Medium and long run economic assimilation of Venezuelan migrants to Peru

Torres, Javier; Beverinotti, Javier; Canavire-Bacarreza, Gustavo Working Paper Medium and long run economic assimilation of Venezuelan migrants to Peru IDB Working Paper Series, No. IDB-WP-1561 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Torres, Javier; Beverinotti, Javier; Canavire-Bacarreza, Gustavo (2024) : Medium and long run economic assimilation of Venezuelan migrants to Peru, IDB Working Paper Series, No. IDB-WP-1561, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0005503 This Version is available at: https://hdl.handle.net/10419/299450 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ Medium and Long Run Economic Assimilation of Venezuelan Migrants to Peru Javier Torres Javier Beverinotti Gustavo Canavire-Bacarreza WORKING PAPER No IDB-WP-1561 Inter-American Development Bank Country Department Andean Group January 2024 Medium and Long Run Economic Assimilation of Venezuelan Migrants to Peru Javier Torres Javier Beverinotti Gustavo Canavire-Bacarreza Inter-American Development Bank Country Department Andean Group January 2024 Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Torres, Javier. Medium and long run economic assimilation of Venezuelan migrants to Peru / Javier Torres, Javier Beverinotti, Gustavo Canavire-Bacarreza. p. cm. — (IDB Working Paper Series ; 1561) Includes bibliographical references. 1. Labor market-Peru. 2. Foreign workers-Peru. 3. WagesPeru. 4. Human capital-Peru. 5. Education, HigherPeru. I. Beverinotti, Javier. II. Canavire, Gustavo. III. InterAmerican Development Bank. Country Department Andean Group. IV. Title. V. Series. IDB-WP-1561 http://www.iadb.org Copyright © 2024 Inter-American Development Bank ("IDB"). This work is subject to a Creative Commons license CC BY 3.0 IGO (https://creativecommons.org/licenses/by/3.0/igo/legalcode ). The terms and conditions indicated in the URL link must be met and the respective recognition must be granted to the IDB. Further to section 8 of the above license, any mediation relating to disputes arising under such license shall be conducted in accordance with the WIPO Mediation Rules. 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Medium and Long Run Economic Assimilation of Venezuelan migrants to Peru* Javier Torres†, Javier Beverinotti‡, and Gustavo Canavire-Bacarreza§ January 8, 2024 Abstract In a span of six years, the proportion of Venezuelans in Perú has surged nearly fourfold, rising from virtually zero to over 4% of the population. This study delves into the dynamics of mediumand long-term labor market integration in Perú, combining data from the Venezuelan Population Residing in Perú Survey and the Peruvian National Household Survey. Our findings reveal that Venezuelan workers experience low returns on foreign postsecondary education and there is minimal relation between foreign work experience and monthly income. Importantly, these outcomes remain consistent irrespective of the time spent in the host country, indicating a gradual economic assimilation process. Lastly, our estimation demonstrates that if Venezuelans’ human capital yielded returns equivalent to Peruvian human capital, the average income of Venezuelans would witness a substantial increase of 20%. Key Words: Immigration, Economic Assimilation, Wage Discount. JEL Classification: J15, J24, J31, J70. *The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the World Bank, the InterAmerican Development Bank, their Board of Directors, or the countries they represent. We thank Rodrigo Chang and Renzo Trujillo for their superb research assistance. †Corresponding Author. Universidad del Pacifico, Department of Economics, Lima, Peru. Email: j.torr[email protected] ‡Inter-American Development Bank, Washington, DC, USA. Email: [email protected]. §World Bank, Washington, DC, USA and Universidad Privada Boliviana, La Paz, Bolivia . Email: [email protected]g. 1 Introduction Since 2017, the migration of Venezuelans across Latin America has emerged as a prominent and pressing issue. The socioeconomic and political crisis in Venezuela has compelled approximately 6 million individuals to seek refuge in other countries. According to the International Organization for Migration (IOM), around 80% of Venezuelan migrants are distributed across seventeen countries, including Perú. By the end of 2022, the Peruvian Superintendence of Migration reported that nearly 1.6 million Venezuelans had entered Perú. This substantial inflow has significantly altered the