Venezuelan Exodus: the Effect of Mass Migration on Labor Market Outcomes

Venezuelan exodus: the eﬀect of mass migration on labor market outcomes

Julieth Santamaria∗ February 1, 2019

Abstract Unexpected and massive immigration flows are the subject of public debate in many countries that host forcibly displaced migrants. Policymakers worry about the effects that free entry has on local labor markets. In addition, forced displacement adds a moral dimension to the agenda when making economic and social policy decisions. In August 2015, the Venezuelan government decided unilaterally to close the border. In mid-2016 and as a consequence of national and international pressure, the border re-opened. This decision along with the political instability in Venezuela caused an unprecedented increase in migration to Colombia and other countries in Latin America. Using a difference-in-differences approach, I exploit the massive immigration flow of Venezuelans to Colombia in recent years and the timing of the events to evaluate its effects on wages and employment. I use two measures of immigration flows: first, the number of registered immigrants; and second, the Internet search intensity of keywords that only Venezuelans would Google. Graph- ical analysis of both measures as well as anecdotal evidence suggests that Internet search is better than migrant counts at identifying where most of the immigrant communities are located. Concerning the effects on labor markets, I find negligible reductions in wages in the informal sector. Among the less educated, there is also a small reduction in wages in the formal sector. Employment regressions suggest mild reductions on employment in the formal sector mostly explained by a transition of Colombians from the formal to the informal sector. All in all, if a Colombian worker earns 10 dollars per hour, the reduction in her salary would be of 2-7 cents of a dollar. According to these calculations, my results suggest a loss of well-being close to zero for Colombians.

Keywords: Migration, Employment, Wages

∗University of Minnesota, Department of Applied Economics, PhD Student. E-mail: [email protected]

1 1 Introduction

The United Nations High Commissioner for Refugees (UNHCR) reported that by the end of 2017 there were 68.5 million forcibly displaced people, 85% of whom were in developing countries. In Latin America, the unprecedented number of the Venezuelan population fleeing to neighboring countries has been the center of attention in the media and political debate in recent years. Figures reported by the International Labor Organization (ILO) and UNHCR suggest that about 3 million Venezuelans have fled the country, 80% of whom have chosen Latin American countries as their main destination. The effects that massive and instantaneous migratory flows have in host countries has been the subject of debate. Yet the literature on forced displacement is small, mainly due to the lack of data on the location and size of the migrant population in the host country.

Latin America is currently witnessing an unprecedented phenomenon for the region: the so-called ‘Venezuelan exodus’. After years of bad government administration, high levels of inﬂation have generated a food crisis in Venezuela. In 2015, political frictions between Colombia and Venezuela led the Venezuelan president to order the permanent closure of the border points. It was not until a year later, in July 2016, that due to protests and international pressure the border was reopened. From this date, millions of Venezuelans have crossed the border with Colombia either to stay there or to continue on their way to other countries in Latin America.

In this paper, I exploit the occurrence of the ‘Venezuelan exodus’ and the timing of the events as a natural experiment to evaluate its effects on wages and employment in the formal and informal sector in Colombia. I use a difference-in-differences approach to evaluate the effect of the massive number of immigrants that Colombia has received recently. I focus on wages and employment by comparing regions with a high influx of migrants to those with a lower number of immigrants after the opening of the borders. The results suggest that there are negligible reductions in wages in the informal sector. Among the less educated, there is also a small reduction in wages in the formal sector. Regressions on employment suggest mild reductions on employment in the formal sector mostly explained by a transition of Colombians from the formal to the informal sector. All in all, if a Colombian worker earns 10 dollars per hour, the reduction in her salary would be of 2-7 cents . According to these calculations, my results suggest a loss of financial well-being close to zero for Colombians.

Finding the impact of immigration on labor market outcomes is diﬃcult for many

2 reasons. First, migrants are a selected group of people who usually have different demo- graphics compared to the population in their country of origin. Second, migrants choose to migrate to places where labor markets can absorb the additional supply, which causes reverse causality. Finally, inter-city migration of natives as a result of the incoming migrants will tend to offset the adverse effects of immigration. For these reasons, using correlations on the number of immigrants on wages and employment will result in biased estimates of the true impact.

Card (1990) suggests that one way to get around these problems is to use a natural experiment, which offers an exogenous increase in the supply of immigrants. Thus, he uses the unanticipated and massive arrival of Cuban migrants (Marielitos) to the Miami labor market to assess its effect on wages and employment. As a control group, he estimates wages and employment equation on observables in four cities: Atlanta, Houston, LA, and Tampa. He uses the coefficients of those estimations to calculate the predicted wages and employment outcomes in Miami. He found virtually no impact on neither outcome. This result was highly controversial at the time because the black population were afraid and protested against the arrival of new immigrants. Some problems arise from his identification strategy: first, he has only one year of pre-treatment period while he uses four years of post-treatment period; second, there was a recession in the US in the early 1980s, which makes wages and employment even more volatile, third, area studies assume that the labor market is autarkic, i.e., they disregard general equilibrium effects caused by inter-city migration; fourth, there is no way to know that the four cities he selected for the wage equation make a good comparison group.

The literature on migration has debated Card’s results. The main argument against his study is that general equilibrium effects make identification harder using area studies. Card (2001) takes a step further to provide evidence of whether general equilibrium forces affect his estimates by testing what he calls the “skating rink mobility model,” i.e., “one migrant who stakes into an area knocks one native off the ice.” His results suggest that between 1980-1990, one immigrant increase in the low-skill population increases the total low-skill population by about one. In other words, inter-city migration is unlikely.

Friedberg (2001) and Borjas (2003) have used country studies to account for general equilibrium eﬀects. Friedberg (2001) used the elimination of emmigration restrictions in the Soviet Union to estimate the impact of the substantial increase of the labor supply on Israeli labor outcomes. He does not have a comparison group. Thus, under the assumption that workers cannot move across occupations, he uses the change of immigrants in

3 occupation j to identify the effect on wage growth. He instruments occupation of Soviet immigrants in Israel using a self-reported measure of their occupation in the Soviet Union. He found negative effects of immigration on wages using OLS estimations and positive effects using IV. Unemployment effects are also negative using the OLS estimation and positive for the IV estimation. There are two salient problems of this identification strategy. First, he founds a weak relationship between the occupation of immigrants in their source and host countries. This suggests that using occupation in the Soviet Union is not a good instrument for the occupation of immigrants in Israel. Second, it is possible that the reason why IV estimates show positive results is that the trend of wages and employment was rising. However, he does not have enough pre-treatment data to control for that.

