Policy Research Working Paper 9605 Integration of Venezuelan Refugees and Migrants in Brazil Mrittika Shamsuddin Pablo Ariel Acosta Rovane Battaglin Schwengber Jedediah Fix Nikolas Pirani Social Protection and Jobs Global Practice March 2021 Policy Research Working Paper 9605 Abstract An unprecedented number of Venezuelans have left behind programs. It finds that even though there is minimum legal the worsening economic and social crisis at home to look constraints and work permits are relatively easy to obtain, for better future prospects. Brazil is hosting about 261,000 Venezuelan refugees and migrants face challenges integrat- Venezuelans as migrants, asylum seekers, or refugees, which, ing into the education system, social protection programs at 18 percent, constitutes the largest share of Brazil’s 1.3 and the formal labor market. The results suggest that Ven- million refugees and migrants population (as of October ezuelan refugees and migrants have faced downgrading in 2020). Although previous literature on other host countries grades at school and occupations at work. They are more found that Venezuelan refugees and migrants are struggling likely to attend overcrowded schools than their host com- to secure high-paying jobs that are commensurate with their munity counterparts and more likely to do inferior jobs education, little is known about their access to education characterized by temporality, lower wages and higher hours and social protection. This paper fills this gap by analyzing worked. Overall, the results suggest that improvement in various administrative and census data to explore whether school capacity, accreditation of Venezuelan education or Venezuelan migrants and refugees face differential access to degrees and relocation to places with favorable employment education, the formal labor market and social protection opportunities may facilitate integration. This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pacosta@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Integration of Venezuelan Refugees and Migrants in Brazil∗ Mrittika Shamsuddin1 , Pablo Ariel Acosta1 , Rovane Battaglin Schwengber1 , Jedediah Fix2 and Nikolas Pirani2 1 World Bank 2 UNHCR Keywords: integration in Brazil, Venezuelan refugees and migrants, forced displace- ment JEL Codes: J15, J31, J61, F22, F15 ∗Funding Disclaimer: This work is part of the program “Building the Evidence on Protracted Forced Displace- ment: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development O ce (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. We further thank FCDO for additional funding support through its Knowledge for Change (KCP) program. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. 1 Introduction The low price of oil along with onerous government spending and excessive international debt have pushed the Republica Bolivariana de Venezuela, once one of the richest countries in Latin America, in to one of the worst economic and social crises of modern day. Economic activity has been contracting with GDP (PPP) shrinking at a rate of 35% in 2019 according to United Nations data. The inflation rate hit 929,797% in 2019 (IMF, 2019), while 89% of the population has been estimated to be living in poverty (ENCOVI, 2018). Health conditions have worsened with an average Venezuelan losing 10 kilos of body weight in 2017 and infant mortality rising to 26 per 1000 live births in 2016 from 14.6 per 1,000 live births in 2010. An average of 89 homicides were reported per 100,000 inhabitants in 2019, which is almost three times the rate of countries that are at war (World Bank, 2019a). These worsening economic and social conditions generated an unprecedented exodus of Venezuelans in search of a better future and ability to avail basic human rights. The Venezuelan refugees and migrants living abroad increased by about six times between 2015 and 2019 as Table 1 suggests. The net migration rate in the Republica Bolivariana de Venezuela, was about –15.69 per 1000 population in 2019, suggesting more than 7% of the population of the Republica Bolivariana de Venezuela, has fled the country since 2014. Although the United States and Spain were the most significant hosts of Venezuelans1 traditionally, 80% of the Venezuelans who were displaced after 2014 were hosted by Latin American countries. Colombia and Peru have been the most significant hosts followed by Chile, Ecuador, the United States and Brazil. As of October 2020, Colombia has about 1.7 million of the Venezuelan refugees and migrants, which is after about 60,000 to 100,000 Venezuelans returned back to reunite with their families during the COVID-19 pandemic (Mazza, 2020). Peru stands second with about 1 million refugees and migrants, while Chile and Ecuador are the third and fourth significant hosts with about 0.47 million and 0.41 million Venezuelan refugees and migrants respectively. Brazil is hosting about 0.26 million Venezuelans (18% of all its total migrant and refugee population) as of the second quarter of 2020, 76 times the number of Venezuelan refugees and migrants in 2015 (Table 1), resulting in a federal decree number 9.285 of February 2018, recognizing it as a humanitarian crisis.2 Most Latin American countries have showed openness in welcoming and granting legal status to Venezuelan refugees and migrants (Selee and Bolter, 2020). However, the sheer size of the Venezuelan refugees and migrants means that addressing their urgent humanitarian needs and providing for their protracted stay may strain the public resources of the Latin American countries, unless they can harness the potentials of the Venezuelans to drive economic growth, which is a development challenge. This will only be possible through economic and social integration of Venezuelans. Greater economic and social inclusion will allow Venezuelans to work in productive jobs, create new job opportunities as business owners, pay taxes and contribute to the social security system, that will pave the path for economic development (UNHCR, 2018). However, our knowledge of living conditions of the Venezuelans in Latin American countries is scant. There are few studies (Olivieri et al. (2020), Graham et al. (2020), Uscategui and Andrea (2019)) that look into the labor market conditions of the Venezuelan refugees and migrants but to the best of our knowledge, there has not been any study that looks into their access to schooling3 and social protection programs. 1 Thispaper uses Venezuelans and Venezuelans migrants, asylum seekers and refugees interchangeably. 2 http://www.planalto.gov.br/ccivil 3/ to2015 2018/2018/decreto/D 9285.htm 0 a 3 This paper concentrates on grades up to high school as data on university is not available. However, the paper finds that inclusion of Venezuelans decreases with level of education, suggesting that integration will be even lower in 2 Table 1: Venezuelan Net Population Displaced Abroad Over the Years 2010 2015 2019 Worldwide 556,641 695,551 4,326,330 South America 62,240 86,964 3,239,730 North America (US+Canada) 196,910 273,418 371,919 Europe (Portugal, Spain, Italy) 203,117 224,328 309,170 Central America & Mexico 21,260 33,065 166,974 Carribbean 19,629 21,74 119,333 Others 53,485 56,702 119,204 South America 2010 2015 2020 Colombia 43,511 48,714 1,717,352 Peru 3,504 4,129 1,043,460 Chile 8,095 54,787 475,702 Ecuador 6,120 8,901 415,835 Brazil 2,844 3,425 261,441 Argentina 1,236 1,240 217,562 Source: Author’s calculation from UN Data This paper attempts to fill this gap by investigating how integrated the Venezuelan refugees and migrants are in the education, formal labor market and social protection sectors of Brazil4 and how di↵erent economic and social factors accelerate or hinder the process of integration. By integration, this paper refers to the definition advocated by OECD (2011) that defines integration as a two-way process of adaptation by migrants and host societies that includes the rights, obligations and access to di↵erent kinds of services and the labor market. Another contribution of this paper lies in the fact that unlike Colombia, Peru and Ecuador, on which previous studies have focused, Brazil has universal access to education, healthcare and social protection irrespective of documentation status and prohibits any kind of discrimination at work (Selee and Bolter, 2020) and consequently it serves as a case study to see whether di↵erent legal constraints can lead to di↵erential observed outcomes.5 This paper refers to this di↵erent legal framework in Brazil as little or minimum legal constraints in line with Selee and Bolter (2020) and Mazza (2020), however in reality, it still might be challenging for Venezuelans to integrate. Measuring integration calls for a benchmark against which outcomes can be assessed and this universities. 4 Access to health service is an important dimension of integration that we do not focus on this paper due to unavailability of data. 5 Olivieri et al. (2020), Graham et al. (2020), Uscategui and Andrea (2019), Selee and Bolter (2020) and Mazza (2020) report that Venezuelans face legal restrictions, di↵erential access to education, health, labor martket and safety net programs and hostility in Colombia, Peru and Ecuador. 3 study compares the outcomes of Venezuelans with those of Brazilians.6 All the integration measures indicate that in spite of little legal barriers, Venezuelans face challenges in accessing education, formal jobs and social protection in Brazil. Venezuelans are 0.47 times as likely to be in school, 0.3 times as likely to be employed in the formal sector, and 0.7 times as likely to be registered in single registry compared to their Brazilian counterparts. The findings suggest that only 42% Venezuelan children enroll in school and even when they do, they experience grade downgrading and capacity constraints, making it harder to attain productive human capital and make future generations self-reliant. In the formal labor market, their employment is of much lower quality than the Brazilian, characterized by high temporality and greater work hours, despite the higher schooling levels suggesting they also face occupation downgrading in Brazil. Controlling for selection into wage employment, there seems to be also a significant wage penalty for Venezuelans. Consequently, more educated Venezuelans and Venezuelans with higher number of children are more likely to register in the unified registry7 than their Brazilian counterparts. Overall, integration seems to be higher where the population of Venezuelan migrants and refugees is lower, controlling for selection, especially in education, supporting previous literature by Lazaer (1999) and Carneiro et al. (2020). It should be noted here that theoretically a larger network of refugees and migrants can provide more information about job prospects, school requirements, social protection benefits and local customs and traditions, promoting integration (Gautier, 2020), but in the case of Brazil, concentration in certain localities like Roraima and most Venezuelan migrants and asylums being new to the situation, seem to be creating an overcrowding e↵ect constraining integration. This paper is divided into six sections. Section 2 starts with a description of the migration trend in Brazil and key demographic characteristics of the Venezuelan refugees and migrants and then moves on to give a brief literature review on forced displacement and economic integration, focusing on the work on Venezuelan refugees and migrants in Latin American countries. Section 3 provides details on the variables and the methodology employed. Section 4 describes the data and presents the summary statistics with an emphasis on the e↵ect of the COVID-19 pandemic.8 Section 5 presents the results, while Section 6 concludes with some policy implications and discusses venue for further research. 2 Refugee and Migration Trends and Literature Review The refugees and migrants population in Brazil has been increasing rapidly from 2016 and the blue bar in Figure 1 shows that the total refugees and migrants population increased from about 0.7 million in 2016 to about 1.4 million in July 2020. From being one of the lowest asylum seeking and migrants population in 2016, among the Latin American countries, Venezuelans in Brazil quickly became the largest refugee and migrant population by 2019. Brazil also has significant number of nationals9 from Bolivia and Haiti, followed by Colombia, Argentina and China. During the period of July 2017 to October 2020, Brazil received about 126,256 migrants and 30,000 asylum seekers 6 Venezuelans could also be compared with the other migrants, but this study concentrates on the Brazilians by birth as this paper is more focused on the extent of local integration in Brazil. 