country’s demographic landscape, with the Venezuelan population growing from virtually zero to over 4% of the total population. Notably, initial estimates highlight the concentration of Venezuelan migrants in the capital, Lima, ranking it as the world’s third-largest city in terms of the number of Venezuelans. While large migration flows can distort labor markets, leading to economic stress due to a sudden increase in the labor supply, the appropriate assimilation of immigrants presents an opportunity for economic growth. Effectively harnessing the (foreign) human capital within an economy has the potential to enhance production and improve the overall material well-being for all individuals. This paper examines economic assimilation by comparing Venezuelans’ labor market income profiles in Perú to that of Peruvians. It implicitly defines assimilation as the difference in labor income between these two groups within similar categories of human capital. Additionally, it specifically focuses on mediumand long-run integration using both the 2022 Survey of the Venezuelan Population Residing in Peru (ENPOVE) and the 2022 Peruvian National Household Survey (ENAHO). Numerous studies have examined the impact of the inflow of Venezuelans on destination countries (host countries). Researchers have scrutinized the effects on overall production and the labor market in Ecuador (Olivieri et al. (2021a), Olivieri et al. (2021b), Caruso et al. (2019), Lebow (2022), and Bahar et al. (2021)), Brazil (Shamsuddin et al. (2021)), and Perú (Asencios and Castellares (2020), Morales-Zurita et al. (2020), and Boruchowicz et al. (2021)).1However, only a limited number of studies have attempted to analyze the economic assimilation of Venezuelan migrants into these host countries in the medium to long run or have examined the welfare effects of recognizing 1For Colombia, Caruso et al. (2019) and Lebow (2022) find a negative effect of the labor supply shock on wages in urban areas, particularly for natives with lower educational levels. In contrast, Bahar et al. (2021) find negative but insignificant effects on the formal employment of natives, concentrated among workers with higher education levels and those from an amnesty program that granted work permits to undocumented Venezuelan immigrants. 1 that foreign human capital is as valuable as native human capital. Our research aims to address this gap in the literature. Lastly, it estimates the increase in monthly labor earnings under the assumption of full recognition of foreign human capital. Comparing monthly labor income profiles, we find that Venezuelan workers’ returns to their foreign postsecondary education are low, with virturally no correlation between foreign work experience and monthly income. As such, Venezuelan workers with a higher level of education and work experience face the largest income differences (less assimilation) to Peruvian workers. Furthermore, there is evidence that these features may be relatively unaffected by time spent in the host country, suggesting . This suggests a slower economic assimilation process. Lastly, our calculations reveal that Venezuelans’ average monthly income would increase by about 20%. if their human capital received the same returns as Peruvian human capital. Among the few studies related to our research, we highlight the work of Graham et al. (2020), who provide a comprehensive descriptive analysis of the medium-term integration of Venezuelans into Colombia; Olivieri et al. (2021b), who use the "Survey of Migrants and Receiving Communities in Ecuador" to study the labor performance of Venezuelans in Ecuador and calculate the income counterfactual;2and Shamsuddin et al. (2021), who use administrative data to analyze Venezuelan migrants’ access to social programs, educational services, and the formal labor market in Brazil.3 Additionally, Torres and Galarza (2021) use the 2018 National Survey of the Venezuelan Population Residing in Peru (ENPOVE 2018) to analyze the labor integration of Venezuelan migrants in Perú in the very short run.4 The remainder of the paper proceeds as follows: Section 2 describes our data. Section 3 presents the econometric specifications used to identify the immigrant wage premium. Section 4 discusses our results, and Section 5 concludes. 2Specifically, this is the overall income effect if Venezuelans were to obtain employment that matches their skills. 3These authors find that Venezuelan migrants face challenges in integrating into the education system, social protection programs, and the formal labor market. 