2528-9705 Örgütsel Davranış Araştırmaları Dergisi Journal Of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 https://doi.org/10.51847/yMjIHy9CUV Geliş tarihi/Recieved: 06.05.2023 – Kabul tarihi/Accepted: 27.08.2023 – Yayın tarihi/Published: 30.09.2023 © 2023 Journal of Organizational Behavior Research. Open Access - This article is under the CC BY license (https://creativecommons.org/licenses/by/4.0/) COVID 19 EFFECT ON VENEZUELAN MIGRANTS’ INCOME: THE PERUVIAN CASE RESEARCH STUDY Jorge GUILLEN1*, Lydia ARBAIZA1 1* ESAN Graduate School of Business, ESAN University, Lima- Peru. *Corresponding Author E-mail: jguillen@esan.edu.pe ABSTRACT This paper shed light on analyzing the effect of the Pandemic on Venezuelan Migrants’ Income in Peru. The Covid 19 Pandemic (Coronavirus Pandemic) enforced governments to apply the “Hammer Blow” which produced recession and consequently unemployment. Governments offset the latter effect by applying some subsidies to the Poor. The subsidy was not delivered to Venezuelan Immigrants in Peru. Therefore, our study attempts to explore the effect of the Coronavirus Pandemic on the Income of Venezuelan Immigrants. The study controls for gender and discrimination. Since 2017, the economic and political situation in Venezuela triggered migration beyond their frontiers. Peru is the second country with the high migration of Venezuelans, the first is Colombia which is the neighborhood. The migration reduced the labor force in the country that receives the population but in some cases, the delinquency rates increased after the wave of Venezuelans relocated to a particular country. We consider the survey: “Encuesta Nacional de la Población Venezolana en Peru” (ENPOVE) to perform the study which was conducted during the peak of migration. Keywords: ENPOVE, Gender inequality, Venezuelan migration in Peru, COVID 19. INTRODUCTION The COVID-19 Pandemic or Coronavirus Pandemic affected the health of the population worldwide. Peru was not alien to the latter phenomenon. For example, As of September 2022, the number of cases hit 4 million people and the deaths achieve 200, 000 people (Portal Digital del Gobierno Peruano 2022). The Peruvian economy was also negatively affected. The Peruvian economy was one of the most affected by the “Lockdown”. The economy and unemployment drop almost one-third during the second quarter of 2020 (Central Bank of Peru, 2020). The policies to lock down the economy performed to avoid an increase in the number of Coronavirus cases produced some heterogeneous economic and health results. Loayza (2020) shows Emerging markets are doing worse than developed countries. The lack of infrastructure, various levels of informality, and capability to cope Pandemic between emerging and developed countries was the key to explaining different health and economic outcome during the pandemic. The drop in unemployment and the increase in informality were significant during the economic lockdown in Peru. The latter situation may affect Immigrants and Locals. However, the negative https://doi.org/10.51847/yMjIHy9CUV https://creativecommons.org/licenses/by/4.0/ GUILLEN & ARBAIZA 67 consequence on Immigrants can be deepened since the former group of the population did not receive a subsidy to offset their drop in income (see Emergency Decrees No. 027-2020 and No. 033-2020). Subsidies were granted to the Independent, Rural population, retired, and vulnerable Peruvian population that may be at risk. The subsidy was relatively low in comparison to some countries in the region. It only considered 200 dollars for 6 months, which does not cover the basic basket of consumption of the poor. The Venezuelan migrants in Peru did not receive any financial support to compensate for their drop in income. Peru is the second country in Latin America that holds Venezuelan migrants. According to the Superintendencia de Migraciones, the number of Venezuelan migrants quadruplicated from 2016-2018 and got steady during the Pandemic. ENPOVE survey considers questions to Venezuelan Immigrants in Peru for 2018 and 2022. In 2018, there is a peak and some restrictions triggered the entry of Venezuelans. Restrictions like Passport and Visa were requested to tackle the flow of migrants. Before1 the Pandemic Venezuelan migrants’ jobs are focused in the areas of provision and business sectors (78.2 percent), with significant duties as cookers and assistant