metadata: authors: 0: author: Shamsuddin,Mrittika
1: author: Acosta,Pablo Ariel
2: author: Battaglin Schwengber,Rovane
3: author: Fix,Jedediah Rooney
4: author: Pirani,Nikolas
count: Venezuela, Republica Bolivariana de,Brazil
docna: 0: docna: Integration of Venezuelan Refugees and
            Migrants in Brazil
docty: Policy Research Working Paper
subtopic: Educational Sciences,Rural Labor Markets,Social Protections & Assistance,Labor Markets,Access & Equity in Basic Education,Educational Populations,Education for Development (superceded),Education For All
teratopic: Social Protections and Labor,Rural Development,Education
abstracts: cdata!: An unprecedented number of Venezuelans
            have left behind the worsening economic and social crisis at
            home to look for better future prospects. Brazil is hosting
            about 261,000 Venezuelans as migrants, asylum seekers, or
            refugees, which, at 18 percent, constitutes the largest
            share of Brazil’s 1.3 million refugees and migrants
            population (as of October 2020). Although previous
            literature on other host countries found that Venezuelan
            refugees and migrants are struggling to secure high-paying
            jobs that are commensurate with their education, little is
            known about their access to education and social protection.
            This paper fills this gap by analyzing various
            administrative and census data to explore whether Venezuelan
            migrants and refugees face differential access to education,
            the formal labor market and social protection programs. It
            finds that even though there is minimum legal constraints
            and work permits are relatively easy to obtain, Venezuelan
            refugees and migrants face challenges integrating into the
            education system, social protection programs and the formal
            labor market. The results suggest that Venezuelan refugees
            and migrants have faced downgrading in grades at school and
            occupations at work. They are more likely to attend
            overcrowded schools than their host community counterparts
            and more likely to do inferior jobs characterized by
            temporality, lower wages and higher hours worked. Overall,
            the results suggest that improvement in school capacity,
            accreditation of Venezuelan education or degrees and
            relocation to places with favorable employment opportunities
            may facilitate integration.
geo_reg_key: 119228,517191
repnme: repnme: Integration of Venezuelan Refugees and
            Migrants in Brazil
display_title: Integration of Venezuelan Refugees
            and Migrants in Brazil
listing_relative_url: /research/2021/03/32958553/integration-venezuelan-refugees-migrants-brazil
topicv3: Labor Markets m1326488 855,Education m1327048 470,Education Access and Equity m1327050 2127,Social Protection and Labor m1327194 697,Jobs m1503931 2670
publishtoextweb_dt: 2021-03-30T00:00:00Z
dois: 10.1596/1813-9450-9605
historic_topic: Social Protections and Labor,Rural Development,Education
origu: HD - LCR Director (HLCDR)
no_of_pages: 63
author_keys: 0: author_key: 1201641
1: author_key: 809643
2: author_key: 1430735
3: author_key: 1251670
model_extractions: 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 the worsening economic and social crisis at home to look for better future prospects. Brazil is hosting about 261,000 Venezuelans as migrants, asylum seekers, or refugees, which, at 18 percent, constitutes the largest share of Brazil’s 1.3 million refugees and migrants population (as of October 2020). Although previous literature on other host countries found that Venezuelan refugees and migrants are struggling to secure high-paying jobs that are commensurate with their education, little is known about their access to education and social protection. This paper fills this gap by analyzing various administrative and census data to explore whether Venezuelan migrants and refugees face differential access to education, the formal labor market and social protection programs. It finds that even though there is minimum legal constraints and work permits are relatively easy to obtain, Venezuelan refugees and migrants face challenges integrating into the education system, social protection programs and the formal labor market. The results suggest that Venezuelan refugees and migrants have faced downgrading in grades at school and occupations at work. They are more likely to attend overcrowded schools than their host community counterparts and more likely to do inferior jobs characterized by temporality, lower wages and higher hours worked. Overall, the results suggest that improvement in school capacity, accreditation of Venezuelan education or degrees and relocation to places with favorable employment 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<sup>∗</sup>