4In regard to the international literature, our work aligns with studies conducted by Friedberg (1992) for the United States, Friedberg (2000) for Israel, Fortin et al. (2016) for Canada, Basilio et al. (2017) for Germany; and Becker and Ferrara (2019) and Brell et al. (2020), who conduct reviews of the literature on the assimilation of forced migrants/refugees in developed countries. 2 2 Data We combine datasets from the 2022 National Survey of the Venezuelan Population Residing in Peru (ENPOVE) and the 2022 Peruvian National Household Survey (ENAHO). Both surveys were conducted by the Peruvian Statistics Bureau (Instituto Nacional de Estadística e Informática, INEI) to collect similar socioeconomic information for different population groups. The ENPOVE 2022 represents the second iteration of the survey focusing on the Venezuelan population in Perú and is unique in its ability to capture mediumto long-run economic assimilation of immigrants.5 The structure of ENPOVE 2022 mirrors that of ENAHO, replicating its main modules and questions. This alignment enables us to utilize both surveys and employ comparable variables for both Peruvian and Venezuelan workers. As argued by Torres and Galarza (2021), ENAHO and ENPOVE complement each other, with ENAHO using the 2017 National Census as a sampling frame for the overall population (with few foreign-born), while ENPOVE augments its sampling frame with information from the National Migration Superintendence. Conducted between February and March of 2022, ENPOVE 2022 collected information on Venezuelan migrants residing in the urban areas of nine regions: Lima (the country’s capital), Ancash, Ica, and Callao in the central coastal area; Piura, Lambayeque, Tumbes, and La Libertad in the northern coastal area; and Cusco and Arequipa in the south.6ENPOVE 2022 gathered information about demographics from all household members (e.g., age, gender, education), migration status, health, employment (e.g., job held in Venezuela before migrating and current employment status), experiences of discrimination, and social networks. Conversely, ENAHO stands out as the primary and most reliable source for comprehensive social, demographic, and economic indicators from Peruvian households. However, it has limitations with regard to the collection of information on foreign-born individuals. Specifically, it lacks data on the past labor profiles of foreigners, including details about their previous occupations. This explains why we need to use both surveys in our analysis. The ENAHO is conducted year-round and is representative at the national and regional (all 25 regions) levels. The main variables used from both surveys include age, gender, education level, occupational 5The initial ENPOVE was conducted in 2018 and involved interviews in urban areas/cities in six regions of the country (Arequipa, Callao, Cuzco, La Libertad, Lima, and Tumbes) with a total of 9,487 observations. The ENPOVE 2022 and 2018 do not share a panel data sample; that is, they do not follow a particular cohort of immigrants. Each survey was conducted independently. We selected ENPOVE 2022 to focus on medium-run assimilation. 6A region is analogous to a US state; Peru has 25 regions. 3 category, wage received from current employment, and region of residence. As mentioned, the ENPOVE also asks about the occupation held in Venezuela, date of arrival, and work permit status. For both ENAHO and ENPOVE, the “main occupation” variable is coded according to the INEI’s Classification of National Occupations (INEI, 2015).7 Working Sample We restrict our analysis to the urban areas of the nine regions surveyed in the ENPOVE and to individuals with a positive wage income and exclude workers who are paid only with in-kind transfers as well as household workers.8Our dependent variable is the logarithm of the monthly wage, calculated from the primary and secondary economic activities of Peruvian and Venezuelan workers in 2022. The education module in both surveys gathers information on the highest degree or diploma attained, distinguishing between complete and incomplete degrees. We create dummy variables for each complete education level, with primary education serving as the reference category for all analyses. The dummy variables represent secondary education, postsecondary technical education, some university education, and undergraduate and graduate degrees. This method is employed to account for potential nonlinear relationships in the benefits of