reasons and channels, including those who migrated through irregular channels (Özden et al., 2011). However, it is possible that these data sources may misreport irregular migrants for various reasons. To the extent such misreporting exists, measurement errors can mute the estimated relationship.

**Income** : Per capita GDP serves as the key measure of income in this paper. The GDP data are taken from the Penn World Tables (PWT) 10.0 release dated June 18, 2021 (Feenstra, Inklaar, and Timmer 2015).<sup>7</sup> This release contains the most comprehensive and up-to-date data on GDP, in purchasing power parity (PPP) terms, covering 183 countries between 1950 and 2019. Per capita GDP is calculated by dividing the expenditure-side real GDP at chained PPPs (in 2017 US$) by the country’s population.<sup>8</sup> GDP for 2020 is computed by applying the annual real per capita GDP growth rate between 2019 and 2020 available from the World Development Indicators (World Bank 2022). Similarly, per capita GDP is back-casted for earlier years based on annual real per capita GDP growth rates for countries for which the GDP series begins only after 1960. Countries that are missing GDP in this data set are omitted from the analysis.

**Education** . Data on the educational attainment of the populations of origin countries are from the Barro-Lee Educational Attainment Dataset (Barro and Lee, 2013).<sup>9</sup> This data set provides harmonized information on the percent of adult population that have no education, some primary education, some secondary education, and some tertiary education for all countries in five-year intervals from 1950 to 2015. This analysis uses the 2015 data to proxy for educational attainment in 2020.

**Education of migrants** : While comprehensive data on the educational attainment of migrants is absent, the analysis turns to data from the United States as a case of migration to high-income destinations. The data on educational attainment come from IPUMS USA, which provides easy access to the harmonized public use microdata samples of the decennial census, as well as the US Census Bureau American Community Survey (ACS) for various years (Ruggles et al. 2022). This study uses the largest available microdata samples for each of the census years: 1960 (5% sample), 1970 (1% state form 1 sample), 1980 (5% state sample), 1990 (5% state sample), and 2000 (5% sample). It uses the ACS one-year samples for years 2010 and 2019, which provide surveys a 1-in-100 national random sample of the population.<sup>10</sup>

IPUMS USA provides harmonized variables across all available samples. This analysis uses harmonized detailed data on birthplace to identify migrants and their country of birth. Migrants with unidentified country of birth are dropped from the analysis. Once migrants are identified, educational attainment is straightforward. Adult (ages 15+) migrants are classified into three groups: low-educated individuals with primary or lower level of education; medium-educated individuals with some secondary education; and high-educated individuals with at least one year of college education. Likewise, working migrants are classified into three groups based on the occupational earnings score provided with the IPUMS USA data: low-earnings occupations are in the bottom third of earning score; mid-earnings occupations are in the middle third; and high-earnings occupations are in the top third.

# 2.2 Empirical strategy

The empirical strategy closely follows the non-parametric estimation outlined in Clemens (2020). That is, the following equation is estimated:

> 7 See <u>https://www.rug.nl/ggdc/productivity/pwt/ for more details.</u>

> 8 All PPPs in this paper are in 2017 US$ as per the PWT 10.0.

> 9 See <u>http://www.barrolee.com/.</u>

> 10 The analysis does not use ACS 2020 because it uses experimental weights to correct for the impacts of the COVID-19 pandemic on the collection of ACS data.

5