Mrittika Shamsuddin<sup>1</sup> , Pablo Ariel Acosta<sup>1</sup> , Rovane Battaglin Schwengber<sup>1</sup> , Jedediah Fix<sup>2</sup> and Nikolas Pirani<sup>2</sup> 1World Bank 2UNHCR

**Keywords: integration in Brazil, Venezuelan refugees and migrants, forced displacement JEL Codes: J15, J31, J61, F22, F15**

> ∗ **Funding Disclaimer:** This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (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 Venezuelans<sup>1</sup> 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.<sup>2</sup> 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 schooling<sup>3</sup> and social protection programs.

> 1This paper uses Venezuelans and Venezuelans migrants, asylum seekers and refugees interchangeably.

> 2http://www.planalto.gov.br/ccivil03 _/ato_ 2015 _−_ 2018 _/_ 2018 _/decreto/D_ 9285 _.htm_

> 3This 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<br>556,641|695,551|4,326,330|
|South America<br>62,240|86,964|3,239,730|
|North America (US+Canada)<br>196,910|273,418|371,919|
|Europe (Portugal, Spain, Italy)<br>203,117|224,328|309,170|
|Central America & Mexico<br>21,260|33,065|166,974|
|Carribbean<br>19,629|21,74|119,333|
|Others<br>53,485|56,702|119,204|
|South America|||
|2010|2015|2020|
|Colombia<br>43,511|48,714|1,717,352|
|Peru<br>3,504|4,129|1,043,460|
|Chile<br>8,095|54,787|475,702|
|Ecuador<br>6,120|8,901|415,835|
|Brazil<br>2,844|3,425|261,441|
|Argentina<br>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 Brazil<sup>4</sup> 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.<sup>5</sup> 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.

> 4Access to health service is an important dimension of integration that we do not focus on this paper due to unavailability of data.

> 5Olivieri 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.<sup>6</sup> 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 registry<sup>7</sup> 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.<sup>8</sup> 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 nationals<sup>9</sup> 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

> 6Venezuelans 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.

> 7The 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.

> 8Impact analysis of the COVID-19 pandemic is not possible due to unavailability of data.

> 9Regular 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.

<!-- Start of picture text --> 2016 2017 2018 2019 2020<br>World Venezuela<br>Haiti Bolivia<br>Colombia Argentina<br>China<br>1,500<br>1,000<br>Total(’000)<br>500<br>0<br><!-- End of picture text --> 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.

<!-- Start of picture text --> 263392<br>252168<br>173298<br>127841<br>2019m1 2019m7 2020m1 2020m7<br>Time<br>Entries Exits<br>Stock<br>30000 300000<br>250000<br>20000<br>200000<br>10000<br>150000<br>0<br>100000<br><!-- End of picture text --> 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%<sup>10</sup> 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 ̧c ̃ao Acolhida”_ (Operation Welcome) and has three main programs: border management and documentation; provision of humanitarian assistance including shelter; and “Interiorization” which involves the voluntary relocation of Venezuelans from Roraima to other cities. The voluntary

> 10Author’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 47,949<sup>11</sup> Venezuelans from Roraima to other Brazilian cities, where there are more opportunities for social and economic integration.

<!-- Start of picture text --> Venezuelan Migrants Venezuelan Asylum Seekers<br>96− 96−<br>91−95 91−95<br>86−90 86−90<br>81−85 81−85<br>76−80 76−80<br>71−75 71−75<br>66−70 66−70<br>61−65 61−65<br>56−60 56−60<br>51−55 51−55<br>46−50 46−50<br>41−45 41−45<br>36−40 36−40<br>31−35 31−35<br>26−30 26−30<br>21−25 21−25<br>16−20 16−20<br>11−15 11−15<br>6−10 6−10<br>0−5 0−5<br>20 15 10 5 0 5 10 15 20 20 15 10 5 0 5 10 15 20<br>Male Female Male Female<br>Migrants Asylum Seekers<br>45.03%<br>51.11% 48.89%<br>54.97%<br>Female Male Female Male<br>Migrants Asylum Seekers<br>2.06%14.02% 13.97% 14.07%<br>83.92% 71.96%<br>Married Single Married Single<br>Others Others<br>Age Group Age Group<br><!-- End of picture text --> 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