education, as suggested by Fortin et al. (2016) in a similar flexible specification. To construct the “work experience” variable, we follow the standard approach to calculate potential labor market experience as the difference between age and years of education, assuming children start school at age 6 and continue uninterrupted. Given that neither the ENPOVE nor the ENAHO directly records the number of years of schooling, this variable is imputed based on the highest degree attained.9 We analyze occupational categories using the International Standard Classification of Occu7This classification is based on the International Standard Classification of Occupations (ISCO-88) developed by the International Labour Organization (ILO). The occupational groups are coded at 3 digits and represent both the groups and subgroups of occupations. 8A similar protocol is followed by Torres and Galarza (2021). 9We impute years of education according to the following rule: Less than incomplete primary education is given 0, incomplete primary education is given 3, complete primary education is given 6, incomplete secondary education is given 9, complete secondary education is given 11, incomplete postsecondary technical education is given 13, complete postsecondary technical education is given 14, incomplete university education is given 15, complete university education is given 16, and postgraduate education is given 17. The years of education imputation is only used to construct the experience (years of experience) variable. All of our educational estimations employ direct information on the highest degree or diploma attained. 4 3 Econometric Specification To measure economic assimilation, we compare the labor market income profiles of Venezuelans and Peruvians, employing two other distinct approaches. Specifically, we examine occupational mobility and compute partial-equilibrium welfare improvements that would result in foreign human capital receiving the same returns as native human capital. 3.1 Income Assimilation We analyze Venezuelan workers’ earnings in contrast to their Peruvian counterparts. Additionally, we assess the disparity in the returns to their individual human capital. To achieve this, we introduce a flexible specification that identifies various combinations of education and work experience, differentiating between the human capital of natives and migrants. Our estimation closely resembles the approaches taken by Friedberg (2000), Fortin et al. (2016), and Torres and Galarza (2021). The estimation includes yir =α0+ C ∑ c=1 (PeruEduc ∗Expc)βc+ C ∑ c=1 (VenEduc ∗Expc)γc+Xiρ+δr+ϵir (1) where yir indicates the logarithm of monthly income for person iresiding in city r. We identify all “C” combinations of education and work experience separating native from foreign human capital. Specifically, we interact educational levels (primary, secondary, technical, and university) with work experience categories (Less than 10 years, 10 to 19, and more than 20 years of experience) for each type of individual (Peruvian vs. Venezuelan). The education categories come directly from the information on the highest degree attained, while the categories for work experience are created from the constructed continuous variable. Xirepresents a female binary indicator, δra region fixed effect, and ϵir the model error. We run equation (1) for our working sample, clustering standard errors at the region level. From the estimates, we compare monthly earnings for natives and immigrants with the same human capital characteristics. These comparisons aid in determining the overall returns to foreign education and work experience. Additionally, we segment the immigrant sample by the year of arrival to distinguish shortand medium-run earnings in the host country. Specifically, we divide the immigrant sample into those who arrived in 2018 or earlier and those who arrived in 2019 or 11 later. Large income penalties for migrants in the short run might diminish or significantly decrease in the medium to long run. 3.2 Occupational Mobility Additionally, we assess whether Venezuelan migrants in Perú hold occupations of comparable quality to those in their home country. As previously mentioned, the ENPOVE gathers information on the occupational category held in Venezuela and classifies it based on the International Standard Classification of Occupations (ISCO-88). The same classification is applied to occupations held in Perú, enabling us to determine whether an immigrant has remained in the same broadly defined occupational