cooks, waiting staff, cleaners, domestic workers, and retail sellers as the most mutual professions amongst. If we consider education levels, a crucial gap exist amongst the expertise of mention migrants as well as their professions (World Bank, 2019). Most Venezuelans are overprepared for their jobs. The main characteristic of Venezuelan jobs is focused on Customer Service since they have better social skills. Most of the Venezuelan migrants compete with Peruvians with lower educational skills. The latter situation is called “skill downgrading” by the latter authors. In 2022, most Venezuelan migrants helped the health sector to fight COVID-19 in Peru (see Embassy of Peru news, 2020). They were mainly nurses, health technicians, and physicians who were recruited in Public Peruvian Hospital´s Intensive Care Unit (ICI). The latter situation is a contribution to the Peruvian country that cannot be captured in ENPOVE 2022.2 Our study attempts to explore the main economic consequences on the income of Venezuelan migrants in Peru after the Coronavirus Pandemic controlling for gender and discrimination effects. The next section will explore Income inequalities in the region focusing attention on Peru. Venezuelan Migration and Income Inequalities in Peru Peru is considered a country with a relatively stable economy and one of the strongest currencies compared to others in the region. Peru has received more than 1.2 million Venezuelan migrants, which makes it the 2nd South American nation with the biggest flow of migrants, the first being Colombia with 2.4 million. It is important to note that since 2019, the number of migrants has been reduced due to a set of changes in Venezuelan politics and economy (Del Aguila Tuesta et al., 2021). According to the national migration superintendence of Peru, by June 2021, 782,000 Venezuelans were reported in the country and around 519,000 went to border countries such as Chile or Bolivia, and another group returned to their country of origin. Only 50% of Venezuelans who remain in the country have a temporary residence permit (PTP) (Del Aguila 1 See the Survey for 2018 2 It is a contribution that can not be measured economically. Some Venezuelan die to help Peruvians combat COVID 19. The Venezuelan volunteers were set in the first row in the ICI units. Örgütsel Davranış Araştırmaları Dergisi Journal of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 68 Tuesta et al., 2021). The data provided by the superintendence indicate that the employment situation of those who have PTP is as follows: 51.6% have a declared job and the rest of migrants, of working age, with temporary or undeclared jobs. In this group, monthly earnings as dependents are 1,432.70 soles on average, while earnings as self-employed are 1,880.96 soles on average. The political and economic crisis in Venezuela has caused a rapid deterioration of the living conditions of its inhabitants. These conditions have forced a massive migration of Venezuelans to different countries of the world (Muñoz-Pogossian & Tufró, 2020). Data from some international agencies and institutions show that there are around 6.5 million Venezuelans who have migrated and entered South American countries mainly (World Bank, 2019; Del Aguila Tuesta et al., 2021). This migration has generated various changes, questions, and challenges that the academy must address to provide decision-making tools (Borjas, 1995; Card, 2001; Felbermayr et al., 2010; Dustmann et al., 2013). Venezuelan migration, in broad terms, has been investigated from perspectives that have shown its results in social terms such as the impact on job insecurity and discrimination Bustillos, Contreras Painemal, Albornoz, Flavio and Bustillos, (2018), insecurity and crimes (Knight & Tribin, 2020), among others. In terms of the possible economic impact, Venezuelan migration has been studied regarding its impacts on the labor market (Bonilla-Mejía et al., 2020; Rodrigues & Shrestha, 2022), in local remunerations (Delgado-Prieto, 2021), and others with a more complex analysis that present their impact on the country's productivity (Acemoglu, 1998; Alesina & Ferrara, 2005; Ager & Brückner, 2013; Alesina et al., 2016; Barbieri et al., 2020; Olga María et al., 2021). Income Inequality In terms of inequality, there is a lot of literature that focuses attention on Latin America. For instance, Campos-Vazquez and Lara (2021) In Mexico, we