7 The Unified Registry of Social Programs (CadUnico) is a database that collects details about low-income families in Brazil, which can be used to identify vulnerable people in the society and develop appropriate benefits for them. The details are given in the Methodology section. 8 Impact analysis of the COVID-19 pandemic is not possible due to unavailability of data. 9 Regular refugees and migrants 4 from Haiti, 38,232 migrants from Colombia, 9,063 asylum seekers from Cuba and 3,986 refugees from the Syrian Arab Republic. 1,500 1,000 Total(’000) 500 0 2016 2017 2018 2019 2020 World Venezuela Haiti Bolivia Colombia Argentina China Figure 1: Migration Trend in Brazil Source: Author’s calculation from SISMIGRA and STIMAR. This section discusses the trends in Venezuelan refugees and migrants in Brazil, reports their key demographics and reviews the available literature on integration, especially focusing on Venezuelans in Latin America. 2.1 Refugee and Migration trends of Venezuelans in Brazil Figure 2 shows the trend of Venezuelan refugees and migrants in Brazil. Although the number of Venezuelans increased quickly from about 118,000 in 2018 to about 265,000 in the first quarter of 2020, the stock started dwindling after the onset of the COVID-19 pandemic. Concentrating on the monthly flows, we see that half of the Venezuelans, who entered Brazil, exited Brazil either to go back to the Republica Bolivariana de Venezuela or move onwards to other countries. When the pandemic hit, the movement across borders was restricted and we see that more Venezuelans were leaving than coming in and as a result the total number started falling. The Federal Police database also reveals that half of the Venezuelan refugees and migrants entered Brazil and requested temporary residence permits, while the other half registered as asylum seekers. Of the migrant and asylum seeker population, 95% have residence permits, as opposed to more permanent forms of stay. As of October 2020, there were 145,462 Venezuelan migrants and refugees, 96,556 Venezuelan asylum 5 seekers and 46,647 Venezuelans refugees, who had entered Brazil since July 2017. The quarterly flow of asylum seekers peaked in the third quarter of 2018, while the quarterly flow of migrants peaked in the last quarter of 2019. The Venezuelan refugees and migrants increased from about 14,000 in the first quarter of 2015 to about 263,000 in the second quarter of 2020. 300000 30000 250000 263392 252168 20000 200000 10000 173298 150000 127841 100000 0 2019m1 2019m7 2020m1 2020m7 Time Entries Exits Stock Figure 2: Refugee and Migration Trend of Venezuelans in Brazil Source: Author’s calculation from SISMIGRA and STIMAR. In Brazil, estimates show that most Venezuelan refugees and migrants enter and settle in Roraima (50%) and Amazonas (19%), which is not surprising given that these states border the Republica Bolivariana de Venezuela at the north. The north region of Brazil is also the poorest traditionally, contributing only 4.7% of GDP in 2016 for instance. The population of Roraima in 2016 was about 0.5 million, one of the lowest among the states in Brazil and contributing only about 0.2% of the Brazilian GDP. Consequently, after the massive Venezuelan inflow, the Venezuelan refugees and migrants quickly comprised of about 30%10 of the population of Roraima and the state received help from the federal government, UNHCR, faith based organizations and civil society partners to manage its response to the influx and provide humanitarian assistance. The initiative is called “Opera¸ ao Acolhida” (Operation Welcome) and has three main programs: border management and c˜ documentation; provision of humanitarian assistance including shelter; and “Interiorization” which involves the voluntary relocation of Venezuelans from Roraima to other cities. The voluntary 10 Author’s calculation from SISMIGRA and STIMAR. It may overestimate the Venezuelan population in Roraima since the database is not updated regularly. 6 relocation program has relocated about 47,94911 Venezuelans from Roraima to other Brazilian cities, where there are more opportunities for social and economic integration. Venezuelan Migrants Venezuelan Asylum Seekers 96− 96− 91−95 91−95 86−90 86−90 81−85 81−85 76−80 76−80 71−75 71−75 66−70 66−70 61−65 61−65 Age Group Age Group 56−60 56−60 51−55 51−55 46−50 46−50 41−45 41−45 36−40 36−40 31−35 31−35 26−30 26−30 21−25 21−25 16−20 16−20 11−15 11−15 6−10 6−10 0−5 0−5 20 15 10 5 0 5 10 15 20 20 15 10 5 0 5 10 15 20 Male Female Male Female Migrants Asylum Seekers 45.03% 48.89% 51.11% 54.97% Female Male Female Male Migrants Asylum Seekers 2.06% 14.02% 13.97% 14.07% 83.92% 71.96% Married Single Married Single Others Others Figure 3: Age, Gender and Civic Status of Venezuelans in Brazil Source: Author’s calculation from SISMIGRA and STIMAR. Available data managed by the Federal Police shows that the Venezuelan population in Brazil has a balanced gender distribution as Figure 3 shows, contrary to the other population movements that are observed in other parts of the world, where men form the dominant movers. Of the Venezuelan migrant population, 49% are women while 51% are men, and 45% of the asylum seekers are women while 54% are men. This Venezuelan balanced gender distribution is observed in other Latin American countries like Colombia and Peru suggesting that this gender equality may be specific to the Venezuelan migration process. Venezuelan refugees and migrants are young, and many are single parents. Of the Venezuelan migrants, 75% are below 50 years old and 50% of the refugees and migrants are between the age of 20 and 40 years old. The presence of about 20% children below the age of 20 years points to the fact that a key component of the Venezuelan migrants and asylum seekers is that there has been substantial family migration. However, about 72% of Venezuelan asylum seekers and 84% of asylum seekers above the age of 25 years report their civic status as single, signifying that most of those families consist of single parents. There is no 11 This number represents those relocated by January 2021. 7 significant di↵erence in proportion of single parents between men and women. According to the data available on the occupation of the Venezuelan population, who entered as migrants in Brazil, at the time of registration approximately 16% worked in the households as governesses, butlers and cooks, 10% worked as vendors, 5% as teachers, 3% as engineers and 3% as administrators, suggesting most of the migrants work in semi-skilled rather than high -skilled jobs in Brazil but the dataset does not contain any information on education.12 Brazil has maintained the same open entry requirements for Venezuelans over the years. Residence permit is available to any Venezuelan, who enters as long as they have some type of identity documents. Since March 2018, Brazil has also begun allowing Venezuelans with this special form of temporary residence to apply for permanent residence status three months before their temporary permits are set to expire. However, this opportunity is only available for Venezuelans with a legal source of income. Those, who do not have such proof of income are permitted to renew their temporary residence permits indefinitely. Brazil has also adopted a prima facie approach in granting refugee status to the Venezuelan asylum seekers since December 2019. This follows the decision in June 2019 by CONARE (Brazil’s National Committee for Refugees) to recognize the situation in the Republica Bolivariana de Venezuela as human rights violation as described under the broader refugee definition contained in the 1984 1984 Cartagena Declaration on Refugees. On a single day, 21,432 Venezuelan asylum seekers were granted refugee status in Brazil on December 6, 2019, making it the country with about 50,000 refugees and the most popular country for refugees in Latin America. 2.2 Past Research Our paper contributes to the literature on forced displacement, and economic and social inte- gration of refugees and migrants in the host communities and aims to extend the literature in two ways. First, to the best of our knowledge, it is the first study to look into the recently displaced Venezuelans’ access to social protection programs and education in a Latin American country, which provides universal access to education, health care and social protection irrespective of legal status. Second, it provides the first evidence outside Europe on the association between education of children and concentration of forcibly displacement individuals. Most past research on the recent episode of Venezuelan forced displacement has focused on the labor market. Descriptive statistics in Cavalcanti et al. (2020) shows that Venezuelans consisted of 12% of the total migrants and refugees working in the formal labor sector in 2019 in Brazil. Graham et al. (2020) find that Venezuelan migrants in Colombia, the country hosting the largest number of Venezuelan refugees and migrants, work in lower-paid and more informal work and that before the COVID-19 pandemic, employed Colombians were earning 43 percent more on average than employed Venezuelans, despite the fact that Venezuelans are highly educated. Caruso et al. (2019) and Penaloza (2019) focus on the impact of the influx of Venezuelans on the labor market in Colombia and find that it reduces wages, especially for male workers in the informal sector. Olivieri et al. (2020) focus on Ecuador and report that although Venezuelans are more educated and more likely to be employed, they tend to work in low wage and informal work and experience significant occupational downgrading. Bahar et al. (2021) provides evidence of asymmetrical e↵ects of a large scale amnesty program that granted work permits to undocumented Venezuelans in Colombia that 12 Race data is available only for a sample of the population showing that 25% of the Venezuelan population is white, while the rest being indigenous, brown and black. The dataset does not include information on the specifics of the source regions in Venezuela, where the refugees and migrants are from. 8 helped labor market outcomes of Venezuelan at the cost of Colombian workers. The World Bank (2019) report on Peru also provides evidence of highly educated Venezuelans struggling to get jobs that are commensurate with their education levels. Incorporating these highly educated Venezuelans into the formal labor market can not only boost labor productivity but also economic growth (Graham et al., 2020). The short-run penalty on wages and employment for the refugees and asylum seekers is also found in Turkey, which has faced a large inflow of Syrian refugees since 2011 (Balkan and Tumen (2016), Becker and Ferrara (2019), DelCarpio and Wagner (2015), Loayza et al. (2018), Tumen (2016) andTumen (2018)) and in many European countries (Fasani et al., 2018). Although to the best of our knowledge, there is no research exploring the e↵ect of Venezuelan displacement on children’s education, Bauer and Kvasnicka (2013), Sarvimaki et al. (2016) and Becker et al. (2020) find that in the long run descendants of forced migrants of World War II tend to acquire more education than their native peers. However, the reasons behind this result seem to vary, while Bauer and Kvasnicka (2013) point to congestion in agriculture leading to more people looking for outside opportunities and acquiring more education, Becker et al. (2020) attribute the reason to labor market competition with the natives. Bilgili (2019) finds that newly arrived refugee and migrant children in the Netherlands have lower enrollment rate in higher education and lower academic resilience than their Dutch counterparts, although their performance in the Netherlands is better than in the other OECD countries. Abu-Ghaida and Silva (2020) surveys the literature on the education outcomes of internally displaced persons and refugees, and find that the secondary gross enrollment rate for refugee adolescents was 