attainment or to the absence of data on education in some receiving countries. Educational categories are built on the basis of country specific information and are compatible with human capital indicators available for all sending countries. A mapping between the country educational classifications is sometimes required to harmonize the data<sup>7</sup> .

By focusing on census and register data, our methodology does not capture illegal immigration for which systematic statistics by education level and country of origin are not available. According to the U.S. Immigration and Naturalization Services, the illegal population residing in the United States amounted to 3.5 million in January 1990 and 7.0 million in January 2000. It is even possible to identify the main countries of origin (in 2000, 68.7 percent were from Mexico, 2.7 from El Salvador, 2.1 from Guatemala, 2.0 from Colombia and Honduras, etc.)<sup>8</sup> . However, there are no accurate data about the educational structure of these illegal migrants. For the other member states of the OECD, data on illegal immigration are less reliable or do not exist. By disregarding illegal migrants, we probably overestimate the average level of education of the immigrant population (it can be reasonably assumed that most illegal immigrants are uneducated). Nevertheless, this limit should not significantly distort our estimates of the migration rate of highly-skilled workers.<sup>9</sup> As far as possible, we turn our attention to the homogeneity and the comparability of the data. This induces a couple of methodological choices:

- To allow comparisons between 1990 and 2000, we consider the same 30 receiving countries in 1990 and 2000. Consequently, Czechoslovakia, Hungary, Korea, Poland, Mexico and Turkey are considered as receiving countries in 1990 despite the fact that they were not members of the OECD. Migration is defined on the basis of the country of birth rather than citizenship. While citizenship characterizes the foreign population, the concept of foreign-born better captures the decision to emigrate<sup>10</sup> . Usually, the number of foreign-

> 6 Bhorat et al. (2002) compare South African emigration data to immigration numbers collected in five important receiving countries (Australia, Canada, New Zealand, UK and USA) and show that total emigration was approximately 3 times larger than South African official statistics showed.

> 7 For example, Australian data mix information about the highest degree and the number of years of schooling. 8 See http://uscis.gov/graphics/shared/aboutus/statistics/III Report 1211.pdf.

9 Note that the problem may not be that important for estimation of brain drain determinants because results do not depend on the extent or share of illegal migrants but rather on their cross-country difference. Hence, estimation is unaffected if the shares are constant across countries or if the difference between the share _mi_ in

> country i and _m j_ in country j is equal to a random (white noise) variable, ∀ _i_ , _j_ , i.e., for _mi_ = _m A_ + ε _i_ , with _mA_

> is the average share,<sup>ε</sup> _i_<sup>is an “iid” random (white noise) variable.</sup>

> 10 In some receiving countries such as Germany, immigrants' children (i.e. the second generation) usually keep their foreign citizenship.

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