In turn, Borjas (2003) acknowledges the improvement of Friedberg to account for general equilibrium effects. However, he argues that occupations are not labor markets and that an increase in the labor supply on certain occupations can have mixed results depending on the substitutability or complementarity of the new supply on the existing one. He argues that immigrants will compete with those natives who have the same level of education and experience. Thus, he runs and analysis on the impact of immigration on native earnings using 20 school-experience cells (4 groups of schooling, and 5 groups of experience) by decade from 1960-2000. He finds that immigration reduces wages. One of the problems in his identification strategy is that he ignores the geographical variation of the labor markets. Another problem is that his regressions have a tiny sample.

A recent set of studies on this literature used exogenous immigration shocks of refugees on native labor markets. On the one hand, using refugees has the advantage that they ar- rive at their host countries on precarious conditions, usually without the ability to work on their past occupations. Their past credentials are generally not valid in their host country; therefore, they only increase competition on only the low-skill native labor markets. On the other hand, the main drawback of working using refugee shocks as natural experiments is that refugees are diﬃcult to track and, to the best of my knowledge, there is no publicly available data with information on the location and quantity of refugee population. To identify where refugees are in national territory, authors have used distance to the source country border. For example, Del Carpio and Wagner (2015) have used the distance from the Turkish-Syrian border as an instrument of the supply shock on the Turkish labor market. They found that Syrian immigration caused decreases in wages and employment for the low educated. Although distance to the border might be exogenous, refugees can locate in areas further from the border for economic reasons.

4 My contribution to the literature on forced migration is twofold: first, contrary to area studies, the study of the Venezuelan exodus provides a natural experiment that allows me to make inference for the whole country; second, I introduce an alternative to measure the location of new immigrants using Google searches. This measure is useful in context where collecting headcounts of new and unexpected massive immigration flows is difficult.

The reminder of this paper is as follows: in Section 2 I provide a summary of the unfold of events that led to the ”Venezuelan Exodus”. In Section 3, I describe my identiﬁcation strategy as well as the two measures I used to measure the shock on the labor force. In Section 4 I describe the data and provide descriptive statistics. In Section 4, I provide a summary of the results. In section 5, I summarize the results and do some ﬁnal remarks.

2 The Venezuelan exodus to Colombia

Due to its geographical and cultural proximity, Colombia and Venezuela have had close relations since their consolidation as independent countries. Many refer to both countries as the ‘sister countries’ due to the large number of trade exchanges and migratory flows that have characterized their relationship. However, both countries have been affected by different local problems that have made this alliance even more important during the past decades. In this section, I briefly explain the time-line of events that led to the closure and posterior opening of the Colombian-Venezuelan border, which is key for the empirical strategy.

Hugo Chávez Fr´ıaswas President of Venezuela until his death and his policies were highly debated. At the beginning of his term, his popularity levels were quite high due to his ideas to fight corruption and to form an inclusive Venezuela in which oil wealth was to be redistributed through social programs to everyone. Not long after his first term began in 1999, he encouraged the creation of a new Constitution that, among other changes, increased the duration of the presidential term from five to six years, changed the bicam- eral legislature to a unicameral one led by the National Assembly, and granted the power to this Assembly to eliminate any governmental institution. This constitutional reform was approved by a referendum (Cameron and Major, 2001).

The wealthier classes and the private sector did not welcome his policies, which encouraged the out-migration of the more aﬄuent during the 2000s and the closure of many ﬁrms. Freitez (2011) narrates that the favorable conditions of Venezuela until the late

5 1990s caused little uncertainty and, therefore, little desire to migrate. Despite the economic boom between 2003-2008 that caused the increase in international oil prices, this situation changed due to Chavez’s political reforms. She suggests that the great uncertainty of the forthcoming socialist economic model promoted by Ch´avez caused increases in the out-migration of Venezuelans during these years. The main destinations in this period were the United States and Spain. This emigration, according to her, occurred among the upper-middle classes, which means, it was a high-skilled emigration (Freitez, 2011). There have not been many studies on migration during this time due to the poor data collection initiatives in Venezuela.

Chavez presidential term ends with his death in 2013 leaving a Venezuela without a private sector, with a weakened oil industry, with oil exports committed to repay debts, with limited cash ﬂow (Hernandez et al., 2016), with crises in its diplomatic relations with its neighboring countries (Romero, 2008), and with an opposition party that is in- creasingly gaining popularity (Lopez and Watts, 2013). It is in this context that Nicol´as Maduro assumes power after being elected president by a narrow margin in April 2013.

When Maduro was elected as President, he displayed even more abuses of power and the economy of Venezuela displayed a growing economic crisis. An example of the former was the increasing violence against the opposition party and its leaders. In discontent with the election of Maduro, the opposition party made a call to protest on streets. The main leader of the opposition, Leopoldo Lopez, was sentenced to 14 years in prison after being accused of causing waves of violence in Venezuela. Alongside, in 2014, the economic crisis was becoming more critical as oil prices begin a strong downward trend. As a result, the Venezuelan population perceives shortages of food, medicines and other basic goods.

Before proceeding, it is worth mentioning that road and pedestrian transportation are the most common means of transportation for people and goods between the sister countries. The main points of Venezuelan migration to Colombia are: the Simón Bolivar International Bridge (Cúcuta),the Páezbridge (Arauca) and the Paraguachón International bridge (Maicao). Because of its easy connection to the rest of the national territory, the entrance through Cúcutahas the greatest number of migratory flows. Due to its physical and commercial isolation, in addition to the difficulty to reach the border, the checkpoints of Maicao and Arauca are not as important.

The Venezuelan exodus began to be sown in 2015. Table 1 provides a summary of the main events beginning with the closure of the border in August. The reason for the

6 Table 1: Timeline of Key Events leading to the Outbreak of Migration from Venezuela 2015 • August 19, 2015 • Maduro orders to close the border crossing in Táchira August 20, 2015 • Maduro declares a State of Emergency and homes of illegal Colombians are marked to be destroyed August 28, 2015 • Maduro declares a State of Exception 2016 • July 6, 2016 • Venezuelan women break through the border controls July 16, 2016 • Venezuela reopens Colombian border to allow shoppers to cross 2017 • October 15,2017 • Maduro’s political party wins 17 out of 22 Governorships October 16,2017 • Opposition party calls for street protests Note: Own creation using information from Escalona (2017), BBC news, El Tiempo, El Espectador, and The Guardian closure was the death of Venezuelan military personnel due to the presence of Colom- bian paramilitaries in the area. The border closure was put in effect only in the State of Táchira (Venezuela) whose point of entry to Colombia is through the SimónBol´ıvar international bridge, located in Villa del Rosario (Colombia). North Santander, the region in which Villa del Rosario is, is a key point for the flow of people and supplies between the two countries, and therefore, it is the most affected department by border closures. On August 20, Maduro decided to take repressive measures against Colombians in Venezuelans. He ordered to search houses in border cities in Venezuela with the pur- pose of finding ‘illegal’ Colombians. During this operation, houses of inhabitants in these areas were marked with symbols to be later destroyed.