31 percent compared to 76 percent globally, and the ter- tiary gross enrollment rate for refugees was 3 percent compared to 38 percent globally. All the top six host countries in Latin America provide universal access to education regardless of their legal status. Argentina, Ecuador, Peru, Panama and Mexico have implemented laws codifying the right to primary and secondary education, Brazil, Chile and Colombia do not have any such law, but have policies to provide for universal education (Selee and Bolter, 2020). However, all the countries are facing varying practical challenges in enrolling Venezuelans in the education system. They have to grapple with the questions of how to enroll students, who lack the documents schools usually require, how to place children in the right grades when they lack school records, and how to respond to di↵ering levels of academic knowledge among children of a similar age. Countries like Peru, where 70 percent of the Venezuelan children are concentrated in the city of Lima, capacity constraint is adding to the di culty of accessing education (Selee and Bolter, 2020). In response, the Peruvian Education Min- istry has introduced a second shift of the school day to make space for more students (UNHCR, 2019). In theory, most of the Latin American countries have constitutions that explicitly recognize protection of economic and social rights. They have adopted a rights-based approach in social protection policies, with some countries committing to explicit social guarantees (Cecchini et al., 2015). In the 2014 Brazil Declaration and Plan of Action,13 the country has also committed to include refugees, asylum-seekers and stateless person to the national social protection programs. But in most countries, the national social protection programs are restricted to a subset of non- nationals at best. In countries like Colombia, Chile and Panama, the refugees and migrants need to have special permission or identity documents, while in Peru and Ecuador, only nationals are eligible for social protection programs. Only Brazil gives non-nationals access to social assistance 13 “A Framework for Cooperation and Regional Solidarity to Strengthen the International Protection of Refugees, Displaced and Stateless Persons in Latin America and the Caribbean.” 9 programs, regardless of their legal status (Mazza, 2020), and as per design, access to Bolsa Famila 14 is conditioned on school attendance and health check-ups, making this study on access to education and social protection of Venezuelan refugees and migrants unique. Research on Turkey (Ozler et al., 2020), which implemented the Emergency Social Safety Net (ESSN), the largest cash transfer program for refugees in 2016, finds positive e↵ects of the program on food consumption, children’s likelihood of attending school and decline in poverty. However, they find that the program causes substantial changes in household composition, with school age children moving from larger ineligible households to smaller eligible ones, suggesting that the design of the program can be improved. Positive results of cash assistance on quantity and quality of food consumption, children’s education, social participation and mental health of migrants and refugees are also found in various studies ((Chaaban et al., 2020), (Caria et al., 2020), (Lehmann and Masterson, 2020), (Shammout and Vandecasteele, 2019), (Valli et al., 2019)). Most Latin American countries, including Brazil, have a well-functioning social assistance system and extending them to Venezuelan refugees and migrants will ensure that Venezuelan families have the ability to pay for basic needs and send their children to school and at the same time it promotes social cohesion by increasing purchasing power of the Venezuelan population and the host communities. 3 Methodology In order to formulate e↵ective policy to help Venezuelans better integrate in the host country, their characteristics and vulnerabilities need to be known and this paper intends to fill this gap by exploring how well the Venezuelan refugees and migrants have integrated in the formal labor market, how much access they have to education and social protection programs and the key challenges that they face. This paper has two objectives: - 1) to measure the extent of Venezuelan migrants and refugees integration in Brazil, and 2) to explore some of the drivers and barriers of integration. Since this paper is focusing on three di↵erent sectors of the economy, the variables and the estimation strategies change accordingly depending on the availability and nature of the data. This section provides a detailed description of the estimation strategies used. 3.1 Integration Measures There is no consensus on the definition of integration. This paper follows the definition advocated by OECD (2011) that defines integration as a two-way process of adaptation by refugees and migrants and host societies, which includes the rights, obligations, access to di↵erent kinds of services and the labor market, along with identifying and respect for a core set of values that bind the non-national population and the host societies for common good. In Brazil, migrants and refugees have the same rights to education, health, jobs and social protection programs as host community, as a result, this paper focuses on the access to education, formal labor market and social protection programs.15 14 The flagship conditional cash transfer program for the poor in Brazil 15 Data on health is not available at this time and is left for future study. 10 Integration is measured as a ratio between the outcome variable of Venezuelans and the outcome of Brazilians. Most commonly used measure, used by Abramitzky et al. (2020), Carneiro et al. (2020), OECD (2015) and many others, is the relative probability of the outcome variable of Venezuelans compared to Brazilians. To measure integration in the education sector, this paper calculates the relative probability of Venezuelans, aged between 4 and 17 years old, the mandatory school age,16 enrolled in regular school compared to the Brazilian cohort. This paper also calculates the relative probability of Venezuelans in the fundamental and high school level of schooling, with the schooling age of 6 to 14 years and 15 to 17 years, respectively. V enezuelansenrolled /V enezuelansSchoolAge Re = (1) Braziliansenrolled /BraziliansSchoolAge To measure integration in the formal labor market, this paper calculates the relative probability of Venezuelans, aged between 15 and 64 years old,17 employed in the formal labor market compared to Brazilian cohort. V enezuelansEmployed /V enezuelansW orkingAge Rf = (2) BraziliansEmployed /BraziliansW orkingAge To measure integration in the social protection program, this paper calculates the relative probability of Venezuelans registering in Cadastro Unico 18 compared to Brazilian cohort and the relative probability of registered Venezuelans to be Bolsa Familia (PBF)19 beneficiaries compared to their Brazilian counterpart. V enezuelansCadastroU nico /V enezuelansP opulation Rc = (3) BraziliansCadastroU nico /BraziliansP opulation V enezuelansP BF /V enezuelansCadastroU nico Rp = (4) BraziliansP BF /BraziliansCadastroU nico This relative probability index has an easy interpretation. A Ri of 0.5 means that Venezuelans are half as likely as Brazilian to be found in sector i. Abramitzky et al. (2020), Carneiro et al. (2020) and Fryer and Levitt (2004) point out that the relative probability index is sensitive to outliers and advocates the use of F-Index , which is a monotonic transformation of the relative probability index. The F Index is measured by the following expression: Ri Fi = 100 ⇤ (5) 1 + Ri where i can be e, f , c and p. This paper reports the F-Index in the main body of the paper. The relative probability index is reported in the appendix. The F Index runs from 0 to 100, with higher number signalling more integration. A F-index of 0 means that Venezuelans are not present at all, while a F-index of 50 means that Venezuelans are as likely to be present as Brazilians and F-index of more than 50 means that Venezuelans are more likely to be present than Brazilians. Although there is no upper bound of relative probability, there is an upper limit of the F-index. 16 Art.4,l Law n. 9394/96. 17 Working-age population. 18 The Unified Registry of Social Programs (Cadastro Unico ) is a database that collects details about low-income families in Brazil, which can be used to identify vulnerable people in the society and develop appropriate benefits for them. 19 The flagship conditional cash transfer program for the poor in Brazil. 11 3.2 Estimating the Relationship between Integration and the Brazilian Environment This paper is conceptually interested in investigating what are some of the drivers and barriers to integration. So depending on the availability of data, this paper estimates slightly di↵erent models for each sector of the economy. 3.2.1 Education Education in Brazil is divided into three levels – basic (educacao infantil), fundamental (Ensino fundamental) and high school (Ensino medio). Education is mandatory for those between the ages of 6 and 17 years, which covers fundamental and high school. Although, Brazil has an gross enrollment rate of in the primary and the secondary school was more than 100% and adult literacy rate was about 93% in 2018, recent World Bank (2019b) report suggests that learning is a major problem with 48% of 10 year olds in Brazil unable to read and understand a simple text although this learning poverty has been declining over last decade. According to its PISA (Program for International Student Assessment) results, Brazil’s performance was grim with approximately 43% of the students appearing to be below level two compared to only 13.4% of the students with similar performance in OECD countries. To explore access to education, this paper first explores the kind of schools that Venezuelans enroll into or are present and then estimate how school characteristics like teacher student ratio, class-sizes, teacher qualifications and grade demotion, along with the size of the Venezuelan refugees and migrants in the municipality a↵ect integration, measured by the F-Index. We focus on regular schooling from basic education to grade 12. The below school selection model is estimated as a linear probability model (Ordinary Least- squares regression) and then as a Probit model20 to show the robustness of results. Z X Vijz = 1 Sijz + 2 Xjz + yz + µijz (6) z where Vijz is a dummy taking the value 1 if school i in municipality j and province z has at least one Venezuelan student enrolled in the school and zero otherwise. Sijz is a vector of school characteristics involving average class size, excluding Venezuelans, teacher-student ratio, proportion of teachers with undergraduate (college) degree, proportion of teachers with MA degree, gender ratio, proportion of white students, average age, type of school (dummy taking the value of 1 if public school and 0 otherwise), total number of Brazilians in the school, proportions of Brazilian who attend classes, lower than his age equivalents, school amenities and access to public services. School amenities include a dummy which takes a value of 1 if the school has simultaneous access to internet, science labs and computer labs and 0 otherwise, while the access to public services is measured as a dummy taking the value of 1 if the school has simultaneous access to electricity, water, sanitary and garbage collection. Xjz includes municipality level characteristics including log of natural number of the total number of Venezuelans living in the municipality,21 while yz includes indicator variable representing the province or state level fixed e↵ects. 20 This estimated Probit model will be used in calculating the inverse Mills ratio for estimating the integration model. This correction term controls for the fact that selection bias of schools which receive Venezuelans. 21 It should be mentioned that the population data on Venezuelans are not updated regularly. 