2023 Volume 8 Issue 2
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COVID 19 Effect on Venezuelan Migrants’ Income: The Peruvian Case Research Study


,
  1. ESAN Graduate School of Business, ESAN University, Lima- Peru.
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 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. Before[1] 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 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 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 2022[5]. 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. 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.

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=x1-x2-(μ1-μ2)s12n1+s22n2

(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

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 Pandemic[6] 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

 

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.

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

 

[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.

[3] Also Wu, Y., Pieters, J., & Heerink, N. (2021) found similar results for the same country.

[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.

[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

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). Responses of the countries of the South American Pacific to Venezuelan migration: comparative study of migration policies in 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). Venezuelan migration in Santiago de Chile: between job insecurity and discrimination. RIEM. International journal of migration studies8(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.

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%20Venezolano%20completo%20PDF%20final.pdf?sequence=-1&isAllowed=y#page=47

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). The incidence of migration on gender wage differences in Colombia. Essays on Economic Policy, 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.


How to cite this article
Vancouver
Guillen J, Arbaiza L. COVID 19 Effect on Venezuelan Migrants’ Income: The Peruvian Case Research Study. J Organ Behav Res. 2023;8(2):66-76. https://doi.org/10.51847/yMjIHy9CUV
APA
Guillen, J., & Arbaiza, L. (2023). COVID 19 Effect on Venezuelan Migrants’ Income: The Peruvian Case Research Study. Journal of Organizational Behavior Research, 8(2), 66-76. https://doi.org/10.51847/yMjIHy9CUV
Issue 1 Volume 11 - 2026