category. Furthermore, the ISCO-88 code can be linked to the Occupational Information Network (O*NET) database. The O*NET utilizes various dimensions to describe an occupation. Following the approach of prior research (for example, Guvenen et al. (2020)), we utilize specific variables from the ONET to identify whether immigrants have undergone a decline in their occupational quality. Specifically, we gauge the “quality of an occupation” based on the level of “writing abilities” it requires. This allows us to evaluate the occupational transition experienced by Venezuelans when they migrated to Perú. 3.3 Counterfactual Income Profile Finally, we explore an alternative income scenario for immigrants. We compute the earnings Venezuelans would receive if their human capital were valued at the same rate as that of natives. In other words, we estimate a counterfactual earnings profile where Venezuelan education and work experience yield identical returns to those of Peruvians.17 Essentially, we predict the earnings of immigrants ( ˆ Yir) using the following equation: ˆ Yir =ˆ α0+ C ∑ c=1 (VenEduc ∗Expc)ˆ βc+Xiˆ ρ+ˆ δr where ˆ βcrepresents the vector of coefficients estimated from equation (1) for Peruvian human capital. From this new counterfactual income profile, we calculate well-being measures and com17Our calculations align with the simulation exercise conducted by Olivieri et al. (2021b). Taking a similar approach, we also assess the impact of having a work permit; however, our estimations do not indicate a significant alteration in income profiles. Additional results can be provided upon request to the corresponding author. 12 pare them to those based on the original income data. Like Olivieri et al. (2021b), we argue that this partial-equilibrium simulation estimates the benefits of fully assimilating immigrants into the Peruvian labor market. 13 4 Results 4.1 Estimated Earnings: Natives vs Immigrants Table 3 and Figures 2 and 3 depict our analysis of the income profiles of immigrants and natives. Using Equation (1), we estimate the logarithm of monthly earnings for all combinations of human capital (education and work experience) in both groups and compare their performances. Figure 2 clearly illustrates the disparities between the earnings profiles of Venezuelans and Peruvians. First, there is a noticeable return to education for Peruvians, with a particularly pronounced increase for graduate education. In other words, regardless of the category of work experience, Peruvians with university-level education earn substantially more than those with technical education. For Venezuelans, this trend is significantly less pronounced. Although Venezuelans with university education earn, on average, more than those with technical, secondary, or primary education, the differences in terms of incomes with any of these groups are not substantial.18 Second, Peruvian workers also exhibit returns to work experience. Within a specific level of education, more work experience is associated with higher monthly earnings, as is common in a labor market that rewards experience (except for primary-educated workers). However, for Venezuelan workers, their foreign work experience has no effect on their monthly earnings. The estimated average income level is not related to their work experience. This last finding is consistent with what other studies found in developed countries (see Fortin et al. (2016), Friedberg (1992), Friedberg (2000)); and what Torres and Galarza (2021) found for Venezuelan immigrants in 2018. Torres and Galarza (2021), however, focused on the very shortterm integration for Venezuelans. It is surprising that after five years of the immigration wave, foreign work experience abroad was still severely undervalued. Lastly, Figure 3 presents the differences in the coefficients for Venezuelans and Peruvians for the comparable education-work experience combinations shown in Table 3. The earnings gap is small, and in some cases zero, for immigrants with low education levels (primary and secondary). However, for those with technical and university education, the gap is substantial and increases with levels of work experience. 18The specific return to education can be calculated from Table 3 as the difference between the coefficients of groups with the same level of work experience (and nationality) but different educational levels. 