> 11This 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.<sup>12</sup> 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 integration 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

> 12Race 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 tertiary 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 difficulty of accessing education (Selee and Bolter, 2020). In response, the Peruvian Education Ministry 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,<sup>13</sup> 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 nonnationals 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_<sup>14</sup> 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.<sup>15</sup>

> 14The flagship conditional cash transfer program for the poor in Brazil

> 15Data 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,<sup>16</sup> 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.

To measure integration in the formal labor market, this paper calculates the relative probability of Venezuelans, aged between 15 and 64 years old,<sup>17</sup> employed in the formal labor market compared to Brazilian cohort.

To measure integration in the social protection program, this paper calculates the relative probability of Venezuelans registering in _Cadastro Unico_<sup>18</sup> compared to Brazilian cohort and the relative probability of registered Venezuelans to be _Bolsa Familia_ (PBF)<sup>19</sup> beneficiaries compared to their Brazilian counterpart.

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: 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.

> 16Art.4,l Law n. 9394/96.

> 17Working-age population.

> 18The 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.

> 19The 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 Leastsquares regression) and then as a Probit model<sup>20</sup> to show the robustness of results.

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,<sup>21</sup> while _yz_ includes indicator variable representing the province or state level fixed e↵ects.

> 20This 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.

> 21It 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.<sup>22</sup> 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 _λ_ s (Inverse Mills ratio).

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 _λijz_ 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.<sup>23</sup>

# **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

> 22This 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.

> 23This 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.<sup>24</sup> 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. _Firmijz_ 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’.<sup>25</sup> The model below shows the second stage estimated in the Heckman model and for the OLS model, we include the same variables except the _λ_ s (Inverse Mills ratio).

where _Ff,ijz_ is the F-index of integration at the firm and all the other covariates are as described before except _λijz_ , which is the inverse mills ratio, which is estimated using the first stage selection model of the Heckman model and _Firmijz_ 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

> 24The Probit model is used as a robustness check.

> 25The 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 official 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 _λ_ s (Inverse Mills ratio).

where _wijz_ refers to individual’s hourly wage and _Mijz_ is individual, _i_<sup>_0_</sup> _s_ socio-demographic characteristics 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, _γ_ 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 coefficients (C). So, it can be expressed as: where the over-line represents the expected value of the variables, _V_ represents Venezuelans, _B_ represents Brazilians and the _γ_ s are the estimated coefficients, while _γ⇤_ represents the weighted average of the other coefficients. 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 targets the elderly (those above 65 years) and the people with disabilities who cannot support themselves.

The Single Registry of Social Programs _(CadUnico)_<sup>_ ́_</sup> 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_<sup>_ ́_</sup> 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_<sup>_ ́_</sup> 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 _Cad Unico_<sup>_ ́_</sup> .

_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.

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, including log of natural number of the total number of Venezuelans living in the municipality. The coefficient, _↵p_ 3, identifies the coverage gap between Venezuelan and Brazilian. This gap is then divided 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 Traffic 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.<sup>26</sup> 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. Difficulty 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 developed Portuguese language classes focused on the needs of Venezuelan students (Selee & Bolter, 2020).

> 26Since 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 official language, along with the tradition of boys being the breadwinner, are most likely to contribute to the phenomenon.

