_Ro_ = ( 1 + δ _n_ )( 1 + δ _s_ ) _Re_ = ( 1 + δ _n_ )( 1 + δ _s_ )( 1 + δ _p_ ) , (3) that is, a combination of natural barriers, selection, and policy-induced wage discrimination. We will return to the subject of selection in detail below. It turns out that the effect of this assumption on the estimates is closely related to the assumption of the independence of φ _fh_ (<sup>_x_′</sup> _i_ ) and φ _ff_ (<sup>_x_′</sup> _i_ ) .

# 2.3 _Data_

We estimate equation (2) using unusually rich and standardized collection of individual level data sets on wage-earners compiled by the World Bank<sup>7</sup> plus the US Census Public Use Microdata Sample (PUMS) five percent file.

A series of steps brings us from the raw collection of data sets to the estimation sample. First, we remove all self-employed people and unpaid family workers from the data, leaving only wage-earners. This has the advantage of increasing the comparability and accuracy of the earnings measures, but has the disadvantage of eliminating a large portion (though not all) of the informal sector from the sample—especially many agriculturists in the poorest countries. Second, we remove all people aged 14 or less and all people aged 66 or greater, as well as all people reporting zero wage earnings. Third, we remove data from twelve transition countries because many of these countries were undergoing extraordinary instability of prices, wages, and currencies at the time the survey was administered. Rather than picking and choosing among them we just eliminated the transition countries as a block.<sup>8</sup> Fourth, we randomly delete US-born US-residents from the PUMS to reduce the size of that group from about 6.13 million to about half a million, due to binding memory constraints in the microcomputer conducting the statistical analysis (the resulting group was 8.15% of its original size, so the sampling weight of each remaining individual was multiplied by 12.257). Fifth, we drop Chad from the sample because the sample of US residents in the public-use data does not happen to contain any working-age wage-earners born in Chad. Finally, we drop Honduras from the sample for reasons described below.

The result is a data set with 2,015,411 wage-earners residing in 43 countries. This comprises 891,158 individuals residing in 42 developing countries, 623,934 individuals born in those same 42 developing countries but residing in the US, and 500,319 individuals born in the US and residing in the US. Each individual record contains the person’s wage in 1999 US dollars at Purchasing Power Parity, country of residence, years of schooling, age, sex, an indicator of urban or rural residence, and indicator variables for the periodicity of the reported wage (weekly, monthly, etc., with monthly as the base group). For those residing in the US, there is additional information on country of birth and year of arrival for the foreign-born. A sampling weight is assigned to each

> 7 The sources for all data are given in the appendix. The basic database is also described in Montenegro and Hirn (2008).

> 8 The twelve we remove are: Armenia, Azerbaijan, Bulgaria, Belarus, Croatia, Hungary, Latvia, Moldova, Republic of Macedonia, Russia, Romania, and Slovakia.

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