On August 28, 2015, the Government of Venezuela declares a state of exception. This is a political instrument that allows the full closure of borders due to circumstances that threat the national security. This closure forbade any transaction of goods and migratory ﬂows from Venezuelan territory to Colombia. It lasted until July 6, 2016, when a group of Venezuelan women dressed in white1 broke through the border controls. They were motivated by the overwhelming scarcity of goods. On July 26, the government of Venezuela decides to reopen the borders. Figure 1 depicts an increase in the entry of

1They wore white to let the Venezuelan military forces know that it was a peaceful march

7 Figure 1: Net migration of foreigners to Colombia by point of entry 50000 40000 30000 20000 10000 Net migration of foreigners to Colombia 0

2014m1 2015m1 2016m1 2017m1 2018m1

Cúcuta Arauca Maicao

Note: Own calculations using data from the Ministry of International Aﬀairs in Colombia. Net migration is calculated as the number of entries minus the number of exits to/from the Colombian territory. foreigners to Colombia through the three land points of entry.

Finally, a more recent event unleashed the peak of the Venezuelan exodus in 2017: the overwhelming defeat of the opposition party in the governor’s elections. The ruling party won 17 out of 22 Governorships. Seemingly, this was the breaking point for the Venezuelans. Since then, he media report massive migratory ﬂows that pass through the Colombian border either to stay in the country or to go to other countries that also host a large number Venezuelan migrants such as Peru and Ecuador.

3 Identiﬁcation Strategy

I exploit the sudden and unprecedented nature of the ‘Venezuelan exodus’ as a natural experiment to evaluate the effect of migration on the employment outcomes of native workers. Specifically, I use a difference-in-differences approach to evaluate the effect of the massive number of immigrants that Colombia has received within a short period on wages and employment by comparing regions with a high influx of migrants to those with a lower number of immigrants after the opening of the borders 2. Under the assumption

2There are 30 departments collected in the data plus 13 metropolitan areas. From here on, the word ‘regions’ will refer to both, departments and metropolitan areas. Thus, there are 43 regions.

8 that the distribution of skills of potential Venezuelan migrants matter, the most preferred speciﬁcation is as follows:

Yi,rse =β0 + β1Opened Bordert + β2mrse + β3(Opened Bordert × mrse) (1) 0 + φ X{it} + τt + αd + κTt + ui,rse

where Yi,rse is the outcome of analysis (either wages or employment) for person i, residing in the region r, with a skill set of s years of schooling and e years of potential experience; Aftert is a dummy that takes the value of 1 after July 2016, i.e., after the re-opening of borders; mrse is a measure of the immigration shock in region r, for natives with s years of schooling and e years of experience; X{it} is a vector of individual characteristics that include potential experience, potential experience squared, years of schooling, gender, a dummy for whether the individual is a part-time worker, and the logarithm of the aggregate labor force; τt is a set of monthly dummies that control for seasonal patterns, αr are regional fixed effects, and Tt is a year trend. The coefficient of interest β3 is an estimate of the effect of immigration shocks on wages and employment in regions with a greater number of immigrants after the re-opening of borders compared to those regions with fewer immigrants before the re-opening of borders. All specifications use clustered standard errors at the regional level.

In a country that is not used to receiving large migratory ﬂows, it is diﬃcult to collect data on the permanent location of these migrants. For this reason, I use two types of approaches to quantifying the labor supply shock:

1. Measure 1 - Number of immigrants: Currently, I have access to the number of Venezuelans per region. However, given the characteristics of current immigrants, the migratory shock will not have the same eﬀects across the distribution of skills. Then, assuming that the distribution of skills prior to the beginning of migratory ﬂows remains the same today, a measure of the migratory shock for those residing in region r with s years of education and e years of experience is:

1 mrse = log(Mr × Vse)

where Mr is the number of Migrants in region r; and Vse is the fraction of Venezue- lans in the cell s, e in Venezuela in 2001. One of the potential problems of this measure is measurement error. In Colombia, as well as in other countries that re- ceive refugee population, registration of immigrants is voluntary. Therefore, counts

9 of immigration are usually underestimated for three reasons: ﬁrst, only whose who want access to cash assistance or other types of subsidies register; second, the registration points are mainly in urban areas; third, registration is costly because of the long waiting times on the line and the transportation costs. For these reasons, I use a second measure to quantify the immigration shock

2. Measure 2 - Internet search intensity of key words: Currently, one of the easiest way to ﬁnd information is to search for topics of interest on the Internet. Therefore, it is reasonable to assume that immigrants who just arrived to a country want to know about working conditions, to inquired about the requirements for acquiring work permits and to connect with host communities. Using a intensity measure for the search of key words such as ”Venezuelans in”, or ”Special Residence Permit”3, another measure of the immigration shock is:

2 mrse = Ir × Vse

where Ir is the search intensity in region r; and Vse is the fraction of Venezuelans in the s, e cell in Venezuela in 2001. One of the main advantages of this measure is that it can also capture regions in which registration centers are not available.It also captures those people in the upper and lower tails of the income distribution for whom the opportunity cost of registering is high.

Given that educational and experience credentials are not necessarily valid in Colom- bia from the point of view of the employer, and the short duration of the work permits, it may be that the distribution of skills before migration does not aﬀect their employability prospects. In particular, it is possible that Venezuelans are being hired much more in low-skill and informal jobs. For this reason, I also explore the following speciﬁcation:

Yi,d|low−skilled=β0 + β1Opened Bordert + β2mr + β3(Opened Bordert × md) (2) 0 + φ X{it} + τt + αr + κTt + ui,dse

where the migration Yi,d|low−skilled is the outcome conditional on being a low skilled worker, that is, these regressions use the sample of people with high-school education

3The Special Residence Permit, or PEP for its acronym in Spanish, is a residence permit that allows Venezuelans to lawfully stay in Colombia for two years. This permit started to be issued in August 2018. Prior that, Venezuelans had access to other temporary residence permits such as the Temporary Residence Permit, or PTP for its acronym in Spanish.

10 completed or less. The two measures for the intensity of migration under this speciﬁca- 1 2 tion will be mr = log(Mr) and mr = Ir, respectively.