18
<!-- Start of picture text --> Venezuela Brazilian<br>Fundamental High School Brazilians Venezuelans<br>(a) enrollment rate by school level (b) Average Age by Grade<br>grade 1 grade 2 grade 3 grade 4 grade 5 grade 6 grade 7 grade 8grade 10grade 11grade 12<br>1 20<br>.8 15<br>.6<br>10<br>.4<br>5<br>.2<br>0<br>0<br><!-- End of picture text --> Figure 4: Individual level characteristics Table 2: Summary statistics of enrolled students in schools by nationality

|||All Brazil|||RR & A|M|
|---|---|---|---|---|---|---|
||(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<br>|0.49<br>|-0.009<sup>_⇤_</sup><br>|0.481<br>|0.487<br>|-0.006<br>|
||(.5)|(.5)|(.004)|(.5)|(.5)|(.009)|
|Age|10.219|10.711|-0.492<sup>_⇤⇤_</sup>|10.511|11.498|-0.987|
||(4.06)|(4.78)|(.195)<br>|(3.849)|(4.915)|(.279)|
|White|0.153|0.335|-0.182<sup>_⇤⇤⇤_</sup>|0.091|0.085|0.006|
||(.36)|(.472)|(.049)|(.288)|(.279)|(.005)|
|Overage|0.681<br>|0.535<br>|0.146 <sup>_⇤⇤⇤_</sup><br>|0.727<br>|0.651<br>|0.077<br>|
||(.466)|(.499)|(.03)|(.445)|(.477)|(.038)|
||||School Cha|racteristics|||
|Public|0.373<br>|0.313<br>|0.059 <sup>_⇤⇤⇤_</sup><br>|0.379<br>|0.403<br>|-0.024<br>|
||(.484)|(.464)|(.019)|(.485)|(.49)|(.026)|
|Classsize|26.145|24.696|1.449 <sup>_⇤⇤_</sup>|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<sup>_⇤⇤_</sup>|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<sup>_⇤⇤_</sup>|0.176|0.22|-0.044|
||(.402)|(.438)|(.021)|(.381)|(.414)|(.009)|
|Computer Lab|0.634|0.562|0.072<sup>_⇤_</sup>|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<br>|0.524<br>|0.084<br>|0.638<br>|0.609<br>|0.029<br>|
||(.488)|(.499)|(.051)|(.481)|(.488)|(.075)|
|Spanish Profcient 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 Profcient Teacher|38.028<br>|32.969<br>|5.059<sup>_⇤⇤_</sup><br>|41.901<br>|37.774<br>|4.127<sup>_⇤_</sup><br>|
||(28.526)|(25.752)|(1.871)|(29.564)|(28.158)|(.513)|
|Teachers with Undergraduate Degrees|93.931|88.641|5.291<sup>_⇤⇤_</sup>|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 <sup>_⇤⇤⇤_</sup>|672.15|706.492|-34.342|
||(407.149)|(436.999)|(24.8)|(394.792)|(725.409)|(16.813)|
|Ln(Concentration)|-4.805|-8.983<br>19|4.178<sup>_⇤⇤_</sup>|-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
<!-- Start of picture text --> 0.96 0.95<br>0.90<br>122366 126049 122394 125084 121746 123858 119036 125761<br>0.69<br>0.00 0.00 0.00 0.00<br>Primary Fundamental High School College Primary Fundamental High School College<br>Brazilian Venezuelan Brazilian Venezuelan<br>(a) Mean Monthly Wage Rate (b) Proportion Downgraded<br>1<br>150000<br>.8<br>Mean Wage 100000 .6.4<br>Fraction Downgraded<br>50,000<br>.2<br>0 0<br><!-- End of picture text --> 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 Brazi|l||RR & A|M|