12 To estimate the association between school and municipal characteristics and extent of integration, the high number of schools (97%) with zero Venezuelan enrollment becomes a problem. Cameron and Trivedi (2010) points out that ordinary least squares regressions will not yield consistent estimates because the censored sample is not representative of the population and suggests the use of Heckman (1979) model as one of the ways to control for the bias. This paper first estimates the relationship between the above covariates and the F-Index in an ordinary least-square linear model (OLS) and then in a two-stage Heckman selection model ((Cameron and Trivedi, 2010) and (Heckman, 1979)). This paper estimates the two-stage Heckman selection model both with and without using any exclusion restriction. Without exclusion restriction, the model identification is based solely on the non-linearity of the functional form. This paper uses the proportion of Brazilians who attend classes lower than their age equivalents as the exclusion restriction and re-estimate the Heckman selection model using the exclusion restriction. An exclusion restriction is a variable or variables that explains variation in the selection variable but does not a↵ect the outcome variable directly. This paper argues that if Venezuelans see that many Brazilians are overaged in a school, they might not be too willing to enroll but once Venezuelans are present in the school, they care about the Venezuelans’ performance and not the Brazilians.22 The model below shows the second stage regression estimated in the Heckman model and for the OLS model, we include the same variables except the s (Inverse Mills ratio). XZ Fe,ijz = ↵1 Sijz + ↵2 Xjz + ↵3 ijz + yz + ⌫ijz (7) z where Fe,ijz is the F-index of integration and all the other covariates are as described before except that Sijz now includes a dummy if the Venezuelan on average are more likely to be mismatched to grade than the Brazilians at the school and ijz is the inverse mills ratio or Heckman correction term (Heckman, 1979), which controls for the fact that not all schools has Venezuelan students. The inverse mills ratio is estimated using the Probit model estimation of our selection model previously discussed.23 3.2.2 Formal Labor Market Like most other Latin American countries, Brazil’s labor market is segmented into informal and formal sectors, with about 40% of the country’s employed workforce working in the informal sector, that is in a job unregulated by the government. This paper concentrates only on the formal labor market as data on the informal sector is not available. Although in most countries, refugees’s access to the labor market is limited to the informal sector (Clemens et al., 2018), in Brazil, refugees also have access to the formal labor market, which in theory should allow Venezuelans to be more integrated and contribute positively to the economy. However, 12% of the total employment in Brazil is public sector employment (OECD, 2017), which bars foreigners from participating. For the formal labor market, this study asks three questions: 1) What are the characteristics of the firms that hire Venezuelans? 2) How do firm characteristics and municipality level Venezuelan refugees and migrants a↵ect integration of Venezuelans in the formal labor market? 3) Is there a 22 This paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian over age a↵ecting the selection model but not the extent of integration in the second stage. 23 This paper shows the results of these models using 2020 education census in the main body. It also re-estimates the model using fixed e↵ect models and the 2019 education census. The results are given in the Appendix (7.2). 13 wage gap between Venezuelans and Brazilians and how much of it can it be explained by individual, firm and municipal level characteristics? To answer the first question, the following firm selection model is estimated as a linear probability model and as a Probit model.24 Z X Pf,ijz = f 1 F irmijz + f 2 Xjz + yz + µf,ijz (8) z where Pf,ijz is a dummy taking the value 1 if the firm i in municipality j and province z has at least one Venezuelan employed and zero otherwise. F irmijz is vector including dummies for the size and industry (agriculture, manufacturing and service) of the firm, gender ratio, average age, proportion of workers who are white, proportions of workers with high school education, proportion of workers with college degrees, proportion of workers in the firms working with temporary contracts and the proportion of Brazilian workers who work in the firm in an occupation that requires education below their highest education. Xjz is log of natural number of the Venezuelan refugees and migrants living in municipality j and province z and yz is the province fixed e↵ects. To answer the second question on how firm characteristics a↵ect integration at the firm level, the high number of firms (80%) with no Venezuelan employees may lead to selection bias as a result, we estimate the model using both ordinary least squares regression and the two-stage Heckman selection model (Heckman, 1979). This paper estimates the two-stage Heckman selection model with and without using any exclusion restriction. Without restriction, the model is identification by the non-linearity of the functional form. It then uses proportion of Brazilian who work in the firm in a position that requires education level below the individual’s education level as the exclusion restriction and re-estimate the Heckman selection model using the exclusion restriction. This paper argues that if Venezuelans see that many Brazilians are downgraded in the firm, they might not be too willing to work there but once Venezuelans are present in the firm, they care about the relative Venezuelans’ performance and not directly the Brazilians’.25 The model below shows the second stage estimated in the Heckman model and for the OLS model, we include the same variables except the s (Inverse Mills ratio). Z X Ff,ijz = ↵f 1 F irmijz + ↵f 2 Xjz + ↵f 3 ijz + yz + ⌫ijz (9) z where Ff,ijz is the F-index of integration at the firm and all the other covariates are as described before except ijz , which is the inverse mills ratio, which is estimated using the first stage selection model of the Heckman model and F irmijz includes a dummy which is 1 if the Venezuelans in the firm are more likely to be downgraded than Brazilians . To answer the third question, a Mincer wage regression is estimated using both ordinary least square estimation and a two-stage Heckman selection model. The selection problem in the Mincer regression arises from the fact that we only observe the wages of workers, who are in wage employment as of December 2019, consequently in the RAIS dataset, this paper only observes wages of about 80% of the workers in the formal sector. In line with previous estimation strategies, this paper estimates the Heckman model with and without using exclusion restrictions. The exclusion restriction that 24 The Probit model is used as a robustness check. 25 The paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian downgraded a↵ecting the selection model but not the extent of integration. 14 this paper uses is the number of non-Venezuelan migrants working at the firm. Although more non-Venezuelan migrants working in the firm may help Venezuelans to find a job or wage employment at the firm, it is unlikely that it will lead them to be more productive and earning a higher wage, especially in an environment, where the o cial language is di↵erent from the native language spoken by Venezuelans. The model below shows the second stage estimated in the Heckman model and for the OLS model, the same variables are included except the s (Inverse Mills ratio). Z X lnwijz = 1 Mijz + 2 Xjz + 3V enezuelanijz + 4 ijz + yz + ⌫ijz (10) z where wijz refers to individual’s hourly wage and Mijz is individual, i0 s socio-demographic character- istics like age, gender, education, occupation dummies, contract types, and the firm’s characteristics like size and industry. V enezuelanijz is an indicator variable which takes the value of 1 if the individual is a Venezuelan by birth and 0 for Brazilians. Thus, 3 captures the wage gap between the Venezuelan and the Brazilian. To identify the underlying causes of the wage gap, an Oaxaca-Blinder decomposition is performed at the mean (Oaxaca and Ransom, 1994). Specifically, the above equation is estimated separately for Venezuelan and Brazilian and then D is calculated, which is the di↵erence in the expected value of Venezuelan and Brazilian wages obtained by estimating the wage equation separately. D can then be decomposed into two parts - one that reflects the di↵erences attributed to the observed characteristics (E) and the other reflects di↵erences in coe cients (C). So, it can be expressed as: D = lnwV + lnwB (11) D = (CharV CharB ) ⇤ + CharB ( B ⇤) + CharV ( V ⇤) (12) where the over-line represents the expected value of the variables, V represents Venezuelans, B represents Brazilians and the s are the estimated coe cients, while ⇤ represents the weighted average of the other coe cients. The first term in the above equation is the explained component of the wage gap, that is E, while the second term is the unexplained component, the C, which is the di↵erence in the return to the observed characteristics of Venezuelans and Brazilians, evaluated at the mean of the characteristics. 3.2.3 Social Protection Programs Providing social security to all irrespective of legal status is a constitutional obligation in Brazil. Social security in Brazil is organized in three main blocks, social welfare and pensions, which is contributory and social assistance and health, which is non-contributory. This paper focuses on social assistance programs that are administered and implemented jointly by the federal, state and municipal governments. The two largest social assistance cash transfer programs are Bolsa Familia and the Continuous Benefit Provision (BPC). Social assistance programs are designed to target the vulnerable at each part of their life cycles. For example, Bolsa Familia (PBF), which is a conditional cash transfer for households living in poverty and extreme poverty, targets not only families but also pregnant mothers, children and adolescents. BPC, Continuous Benefit Provision tar- gets the elderly (those above 65 years) and the people with disabilities who cannot support themselves. ´ The Single Registry of Social Programs (CadUnico) is a database that collects details about 15 low-income families, which can be used to identify vulnerable people in the society and develop ´ appropriate benefits for them. CadUnico aims to include families with monthly income of up to half a minimum wage per person, or total monthly income of up to three minimum wages. Families with an income above half a minimum wage can also be registered, as long as their inclusion is linked to receipts of social programs, implemented by municipalities, states or the federal government. ´ CadUnico enrollment records show a significant increase in the total number of Venezuelans enrolled in the register, jumping from 1,969 in January 2018 to 74,185 in July 2020 and 77,291 in September 2020. Due to unavailability of recent census data and PNAD surveys not including nationality, there is not enough information at present on the Venezuelans to explore the determinants of registering, thus, this paper concentrates on the determinants of receiving treatment under Bolsa Familia, once ´ registered in CadUnico . Bolsa Familia is one of the social assistance programs that uses the Cadastro Unico to identify the low-income families and assess their socioeconomic conditions. Bolsa Familia is a conditional cash transfer for households living in poverty and extreme poverty. In July 2020, 13.5 million families, reaching 44.5 million people - approximately 21% of Brazil’s total population - benefited from Bolsa Fam´ ılia. The total amount disbursed is approximately R$ 30.6 billion (USD$ 6.1 billion) per year, equivalent to 0.45% of the national GDP. Bolsa Familia benefits are given depending on the family’s composition and per capita income. The basic benefit is equal to R$ 89.0 (USD$ 18.0) and is paid only to extremely poor families, whose income per capita does not exceed 89.0 reais per month. Variable benefits are R$ 41.0 (USD$ 8.2) and are available for families with children between 0 and 6 months of age, children under 15, pregnant women and nursing mothers, up to a maximum of 5 beneficiaries per family. The variable youth benefit has a cash transfer of 48 reais (USD$ 9.2), and is intended for families with