The use of a difference-in-differences model imposes the assumption of parallel trends. That is, had not the migration shock happened, trends on wages and employment would have been parallel in high migration areas as compared to areas with fewer levels of immigrants. One way to provide evidence that this assumption holds for the outcomes of analysis is to compare the trends prior to the migration shock. Figure B1 depicts the trends for the three main variables of interest prior to the re-opening of borders. The figures suggest that this assumption holds.

The involuntary nature these migration flows, the timing of the events, and the characteristics of Venezuelan immigrants have several advantages over previous studies in the literature. First, this type migration shock is mostly unrelated to the immigrants’ location and employment preferences, which reduces the selection effect that tends to underestimate the effects of migration (Tumen, 2015). Second, the sudden re-opening of the Colombian-Venezuelan borders allows me to situate the start of migration shock at a point in time. Finally, the short duration of the special work permits (2 years) and the fact that past credentials are difficult to validate in Colombia, allows me to target the impacts on the informal sector, which is where Venezuelans are more employable. Data for this analysis and descriptive statistics are presented in the next section.

4 Data and Descriptive Statistics

Information on wages and employment come from the Colombian Great Integrated House- hold Survey (or GEIH by its acronym in Spanish), which has information at the household level in 23 departments, 13 metropolitan areas and 11 intermediate cities 4. Although GEIH provides information since 2009, I use monthly data from January 2013 to Au- gust 2018 in this analysis. This survey also provides detailed demographic information of Colombian households including age, gender, years of education, municipality or department of residence, type of job, hours worked, labor earnings, ﬁrm size, among others. Every month, this survey reports information on around 52.000 people, which for the time frame of analysis sets the initial set of observations to 3.579.230.

4Colombia has 32 departments and 1023 municipalities

11 Table 2: Descriptive statistics of the native population (1) (2) (3) VARIABLES Formal and Informal Formal Informal

Age 30.7 41.9 36.9 Female (%) 53.9 45.8 44.8 Education (years) 9.1 12.7 7.4 Education (%) None 4.5 0.5 5.7 Primary 21.9 6.6 33.9 Secondary (6th to 9th grade) 21.1 6.4 21.2 High School (10th to 13th grade) 26.1 24.2 32.1 Higher education 27.3 62.1 7.1 Labor force (%) 90.8 - - Unemployed (%) 11.6 - - Informal (%) 54.7 - -

Observations 3,578,290 912,250 1,103,042 Note: Own calculations using GEIH, Jan 2013 - Aug 2018. Each column describes the sample of analysis. Unweighed means are shown.

Descriptive statistics of the sample are presented in Table 2. The average Colombian in the sample is 30.7 years old, and has completed 9 years of education. People working in the informal sector have around 5 fewer years of education as compared to people working in the formal sector. Finally, employment has been on average 11.6%. Among those who are employed, 54.7% work in the informal sector. Overall, this suggests that the formal and the informal sector are large and attracts diﬀerent types of workers.

The number of Venezuelans currently residing in Colombia come from the adminis- trative registry of Venezuelan migrants (or RAMV). This registry began in 2018 as a government initiative to know the location and characteristics of the Venezuelan population in the country. The goal of this survey is to help in the designing of better humanitarian policies for the migrant population. To the best of my knowledge, only aggregate data are available to the public. Figure B2 depicts the distribution of Venezuelans in Colombia.

As expected, there is a large number of Venezuelans in two of the three largest border points: Guajira, north of Colombia, and north of Santander, which is the main entry point for migrants. Other regions that stand out for their high number of migrants are Bogot´a(in the center of the country), and Atl´antico (on the north coast of the country). According to this map, Venezuelan migrants are concentrated in only some regions of the country. However, this registry is an imperfect measure of the number of Venezuelans

12 currently residing in Colombia. One of the problems is that although there are several registration units, registration is voluntary. Only those who want to have a work permit and who want to access other types of beneﬁts are those who will be willing to pay the opportunity cost. Those who have employment options in the informal market, or in rural areas do not have the incentive to go to a registration unit.

As an alternative measure of the migration shocks, I obtained information from Google Trends on two keywords that mostly Venezuelans would be interested on searching on the Internet. The ﬁrst keyword is ”Venezuelans”. When immigrants enter a new country, they will search for host communities and ONG’s recommendations on where to go and how to seek for help. The second keyword is ”PEP”, which is the acronym in Spanish for an Special Residence Permit that mostly Venezuelans who are interested on acquiring would search. Finally, it is important to mention that anecdotal evidence suggests that Venezuelans use their cellphones or coﬀee shops to access social media and share experi- ences of their journey.

Figure B3 displays the distribution of search of these two words in the national territory. The map depicts a new region on the top of list of searches: Arauca. The latter is the third entry point that is rural and, therefore, makes it difficult for the government to access information in those areas. Notice as well that Nariño, which is where the border with Ecuador is located, now stands out over other regions that have low internet search levels of these key words. It is necessary to remember that those Venezuelans who plan to leave the country to other Latin American countries have to go through this point. This gives indications that the Internet search measure is a more accurate measure of the magnitude of the migratory shock. However, one of the disadvantages is that is a difficult measure to interpret and therefore, in the regression analysis, I will be able to interpret only the direction of the coefficient but not its size.

Skills distribution of Venezuelans come from IPUMS international 2001, which is the most recent wave of information of Venezuelans in Venezuela. I dropped information of foreign-born residents of Venezuela in the year of the Census. At this point, it is worth highlighting a diﬀerence between the Colombian and the Venezuelan education system. In Colombia, primary school ends at ﬁfth grade while in Venezuela it ends at sixth grade. Similarly, high school education in Colombia ends in eleventh grade while in Venezuela it ends in twelfth grade.

Figure 2 depicts this diﬀerence between the two education systems. The graph on the

13 Figure 2: Distribution of skills Venezuela and Colombia Venezuela 2001 Colombia 2005

20000 15 15

15000

15000 10 10 10000 10000 Schooling (years) Schooling (years)

5 5000 5 5000

0 0 10 20 30 40 50 10 20 30 40 50

Experience (years) Experience (years) Note: Produced by the author using information from IPUMS International. left displays the distribution of Venezuelans in the 1% sample of the Venezuelan Census in 2001 across the school and experience dimensions. The graph on the right depicts the distribution of Colombians in the 1% sample of the Colombian Census in 2005 across the school and experience dimensions 5. Colors in the graph indicate the number of people in each school and experience cell, red meaning a high concentration of population on that cell. The graph shows that age-experience distributions between Colombia and Venezuela were similar in the 2000s. Both of the countries have a large mass of people in grades that indicate completion of primary and secondary.