|---|---|---|---|---|---|---|
||(1)<br>Venezuelans|(2)<br>Brazilians|(3)<br>Di↵erence (1)-(2)|(4)<br>Venezuelans|(5)<br>Brazilians|(6)<br>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<br>|40.526<br>|1.978<sup>_⇤⇤⇤_</sup><br>|42.535<br>|40.213<br>|2.322<sup>_⇤⇤_</sup><br>|
||(5.498)|(7.818)|(.15)|(5.168)|(7.738)|(.043)|
|Female|.302|.512|-.21<sup>_⇤⇤⇤_</sup>|.239|.472|-.233<sup>_⇤⇤_</sup>|
||(.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<sup>_⇤⇤⇤_</sup>|31.225|34.596|-3.372|
||(9.102)|(12.007)|(.336)|(8.637)|(11.385)|(.906)|
|Fundamental|.095|.152|-.057<sup>_⇤⇤⇤_</sup>|.084|.113|-.029|
||(.293)|(.359)|(.007)|(.278)|(.316)|(.006)|
|High School|.73|.683|.047<sup>_⇤_</sup>|.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<sup>_⇤⇤⇤_</sup>|.008|.047|-.039<sup>_⇤_</sup>|
||(.095)|(.189)|(.003)|(.087)|(.212)|(.005)|
|Admin|.034|.07|-.036<sup>_⇤⇤⇤_</sup>|.035|.072|-.037<sup>_⇤_</sup>|
||(.181)|(.255)|(.004)|(.184)|(.258)|(.004)|
|Commerce|.116|.242|-.126<sup>_⇤⇤⇤_</sup>|.124|.256|-.133|
||(.32)|(.428)|(.011)|(.329)|(.437)|(.054)|
|Personal|.455|.387|.068<sup>_⇤_</sup>|.526|.413|.113|
||(.498)|(.487)|(.036)|(.499)|(.492)|(.021)|
|Agriculture|.028|.047|-.018<sup>_⇤⇤_</sup>|.036|.013|.023|
||(.166)|(.211)|(.008)|(.185)|(.113)|(.018)|
|Industry|.354|.196|.158<sup>_⇤⇤⇤_</sup>|.268|.177|.09<sup>_⇤_</sup>|
||(.478)|(.397)|(.046)|(.443)|(.382)|(.01)|
|frm_>_10|.007|.013|-.007<sup>_⇤⇤⇤_</sup>|.005|.002|.003|
||(.083)|(.115)|(.002)|(.07)|(.048)|(.003)|
|Downgraded|.857|.721|.136<sup>_⇤⇤⇤_</sup>|.857|.755|.102|
||(.351)|(.449)|(.012)|(.35)|(.43)|(.025)|
|Temporary|.006|.005|.001|.002|.007|-.005<sup>_⇤⇤_</sup>|
||(.076)|(.068)|(.002)|(.043)|(.084)|(0)|
|Tenure (Months)|66.323|403.108|-336.785<sup>_⇤⇤⇤_</sup>|90.48|427.69|-337.21<sup>_⇤_</sup>|
||(67.515)|(588.971)|(22.347)|(77.059)|(621.83)|(37.265)|
|Total Non-Venezuelan Migrants|5.354|3.135|2.218<sup>_⇤⇤_</sup>|3.580|3.507|0.726<sup>_⇤⇤⇤_</sup>|
||(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 December 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_<sup>_ ́_</sup> 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_<sup>_ ́_</sup> 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 _Unico ́_ haveregisteredhigh schoolhavingdegreestertiarycomparededucation.to 42%Figureof Venezuelans6b shows thatin20% _Cadastro_ of Brazilians _Unico ́_ havingin _Cadastro_ 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 educated and the overall results seem to be consistent all across Brazil including Roraima and Amazonas.