adolescents aged between 16 and 17 years, and has a maximum limit of 2 beneficiaries per family. Both variable benefits and variable youth benefits depend on compliance with conditions related to minimum school attendance, vaccination, in the case of children of vaccination age, and the use of health services for pregnant women. It also comprises of the Benefit of Overcoming Extreme Poverty (BSP), which is a variable amount paid to those families under Bolsa Fam´ ılia, who even after receiving other types of benefits do not achieve per capita monthly family income of R$ 89.0. The average monthly payment of Bolsa Fam´ ılia is equivalent to approximately R$ 170.0 (USD$ 34.0) per family, as of March 2020. This paper is interested in analyzing whether there is a gap in the coverage rate of PBF between Venezuelans and Brazilians, who are registered in CadUnico. We then analyze the underlying causes of the gap using the Blinder-Oaxaca decomposition technique for non-linear regression models, developed in Oaxaca and Ransom (1994). The below model is estimated using both OLS and Probit model. XZ Iijz = ↵p1 Mijz + ↵p2 Xjz + ↵p3 V enezuelanijz + yz + µp,ijz (13) z where Iijz is an indicator variable taking the value of 1 if household i living in municipality j and province z is PBF beneficiary and 0 otherwise. Mijz is a vector including household characteristics like income per capita, household size, dummy equal to 1 if family has children between 0 and 6 years old, 7-15 years old and 15-17 year old respectively, dummy for access to public services like 16 electricity, water and etc., education of household head, sex of household head, age of household head and employment status of household head. Xjz includes municipal level characteristics, in- cluding log of natural number of the total number of Venezuelans living in the municipality. The coe cient, ↵p3 , identifies the coverage gap between Venezuelan and Brazilian. This gap is then di- vided into the explained and the unexplained component as discussed in (Oaxaca and Ransom, 1994). 4 Data 4.1 Data Sources The data for this analysis comes from five sources. The education data comes from the 2019 and 2020 School Census; the labor market data comes from the 2019 Annual Report on Social Information (RAIS); the social assistance data comes from the Cadastro Unico ; and the population data comes from National Migration Registry System (SISMIGRA) and International Tra c System (STI-MAR) for Venezuelans and from Brazilian Institute of Geography and Statistics Foundation’s (IBGE) population estimation counts for Brazilians (Summary of Social Indicators). The School Census on basic education is carried out annually by INEP (Anisio Teixeira National Institute for Educational Research and Studies). It collects information on early childhood education, elementary education, high school education and professional education, irrespective of whether the organization is public or private. It contains information on school amenities, infrastructures and management, as well as, detailed information on students and teachers. Information on the teachers include their level of training, teaching activities, places of origin along with sex, gender and race, while the information on the students include demographics data along with places of origin. One caveat in this data is that it does not include any data on student’s socio-economic or family background. In 2019, it included data on about 176,000 schools, 2.3 million teachers and 50 million students, with 20,272 (0.05% of all students) Venezuelan students in regular traditional school, all over Brazil. In 2020, Brazilian students in regular school increased to 37,738. The RAIS dataset is an administrative data managed by the Ministry of Economy. It covers all formally employed wage earners, either public or private, and is collected annually, including data on demographics, income, occupation, nationalities, new hires and terminations during the year. In 2019, it contains information about 28,910 Venezuelans with about 19,746 employed in the formal sector as of December 31, 2019. Cadastro Unico is a database that collects details about low-income families and is used to identify vulnerable people in the society to develop appropriate benefits for them. Apart from income, it contains information on beneficiary status of Bolsa Familia program, living conditions, demographics, education and labor market outcomes. This paper uses Cadastro Unico of December 2017, December 2018, December 2019 and July 2020 for our analysis. On average, Cadastro Unico includes information on about 78 million people (28 million households) and as of September 2020, there were about 77,291 Venezuelans (30,500 households) registered in it. The SISMIGRA is an administrative record, maintained by the Federal Police, of migrants, who applied for residence permits and contains information on age, sex, country of birth and municipality 17 of residence. The STI-MAR contains the same information on those who have requested asylum in Brazil and is also maintained by the Federal Police. IBGE publishes population estimates of Brazil by municipality every year. It should be noted here that the population estimates of Venezuelan from the two sources may be an overestimate due to double counting and lack of updating of STI-MAR. 4.2 Summary Statistics 4.2.1 Education The Venezuelan refugees and migrants, 20% of whom are below 20 years and with majority located in Roraima, have the potential to strain the education system. However, according to the education census of 2020, only about 37,738 or 45% of the Venezuelan school age children have been enrolled in school. The gross enrollment rate among the 0-5 years old cohort is about 18% while it is about 74% in the 6-14 years old cohort and only 40% in the 15-19 years old cohort. The Brazilians enrollment rate is much higher across all age groups. Figure 4a reveals that gross enrollment rate in the fundamental level (grade 1 to grade 9) is only about 74% among Venezuelans, compared to 100% among Brazilians, while in high school, the enrollment rate is about 40% among Venezuelans, compared to 80% among Brazilians. The drop-out rate between fundamental and high school seems to be the higher among Venezuelans than the Brazilians.26 Among Brazilians enrolled in school, 33% are white, while only 15% of the Venezuelans enrolled in school are white. Among the white Venezuelans in school, 65% are in fundamental level and 11% are in high school, while among the non-white Venezuelans, 70% are in fundamental level and 11% are in high school. Given that about half of the Brazilian population is white, the relatively low presence of Brazilians white in the regular schooling needs to be explored further. Although race data on all Venezuelan refugees and migrants are not available, data for 2019 suggests that about 25% of the Venezuelans refugees and migrants are white, suggesting that white-nonwhite schooling disparity exists for both Venezuelan refugees and migrants and Brazilians. Figure 4b shows that average Venezuelans tend to be older than Brazilians in lower grades, from grade 1 to grade 5, suggesting that Venezuelans are more likely to be mismatched to class. Di culty in evaluating Venezuelan students’ prior knowledge and language barrier may lead to Venezuelans being enrolled into the lowest grade possible, which may not only demotivate Venezuelan students from learning but also add to additional cost to the government. Some cities in Brazil are already responding to this mismanagement. For example, in Manaus, some teachers and school sta↵ have received Spanish-language training, and in the city of Pacaraima, schools have devel- oped Portuguese language classes focused on the needs of Venezuelan students (Selee & Bolter, 2020). 26 Since there is no data on those, who are not dropping out, this paper cannot investigate the reasons behind the high dropout rate but better outside opportunities, lower returns to higher education, lacking the knowledge of the o cial language, along with the tradition of boys being the breadwinner, are most likely to contribute to the phenomenon. 18 20 1 .8 15 .6 10 .4 5 .2 0 1 2 3 4 5 6 7 8 10 11 12 e e e e e e e e ad ad ad ad ad ad ad ad e e e ad ad ad 0 gr gr gr gr gr gr gr gr gr gr gr Venezuela Brazilian Fundamental High School Brazilians Venezuelans (a) enrollment rate by school level (b) Average Age by Grade Figure 4: Individual level characteristics Table 2: Summary statistics of enrolled students in schools by nationality All Brazil RR & AM (1) (2) (3) (4) (5) (6) Venezuelans Brazilians Di↵erence (1)-(2) Venezuelans Brazilians Di↵erence (4)-(5) Total Students 37,738 42,930,024 22,481 1,194,065 Female 0.481 0.49 -0.009⇤ 0.481 0.487 -0.006 (.5) (.5) (.004) (.5) (.5) (.009) Age 10.219 10.711 -0.492⇤⇤ 10.511 11.498 -0.987 (4.06) (4.78) (.195) (3.849) (4.915) (.279) White 0.153 0.335 -0.182⇤⇤⇤ 0.091 0.085 0.006 (.36) (.472) (.049) (.288) (.279) (.005) ⇤⇤⇤ Overage 0.681 0.535 0.146 0.727 0.651 0.077 (.466) (.499) (.03) (.445) (.477) (.038) School Characteristics Public 0.373 0.313 0.059 ⇤⇤⇤ 0.379 0.403 -0.024 (.484) (.464) (.019) (.485) (.49) (.026) ⇤⇤ Classsize 26.145 24.696 1.449 26.966 26.307 0.66 (6.056) (7.571) (.694) (5.859) (8.781) (.955) Teacher - Student Ratio 0.915 0.954 -0.039 0.854 0.873 -0.019 (.515) (.684) (.056) (.421) (.766) (.098) Public Services 0.488 0.677 -0.189⇤⇤ 0.295 0.129 0.166 (.5) (.468) (.088) (.456) (.335) (.057) Amenities 0.159 0.181 -0.022 0.143 0.177 -0.035 (.366) (.385) (.019) (.35) (.382) (.009) Science Lab 0.203 0.259 -0.056⇤⇤ 0.176 0.22 -0.044 (.402) (.438) (.021) (.381) (.414) (.009) Computer Lab 0.634 0.562 0.072⇤ 0.637 0.535 0.102 (.482) (.496) (.04) (.481) (.499) (.05) Internet 0.946 0.94 0.005 0.923 0.772 0.151 (.227) (.237) (.023) (.266) (.42) (.036) Library 0.608 0.524 0.084 0.638 0.609 0.029 (.488) (.499) (.051) (.481) (.488) (.075) Spanish Proficient Teacher 2.025 0.695 1.33 3.104 1.973 1.131 (8.77) (3.18) (1.119) (11.126) (8.179) (1.623) Portugese Proficient Teacher 38.028 32.969 5.059⇤⇤ 41.901 37.774 4.127⇤ (28.526) (25.752) (1.871) (29.564) (28.158) (.513) ⇤⇤ Teachers with Undergraduate Degrees 93.931 88.641 5.291 94.028 87.307 6.721 (12.581) (20.027) (2.467) (12.302) (25.001) (2.207) Teachers with MA 3.324 3.102 0.222 3.096 2.374 0.723 (5.64) (6.822) (.325) (5.402) (6.951) (.397) Total enrollment 638.219 553.232 84.988 ⇤⇤⇤ 672.15 706.492 -34.342 (407.149) (436.999) (24.8) (394.792) (725.409) (16.813) ⇤⇤ Ln(Concentration) -4.805 -8.983 19 4.178 -2.418 -4.632 2.215 (3.306) (1.183) (1.612) (1.944) (1.319) (.995) ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All refers to all of Brazil, RR stands for Roraima and AM stands for Amazonas. The di↵erence in column (3) refers to the di↵erence between column (1) and column (2), while the di↵erence in column (6) refers to the di↵erence between column (4) and column (5) . The sample is restricted to only regular schools. Overaged refers to students who are going to a lower grade compared to his or her age cohort Table 2 shows summary statics of Brazilian and Venezuelan children enrolled in school up to grade 12. The di↵erences in averages are posted in columns (3) and (6) and the standards errors are clustered at the province level. Out of the 38,000 Venezuelan children attending regular schools, about 22,000 are in Roraima and Amazonas. In line with Venezuelan refugees and migrants population having a balanced gender distribution, about 48.5% of Venezuelans enrolled are female compared to 49% of Brazilians enrolled. Venezuelans across Brazil, on average, attend larger class-sizes, but both Venezuelans and Brazilians attend same class sizes in Roraima and Amazonas. Di↵erences in teacher student ratios seem to be not significant between the two nationalities. About 31% of Brazilians, compared to 37% of Venezuelans attend public schools . Among the private schools, most Venezuelans attend community or philanthropic schools. Table 20 shows that among public schools, 3% of Federal schools, 25% of Provincial schools and 12% of Municipal schools have Venezuelan students and on average federal and municipal schools have better trained teachers, while the private schools have better access to amenities. More Venezuelans than Brazilians attend schools that have simultaneous access to public services like water and sewage