To solve for the mismatch in completion grades and to make the skills distribution comparable between the two countries, I subtract one from the number of years of education of the Venezuelan population. Therefore, a person who reported six years of education in Colombia was assigned to have ﬁve years of education to make the credentials of graduation comparable between Colombians and Venezuelans. I also assign the value of 1 to those who originally reported having one year of education and a value of 0 to those who reported having no education in Venezuela.

Using the skills distribution of Venezuelans in 2001 to approximate the skill distribution of Venezuelans in the past ﬁve years seems reasonable. ENCOVI (2014), a survey conducted by three universities in Venezuela, reports that more than 70% of the population of age 25 and above in the third quintile of the income distribution have completed

5The graph on the right uses data from IPUMS International in 2005. Data from this source is only used for comparison purposes in this graph, but it is not used in the regression analysis.

14 high school as their maximum educational achievement. That seems to suggest that in recent years Venezuelans’ skills distribution has not changed. However, to the best of my knowledge, there is no data to test for this hypothesis.

5 Results

5.1 Migration shock 1: Number of migrants

Using the specification in equation 1, I estimate the effects of wages and employment. Table 3 shows the estimate of the interaction coefficient. The dependent variable is log hourly real wages and all regressions use controls such as experience, experienced squared, school years, sex, log of the workforce, and a dummy for whether the individual works part time. Assuming that the education distribution of Venezuelans before migration is of relevance, I found significant effects that suggest a reduction of wages in the informal sector accompanied with an increase in wages in the formal sector. However, these effects are very close to zero. For every additional 1% increase in the immigrant population in region r for the skills level s, e, there is a reduction of 0.008% of wages in the informal sector and a increase of 0.006%. This coefficient does not change in size after the inclusion of monthly FE, Area FE or a time trend.

15 1 Table 3: Eﬀects of immigration on log wages - Migration shock md,s,e Sector of employment (1) (2) (3) (4)

Formal and informal sector 0.002 0.002 0.003 0.003 (0.003) (0.003) (0.003) (0.003)

Observations 1,634,083 1,634,083 1,634,083 1,634,083 R-squared 0.321 0.321 0.341 0.341

Formal sector 0.006*** 0.006*** 0.006*** 0.006*** (0.002) (0.002) (0.002) (0.002)

Observations 740,397 740,397 740,397 740,397 R-squared 0.258 0.258 0.268 0.268

Informal sector -0.008** -0.008** -0.007* -0.007* (0.003) (0.003) (0.004) (0.004)

Observations 893,686 893,686 893,686 893,686 R-squared 0.107 0.107 0.130 0.130

Controls XXXX Month FE XXX Area FE XX Trend X

Note: Each coefficient in the table corresponds to the coefficient associated with interaction term in equation 1 (i.e. β3). The sample used is described in the rows while the columns describe the specification used. The dependent variable is log of hourly real wages. Controls include experience, experience squared, school years, sex log of the workforce, and a dummy for whether the individual works part or full time. Clustered standard errors at the department level in parentheses. Extended versions of these tables are in the Appendix B (see Tables B1, B2, and B3). *** p<0.01, ** p<0.05, * p<0.1

Table 4 displays the estimations for the model in equation 2. The results suggest that if we assume that the immigration of Venezuelans only aﬀects the low-skilled, then a 1% increase in the immigrant population will cause a 0.014% reduction in real wages.

16 1 Table 4: Eﬀect on log real wages of the low skilled - Migration shock md (1) (2) (3) VARIABLES Formal and Informal Formal Informal

Immigration shock × Opened border -0.012* -0.005 -0.014** (0.006) (0.004) (0.006)

Controls XXX Month FE XXX Area FE XXX Trend XXX

Observations 1,101,989 312,920 789,069 R-squared 0.144 0.113 0.105 Note: Each coefficient corresponds to the interaction coefficient in equation 2. OLS regressions use the most preferred specification in Table 3. Columns describe the sample used. The dependent variable is log of hourly real wages. These regressions are run using the sample of individual with completed high school degrees or less. Clustered standard errors at the department level in parentheses. An extended version of this table can be consulted in Appendix B (see Table B4). *** p<0.01, ** p<0.05, * p<0.1

On the other hand, table5 presents the results for both types of specifications on the employment transition. Assuming that the distribution of skills of Venezuelan matter, I do not find evidence of effects on the employment, nor on informality. However, if we assume that Venezuelans are competing only with the low-skilled, I find weak evidence of employment in the formal sector accompanied with an increase in the transition from the formal to the informal sector for the native population.

17 1 1 Table 5: Eﬀect on employment transitions - Migration shock mdse and md Type of transition VARIABLES EU FU IU IF

1 mdse× Opened border 0.001 -0.001 -0.002 0.000 (0.001) (0.001) (0.001) (0.000)

Observations 1,984,945 1,049,724 1,170,794 1,750,565 R-squared 0.041 0.115 0.161 0.364

1 md× Opened border 0.002 -0.004* -0.003 0.002** (0.001) (0.002) (0.002) (0.001)

Observations 1,984,945 1,049,724 1,170,794 1,749,372 R-squared 0.041 0.113 0.130 0.323

Controls XXXX Month FE XXXX Area FE XXXX Trend XXXX Note: Each coefficient corresponds to a different OLS regression. Each column describes the outcome: EU is 1 if employed 0 if unemployed; FU is 1 if employed in the formal sector, 0 if unemployed; IU is 1 if employed in the informal sector, 0 if unemployed; and IF is 1 if employed in the informal sector, 0 if employed in the formal sector. Rows describe the type of model. The first row displays interaction terms equation of running a regression using specification in 1 while the second row displays estimates of equation 2. OLS regressions use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Clustered standard errors at the department level in parentheses. An extended version of this table can be consulted in Appendix B (see Tables B5 and B6). *** p<0.01, ** p<0.05, * p<0.1

5.2 Migration shock 2

Using the internet search intensity seems a better measure to capture where Venezuelans are in Colombia. The results of the OLS regressions on log real wages are presented in table 6. These results suggest an increase on wages in the formal sector accompanied by a decrease in wages in the informal sector. However, among the low skilled the results diﬀer. I ﬁnd an overall reduction of wages in both, the formal and the informal sector.

18 2 2 Table 6: Eﬀect on log hourly wages - Migration shock mdse and md VARIABLES Formal and Informal Formal Informal

2 mdse× Opened border -0.127 0.215*** -0.336*** (0.088) (0.074) (0.078)

Observations 1,574,205 685,528 859,622 R-squared 0.322 0.261 0.128

2 md× Opened border -0.002*** -0.001*** -0.002*** (0.000) (0.000) (0.000)

Observations 1,063,107 283,822 759,240 R-squared 0.145 0.112 0.105

Controls XXX Month FE XXX Area FE XXX Trend XXX Note: Each coefficient corresponds to a different OLS regression in which log of hourly wages is used as dependent variable. Each column describes the sample of analysis. Rows describe the type of model. The first row displays interaction terms equation of running a regression using specification in 1 while the second row displays estimates of equation 2. OLS regressions use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Clustered standard errors at the department level in parentheses. An extended version of this table can be consulted in Appendix B (see Tables B7 and B8). *** p<0.01, ** p<0.05, * p<0.1

Results on employment are present in table 7. I ﬁnd increase in the transition from informality to informality and a decrease in the transition from unemployment to the formal sector. The net out of this eﬀects suggest an increase in overal employment for Colombians.