14 Table 3 – Regression Results on Log Monthly Earnings (1) Native Primary Education - 20 years work exp. 0.237 (0.280) Native Primary Education - 30 years work exp. 0.247 (0.270) Native Secondary Education - 10 years work exp. 0.174 (0.244) Native Secondary Education - 20 years work exp. 0.427 (0.264) Native Secondary Education - 30 years work exp. 0.477 (0.269) Native Technical Education - 10 years work exp. 0.378 (0.278) Native Technical Education - 20 years work exp. 0.652∗∗ (0.250) Native Technical Education - 30 years work exp. 0.788∗∗ (0.262) Native University Education - 10 years work exp. 0.821∗∗ (0.262) Native University Education - 20 years work exp. 1.177∗∗∗ (0.264) Native University Education - 30 years work exp. 1.494∗∗∗ (0.254) Immigrant Primary Education - 20 years work exp. 0.381 (0.269) Immigrant Primary Education - 30 years work exp. 0.450 (0.275) Immigrant Secondary Education - 10 years work exp. 0.363 (0.275) Immigrant Secondary Education - 20 years work exp. 0.449 (0.266) Immigrant Secondary Education - 30 years work exp. 0.447 (0.272) Immigrant Technical Education - 10 years work exp. 0.453 (0.265) Immigrant Technical Education - 20 years work exp. 0.525∗ (0.268) Immigrant Technical Education - 30 years work exp. 0.482 (0.265) Immigrant University Education - 10 years work exp. 0.553∗ (0.275) Immigrant University Education - 20 years work exp. 0.606∗ (0.268) Immigrant University Education - 30 years work exp. 0.567∗ (0.276) Female -0.301∗∗∗ (0.0210) Constant 6.695∗∗∗ (0.266) Observations 17175 R20.279 Region FE Yes Standard errors in parentheses ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01 Ommited Cuszo 15 Figure 2 – Comparing Logarithm Monthly Wage - Peruvian vs Immigrants 16 Figure 3 – Differences in Logarithm Monthly Wage by Human Capital - Peruvian vs Immigrants 4.2 Immigrants Performance by the time of Arrival We further explore whether differences in (monthly) income between migrants and natives are linked to the time since arrival. Venezuelans with several years in Perú might have assimilated better into the Peruvian labor market, potentially showing higher returns to their human capital. Table 4 and Figure 4 investigate this possibility. Table 4 divides our immigrant sample into two groups. Those who arrived in Peru up through the end of 2018 (i.e., 2016, 2017, and 2018) are on the left and those who arrived after 2018 (i.e., 2019, 2020, 2021, and a few months of 2022) are on the right. Figure 4 summarizes our main findings, presenting our predictions of the logarithm of monthly income by years of work experience and levels of education. For clarity we present the predictions for primary, secondary, and university education levels.19 The results are both interesting and concerning. Time spent in Perú, at least in the first five years, does not appear significantly correlated with higher income. Regardless of the level of 19Estimates for technical education are available upon request. 17 education attained, Venezuelans who arrived up to five years ago seem to have similar income profiles as those who arrived three to one year ago. Table 4 and Figure 4 provide evidence of a lack of significant labor market assimilation. Even for university-educated immigrants, where natives show large income gains related to work experience, we find that the average level of (monthly) income for a recently arrived, universitygraduate Venezuelan is similar to the income of a Venezuelan who has been in the country for years. This behavior diverges from findings in other papers in the literature (for example, Chiswick (1977), Chiswick (1980), Boudarbat et al. (2010), Friedberg (2000), Bratsberg et al. (2014), and Fortin et al. (2016)), where immigrants generally increase their income profiles over time in the host country. However, those studies typically focus on the assimilation of immigrants from developing countries into developed economies. This result could be attributed to imperfect skill recognition by the Peruvian market or to returns to country-specific human capital, making it challenging to achieve high incomes with Venezuelan human capital. Lastly, it could also be linked to the complex economic conditions Perú experienced from 2020 to 2022 due to the COVID-19 pandemic. 