find that a 10% increase in men's relative labor supply increases the wage gap between women and men by about 1.1 percentage points. However, the results suggest greater elasticity of substitution between men and women than assumed in previous studies. In China, authors like Xing, Yuan and Zhang, (2022) found We found that the labor market is denser and more diverse in big cities, easing the problem of collocation for married couples. Also for the same country Magnani and Zhu, (2012), found that on average, male migrants earn 30.2% more hourly wages than female migrants. The gender wage gap is not uniform across migrants' wage distribution, and wage differentials are found to be much higher at the top end than at the bottom and the middle of the wage distribution (Wu et al., 2021).3 For France, Edo and Toubal, (2017) found that changes in the supply of female labor widen the gender wage gap when males and females are defective alternatives in manufacturing. From the immigration perspective, Hayfron, (2002) Exploring the possibility that being both 'woman' and 'migrant' imposes an income disadvantage on Norwegian female immigrants. Gindling (2009) examines the impact of resettlement from non-industrial Nicaragua on the 3 Also Wu, Y., Pieters, J., & Heerink, N. (2021) found similar results for the same country. GUILLEN & ARBAIZA 69 labor market in another developing country, Costa Rica. Same study performed Koechlin, Vega and Solórzano (2018) and Muncial (2018) for the Peruvian and Colombian case respectively. We find little evidence to support the hypothesis that Nicaraguan migration to Costa Rica was an important factor contributing to falling earnings, increased inequality, or stagnating poverty in Costa Rica. In Europe, a migration study made by Adsera and Chiswick, (2007) found that approximately 40% of foreign-born children had a significant negative impact on personal income compared to destination-born children. These differences depend not only on gender, but also on origin and destination. Immigrant incomes catch up with native-born incomes after about 18 years in destination. While education is more important to women's income, language proficiency is relatively more important for men. Also, Nicodemo and Ramos (2012) indicated that, on average, immigrant women earn less than native women in the Spanish labor market. Piazzalunga (2015) investigates the gender and ethnic wage differentials for female migrants in Italy by applying the Oaxaca–Blinder decomposition, with and without Heckman correction, to account for self-selection in the labor market. They found a gender wage gap of nearly 15 percent, more than 60 percent of which is unexplained by observable differences (Conover et al., 2021; Nieto, 2021).4 On the other side, Salas (2015) found for a Colombian dataset that there is a gap in gender within Colombia (Ortega & Peri, 2014; Otero-Cortés et al., 2022). The latter result is deepened when the male comes from the city and the female is an immigrant. Our study will focus attention on the gender and income inequalities of Venezuelan Migrants in Peru over two periods of time: before and after the Coronavirus Pandemic. We hypothesize that there is gender income inequality during the period before and after COVID Pandemic. We will control by discrimination effect on the latter disparities. The next section will describe the data and variables considered to test the hypothesis. MATERIALS AND METHODS We use the “Encuesta Dirigida a la Población Venezolana (ENPOVE)”. The survey is conducted in 2018 and 2022. For 2018 the survey considers 3,611 houses (3,680 houses for 2022). However, the respondents in 2018 are not the same for 20225. The survey covers Tumbes, La Libertad, Lima-Callao, Arequipa, and Cusco, which are the cities that consider 85% of the Venezuelan immigrant population. For 2022, the regions covered were the same as in 2018. The survey attempts to deliver reliable data on the conditions of health, employment, and housing in which they live, as well as some socioeconomic characteristics. The survey is relevant to shape decisions and capture the needs of the Venezuelan population that has arrived in Peru. The survey for 2018 and 2022 considers gender, age, socioeconomic and ethnic self-perception issues. Both survey also studies main aspects of the labor market for the migrants as well as perceptions of violence and discrimination. Covid questions and Vaccine coverage is under consideration in the 2022 Survey. In 2019 and 2020, the situation for Venezuelans in Peru changed compared to the reality of 2018. This is in part due to changes in immigration regulations and global phenomena. 