19 2 2 Table 7: Eﬀect on employment transitions - Migration shock mdse and md Type of transition VARIABLES EU FU IU IF

2 mdse× Opened border 0.053* -0.112** -0.048 0.101*** (0.030) (0.042) (0.046) (0.028)

Observations 1,913,026 1,015,723 1,125,743 1,684,586 R-squared 0.041 0.115 0.145 0.343

2 md× Opened border 0.000* -0.001*** -0.000 0.000*** (0.000) (0.000) (0.000) (0.000)

Observations 1,235,459 445,572 926,796 1,098,550 R-squared 0.042 0.137 0.062 0.148

20 References

Borjas, George J, “The labor demand curve is downward sloping: Reexamining the impact of immigration on the labor market,” The quarterly journal of economics, 2003, 118 (4), 1335–1374.

Cameron, Maxwell A. and Flavie Major, “Venezuela’s Hugo Chavez: Savior or Threat to Democracy,” Latin American Research Review, 2001, 36 (3), 255–266.

Card, David, “The impact of the Mariel boatlift on the Miami labor market,” ILR Review, 1990, 43 (2), 245–257.

, “Immigrant inﬂows, native outﬂows, and the local labor market impacts of higher immigration,” Journal of Labor Economics, 2001, 19 (1), 22–64.

Carpio, Ximena V Del and Mathis Wagner, The impact of Syrians refugees on the Turkish labor market, The World Bank, 2015.

Escalona, Alejandro, “Venezuela: cronologia de los ultimos 20 anos,” 7 2017.

Freitez, Anitza, “La emigracion desde Venezuela durante la ultima decada.,” Revista temas de coyuntura, 2011, (63).

Friedberg, Rachel M, “The impact of mass migration on the Israeli labor market,” The Quarterly Journal of Economics, 2001, 116 (4), 1373–1408.

Hernandez, Igor, Francisco Monaldi et al., “Weathering Collapse: An Assessment of the Financial and Operational Situation of the Venezuelan Oil Industry,” Technical Report, Center for International Development at Harvard University 2016.

Lopez, Virginia and Jonathan Watts, “Nicolas Maduro narrowly wins Venezuelan presidential election,” 04 2013.

Romero, Simon, “Crisis at Colombia Border Spills Into Diplomatic Realm,” New York Times, 2008, 4.

Tumen, Semih, “The use of natural experiments in migration research,” IZA World of Labor, 2015.

21 Appendix A: Deﬁnitions

Experience: It is potential experience. That is the age reported by the individual minus the years of schooling.

Hourly real wages: They are deﬁned as the labor earnings received last month, adjusted by a monthly price index, and divided by the number of hours that an individual usually works per month. The latter is deﬁned as four (weeks) times the number of hours that a person usually works per week. Wages in the formal sector were also adjusted by the contribution employers make to health insurance and retirement plants of their employees. In Colombia, employers contribute 8.5% of their wages to their employees’ health insurance plan and 12% to their retirement plans. I also dropped observations below the 1% and above the 99% of the wage distribution.

Labor force: It is deﬁned as the economically active. That is, those people who are 12+ years old and, who are either working or looking for employment6.

6Although the oﬃcial deﬁnition from the National Bureau of Statistics in Colombia takes into account people who are 10+ years old in rural areas, and those who are 12+ years old in urban areas, I dropped those people aged 10-11 years old from the sample.

22 Appendix B: Extended tables

B1.3 Figures

Figure B1: Trends in main outcomes before borders opened 70 60 50 40 30 20 Informality ( as % of employed) 10 0 2013m1 2014m1 2015m1 2016m1 Date

Below average Above average 20 16 12 8 4 Unemployed (as % of labor force) 0 2013m1 2014m1 2015m1 2016m1 date

Below median Above median 8000 7000 6000 5000 Hourly wage (COP) 4000 3000 2013m1 2014m1 2015m1 2016m1 Date

Below average Above average

Note: Light blue lines display averages for the corresponding outcome in departments and municipalities with migration shocks below the median. Similarly,23 dark blue lines display averages in municipalities and departments that received an inﬂow of migrants above the median. Figure B2: Municipalities close to the border with Venezuela

Note: Produced by the author using information collected using Google Maps on the road distance from Villa del Rosario to any other point in the map. The location of three main entry points from Venezuela to Colombia by land are displayed in yellow.

24 Figure B3: Location of Venezuelans estimated using the search for key terms on the Internet

Note: Produced by the author using information from Google Trends from 2013-2015. The map displays intensity of Internet search at the Department level. Key terms include: “PEP” 7, “permiso de trabajo” (work permit), and “venezolanos” (Venezuelans). Google Trends does not provide information for Departments displayed in gray.

25 B1.4 Tables

Table B1: Eﬀect on log real wages in the formal and informal sector - Migration shock 1 md,s,e VARIABLES (1) (2) (3) (4)

Immigration shock × Opened border 0.002 0.002 0.003 0.003 (0.003) (0.003) (0.003) (0.003) Opened border -0.020* -0.020* -0.016 -0.011 (0.010) (0.010) (0.010) (0.008) Immigration shock -0.054*** -0.054*** -0.088*** -0.088*** (0.006) (0.006) (0.002) (0.002) Experience 0.021*** 0.021*** 0.021*** 0.021*** (0.001) (0.001) (0.001) (0.001) Experience squared -0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) 0.086*** 0.086*** 0.074*** 0.074*** (0.004) (0.004) (0.002) (0.002) Female -0.200*** -0.200*** -0.209*** -0.209*** (0.006) (0.006) (0.007) (0.007) Part time -0.014 -0.014 -0.002 -0.002 (0.015) (0.015) (0.015) (0.015) Log workforce 0.138*** 0.138*** -0.274*** -0.274*** (0.017) (0.017) (0.024) (0.024)

Constant 0.342 0.349 7.577*** 11.691** (0.254) (0.254) (0.439) (5.600)