18 Table 4 – Regression Results on Log Monthly Earnings: by Time of entry (1) (2) Up to 2018 After 2018 Native Primary Education - 20 years work exp. 0.230 0.238 (0.275) (0.276) Native Primary Education - 30 years work exp. 0.240 0.252 (0.266) (0.266) Native Secondary Education - 10 years work exp. 0.168 0.177 (0.240) (0.239) Native Secondary Education - 20 years work exp. 0.420 0.429 (0.260) (0.259) Native Secondary Education - 30 years work exp. 0.470 0.480 (0.265) (0.264) Native Technical Education - 10 years work exp. 0.371 0.382 (0.275) (0.274) Native Technical Education - 20 years work exp. 0.645∗∗ 0.656∗∗ (0.246) (0.245) Native Technical Education - 30 years work exp. 0.781∗∗ 0.791∗∗ (0.258) (0.258) Native University Education - 10 years work exp. 0.815∗∗ 0.826∗∗ (0.258) (0.258) Native University Education - 20 years work exp. 1.170∗∗∗ 1.181∗∗∗ (0.260) (0.259) Native University Education - 30 years work exp. 1.487∗∗∗ 1.498∗∗∗ (0.249) (0.249) Immigrant Primary Education - 20 years work exp. 0.395 0.373 (0.283) (0.257) Immigrant Primary Education - 30 years work exp. 0.467 0.441 (0.273) (0.270) Immigrant Secondary Education - 10 years work exp. 0.264 0.390 (0.255) (0.275) Immigrant Secondary Education - 20 years work exp. 0.465 0.436 (0.272) (0.257) Immigrant Secondary Education - 30 years work exp. 0.467 0.428 (0.269) (0.268) Immigrant Technical Education - 10 years work exp. 0.535∗0.391 (0.263) (0.265) Immigrant Technical Education - 20 years work exp. 0.518∗0.531∗ (0.263) (0.261) Immigrant Technical Education - 30 years work exp. 0.462 0.491∗ (0.259) (0.263) Immigrant University Education - 10 years work exp. 0.479 0.607∗ (0.293) (0.276) Immigrant University Education - 20 years work exp. 0.609∗∗ 0.599∗ (0.264) (0.263) Immigrant University Education - 30 years work exp. 0.589∗0.509 (0.269) (0.277) Female -0.298∗∗∗ -0.310∗∗∗ (0.0247) (0.0197) Constant 6.705∗∗∗ 6.698∗∗∗ (0.260) (0.260) Observations 14325 14792 R20.287 0.292 Standard errors in parentheses ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01 19 Figure 4 – Monthly Earnings by time of arrival - Peruvian vs Immigrants 20 References Asencios, R. and Castellares, R. 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Working Papers 179, Peruvian Economic Association. 28 Figure 7 – Log income histogram, - Counterfactual: Peruvian returns 29 A Appendix 30 Figure 8 – Log Income Histogram, - Contrafactual 1: PTP 31 Figure 9 – Log income histogram, - Contrafactual 2: Writing occupational upgrading 32 Table 1 – Regression Results on Log Monthly Earnings: by Writting Skills (1) (2) (3) Low Medium High Native Primary Education - 20 years work exp. 0.328 -0.354∗∗∗ 0 (0.260) (0.0547) (.) Native Primary Education - 30 years work exp. 0.216 -0.224∗∗∗ 0.840∗∗ (0.279) (0.0547) (0.257) Native Secondary Education - 10 years work exp. 0.108 -0.265∗∗∗ 0.415∗∗∗ (0.245) (0.0769) (0.0670) Native Secondary Education - 20 years work exp. 0.381 -0.0853 0.944∗∗∗ (0.256) (0.0473) (0.115) Native Secondary Education - 30 years work exp. 0.425 -0.0366 1.104∗∗∗ (0.259) (0.0407) (0.0407) Native Technical Education - 10 years work exp. 0.265 -0.125∗∗ 0.688∗∗∗ (0.276) (0.0398) (0.0378) Native Technical Education - 20 years work exp. 0.504∗0.0950 1.000∗∗∗ (0.233) (0.0592) (0.0335) Native Technical Education - 30 years work exp. 0.523∗0.184∗∗ 1.240∗∗∗ (0.255) (0.0604) (0.0319) Native University Education - 10 years work exp. 0.668∗0.175 1.067∗∗∗ (0.325) (0.0962) (0.0332) Native University Education - 20 years work exp. 0.607∗∗ 0.583∗∗∗ 1.429∗∗∗ (0.259) (0.101) (0.0242) Native University Education - 30 years work exp. 0.695∗∗ 0.764∗∗∗ 1.751∗∗∗ (0.272) (0.157) (0.0161) Immigrant Primary Education - 20 years work exp. 0.353 -0.0779 0.520∗∗∗ (0.269) (0.0653) (0.0727) Immigrant Primary Education - 30 years work exp. 0.431 -0.0179 0.618∗∗∗ (0.267) (0.0441) (0.0725) Immigrant Secondary Education - 10 years work exp. 0.417 0.192 0.346 (0.267) (0.144) (0.203) Immigrant Secondary Education - 20 years work exp. 0.419 -0.0514 0.795∗∗∗ (0.249) (0.0403) (0.0722) Immigrant Secondary Education - 30 years work exp. 0.448 -0.0683 0.566∗∗∗ (0.266) (0.0429) (0.0421) Immigrant Technical Education - 10 years work exp. 0.404 -0.0552 0.719∗∗∗ (0.250) (0.0691) (0.0757) Immigrant Technical Education - 20 years work exp. 0.518∗0.0254 0.708∗∗∗ (0.264) (0.0557) (0.0160) Immigrant Technical Education - 30 years work exp. 0.427 0.0110 0.680∗∗∗ (0.261) (0.0493) (0.0217) Immigrant University Education - 10 years work exp. 0.479 0 0.779∗∗∗ (0.293) (.) (0.119) Immigrant University Education - 20 years work exp. 0.486 0.193∗∗ 0.743∗∗∗ (0.287) (0.0644) (0.0283) Immigrant University Education - 30 years work exp. 0.474 0.212∗∗∗ 0.679∗∗∗ (0.266) (0.0504) (0.0391) Female -0.346∗∗∗ -0.322∗∗∗ -0.239∗∗∗ (0.0340) (0.0181) (0.0238) Constant 6.702∗∗∗ 7.202∗∗∗ 6.507∗∗∗ (0.263) (0.0510) (0.00465) Observations 5380 6260 4310 R20.136 0.182 0.375 Standard errors in parentheses ∗p<0.10, ∗∗ p<0.05, ∗∗∗ p<0.01 33