 2008). This paper uses a recently constructed database on emigration ‡ows by schooling level between 1990 and 2000, constructed using data across all countries in the world. Using a range of parameters and an aggregate representation of labor markets of receiving countries, we show that residents of Western European countries experienced wage gains, on average, from 22
immigration while they experienced wage losses due to emigration. The magnitude of the wage losses due to emigration is roughly equal to or larger than the gains from immigration. This is due to the fact that both immigrants and emigrants in European countries are, on average, more educated relative to non-migrants. Moreover immigrants are generally imperfect substitutes for non-migrants bringing skills that only partially compensate the losses due to emigration. Our analysis also ...nds that immigration in Europe was somewhat more bene...cial to the less educated natives, reducing their wage gap with highly educated, while the opposite is true of emigration. These surprising results imply that several European countries should begin to discuss more seriously the causes and e¤ects of their signi...cant emigration rates, especially of their highly educated professionals, rather than obsessing with immigration that has mostly been bene...cial in economic terms.

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# A Data appendix

Migration data presented in Section 3 follow Docquier et al. (2010) who construct comprehensive 195x195 matrices of bilateral migration stocks. These matrices cover the two skill groups (college graduates and less educated individuals), and two years (1990 and 2000). Migration is de...ned on the basis of country of birth which is time invariant (contrary to the concept of citizenship, which changes with naturalization) and independent of the changes in policies regarding naturalization. However, this de...nition does not account for whether education has been acquired in the home or in the host country. This induces a potential over-estimation of the intensity of the brain drain migration of children and students can represent an important fraction of total immigration for certain countries.<sup>17</sup> The methodology is described in detail in Docquier et al. (2010) and it consists of three main steps. The starting point (step 1) is the database described in Docquier, Lowell and Marfouk (2009) documenting bilateral migration stocks in OECD host countries. It is based on a collection of census and register immigration data by country of birth and educational level in the 30 OECD countries in 1990 and 2000. This OECD set of destinations does not include the three member states which joined the OECD in 2010 (Chile, Israel and Slovenia) and future members (Estonia and Russia). This database characterizes the education level, origin and destination of about 57.4 million migrants in 2000 and 40.8 million migrants in 1990. High-skill migration represents 35.4 percent of the total in 2000 (30.1 percent in 1990). The second step consists of a collection of similar immigration data from 46 nonOECD destinations in 2000 and 31 destinations in 1990. In the third step, data collected in steps 1 and 2 are used to estimate the determinants of the size of bilateral migration (Mijt<sup>s)from countryitocountryjintheeducationgroupsatyeart.Then,basedonthese</sup> estimations, the size of the migrant stock from each origin country to the remaining 119 non-OECD host countries in 2000 (and 134 in 1990) for each education level is predicted. The ...nal 195x195 database combines recorded migration stocks (when census and register data are available) and out-of-sample predictions (when o¢ cial statistics are missing).

Table A1 provides summary statistics for migration stocks which are comparable with labor force data. The database characterizes the origin, destination and education level of about 100.5 million adult migrants in 2000, and 80.2 million in 1990. Table A2 distinguishes observed and imputed values for immigrants to countries included in our sample. Table A3 shows stock of immigrants and emigrants in 1990 and 2000 as percentage of the skill-speci...c labor force. Table A4 describes the distribution of the labor force between college graduates and less educated as percentage of total.

> 17Beine et al (2007) have estimated the age-of-entry structure of high-skill immigration and proposed alternative measures of the brain drain excluding those who left their home country before age 12, 18 or 22. The corrected rates are obviously lower than those calculated without age-of-entry restrictions. However, the correlation between corrected and uncorrected rates is extremely high and the country rankings by brain drain intensities are basically una¤ected by the correction. This should mitigate concerns about children migration possibly leading to cross-sectional biases in the brain drain estimates.

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