4 Conover, E., Khamis, M., & Pearlman, S. (2021) and Nieto (2021) found similar results in the Latin American region. 5 If they were the same, we could have applied difference and difference models. Örgütsel Davranış Araştırmaları Dergisi Journal of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 70 Therefore, a second study is needed to provide up-to-date information on Peru's Venezuelan population and support public policy decisions based on solid and reliable data. The Instituto Nacional de Estadística e Informática (INEI) along with the support of the World Bank, the United Nations Refugee Agency (UNHCR), the International Organization for Migration (IOM), the United Nations Population Fund (UNFPA), and the United Nations Fund for Children (UNICEF), carried out the Surveys Addressed to the Venezuelan Population living in the country (ENPOVE). We have gathered some variables to conduct our research. Table 1, below shows the variables collected to perform our research: Table 1. Summary of variables for the research Variables 2018 Mean Standard Deviation Minumun Maximum DISCRIMINATION 0.35 0.48 0 1 SEX 1.47 0.50 0 1 TOTAL INCOME 841.00 663.58 0 10000 Variables 2022 Mean Standard Deviation Minumun Maximum DISCRIMINATION 0.35 0.48 0 1 SEX 1.69 0.45 0 1 TOTAL INCOME 400.17 610.11 0 9500 Elaboration: Own Source: ENPOVE (2018, 2022) The next section will show the estimation method to test the hypothesis as well as the results of the inference. RESULTS AND DISCUSSION Since respondents are not the same for both periods of surveys, we can not study over time of the surveyed. We can test for any difference within a period of time and test our hypothesis of gender income inequality during the period before and after COVID Pandemic. We will control by discrimination effect as well. The test used in the paper is the difference in means. The latter test provides a confidence interval for the difference between the two means, indicating the range of values over which the difference between the means of the two populations may exist. This test is commonly used by medical researchers wishing to estimate the difference in mean responses of patients who received two different treatments. The confidence interval for the difference between the two means contains all the values of (µ1- µ2) (the difference between the two sample means) which would not be rejected in the two-sided hypothesis test of H0: µ1=µ2, against the alternative hypothesis Ha: µ1≠µ2. GUILLEN & ARBAIZA 71 The size of our two samples (one for 2018 and the other for 2022) permits us to infer and consider a normal distribution for the inference estimation. The statistical test is Z and we will consider the rejection of the null hypothesis (Ho) at 95% of confidence. The Z is constructed as follows: (𝑍) = (�̅�1 − �̅�2) − (𝜇1 − 𝜇2) √ 𝑠1 2 𝑛1 + 𝑠2 2 𝑛2 (1) Where x1 and x2 are sample means. The symbols: µ1 and µ2 are population mean. The variables σ1 and σ2 are standard deviations and n1 and n2 are sample sizes. Before estimation, we must clean any outliers. The sample in 2018, contains some outlier that needs to be removed. The Figure 1 below show the data before cleaning. Figure 1. Outliers in the 2018 sample For 2022, we have applied the same procedure and then we can proceed with the estimation. The following Table 2 show the results for the test of difference in means, controlling for gender and discrimination perception across the two periods under. Table 2. Two-sample t-test with unequal variances Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] 2018 9,577 691.4874 5.585883 546.6465 680.5379 702.4369 2022 12,085 291.4203 3.962894 435.648 283.6524 299.1882 combined 21,662 468.2942 3.578919 526.7459 461.2793 475.3091 diff 400.0671 6.848841 386.6428 413.4915 diff = mean (0) - mean (1) t = 58.4138 Ho: diff = 0 Welch's degrees of freedom = 18024.9 0 2 ,0 0 0 4 ,0 0 0 6 ,0 0 0 8 ,0 0 0 1 0 ,0 0 0 in g to t Örgütsel Davranış Araştırmaları Dergisi Journal of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 72 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 Table 2, above shows a drop in income after COVID Pandemic6 between 2018 and 2022. The