<!-- Start of picture text --> Brazilian Venezuelan<br>3.06% 10.45%1.66%0.28%<br>17.74%<br>15.32%<br>48.52%<br>20.91%<br>72.28%<br>9.78%<br><R$89 >=R$89 & <178 Brazilian Venezuelan<br>>=R$178 & <R$522 >=R$522 & <R$1045<br>>=R$1045 Fundamental High School<br>Graphs by nationality College<br>(a) Household Income (b) Education Level<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Figure 6: Fraction of those registered in _Cadastro Unico_<sup>_ ́_</sup> by income brackets, education and nationality 23
Table 4: Summary statistics of households registered in _Cadastro Unico_<sup>_ ́_</sup>

|||All Brazil|||RR & A|M|
|---|---|---|---|---|---|---|
||(1)<br>Venezuelans|(2)<br>Brazilians|(3)<br>Di↵erence (1)-(2)|(4)<br>Venezuelans|(5)<br>Brazilians|(6)<br>Di↵erence (4)-(5)|
|Income per Capita|85.281<br>(143.137)|307.036<br>(417.804)|-221.755<sup>_⇤⇤⇤_</sup><br>(20.503)|85.745<br>(119.18)|244.888<br>(381.645)|-159.143<sup>_⇤⇤_</sup><br>(11.516)|
|Extreme Poverty|.722|.485|.237<sup>_⇤⇤⇤_</sup>|.699|.565|.134<sup>_⇤_</sup>|
||(.448)|(.5)|(.039)|(.459)|(.496)|(.016)|
|Poverty excluding Extreme Poor|.106|.209|-.104<sup>_⇤⇤⇤_</sup>|.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<sup>_⇤⇤⇤_</sup>|.232|.029|.203<sup>_⇤_</sup>|
||(.444)|(.183)|(.029)|(.422)|(.167)|(.018)|
|Secondary|.637|.427|.211<sup>_⇤⇤⇤_</sup>|.651|.472|.18<sup>_⇤⇤_</sup>|
||(.481)|(.495)|(.016)|(.477)|(.499)|(.007)|
|Children - 0_<_Age _<_=5|.259|.248|-.037<sup>_⇤⇤⇤_</sup>|.244|.33|-.069<sup>_⇤⇤_</sup>|
||(.438)|(.432)|(.007)|(.429)|(.47)|(.004)|
|Family Members|2.805|2.704|.101|2.658|3.143|-.486|
||(1.671)|(1.458)|(.15)|(1.598)|(1.835)|(.137)|
|Head- Months Worked|5.567|8.365|-2.798<sup>_⇤⇤⇤_</sup>|5.539|8.008|-2.469<sup>_⇤_</sup>|
||(4.174)|(4.185)|(.21)|(4.189)|(4.428)|(.219)|
|Head - White|.153|.285|-.132<sup>_⇤⇤⇤_</sup>|.115|.069|.046<sup>_⇤_</sup>|
||(.36)|(.451)|(.04)|(.319)|(.253)|(.006)|
|Head - Female|.664|.816|-.152<sup>_⇤⇤⇤_</sup>|.665|.798|-.133<sup>_⇤⇤_</sup>|
||(.472)|(.388)|(.008)|(.472)|(.401)|(.007)|
|Head Age|36.624|43.479|-6.855<sup>_⇤⇤⇤_</sup>|37.007|41.582|-4.575 <sup>_⇤_</sup>|
||(12.263)|(16.044)|(.534)|(12.663)|(16.251)|(.53)|
|Head Employed|.542|.499|.043|.618|.533|.085<sup>_⇤_</sup>|
||(.498)|(.5)|(.042)|(.486)|(.499)|(.01)|
|Head Self-Employed|.461|.301|.16<sup>_⇤⇤_</sup>|.592|.36|.232<sup>_⇤⇤_</sup>|
||(.498)|(.459)|(.075)|(.491)|(.48)|(.005)|
|Head - Agriculture|.019|.142|-.12<sup>_⇤⇤⇤_</sup>3|.021|.191|-.171<sup>_⇤⇤_</sup>|
||(.137)|(.349)|(.024)|(.142)|(.393)|(.009)|
|Observations|18,480|23,430,686||12,159|632,463||

> _⇤ 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, who have updates information in the last 24 months.