and access to internet services in Roraima but overall Brazilians seem to attend schools with better access to public services. These schools are more likely to have teachers who have either an undergraduate or a MA degree. Only about 3% of the teacher in Roraima and Amazonas, at schools with Venezuelan children, are proficient in Spanish language, which might be a major deterrent for Venezuelan children, who only speak Spanish and understand little or no Portuguese. While Venezuelans attend schools with higher enrollment than Brazilians on average across Brazil, they attend schools with lower enrollment in Roraima and Amazonas. This partly may reflect the fact that on average schools in Roraima and Amazonas are bigger than the average school in Brazil. Another interesting finding is that higher proportion of Venezuelans (68%) are attending classes that are below the grades consistent with their age than the Brazilian cohort (53%). This phenomenon may discourage students from attending schools, that may lead to inappropriate behavior like bullying, which will exaggerate the challenges that they already face in integrating. However, the average age of Venezuelans is lower since more Venezuelans are enrolled in grades below 4. About 20% of Venezuelans enrolled in school are enrolled in grade 1. Figure 16 shows that the grade distribution of Venezuelans are skewed to the left compared to the grade distribution of Brazilians. Overall, descriptive statistics reveal that being demoted to lower class and shortages in having Spanish speaking teachers are major obstacles for Venezuelans to access education and Brazilian government and the international organizations are in the right direction to train teachers and school sta↵s Spanish language (Selee & Bolter, 2020) and o↵er Portuguese language courses to Venezuelan students. At the same time, resources needs to be diverted to increase capacity of schools. Another factor that is not observed in the data is the knowledge of Venezuelans about their rights and low attendance of Venezuelan in school may reflect the lack of knowledge about the education system in Brazil and constraints in accessing equivalence certificates. So, facilitation of credentials certification and provision of information on how to enroll and which documents are needed to enroll may promote more Venezuelans to access education. 4.2.2 Labor Market As of December 2019, about 29,000 Venezuelans are in RAIS, with about 19,000 employed, compared to 47 million Brazilians.Table 3 shows the summary statistics of the characteristics of Venezuelans and Brazilians, who are employed in the formal sector and represented in the RAIS dataset. The di↵erences in averages are posted in columns (3) and (6) and the standard errors are 20 150000 0.96 1 0.95 0.90 126049 125761 122366 122394 125084 121746 123858 119036 .8 Fraction Downgraded 100000 0.69 Mean Wage .4 .6 50,000 .2 0.00 0.00 0.00 0.00 0 0 Primary Fundamental High School College Primary Fundamental High School College Brazilian Venezuelan Brazilian Venezuelan (a) Mean Monthly Wage Rate (b) Proportion Downgraded Figure 5: Performances in the Formal Labor Market by Nationality clustered at the province level. Out of the 19,000 Venezuelans in the formal sector, about 5,609 are in Roraima and Amazonas. Summary statistics reveal that an average employed Venezuelan earns about 3.4 percentage points more in monthly wage than an average Brazilian but the di↵erence is statistically insignificant. In Roraima and Amazonas, the raw wage penalty is about 0.5 percentage points but is again statistically insignificant. Figure 5a shows that there is a wage premium for Venezuelans across the di↵erent education level. According to Table 3, Venezuelan formal workers are younger, less likely to be female and white and more likely to have completed high school than their Brazilian peers. They are more likely to worker longer hours a week than the Brazilian formal workers. They are also more likely to be occupationally downgraded than Brazilians. While 72% of the Brazilian report to work in an occupation, where the education requirement is lower than their acquired highest education, 86% of the Venezuelans report so, suggesting that occupation downgrading is more prevalent among Venezuelans. Venezuelans work mainly as industrial workers, especially as machine and vehicle operators and as workers in hotel industry, personal services, hygiene and security services. Figure 5b shows that incidence of being occupationally downgraded at work is higher for high school and college educated Venezuelans than for high school and college educated Brazilians. On average, employed Venezuelans seem to have been in Brazil for about 1.5 years and work in firms that have higher number of non-Venezuelan migrants. 21 Table 3: Summary statistics of those employed in the formal sector by nationality All Brazil RR & AM (1) (2) (3) (4) (5) (6) Venezuelans Brazilians Di↵erence (1)-(2) Venezuelans Brazilians Di↵erence (4)-(5) Total Employed 19,746 47,365,435 5,609 694,801 Ln(Wage) 11.714 11.681 .034 11.657 11.663 -.005 (.249) (.195) (.023) (.176) (.236) (.021) Hours Worked 42.504 40.526 1.978⇤⇤⇤ 42.535 40.213 2.322⇤⇤ (5.498) (7.818) (.15) (5.168) (7.738) (.043) Female .302 .512 -.21⇤⇤⇤ .239 .472 -.233⇤⇤ (.459) (.5) (.023) (.427) (.499) (.005) White .259 .349 -.09 .098 .094 .005 (.438) (.477) (.056) (.298) (.292) (.01) Age 31.447 34.748 -3.301⇤⇤⇤ 31.225 34.596 -3.372 (9.102) (12.007) (.336) (8.637) (11.385) (.906) Fundamental .095 .152 -.057⇤⇤⇤ .084 .113 -.029 (.293) (.359) (.007) (.278) (.316) (.006) High School .73 .683 .047⇤ .798 .756 .042 (.444) (.465) (.026) (.401) (.43) (.015) College .134 .105 .029 .086 .1 -.014 (.341) (.307) (.019) (.28) (.3) (.037) Scientist .009 .037 -.028⇤⇤⇤ .008 .047 -.039⇤ (.095) (.189) (.003) (.087) (.212) (.005) Admin .034 .07 -.036⇤⇤⇤ .035 .072 -.037⇤ (.181) (.255) (.004) (.184) (.258) (.004) Commerce .116 .242 -.126⇤⇤⇤ .124 .256 -.133 (.32) (.428) (.011) (.329) (.437) (.054) Personal .455 .387 .068⇤ .526 .413 .113 (.498) (.487) (.036) (.499) (.492) (.021) Agriculture .028 .047 -.018⇤⇤ .036 .013 .023 (.166) (.211) (.008) (.185) (.113) (.018) Industry .354 .196 .158⇤⇤⇤ .268 .177 .09⇤ (.478) (.397) (.046) (.443) (.382) (.01) firm>10 .007 .013 -.007⇤⇤⇤ .005 .002 .003 (.083) (.115) (.002) (.07) (.048) (.003) Downgraded .857 .721 .136⇤⇤⇤ .857 .755 .102 (.351) (.449) (.012) (.35) (.43) (.025) Temporary .006 .005 .001 .002 .007 -.005⇤⇤ (.076) (.068) (.002) (.043) (.084) (0) Tenure (Months) 66.323 403.108 -336.785⇤⇤⇤ 90.48 427.69 -337.21⇤ (67.515) (588.971) (22.347) (77.059) (621.83) (37.265) Total Non-Venezuelan Migrants 5.354 3.135 2.218⇤⇤ 3.580 3.507 0.726⇤⇤⇤ (6.985) (4.061) (0.967) (4.841) (3.803) (0.052) ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All refers to all of Brazil, RR stands for Roraima and AM stands for Amazonas. The di↵erence in column (3) refers to the di↵erence between column (1) and column (2), while the di↵erence in column (6) refers to the di↵erence between column (4) and column (5) . The sample is restricted to those employed on 31st De- cember 2019. Downgraded refers to the proportion of employed who are working at an occupation, where education requirement is lower than the individual’s. Overall, it seems that Venezuelans work longer hours and in more contact-based jobs than Brazilians even though they are on average better educated. They are more likely to be downgraded, suggesting that just like the education sector, facilitation of credential verification and o↵ering Portuguese language training may encourage more Venezuelans to enter the formal labor market as will specialized counsellors who are proficient in Spanish language and knowledgeable about Venezuelan community. 22 4.2.3 Social Protection Table 4 shows the summary statistics of those enrolled in Cadastro Unico ´ in December 2019 by their nationality. The di↵erences in household characteristics between Venezuelans and Brazilians are posted in column (3) and (6) and the standards errors are clustered at the province level and seems to be stark and statistically significant. Compared to 23 million Brazilian, about 18,000 ´ Venezuelans are registered in the single registry. Venezuelans registering in Cadastro Unico are on average poorer than their Brazilian peers. Average income of registered Venezuelan is R$85, while that of Brazilian is R$307. 72.3% of Venezuelan live in extreme poverty, with an income less than R$89 while 48% of Brazilians registered have an income less than R$89 (Figure 6a). Venezuelans registered are also more educated with 27% having some tertiary education compared to 3% of Brazilians registered having tertiary education. Figure 6b shows that 20% of Brazilians in Cadastro ´ Unico have high school degrees compared to 42% of Venezuelans in Cadastro Unico ´ having high school degrees. Except in Roraima and Amazonas, registered Venezuelans’ family sizes (2.8) are also slightly bigger than the registered Brazilian family size (2.7). According to Table 4, Venezuelans seem to have more younger children in the age cohort 0 to 5 years than Brazilians. Registered Venezuelan households are also less likely to have female heads and average age of heads are lower than the registered Brazilian households. Conditions of living seems to be almost similar for both Brazilians and Venezuelans, with about 54% reporting that they have simultaneous access to water supply, garbage collection, adequate sanitation and electricity. However, Venezuelan heads of the households are more likely to be employed and be self-employed than Brazilian heads, although the months worked is substantially lower. Overall, the summary statistics reveal that Venezuelans who register for social assistance are poverty-stricken and work in low quality jobs, although they are more edu- cated and the overall results seem to be consistent all across Brazil including Roraima and Amazonas. Brazilian Venezuelan .4 0.28% 3.06% 1.66% 10.45% 17.74% .3 15.32% 48.52% .2 20.91% 72.28% 9.78% .1 0 =R$89 & <178 Brazilian Venezuelan >=R$178 & =R$522 & =R$1045 Fundamental High School College Graphs by nationality (a) Household Income (b) Education Level ´ Figure 6: Fraction of those registered in Cadastro Unico by income brackets, education and nationality 23 ´ Table 4: Summary statistics of households registered in Cadastro Unico All Brazil RR & AM (1) (2) (3) (4) (5) (6) Venezuelans Brazilians Di↵erence (1)-(2) Venezuelans Brazilians Di↵erence (4)-(5) Income per Capita 85.281 307.036 -221.755⇤⇤⇤ 85.745 244.888 -159.143⇤⇤ (143.137) (417.804) (20.503) (119.18) (381.645) (11.516) Extreme Poverty .722 .485 .237⇤⇤⇤ .699 .565 .134⇤ (.448) (.5) (.039) (.459) (.496) (.016) Poverty excluding Extreme Poor .106 .209 -.104⇤⇤⇤ .108 .149 -.041 (.307) (.407) (.013) (.311) (.356) (.007) Infrastructure .548 .542 .006 .501 .354 .147 (.498) (.498) (.073) (.5) (.478) (.069) Tertiary .27 .035 .235⇤⇤⇤ .232 .029 .203⇤ (.444) (.183) (.029) (.422) (.167) (.018) Secondary .637 .427 .211⇤⇤⇤ .651 .472 .18⇤⇤ (.481) (.495) (.016) (.477) (.499) (.007) Children - 0=R$89 & <178 >=R$178 & 10 -0.0471 0.0367 -0.0585 -0.194 0.134 -0.255 (0.0493) (0.270) (0.0501) (0.334) (0.743) (0.366) Brazilian Downgraded -0.0397 -0.0340 -0.0389 -0.322⇤⇤ -0.114⇤⇤⇤ -0.373⇤⇤ (0.0208) (0.00802) (0.0225) (0.125) (0.0211) (0.142) Non-Venezuelan Migrants 0.729⇤⇤ 0.393 0.780⇤ 2.598⇤⇤ 1.303 2.762⇤⇤ (0.253) (0.331) (0.298) (0.846) (0.992) (0.983) Ln(Concentration) 0.0511 0.0262 0.0314 -0.0842⇤⇤⇤ 0.0700⇤⇤⇤ -0.0891⇤⇤⇤ (0.0257) (0.00293) (0.0224) (0.00609) (0.00709) (0.00578) Observations 14955 895 14060 14955 895 14060 R2 0.211 0.095 0.182 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. Columns 1, 2 and 3 are estimated as a linear probability model, while columns 4, 5 and 6 report the marginal e↵ects at the means of the estimated Probit model. Brazilian downgraded refers to the proportion of Brazilians employed who are working at an occupation, where education requirement is lower than the individual’s. Table 9 provides further evidence of occupational downgrading being the major barrier to integration in the labor market. It shows that Venezuelans are less likely than Brazilians to work in firms, which downgrades Venezuelans more than the Brazilians. Column (1) shows that firms, which downgrades Venezuelans more than Brazilians, have a predicted F-index that is 37 points lower. This