Controls XXXX Month FE XXX Area FE XX Trend X

Observations 1,634,083 1,634,083 1,634,083 1,634,083 R-squared 0.321 0.321 0.341 0.341 Note: OLS regression model. Columns describe the speciﬁcation used. The dependent variable is log of hourly real wages. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

26 1 Table B2: Eﬀect on log real wages in the formal sector - Migration shock md,s,e VARIABLES (1) (2) (3) (4)

Immigration shock × Opened border 0.006*** 0.006*** 0.006*** 0.006*** (0.002) (0.002) (0.002) (0.002) Opened border -0.039*** -0.039*** -0.036*** -0.005 (0.010) (0.010) (0.010) (0.005) Immigration shock -0.030*** -0.030*** -0.040*** -0.040*** (0.006) (0.006) (0.003) (0.003) Experience 0.019*** 0.019*** 0.019*** 0.019*** (0.001) (0.001) (0.001) (0.001) Experience squared -0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) 0.081*** 0.081*** 0.076*** 0.076*** (0.004) (0.004) (0.002) (0.002) Female -0.120*** -0.120*** -0.123*** -0.123*** (0.005) (0.005) (0.005) (0.005) Part time 0.108*** 0.107*** 0.105*** 0.105*** (0.014) (0.014) (0.014) (0.014) Log workforce 0.073*** 0.073*** 0.096*** 0.094*** (0.014) (0.014) (0.014) (0.014)

Constant 1.553*** 1.551*** 1.222*** 23.259*** (0.207) (0.206) (0.275) (5.200)

Controls XXXX Month FE XXX Area FE XX Trend X

Observations 740,397 740,397 740,397 740,397 R-squared 0.258 0.258 0.268 0.268 Note:Note: OLS regression model. Columns describe the speciﬁcation used. The dependent variable is log of hourly real wages. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

27 1 Table B3: Eﬀect on log real wages in the informal sector - Migration shock md,s,e VARIABLES (1) (2) (3) (4)

Immigration shock × Opened border -0.008** -0.008** -0.007* -0.007* (0.003) (0.003) (0.004) (0.004) Opened border 0.018 0.017 0.018 0.009 (0.010) (0.011) (0.011) (0.010) Immigration shock -0.018** -0.018** -0.033*** -0.033*** (0.006) (0.006) (0.002) (0.002) Experience 0.024*** 0.024*** 0.023*** 0.023*** (0.001) (0.001) (0.000) (0.000) Experience squared -0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) 0.047*** 0.047*** 0.043*** 0.043*** (0.002) (0.002) (0.002) (0.002) Female -0.246*** -0.246*** -0.257*** -0.257*** (0.011) (0.011) (0.011) (0.011) Part time 0.059*** 0.060*** 0.077*** 0.077*** (0.019) (0.019) (0.019) (0.019) Log workforce 0.095*** 0.095*** -0.445*** -0.444*** (0.017) (0.017) (0.021) (0.021)

Controls XXXX Month FE XXX Area FE XX Trend X

Observations 893,686 893,686 893,686 893,686 R-squared 0.107 0.107 0.130 0.130 Note: OLS regression model. Columns describe the speciﬁcation used. The dependent variable is log of hourly real wages. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

28 1 Table B4: Eﬀect on log real wages of the low skilled - Migration shock md (1) (2) (3) VARIABLES Formal and Informal Formal Informal

Immigration shock × Opened border -0.012* -0.005 -0.014** (0.006) (0.004) (0.006) Opened border 0.099* 0.046 0.113** (0.051) (0.036) (0.050) Immigration shock -0.008*** 0.116*** 1.580*** (0.001) (0.035) (0.048) Experience 0.026*** 0.018*** 0.027*** (0.001) (0.001) (0.000) Experience squared -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) Education (years) 0.051*** 0.040*** 0.036*** (0.002) (0.002) (0.002) Female -0.250*** -0.121*** -0.274*** (0.009) (0.006) (0.011) Part time -0.036* 0.164*** 0.087*** (0.017) (0.011) (0.019) Log workforce 0.005 -0.177*** -2.603*** (0.006) (0.059) (0.081)

Constant -5.615 7.057 22.599*** (6.217) (4.730) (7.282)

Controls XXX Month FE XXX Area FE XXX Trend XXX

Observations 1,101,989 312,920 789,069 R-squared 0.144 0.113 0.105 Note: OLS regression suse the most preferred speciﬁcation in table 3. Columns describe the sample used. The dependent variable is log of hourly real wages. These regressions are run using the sample of individual with completed high school degrees or less. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

29 1 Table B5: Eﬀect on employment transitions - Migration shock mdse Type of transition VARIABLES EU FU IU IF

Opened border 0.009*** -0.019*** -0.007** 0.009*** (0.002) (0.003) (0.003) (0.002) 1 mdse 0.000 -0.011*** 0.052*** 0.066*** (0.001) (0.001) (0.003) (0.004) 1 mdse× Opened border 0.001 -0.001 -0.002 0.000 (0.001) (0.001) (0.001) (0.000) Experience (years) -0.009*** 0.018*** 0.015*** 0.004*** (0.000) (0.000) (0.001) (0.000) Experience squared 0.000*** -0.000*** -0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) -0.003*** 0.025*** -0.008*** -0.035*** (0.000) (0.002) (0.001) (0.001) Female 0.046*** -0.105*** -0.050*** 0.034*** (0.003) (0.006) (0.005) (0.003) Log workforce 0.202*** -0.390*** -0.188*** 0.158*** (0.004) (0.009) (0.008) (0.009) Part-time 0.172*** (0.012)

Month FE XXXX Area FE XXXX Trend XXXX

Observations 1,984,945 1,049,724 1,170,794 1,750,565 R-squared 0.041 0.115 0.161 0.364 Note: Coefficients come from OLS regressions that use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Each column describes the outcome: EU is 1 if employed 0 if unemployed; FU is 1 if employed in the formal sector, 0 if unemployed; IU is 1 if employed in the informal sector, 0 if unemployed; and IF is 1 if employed in the informal sector, 0 if employed in the formal sector. Rows describe the type of model. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

30 1 Table B6: Eﬀect on employment transitions - Migration shock md Type of transition VARIABLES EU FU IU IF

Opened border -0.011 0.018 0.015 -0.004 (0.013) (0.019) (0.020) (0.007) 1 md 0.007*** -0.032*** 0.431*** -0.661*** (0.000) (0.002) (0.019) (0.021) 1 md× Opened border 0.002 -0.004* -0.003 0.002** (0.001) (0.002) (0.002) (0.001) Experience (years) -0.009*** 0.018*** 0.015*** 0.004*** (0.000) (0.000) (0.001) (0.000) Experience squared 0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) -0.003*** 0.031*** -0.018*** -0.061*** (0.001) (0.002) (0.001) (0.002) Female 0.045*** -0.103*** -0.054*** 0.025*** (0.003) (0.006) (0.006) (0.004) Log workforce 0.009*** 0.035*** -0.732*** 1.052*** (0.001) (0.002) (0.032) (0.036) Part-time 0.173*** (0.012)