Table 5 shows that about 6,000 Venezuelans receive _Bolsa Familia_ , compared to 11 million Brazilians. It reports the demographics of registered households receiving _Bolsa Familia_ cash transfers in December 2019 by their nationality and reveals important di↵erences between the two groups. The average income of those receiving PBF is lower and about 84% of Brazilians live in extreme poverty compared to 85% Venezuelans (Figure 7a). The average income of Venezuelan is lower than Brazilian, while the extreme poverty rate is higher. They are more educated and have lower number of children than their fellow Brazilian PBF beneficiaries. Figure 7b shows that 42% of the Venezuelan PBF beneficiaries have high school education, while 15% have college degrees, compared to 19% of Brazilian PBF beneficiaries having high school degrees and 1% of Brazilian PBF beneficiaries having college degrees. Table 5 shows that the Venezuelan PBF beneficiary households are also less likely to be led by women. Venezuelan heads of the households, who are receiving PBF, are also less likely to be employed and even when employed, are more likely to be self-employed and work for lower number of months that their Brazilian peers. Venezuelans receiving PBF are also more likely to have simultaneous access to water supply, garbage collection, adequate sanitation and electricity than the Brazilian counterparts, which is a result of most of them living in urban areas and in shelters.

24
<!-- Start of picture text --> Brazilian Venezuelan<br>3.59% 2.19%<br>12.66% 12.32%<br>83.75% 85.49%<br>Brazilian Venezuelan<br><R$89 >=R$89 & <178<br>>=R$178 & <R$522 Fundamental High School<br>Graphs by nationality College<br>(a) Household Income (b) Education Level<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Figure 7: Fraction of those receiving _Bolsa Familia_ by income brackets, education and nationality 25
Table 5: Summary statistics of households registered in _Cadastro Unico_<sup>_ ́_</sup> , who are receiving _Bolsa Familia_

|||All Brazil|||RR & A|M|
|---|---|---|---|---|---|---|
||(1)<br>Venezuelans|(2)<br>Brazilians|(3)<br>Di↵erence (1)-(2)|(4)<br>Venezuelans|(5)<br>Brazilians|(6)<br>Di↵erence (4)-(5)|
|Income per Capita|46.445|52.625|-6.18<sup>_⇤_</sup>|49.392|51.144|-1.752|
||(55.479)|(64.375)|(3.361)|(47.943)|(59.27)|(2.258)|
|Extreme Poverty|.855|.838|.017|.851|.833|.019|
||(.353)|(.369)|(.021)|(.356)|(.373)|(.013)|
|Poverty excluding extreme poor|.022|.036|-.014<sup>_⇤⇤_</sup>|.014|.03|-.016<sup>_⇤_</sup>|
||(.145)|(.186)|(.006)|(.117)|(.171)|(.002)|
|Infrastructure|.517|.443|.073|.486|.279|.207|
||(.5)|(.497)|(.061)|(.5)|(.449)|(.038)|
|Tertiary|.258|.012|.246<sup>_⇤⇤⇤_</sup>|.233|.012|.221<sup>_⇤⇤⇤_</sup>|
||(.437)|(.108)|(.019)|(.423)|(.109)|(.002)|
|Secondary|.622|.435|.187<sup>_⇤⇤⇤_</sup>|.633|.505|.128<sup>_⇤⇤⇤_</sup>|
||(.485)|(.496)|(.018)|(.482)|(.5)|(.002)|
|Children - 0_<_Age _<_=5|.264|.367|-.074 <sup>_⇤⇤⇤_</sup>|.242|.466|-.14<sup>_⇤⇤_</sup>|
||(.441)|(.482)|(.011)|(.428)|(.499)|(.005)|
|Family Members|2.959|3.136|-.177|2.801|3.812|-1.011|
||(1.725)|(1.52)|(.178)|(1.637)|(1.819)|(.174)|
|Head-Months Worked|4.853|7.32|-2.467<sup>_⇤⇤⇤_</sup>|4.854|7.342|-2.488<sup>_⇤⇤_</sup>|
||(3.897)|(4.312)|(.229)|(3.921)|(4.482)|(.152)|
|Head-White|.138|.222|-.084<sup>_⇤⇤_</sup>|.11|.055|.055<sup>_⇤⇤⇤_</sup>|
||(.345)|(.416)|(.033)|(.312)|(.228)|(0)|
|Head-Female|.635|.887|-.252<sup>_⇤⇤⇤_</sup>|.634|.878|-.243<sup>_⇤⇤_</sup>|
||(.482)|(.316)|(.012)|(.482)|(.328)|(.011)|
|Head Age|36.741|37.336|-.595<sup>_⇤⇤_</sup>|36.795|35.187|1.608<sup>_⇤⇤_</sup>|
||(11.393)|(11.268)|(.264)|(11.614)|(10.635)|(.104)|
|Head Employed|.519|.558|-.039|.587|.631|-.043<sup>_⇤_</sup>|
||(.5)|(.497)|(.044)|(.492)|(.483)|(.005)|
|Head Self-Employed|.454|.392|.062|.572|.448|.124<sup>_⇤⇤_</sup>|
||(.498)|(.488)|(.077)|(.495)|(.497)|(.002)|
|Head - Agriculture|.016|.214|-.197<sup>_⇤⇤⇤_</sup>|.015|.268|-.253<sup>_⇤⇤_</sup>|
||(.126)|(.41)|(.03)|(.122)|(.443)|(.012)|
|Observations|6,372|11,373,380||4,246|369,958||