difference ranges from 386 to 416 soles within a month. Tables 3 and 4 show an assessment by Gender. For 2018, there is gender inequality as well as for 2022. Male earns more than Female and the gap deepens after the COVID-19 Pandemic. On average, the gap can hit until 70 soles more in favor of men. Table 3. Two-sample t-test with equal variances Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] Male 4,681 908.2442 9.952229 680.91 888.7331 927.7552 Female 4,155 765.2513 9.852834 635.1067 745.9344 784.5681 combined 8,836 841.0038 7.059366 663.5804 827.1658 854.8418 diff 142.9929 14.06256 115.427 170.5588 diff = mean (Hombre) - mean (Mujer) t = 10.1683 Ho: diff = 0 degrees of freedom = 8834 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 We can infer from the previous result that the Pandemic has deteriorated the income, increasing the gap gender as well. This result is interesting for policymakers that look for reducing any inequality and the negative economic consequences of the latest Pandemic. As we have seen in the previous section, the Pandemic has deteriorated many economic and financial variables and we have shed light on some relationships Table 4. Two-sample t-test with equal variances Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] 1. Male 4,932 517.6298 9.733241 683.548 498.5483 536.7113 2. Female 5,088 286.3245 7.064107 503.8843 272.4758 300.1732 combined 10,020 400.1765 6.0951 610.1192 388.2289 412.1242 diff 231.3053 11.97127 207.8392 254.7714 diff = mean (1.Hombre) - mean (2.Mujer) t = 19.3217 Ho: diff = 0 degrees of freedom = 10018 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 1.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 0.0000 6 We can assume normality since there is a large amount of observations on each dataset. The dataset is clean for outliers and the variance are similar between groups. Variance ratio test show similar results GUILLEN & ARBAIZA 73 Next Tables 5 and 6 test whether there is a gap between people who feel discriminated or not. The First consider the 2018 and the next 2022 surveys. The results show that the people who feel discriminated against have better income in comparison to the group that does not feel left behind. The result goes against the line of the literature on immigration discrimination. The gap between groups who feel discriminated shortens in 2022. Table 5. Two-sample t-test with equal variances Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] Not Discrim. 2,724 453.4938 11.73765 612.6109 430.4782 476.5094 Discriminated 304 426.5658 46.64179 813.2273 334.783 518.3486 combined 3,028 450.7903 11.54891 635.505 428.1458 473.4348 diff 26.92797 38.43201 -48.42753 102.2835 diff = mean (0) - mean (9. En tu) t = 0.7007 Ho: diff = 0 degrees of freedom = 3026 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.7582 Pr(|T| > |t|) = 0.4836 Pr(T > t) = 0.2418 Table 6. Two-sample t test with equal variances Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] Not Discrim. 2,819 1070.812 10.41181 552.8075 1050.396 1091.227 Discriminated 232 1109.586 38.04787 579.5279 1034.621 1184.551 combined 3,051 1073.76 10.04567 554.8812 1054.063 1093.457 diff -38.77457 37.89887 -113.0845 35.53535 diff = mean (0) - mean (¿En tu) t = -1.0231 Ho: diff = 0 degrees of freedom = 3049 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1532 Pr(|T| > |t|) = 0.3063 Pr(T > t) = 0.8468 CONCLUSION We have concluded that the COVID 18 Pandemic deepened gender disparities. The latter relationship broadens after the worldwide recession of 2020. The late economic event deteriorated income but also inequalities as well. However, we concluded that people who perceived themselves as discriminated against did not suffer from a drop in income. Conversely, people who do not feel discriminated against gain lower salaries than the control group discriminated against. This result contrasts the literature on discrimination. The prejudice does not come from racial issues since Peru is a country with a variety of races and hybrid breeds. The feeling of being left behind comes from nationality and migration status. Örgütsel Davranış Araştırmaları Dergisi Journal of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 74 Our results permit us to explore inequalities in gender before and after the event of COVID 18 Pandemic. Most of the paper focuses attention on the economic consequences of the Pandemic, missing any