result is robust even after controlling for the fact that some firms do not have any Venezuelans in their payroll with and without exclusion restrictions. Without exclusion restriction, the F-index is 37 points lower and with proportion of Brazilians downgraded as the exclusion restriction, the F-index is 41 points lower. The results also suggest bigger firms can be associated lower F-index and integration. The table also shows that integration is higher in firms that have higher number of non-Venezuelan migrants, but this result is robust except in Roraima and Amazonas, suggesting that the added advantage of having network and information about possible job opportunities outweigh the negative e↵ect of higher competition due to higher labor supply. However, there is 36 some evidence that once controlled for selection, high number of Venezuelan refugees and migrants in the municipality can have a negative e↵ect on the extent of integration. This result is primarily driven by the situation in Roraima and Amazonas where the negative e↵ect of higher competition due to higher labor supply is obstructing more Venezuelans to integrate in the formal labor market. Table 9: Determinants of Integration in Formal Labor Market (All) (RR&AM) (Rest) (All) (RR&AM) (Rest) (All) (RR&AM) (Rest) OLS OLS OLS Heckman Heckman Heckman Heckman Heckman Heckman Female Proportions -12.14⇤⇤⇤ -6.421 -12.33⇤⇤⇤ 2.145 -49.73 6.481 2.503 -40.22 4.041 (1.829) (3.911) (2.107) (11.01) (14.00) (10.34) (9.332) (27.06) (9.413) White Proportions 9.231⇤ 8.222 10.84⇤ 0.443 34.76 -0.644 0.188 25.03 0.627 (4.312) (5.397) (4.720) (6.606) (14.95) (8.360) (5.957) (27.95) (7.810) Temporary Proportion 10.71⇤ 11.78 8.904⇤ 39.07 -77.04 45.92 39.45⇤ -57.81 43.29 (4.223) (6.998) (4.307) (20.20) (35.55) (23.29) (18.74) (67.20) (21.68) High School 22.37⇤ 2.556 20.97⇤⇤⇤ 17.04 18.85 13.84 15.58⇤ 0.797 22.64⇤⇤⇤ (9.271) (39.17) (5.199) (12.75) (32.03) (7.936) (7.299) (9.508) (5.958) College 0.343 -39.82 6.250 -22.70 32.81 -23.62 -23.28 15.20 -19.69 (7.028) (33.59) (4.739) (22.08) (3.446) (21.60) (19.95) (23.24) (20.07) Agriculture 4.808 0.157 6.449 -5.038 30.25 -6.900 -5.321 22.21 -5.000 (2.528) (1.050) (3.315) (7.490) (10.43) (8.610) (6.412) (25.14) (7.876) Manufacturing 2.493 5.662 1.316 -2.845 22.89⇤⇤ -5.590 -3.044 17.69 -4.361 (1.811) (6.617) (1.116) (5.321) (0.144) (4.319) (4.262) (6.962) (3.902) Construction -4.809⇤⇤⇤ -2.755 -4.658⇤⇤ 3.477 -26.68 6.336 3.632 -21.70 5.277 (1.184) (0.285) (1.417) (6.000) (10.03) (5.744) (5.272) (17.10) (5.310) Trade -2.880⇤ 3.097 -4.786⇤⇤⇤ 0.909 -8.162 0.280 0.881 -6.946 0.338 (1.390) (0.551) (1.162) (2.843) (3.976) (2.989) (2.969) (5.717) (2.884) Firm Size>10 -25.75⇤⇤ -6.132 -27.83⇤⇤⇤ -28.46⇤⇤ 2.331 -29.43⇤⇤⇤ -28.56⇤⇤ -2.017 -28.94⇤⇤⇤ (7.470) (0.680) (7.223) (8.264) (0.971) (7.343) (7.934) (1.156) (7.480) Brazilian Downgraded -3.501 -27.29 10.31⇤ -2.232 -29.07 12.48⇤ (13.66) (39.42) (4.515) (14.60) (39.17) (5.075) Non-Venezuelan Migrants 367.8⇤⇤⇤ 123.7 428.8⇤⇤⇤ 414.3⇤⇤⇤ 2.481 493.3⇤⇤⇤ 416.8⇤⇤⇤ 66.18 479.3⇤⇤⇤ (61.68) (130.5) (55.71) (83.42) (69.02) (61.57) (68.53) (12.58) (57.42) Ln(Concentration) -102.4⇤⇤⇤ -8.579⇤ -102.9⇤⇤⇤ -3.945 -12.27⇤ 6.420 -3.847 -10.88 4.930 (3.836) (0.388) (4.415) (2.589) (0.755) (7.854) (2.133) (2.953) (7.258) Mismatch -36.87⇤⇤⇤ -28.05 -3.290 -37.26⇤⇤⇤ -28.01 -1.581 -41.16⇤⇤⇤ -34.43 0.353 (4.381) (17.44) (3.147) (4.535) (17.23) (3.117) (1.784) (8.210) (3.098) 19.68 10.76 (15.58) (21.63) -31.36 -154.0 (15.30) (161.5) 21.06 24.08 (14.77) (15.03) Observations 2743 475 2268 2743 475 2268 2743 475 2268 R2 0.492 0.519 0.265 0.494 0.523 0.267 0.494 0.508 0.265 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All regressions include province fixed e↵ect. Rest refers to the Brazil provinces outside RR and AM. Mismatch is a dummy if the rate of occupational downgrading at firm is higher for Venezuelans than Brazilians. Brazilians Downgraded is the fraction of Brazilians who are occupationally downgraded would and is used as the exclusion restriction in columns 7 to 9. Columns 4, 5 and 6 are estimated without using exclusion restrictions. All variables (except concentration index) are at the firm level. refers to the Inverse Mill’s ratio and the first stage regressions are shown in the appendix. The first stage regressions used to estimate the inverse mills ratio ( ) for the Heckman model is given the appendix (Table 18). Proportion of Brazilian downgraded at the firm is significant in the first stage although it has no significance in the second stage that estimates its e↵ect on extent of integration (Table 9). The likelihood ratio test has a p-value of 0 suggesting that the first stage model is overall well modelled and that the overall model is significant. However, in the second stage of Table 9 is not significant, suggesting that either selection bias is not a problem or that the exclusion restriction used in the analysis is weak. Even though the Venezuelan refugees and migrants face higher obstacles than Brazilian in 37 accessing the formal labor market, least square estimations suggest that the wage penalty is statistically insignificant after controlling for individual characteristics, occupations and firm size and remains statistically insignificant even after controlling for selection into wage earning employment. For example, once they are in the formal labor market, Venezuelans are more likely to find a waged employment (Table 19) that gives them a lower wage on average than Brazilians (Table 10). This result is in contradiction to results found in other Latin American countries hosting Venezuelans. For example, Olivieri et al. (2020) find a very high significant wage gap between Ecuadorean and Venezuelan, penalizing the Venezuelan refugees and migrants but they did not control for selection and were including both the formal and informal sector workers. Table 10 shows that controlling for the e↵ect of higher education on the likelihood of getting a waged employment, having a college degree leads to higher wage across Brazil other than in Roraima and Amazonas. Column (9) of Table 10 shows that controlling for selection using number of non-Venezuelan migrants working in the firm as exclusion restriction, having a high school education is associated with a 0.02 percentage points higher wage outside Roraima and Amazonas but no significant e↵ect in Roraima and Amazonas, suggesting that congestion in the formal labor market in Roraima and Amazonas may be a problem and the ”interiorization” strategy has a crucial role to play in matching Venezuelan refugees and migrants to the right jobs. The first stage regressions used to estimate the inverse mills ratio ( ) for the Heckman model is given the appendix (Table 19). The exclusion restriction, which is the number of non-Venezuelan migrants in the firm is significant in the first stage although it has no significance in the second stage that estimates its e↵ect on the natural logarithm of wage (Table 10). The likelihood ratio test has a p-value of 0 suggesting that the first stage model is overall well modelled and that the overall model is significant. s in the second stage of Table 10 in columns (4), (6),(7) and (9) are significant, suggesting evidence of selection. However, the results should be interpreted with caution as our exclusion restriction may be theoretically weak and as there are other omitted variables that may a↵ect wage and are controlled for in the estimations. 38 Table 10: Determinants of wage (All) (RR&AM) (Rest) (All) (RR&AM) (Rest) (All) (RR&AM) (Rest) OLS OLS OLS Heckman Heckman Heckman Heckman Heckman Heckman Venezuelans 0.009 -0.020 0.027⇤⇤ -0.059⇤⇤⇤ -0.088 -0.048⇤⇤⇤ -0.044 -0.029 -0.034 (0.014) (0.028) (0.008) (0.015) (0.013) (0.010) (0.022) (0.032) (0.021) Age 0.002⇤⇤⇤ 0.003⇤ 0.002⇤⇤⇤ 0.020⇤⇤⇤ 0.027 0.020⇤⇤⇤ 0.019⇤⇤⇤ 0.023⇤ 0.019⇤⇤⇤ (0.000) (0.000) (0.000) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) White 0.002 -0.002 0.002 0.010 0.006 0.010 0.009 -0.001 0.009 (0.005) (0.002) (0.005) (0.005) (0.003) (0.005) (0.004) (0.001) (0.004) Ln(Hours Worked) 0.309⇤⇤⇤ 0.200⇤ 0.311⇤⇤⇤ 0.298⇤⇤⇤ 0.183⇤ 0.300⇤⇤⇤ 0.299⇤⇤⇤ 0.185⇤ 0.301⇤⇤⇤ (0.014) (0.004) (0.014) (0.013) (0.005) (0.013) (0.013) (0.004) (0.013) Female -0.016⇤⇤⇤ -0.009 -0.016⇤⇤⇤ -0.071⇤⇤⇤ -0.046 -0.071⇤⇤⇤ -0.059⇤⇤⇤ -0.015 -0.059⇤⇤⇤ (0.003) (0.002) (0.003) (0.005) (0.005) (0.005) (0.015) (0.004) (0.014) High School 0.021⇤⇤⇤ 0.018 0.021⇤⇤⇤ 0.023⇤⇤⇤ 0.035 0.023⇤⇤⇤ 0.023⇤⇤⇤ 0.014 0.023⇤⇤⇤ (0.002) (0.009) (0.003) (0.002) (0.012) (0.002) (0.003) (0.006) (0.003) College 0.058⇤⇤⇤ 0.059 0.058⇤⇤⇤ 0.135⇤⇤⇤ 0.173 0.135⇤⇤⇤ 0.113⇤⇤⇤ 0.059 0.113⇤⇤⇤ (0.005) (0.015) (0.005) (0.009) (0.039) (0.009) (0.025) (0.006) (0.025) Temporary 0.046 0.017 0.047 0.042 0.018 0.043 0.042 0.017 0.043 (0.027) (0.003) (0.028) (0.026) (0.002) (0.027) (0.026) (0.003) (0.027) Scientist 0.051⇤⇤⇤ 0.009 0.052⇤⇤⇤ 0.047⇤⇤⇤ 0.004 0.048⇤⇤⇤ 0.046⇤⇤⇤ 0.003 0.047⇤⇤⇤ (0.011) (0.008) (0.011) (0.011) (0.009) (0.011) (0.011) (0.008) (0.011) Administration 0.033⇤ 0.020 0.033⇤ 0.032⇤ 0.016 0.032⇤ 0.030 0.015 0.030 (0.015) (0.006) (0.015) (0.015) (0.005) (0.015) (0.017) (0.005) (0.017) Commerce -0.010 -0.043 -0.009 -0.003 -0.036 -0.002 -0.003 -0.035 -0.002 (0.006) (0.013) (0.006) (0.006) (0.011) (0.006) (0.006) (0.010) (0.007) Personal 0.015 -0.012 0.015⇤ 0.015 -0.015 0.015 0.014 -0.014 0.014 (0.007) (0.012) (0.007) (0.008) (0.013) (0.008) (0.007) (0.012) (0.007) Industrial 0.018 0.042⇤⇤ 0.017 0.016 0.036⇤⇤ 0.016 0.015 0.038⇤ 0.015 (0.009) (0.000) (0.009) (0.009) (0.000) (0.009) (0.009) (0.001) (0.009) Firm>10 -0.029⇤⇤⇤ -0.004 -0.029⇤⇤⇤ -0.030⇤⇤⇤ -0.009 -0.030⇤⇤⇤ -0.029⇤⇤⇤ -0.008 -0.029⇤⇤⇤ (0.006) (0.023) (0.007) (0.007) (0.020) (0.007) (0.007) (0.020) (0.007) Total Non-Venezuelan Migrants -0.000 0.000 -0.000 -0.000 -0.001 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) -0.344⇤⇤⇤ -0.254⇤ (0.037) (0.101) -0.395 -0.039 (0.081) (0.022) ⇤⇤⇤ -0.343 -0.256⇤ (0.038) (0.101) Observations 15832423 238293 15594130 15832423 238293 15594130 15832423 238293 15594130 R2 0.193 0.123 0.194 0.208 0.153 0.209 0.208 0.152 0.209 Standard error in parentheses and are clustered at the province level. ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. All regressions include state fixed e↵ect. Columns (1), (4) and (7) use all data, columns (2), (5), and (8) use data on Roraima (RR) and Amazonas (AM) and columns (3), (6) and (9) show the results from the rest of Brazil. Columns 4, 5 and 6 are estimated without using exclusion restrictions. Columns (7), (8) and (9) use total non-Venezuelan migrants at the firm as the exclusion restriction. Table 23 shows the returns to di↵erent characteristics by nationality controlling for selection and provides further evidence that although higher education leads to higher wage for Brazilian, the same is not true for Venezuelan refugees and migrants. Higher education seems to have no or smaller e↵ect on wage of Venezuelans controlling for selection, providing further evidence that facilitation of credential verification and validation is of utmost important to promote integration of asylum seekers and migrants. Table 11 shows the Oaxaca decomposition and shows that depending on observed characteristics, Venezuelans should be paid even more but some unobserved or unexplained is dampening the wage premium of Venezuelans controlling for selection, suggesting that other variables like language skills and being relocated through the ”interiorization” strategy that are not controlled for in the regressions are important factors in explaining formal labor market performances of Venezuelans. 39 Table 11: Decomposition of the wage gap (Brazil) (RR & AM) (Rest) (Brazil) (RR & AM) (Rest) OLS OLS OLS Heckman Heckman Heckman Di↵erential Brazilian 11.682⇤⇤⇤ 11.663⇤⇤⇤ 11.682⇤⇤⇤ 11.682⇤⇤⇤ 11.663⇤⇤⇤ 11.682⇤⇤⇤ (0.012) (0.001) (0.012) (0.012) (0.001) (0.012) Venezuelan 11.715⇤⇤⇤ 11.658⇤⇤⇤ 11.746⇤⇤⇤ 11.715⇤⇤⇤ 11.658⇤⇤⇤ 11.746⇤⇤⇤ (0.022) (0.019) (0.015) (0.022) (0.019) (0.015) Di↵erence -0.033 0.006 -0.064⇤⇤⇤ -0.033 0.006 -0.064⇤⇤⇤ (0.020) (0.021) (0.012) (0.020) (0.021) (0.012) Decomposition Explained -0.029⇤⇤ -0.017⇤⇤ -0.044⇤⇤⇤ -0.077⇤⇤⇤ -0.023⇤⇤ -0.098⇤⇤⇤ (0.011) (0.007) (0.008) (0.021) (0.008) (0.022) Unexplained -0.004 0.023 -0.020⇤⇤ 0.044⇤ 0.029 0.034 (0.013) (0.028) (0.007) (0.021) (0.028) (0.021) Observations 15832423 238293 15594130 15832423 238293 15594130 Standard error in parentheses and are clustered at the province level. ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001 Oaxaca decomposition conducted controlling for selection. First column uses all data, second column uses data on Roraima (RR) and Amazonas (AM) and the third column shows the results from the rest of Brazil. Total non-Venezuelan migrants are used as the exclusion restriction for the Heckman selection models. Overall, the results suggest that just like the education sector, equivalency of education or occupational downgrading is a major barrier to integration in the formal labor market. Although race plays an important role in the Brazilian labor market (M´ arquez et al., 2007), this paper does not find a statistically significant di↵erential e↵ect of race on Venezuelan asylum seekers and migrants’ performance in the formal labor market. The importance of Portuguese language skills and relocation program in promoting integration and productivity of Venezuelan refugees and migrants are also highlighted in the results, implying that having specialized employment counsellors for the Venezuelans may help them to access the formal labor market more. 5.3 Social Protection 5.3.1 Integration Overall, Venezuelans seem to be less likely to register in Cadastro ’Unico than Brazilians, but if they do register, they are equally likely or more likely to have access to Bolsa Familia programs. However, on average, integration have continuously improved over the last 4 years, as Figure 13, shows, with Venezuelans being 0.7 times as likely to be registered as Brazilians in July 2020, while they were only 0.2 times as likely to be registered in December 2018, with an F-index of 17 in December 2018 and 42 in December 2020. Venezuelans still constitute a small fraction of the ´ vulnerable people registered in CadUnico , with 0.01 Venezuelans registered for each Brazilian in 2018, which increased to 0.10 Venezuelans registered for each Brazilian in 2020. Integration in CadUnico varies across states. Relative probability and the F-index of registering in CadUnico is the highest 40 in Rio Grande do Sul and lowest in Roraima, while the concentration of registered Venezuelans is the highest in Roraima and lowest in Tocantins. Overall, it seems that integration is more in states that have lower number of Venezuelans. Venezuelans registered are also more likely to be living in urban areas than the registered Brazilians. PBF coverage rate seems to be slightly higher for Venezuelans than for Brazilians, although in numbers, there are fewer Venezuelans receiving the program compared to Brazilians. In July 2020, the relative probability index suggests that it is about 1.2 times as likely for Venezuelans registered to receive PBF as Brazilians registered, with an F-Index of 56. The figure below also shows that there has not been any di↵erential access to Bolsa Familia for the Venezuelans except in 2019. Morgandi et al. (2020) reports that approximately 1.5 million families (about 10% of total families benefitted) were in the waiting list in 2019 and this deterioration in program coverage and fall in the value of the benefit opened up discussion on the need of reform of the Bolsa Familia program, which resulted in the introduction of the 13th payment for December 2019. The rest of our analysis focuses on whether controlling for eligibility, Venezuelans still face a di↵erential coverage rate and what factors influences the coverage gap in 2019. 60 80 50 60 40 F−Index F−Index 40 30 20 20 10 0 2017 2018 2019 2020 RO AC AM RR PA AP TO MA PI CE RN PB PE AL SE BA MG ES RJ SP PR SC RS MS MT GO DF Time CadUnico PBF CadUnico PBF (a) Over time (b) Over regions Figure 13: Measure of Integration in Social Protection over Time and Regions 5.3.2 Coverage Gap and Decomposition Table 12 shows the results after estimating equation (13) and clustering the standard errors at the province level. It reveals that the coverage gap in 2019 remains after controlling for income per capita and family composition, which are the two main criteria for eligibility of PBF. Venezuelans are found to be 0.12 to 0.19 percentage points less likely to receive Bolsa Familia than Brazilian, controlling for their income and family characteristics and conditional on them enrolling in Cad’Unico. However, this coe cient should be interpreted with caution since the estimation results do not control for selection in to Cadastro ’Unico and there are likely many omitted variables that the estimations do not control for, like the availability of required documents.Table 13 further shows that only 0.05 of the 0.19 percentage points di↵erence can be explained by endowment di↵erence (explained), the other 0.14 percentage points relate to unobserved characteristics or to di↵erences in returns to the characteristics. One obvious candidate to explain this unexplained gap may be propensity to register in the Single Registry, which cannot be controlled for because of unavailability of data on the 41 population who is not enrolling. Other reason may be lack of knowledge about the social protection system in Brazil. Not being fluent in Portuguese, the o cial language of Brazil, is obviously a big obstacle for integration. Table 12: Relationship between likelihood of being PBF beneficiary, nationality and other household characteristics (All) (RR&AM) (Rest) (All) (RR&AM) (Rest) OLS OLS OLS Probit Probit Probit Venezuela -0.185⇤⇤⇤ -0.192 -0.125⇤⇤⇤ -0.531⇤⇤ -0.580⇤ -0.566⇤⇤ (0.0493) (0.101) (0.0272) (0.170) (0.293) (0.183) Income per capita -0.000256⇤⇤⇤ -0.000335 -0.000254⇤⇤⇤ -0.00683⇤⇤⇤ -0.00663⇤⇤⇤ -0.00683⇤⇤⇤ (0.0000161) (0.0000388) (0.0000159) (0.000122) (0.000280) (0.000121) Extreme Poverty 0.426⇤⇤⇤ 0.302 0.430⇤⇤⇤ 0.290⇤⇤⇤ 0.174⇤⇤⇤ 0.289⇤⇤⇤ (0.0147) (0.0308) (0.0150) (0.0638) (0.0142) (0.0637) Infrastructure -0.0434⇤⇤⇤ -0.0301 -0.0439⇤⇤⇤ -0.162⇤⇤⇤ -0.113⇤⇤ -0.165⇤⇤⇤ (0.00429) (0.00748) (0.00438) (0.0164) (0.0378) (0.0166) Tertiary -0.0896⇤⇤⇤ -0.0886 -0.0895⇤⇤⇤ -0.490⇤⇤⇤ -0.318⇤⇤⇤ -0.492⇤⇤⇤ (0.00456) (0.0119) (0.00468) (0.0183) (0.0857) (0.0183) Secondary -0.0571⇤⇤⇤ -0.0237 -0.0580⇤⇤⇤ -0.185⇤⇤⇤ -0.0791⇤⇤ -0.184⇤⇤⇤ (0.00287) (0.00812) (0.00282) (0.00922) (0.0263) (0.00928) Children - 1510 -0.167 0.385 -0.251 (0.332) (0.929) (0.363) Brazilian Downgraded -0.208⇤ -0.939⇤ 0.0398 (0.007) (0.400) (0.117) Non-Venezuelan Migrants 2.329⇤⇤ 0.0559 2.631⇤⇤ (0.868) (1.134) (0.977) Ln(Concentration) -0.0941⇤⇤⇤ 0.0463⇤⇤⇤ -0.0979⇤⇤⇤ (0.00616) (0.00416) (0.00637) Observations 14955 895 14060 LR 3235.73 109.23 2594.58 p-value 0.00 0.08 0.00 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All regressions include province fixed e↵ect. Rest refers to the Brazil provinces outside RR and AM. Total Non-Venezuelan Migrants is used as the exclusion restriction. 52 Table 19: Determinants of Likelihood of Venezuelans being in Waged Employment in Formal Sector (1) (2) (3) All RR&AM Rest Age -0.037⇤⇤⇤ -0.030⇤⇤⇤ -0.037⇤⇤⇤ (0.005) (0.008) (0.005) Age Square 0.000⇤⇤ 0.000 0.000⇤⇤ (0.000) (0.000) (0.000) White -0.050⇤⇤⇤ -0.048⇤ -0.049⇤⇤⇤ (0.013) (0.020) (0.012) Female 0.371⇤⇤⇤ 0.209⇤⇤⇤ 0.374⇤⇤⇤ (0.025) (0.019) (0.025) High School -0.018 -0.140⇤⇤⇤ -0.016 (0.010) (0.023) (0.010) College -0.564⇤⇤⇤ -0.715⇤⇤⇤ -0.563⇤⇤⇤ (0.039) (0.022) (0.040) Non-Venezuelan Migrants 0.000⇤⇤⇤ 0.003⇤⇤⇤ 0.000⇤⇤⇤ (0.000) (0.000) (0.000) Venezuelans 0.539⇤⇤⇤ 0.453⇤⇤⇤ 0.612⇤⇤⇤ (0.067) (0.079) (0.076) Ln(Concentration) -0.132⇤⇤⇤ 0.000274 -0.132⇤⇤⇤ (0.00293) (0.00274) (0.00294) Observations 20018567 307556 19711011 LR 160000.00 58653.65 1600000.00 p-value 0.00 0.00 0.00 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All regressions include province fixed e↵ect. Rest refers to the Brazil provinces outside RR and AM. Total Non-Venezuelan Migrants is used as the exclusion restriction. 53 7.4 Additional Information 7.4.1 Education .2 .15 Density .1 .05 0 0 2 4 6 8 10 12 grade Venezuelans Brazilians Figure 16: Distribution of Venezuelans and Brazilians by Grades 54 Table 20: Characteristics of Schools by Type of Administration (1) Federal Provincial Municipal Private Total Venezuelan Students 0.0278 0.245 0.116 0.0213 0.116 (0.264) (3.856) (3.127) (0.213) (2.903) Student Body 409.0 459.9 204.0 210.6 248.4 (311.8) (360.1) (216.4) (266.4) (273.9) Library 0.980 0.606 0.228 0.539 0.362 (0.139) (0.489) (0.420) (0.498) (0.481) Science Lab 0.853 0.308 0.0271 0.199 0.114 (0.355) (0.462) (0.162) (0.399) (0.318) Computer Lab 0.972 0.746 0.241 0.322 0.345 (0.165) (0.435) (0.428) (0.467) (0.475) Internet 0.997 0.875 0.662 0.923 0.756 (0.0572) (0.330) (0.473) (0.267) (0.429) Public Water 0.768 0.838 0.654 0.950 0.750 (0.423) (0.368) (0.476) (0.218) (0.433) Public Electricity 1 0.977 0.948 0.998 0.964 (0) (0.150) (0.222) (0.0390) (0.186) Public Sanitation 0.656 0.617 0.424 0.893 0.561 (0.475) (0.486) (0.494) (0.310) (0.496) Amenities 0.0196 0.0110 0.00681 0.0558 0.0184 (0.139) (0.105) (0.0823) (0.230) (0.135) Public Services 0.612 0.601 0.402 0.866 0.539 (0.488) (0.490) (0.490) (0.340) (0.498) Teacher-Spanish 1.551 1.503 0.420 1.074 0.748 (1.704) (6.739) (4.449) (4.639) (4.954) Teacher - Undergraduate 98.39 92.72 75.24 66.65 76.31 (6.110) (19.44) (32.49) (32.46) (31.73) Teacher - MA 51.18 3.299 0.825 1.451 1.549 (18.01) (6.148) (3.569) (4.829) (5.458) Observations 611 28,789 106,284 38,611 174,400 55 7.4.2 Social Protection Table 21: Factors a↵ecting the likelihood of receiving Bolsa Familia by nationality Brazil RR & AM Rest Brazilian Venezuelan Brazilian Venezuelans Brazilians Venezuelans ⇤⇤⇤ Income per Capita -0.000256 -0.000350⇤⇤⇤ -0.000327 -0.000517 -0.000254 ⇤⇤⇤ -0.000242⇤⇤⇤ (0.0000160) (0.0000780) (0.0000467) (0.000159) (0.0000159) (0.0000432) Extreme Poverty 0.427⇤⇤⇤ 0.231⇤⇤⇤ 0.303 0.250 0.430⇤⇤⇤ 0.140⇤⇤ (0.0147) (0.0510) (0.0306) (0.0564) (0.0150) (0.0382) ⇤⇤⇤ ⇤⇤⇤ Infrastructure -0.0434 0.00714 -0.0302 0.0279 -0.0439 -0.0875⇤ (0.00429) (0.0202) (0.00584) (0.00920) (0.00438) (0.0381) Tertiary -0.0899⇤⇤⇤ 0.0144 -0.103⇤ 0.0439 -0.0896⇤⇤⇤ -0.0609⇤⇤ (0.00463) (0.0263) (0.00676) (0.0203) (0.00469) (0.0189) ⇤⇤⇤ Secondary -0.0571 -0.00881 -0.0231 0.00373 -0.0580⇤⇤⇤ -0.0325 (0.00287) (0.0111) (0.00774) (0.00937) (0.00281) (0.0212) Children - 1510 -0.031 -0.055 -0.008 0.002 -0.031⇤⇤⇤ -0.085⇤ (0.007) (0.031) (0.019) (0.018) (0.007) (0.031) Observations 15820505 11918 234077 4216 15586428 7702 R2 0.207 0.210 0.154 0.080 0.208 0.227 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. The standard error is in parentheses and is clustered at the province level. All regressions include province fixed e↵ect. Rest refers to the Brazil provinces outside RR and AM. 57 Table 23: Factors a↵ecting wages by nationality controlling for selection Brazil RR & AM Rest Brazilian Venezuelan Brazilian Venezuelans Brazilians Venezuelans Age 0.019⇤⇤⇤ 0.009⇤⇤⇤ 0.023⇤ 0.010 0.019⇤⇤⇤ 0.009⇤⇤⇤ (0.001) (0.001) (0.001) (0.004) (0.001) (0.001) Age2 -0.000⇤⇤⇤ -0.000⇤⇤⇤ -0.000⇤ -0.000 -0.000⇤⇤⇤ -0.000⇤⇤⇤ (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) White 0.009 0.016⇤⇤ -0.001 0.023 0.009 0.015⇤ (0.004) (0.005) (0.002) (0.011) (0.004) (0.007) Ln(Hours Worked) 0.299⇤⇤⇤ 0.188⇤⇤⇤ 0.186 ⇤ 0.122 0.301⇤⇤⇤ 0.227⇤⇤⇤ (0.013) (0.032) (0.004) (0.027) (0.013) (0.034) Female -0.059⇤⇤⇤ -0.052⇤⇤⇤ -0.015 -0.050 -0.059⇤⇤⇤ -0.055⇤⇤⇤ (0.015) (0.008) (0.004) (0.008) (0.015) (0.010) High School 0.023⇤⇤⇤ 0.001 0.014 0.010 0.023⇤⇤⇤ 0.004 (0.003) (0.007) (0.005) (0.024) (0.003) (0.008) ⇤⇤⇤ College 0.113 0.059⇤⇤ 0.060 0.125 0.114⇤⇤⇤ 0.065⇤⇤⇤ (0.025) (0.017) (0.005) (0.054) (0.025) (0.017) Temporary 0.042 -0.016 0.017 0.028⇤ 0.043 -0.020 (0.026) (0.015) (0.003) (0.001) (0.027) (0.017) ⇤⇤⇤ Scientist 0.046 -0.120⇤ 0.002 0.005 0.047⇤⇤⇤ -0.168⇤⇤ (0.011) (0.052) (0.006) (0.040) (0.011) (0.060) Administration 0.030 0.014 0.014 0.054 0.030 -0.008 (0.017) (0.025) (0.003) (0.024) (0.017) (0.013) Commerce -0.003 0.030 -0.037 0.067 -0.002 -0.000 (0.007) (0.028) (0.008) (0.051) (0.007) (0.014) Personal 0.014 0.038⇤ -0.016 0.052 0.014 0.021⇤ (0.007) (0.018) (0.010) (0.030) (0.007) (0.010) Industrial 0.015 0.044⇤ 0.038⇤ 0.066 0.015 0.022 (0.009) (0.020) (0.002) (0.027) (0.009) (0.017) firm>10 -0.029⇤⇤⇤ -0.058 -0.006 0.001 -0.029⇤⇤⇤ -0.090⇤⇤ (0.007) (0.032) (0.020) (0.017) (0.007) (0.032) -0.255⇤ -0.241⇤⇤ (0.102) (0.067) -0.040 -0.439 (0.023) (0.082) -0.256⇤ -0.269⇤⇤ (0.101) (0.074) Observations 15820505 11918 234077 4216 15586428 7702 R2 0.208 0.209 0.154 0.077 0.209 0.226 ⇤ p < 0.05, ⇤⇤ p < 0.01, ⇤⇤⇤ p < 0.001. 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