Month FE XXXX Area FE XXXX Trend XXXX

Observations 1,984,945 1,049,724 1,170,794 1,749,372 R-squared 0.041 0.113 0.130 0.323 Note: Coefficients come from OLS regressions that use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Each column describes the outcome: EU is 1 if employed 0 if unemployed; FU is 1 if employed in the formal sector, 0 if unemployed; IU is 1 if employed in the informal sector, 0 if unemployed; and IF is 1 if employed in the informal sector, 0 if employed in the formal sector. Rows describe the type of model. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

31 2 Table B7: Eﬀect on log hourly wages - Migration shock mdse VARIABLES Formal and informal Formal Informal

Opened border -0.003 -0.011* 0.015 (0.006) (0.006) (0.010) 2 mdse -1.887*** -0.478*** -0.484*** (0.285) (0.161) (0.130) 2 mdse× Opened border -0.127 0.215*** -0.336*** (0.088) (0.074) (0.078) Experience 0.020*** 0.018*** 0.022*** (0.001) (0.001) (0.000) Experience squared -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) Years of schooling 0.097*** 0.096*** 0.046*** (0.002) (0.002) (0.002) Female -0.204*** -0.118*** -0.254*** (0.007) (0.005) (0.011) Part time 0.005 0.120*** 0.082*** (0.014) (0.014) (0.018) Log workforce -0.063* 0.192*** -0.405*** (0.034) (0.013) (0.025)

Constant 9.332 21.310*** 4.695 (6.006) (5.413) (7.199)

Observations 1,574,205 685,528 859,622 R-squared 0.322 0.261 0.128 Note: Estimates come from an OLS regression in which log of hourly wages is used as dependent variable. Each column describes the sample of analysis. Regressions use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

32 2 Table B8: Eﬀect on log hourly wages - Migration shock md VARIABLES Formal and informal Formal Informal

Opened border 0.038*** 0.024*** 0.043*** (0.011) (0.007) (0.013) 2 md -0.010*** 0.001*** -0.009*** (0.000) (0.000) (0.000) 2 md× Opened border -0.002*** -0.001*** -0.002*** (0.000) (0.000) (0.000) Experience 0.026*** 0.017*** 0.027*** (0.001) (0.001) (0.000) Experience squared -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) Education (years) 0.051*** 0.040*** 0.036*** (0.002) (0.002) (0.002) Female -0.248*** -0.121*** -0.272*** (0.009) (0.006) (0.011) Part time -0.029* 0.177*** 0.094*** (0.017) (0.010) (0.018) Log workforce 0.093*** 0.013*** 0.053*** (0.001) (0.001) (0.001) Constant -6.424 4.530 -5.790 (6.462) (4.601) (7.343)

Observations 1,063,107 283,822 759,240 R-squared 0.145 0.112 0.105 Note: Estimates come from an OLS regression in which log of hourly wages is used as dependent variable. Each column describes the sample of analysis. Regressions use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

33 2 Table B9: Eﬀect on employment transitions - Migration shock mdse Type of transition VARIABLES EU FU IU IF

Opened borders 0.007*** -0.015*** -0.007** 0.007*** (0.002) (0.003) (0.002) (0.002) 2 mdse -0.087** -0.383*** 1.199*** 1.644*** (0.042) (0.065) (0.167) (0.205) 2 mdse× Opened borders 0.053* -0.112** -0.048 0.101*** (0.030) (0.042) (0.046) (0.028) Experience (years) -0.009*** 0.018*** 0.017*** 0.005*** (0.000) (0.000) (0.001) (0.000) Experience squared 0.000*** -0.000*** -0.000*** -0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) -0.003*** 0.029*** -0.016*** -0.053*** (0.001) (0.002) (0.001) (0.002) Female 0.045*** -0.104*** -0.051*** 0.033*** (0.004) (0.006) (0.006) (0.003) Log workforce 0.207*** -0.349*** -0.286*** 0.036* (0.005) (0.011) (0.018) (0.021) Part-time 0.170*** (0.013) Constant 1.411 -4.633 0.917 4.672*** (2.159) (3.376) (3.042) (1.374)

Observations 1,913,026 1,015,723 1,125,743 1,684,586 R-squared 0.041 0.115 0.145 0.343 Note: Coefficients come from OLS regressions that use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Each column describes the outcome: EU is 1 if employed 0 if unemployed; FU is 1 if employed in the formal sector, 0 if unemployed; IU is 1 if employed in the informal sector, 0 if unemployed; and IF is 1 if employed in the informal sector, 0 if employed in the formal sector. Rows describe the type of model. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

34 2 Table B10: Eﬀect on employment transitions - Migration shock md Type of transition VARIABLES EU FU IU IF

Opened borders 0.001 -0.005 -0.001 0.005 (0.003) (0.006) (0.004) (0.004) 2 md 0.003*** -0.009*** -0.002*** 0.007*** (0.000) (0.000) (0.000) (0.000) 2 md× Opened borders 0.000* -0.001*** -0.000 0.000*** (0.000) (0.000) (0.000) (0.000) Experience (years) -0.009*** 0.026*** 0.011*** 0.001 (0.000) (0.001) (0.001) (0.001) Experience squared 0.000*** -0.000*** -0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) Education (years) -0.002*** 0.030*** -0.002*** -0.026*** (0.001) (0.002) (0.001) (0.001) Female 0.052*** -0.188*** -0.045*** 0.046*** (0.004) (0.012) (0.005) (0.004) Log workforce -0.005*** 0.062*** -0.020*** -0.091*** (0.000) (0.001) (0.000) (0.001) Part-time 0.229*** (0.015) Constant 5.709** -16.382*** -3.935 8.070*** (2.174) (4.382) (2.778) (1.994)

Observations 1,235,459 445,572 926,796 1,098,550 R-squared 0.042 0.137 0.062 0.148 Note: Coefficients come from OLS regressions that use the most preferred specification that includes controls, monthly fixed effects, area fixed effects and a time trend. Each column describes the outcome: EU is 1 if employed 0 if unemployed; FU is 1 if employed in the formal sector, 0 if unemployed; IU is 1 if employed in the informal sector, 0 if unemployed; and IF is 1 if employed in the informal sector, 0 if employed in the formal sector. Rows describe the type of model. Clustered standard errors at the department level in parentheses. *** p<0.01, ** p<0.05, * p<0.1