effect on immigrants that come from one emergent country to another. ACKNOWLEDGMENTS: None CONFLICT OF INTEREST: None FINANCIAL SUPPORT: None ETHICS STATEMENT: None References Acemoglu, D. (1998). Why do new technologies complement skills? Directed technical change and wage inequality. The Quarterly Journal of Economics, 113(4), 1055-1089. Adsera, A., & Chiswick, B. R. (2007). Are there gender and country of origin differences in immigrant labor market outcomes across European destinations? Journal of Population Economics, 20(3), 495-526. Ager, P., & Brückner, M. (2013). Cultural diversity and economic growth: Evidence from the US during the age of mass migration. European Economic Review, 64, 76-97. Alesina, A., & Ferrara, E. L. (2005). Ethnic diversity and economic performance. Journal of Economic Literature, 43(3), 762-800. Alesina, A., Harnoss, J., & Rapoport, H. (2016). Birthplace diversity and economic prosperity. Journal of Economic Growth, 21(2), 101-138. Barbieri, N. G., Ramírez Gallegos, J., Ospina Grajales, M. D. P., Cardoso Campos, B. P., & Polo Alvis, S. (2020). Respuestas de los países del pacífico suramericano ante la migración venezolana: estudio comparado de políticas migratorias en Colombia, Ecuador y Perú. Diálogo andino, (63), 219-233. doi:10.4067/s0719-26812020000300219 Bonilla-Mejía, L., Morales, L. F., Hermida-Giraldo, D., & Flórez, L. A. (2020). The Labor Market of Immigrants and Non-Immigrants Evidence from the Venezuelan Refugee Crisis. Borradores de Economía; No. 1119. Retrieved from: https://repositorio.banrep.gov.co/handle/20.500.12134/9872 Borjas, G. J. (1995). The economic benefits from immigration. Journal of Economic Perspectives, 9(2), 3- 22. Bustillos, F. S., Painemal, C. C., & Albornoz, L. (2018). La migración venezolana en Santiago de Chile: entre la inseguridad laboral y la discriminación. RIEM. Revista internacional de estudios migratorios, 8(1), 81-117. Available from: https://ojs.ual.es/ojs/index.php/RIEM/article/view/2164 Campos-Vazquez, R., & Lara, J. (2021). International migration and the gender wage gap. Journal of Demographic Economics, 87(2), 213-232. https://doi.org/10.4067/s0719-26812020000300219 https://repositorio.banrep.gov.co/handle/20.500.12134/9872 https://ojs.ual.es/ojs/index.php/RIEM/article/view/2164 GUILLEN & ARBAIZA 75 Card, D. (2001). Immigrant inflows, native outflows, and the local labor market impacts of higher immigration. Journal of Labor Economics, 19(1), 22-64. Central Bank of Peru. (2020). Inflation Report. Available from: https://www.bcrp.gob.pe/publicaciones/reporte-de-inflacion.html Clemens, M. A. (2011). Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives, 25(3), 83-106. Coe, D. T., Helpman, E., & Hoffmaister, A. W. (2009). International R&D spillovers and institutions. European Economic Review, 53(7), 723-741. Conover, E., Khamis, M., & Pearlman, S. (2021). Gender Imbalances and Labor Market Outcomes: Evidence from Large-Scale Mexican Migration. IZA Journal of Development and Migration, 12(1). Decree 27 and 33. (2020). Framework of Emergency State. El Peruano. Del Aguila, R., Ríos, F., & Torres, M. (2021). Características Sociodemográficas de la Migración Venezolana en el Perú Feb 2017 - Jun 2021. Available from: https://cdn.www.gob.pe/uploads/document/file/1260593/Caracteristicas-sociodemograficas-de- ciudadanos-venezolanos-julio2020.pdf Delgado-Prieto, L. (2021). Dynamics of Local Wages and Employment: Evidence from the Venezuelan Immigration in Colombia. Available from: https://conference.iza.org/conference_files/AMM_2021/delgado-prieto_l31183.pdf Dustmann, C., Frattini, T., & Preston, I. P. (2013). The effect of immigration along the distribution of wages. Review of Economic Studies, 80(1), 145-173. Edo, A., & Toubal, F. (2017). Immigration and the gender wage gap. European Economic Review, 92, 196- 214. Embassy of USA in Peru. (2022). Available from: https://pe.usembassy.gov/es/medicos-venezolanos-se- colegian-y-suman-esfuerzos-con-colegas-peruanos-para-vencer-al-covid-19/ Felbermayr, G. J., Hiller, S., & Sala, D. (2010). Does immigration boost per capita income? Economics Letters, 107(2), 177-179. Gindling, T. H. (2009). South–South migration: The impact of Nicaraguan immigrants on earnings, inequality and poverty in Costa Rica. World Development, 37(1), 116-126. Knight, B. G., & Tribin, A. (2020). Immigration and violent crime: Evidence from the Colombia-Venezuela border (No. w27620). National Bureau of Economic Research. Available from: doi:10.2139/ssrn.3667092 Koechlin, J., Vega, E., & Solórzano, X. (2018). Migración venezolana al Perú: proyectos migratorios y respuesta del Estado. In J. Koechlin & J. Eguren (Eds.), El Éxodo Venezolano: Entre el Exilio y la Migración (pp. 47-96). Available from: https://repositorio.comillas.edu/xmlui/bitstream/handle/11531/45334/17576%20Exodo%20Venez olano%20completo%20PDF%20final.pdf?sequence=-1&isAllowed=y#page=47 https://cdn.www.gob.pe/uploads/document/file/1260593/Caracteristicas-sociodemograficas-de-ciudadanos-venezolanos-julio2020.pdf https://cdn.www.gob.pe/uploads/document/file/1260593/Caracteristicas-sociodemograficas-de-ciudadanos-venezolanos-julio2020.pdf https://conference.iza.org/conference_files/AMM_2021/delgado-prieto_l31183.pdf https://pe.usembassy.gov/es/medicos-venezolanos-se-colegian-y-suman-esfuerzos-con-colegas-peruanos-para-vencer-al-covid-19/ https://pe.usembassy.gov/es/medicos-venezolanos-se-colegian-y-suman-esfuerzos-con-colegas-peruanos-para-vencer-al-covid-19/ https://doi.org/10.2139/ssrn.3667092 https://repositorio.comillas.edu/xmlui/bitstream/handle/11531/45334/17576%20Exodo%20Venezolano%20completo%20PDF%20final.pdf?sequence=-1&isAllowed=y#page=47 https://repositorio.comillas.edu/xmlui/bitstream/handle/11531/45334/17576%20Exodo%20Venezolano%20completo%20PDF%20final.pdf?sequence=-1&isAllowed=y#page=47 Örgütsel Davranış Araştırmaları Dergisi Journal of Organizational Behavior Research Cilt / Vol.: 8, Sayı / Is.: 2, Yıl/Year: 2023, Sayfa/Pages: 66-76 76 Loayza N. (2020). Costs and Trade-Offs in the Fight against the COVID-19 Pandemic: A Developing Country Perspective. Research & Policy Brief N°35. World Bank. Magnani, E., & Zhu, R. (2012). Gender wage differentials among rural–urban migrants in China. Regional Science and Urban Economics, 42(5), 779-793. Mundial, B. (2018). Migración desde Venezuela a Colombia. impactos y estrategia de respuesta en el corto y mediano plazo. Available from: http://www.healthandmigration.info/handle/123456789/492 Muñoz-Pogossian, B., & García Tufró, P. (2020). Venezuelan Migration Crisis: Medium and Long-Term Impacts. Jack Gordon Institute Research Publications. Available from: https://digitalcommons.fiu.edu/jgi_research/34/ Nicodemo, C., & Ramos, R. (2012). Wage differentials between native and immigrant women in Spain: accounting for differences in support. International Journal of Manpower, 33(1), 118-136. Nieto, A. (2021). Native-immigrant differences in the effect of children on the gender pay gap. Journal of Economic Behavior & Organization, 183, 654-680. Olga María, M. O., Camilo, J. R. I., Laura María, M. G., & Vanessa, M. R. (2021). Crisis or opportunity: Impact of Venezuelan migration on Colombian productivity. Desarrollo y Sociedad, (89), 13-56. doi:10.13043/DYS.89.1 Ortega, F., & Peri, G. (2014). Openness and income: The roles of trade and migration. Journal of international Economics, 92(2), 231-251. Otero-Cortés, A., Tribín-Uribe, A. M., & Mojica-Urueña, T. (2022). The Heterogeneous Labor Market Effects of the Venezuelan Exodus on Female Workers: Evidence from Colombia. Documento sobre economía regional y urbana No. 311. Available from: https://repositorio.banrep.gov.co/handle/20.500.12134/10459 Rodrigues, H. S., & Shrestha, S. (2022). Labor Market Impacts of a Refugee Crisis in Brazil. Available from: https://ageconsearch.umn.edu/record/322349/files/24165R.pdf Salas, R. (2015). La incidencia de la migración sobre las diferencias salariales de género en Colombia. Ensayos sobre Política Económica, 33(77), 103-116. World Bank. (2019). An opportunity for all: Venezuelan migrants and refugees and peru’s development. Technical report, World Bank. Available from: https://openknowledge.worldbank.org/handle/10986/32816. License: CC BY 3.0 IGO. Wu, Y., Pieters, J., & Heerink, N. (2021). The gender wage gap among China’s rural–urban migrants. Review of Development Economics, 25(1), 23-47. Xing, C., Yuan, X., & Zhang, J. (2022). City size, family migration, and gender wage gap: Evidence from rural-urban migrants in China. Regional Science and Urban Economics, 97, 103834. http://www.healthandmigration.info/handle/123456789/492 https://digitalcommons.fiu.edu/jgi_research/34/ https://doi.org/10.13043/DYS.89.1 https://repositorio.banrep.gov.co/handle/20.500.12134/10459 https://ageconsearch.umn.edu/record/322349/files/24165R.pdf