> _⇤ 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, who have updates information in the last 24 months.

Overall, the descriptive statistics show that Venezuelans who register in the single registry are poorer but more educated than their Brazilian counterparts. However, the lower registration rate in _Cadastro Unico_<sup>_ ́_</sup> may signal that many Venezuelan refugees and migrants may not be aware of their rights and guarantee accesses, suggesting a need to provide continued information assistance and language training.

# **4.3 Impact of the COVID-19 Pandemic**

The COVID-19 pandemic has a↵ected millions of people, causing disruptions on all branches of the economy as countries imposed travel restrictions and lockdowns to subdue the spread of the virus and prevent it from overwhelming the health care system. Reports show that vulnerability to the COVID-19 virus varies with age, health and social conditions (Garcini et al., 2020). Mazza (2020) describes that beginning mid-March 2020, all South American countries officially closed their borders and there was a dramatic reduction in cross-border movements. Figure 2 supports this 26
for Brazil with only a few hundred exiting the country and very few entering. Total estimates of returnees between March and September 2020 range from 60,000 to 100,000 (Mazza, 2020), with only about 2,300 returnees from Brazil and most of the returnees are from Colombia as it received the highest displaced Venezuelans before the start of the Covid-19 pandemic.

<!-- Start of picture text --> 2018m7 2019m1 2019m7 2020m1 2020m7 2021m1<br>Time<br>3000<br>2000<br>Relocated<br>1000<br>0<br><!-- End of picture text --> Figure 8: E↵ect of Covid-19 pandemic on _Opera ̧c ̃ao Acolhida_ Figure 8 shows that the number of beneficiaries of _Opera ̧c ̃ao Acolhida_ decreased substantially from April 2020. There is a steep decline in the beneficiaries of relocation from Roraima to the other states, from about 3,000 in February to about 1,000 in April and onwards. This may have led to overcrowding in shelters and hospitals and to shortages of food in Roraima and further research needs to be conducted to quantify the actual e↵ects.

In order to flatten the curve of hospitalizations of COVID-19 patients, over 180,000 schools in Brazil have been closed. Teachers are learning to deliver courses online and parents are learning to become learning facilitators in midst of the additional stress of facing or the possibility of facing an adverse income loss. However, access to computer and digital tools are not universal and it is becoming exceptionally difficult to teach low income children and the Venezuelan migrant and refugee children are most likely to drop out. Figure 9 shows that most students do not have access to computers and the students in the northern regions in Brazil lag behind in their access to computers.

27
<!-- Start of picture text --> Central North North−east South South−east<br>Television Radio<br>Computer<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 9: Percentage of students having access to means of online education Source: SAEB 2017 However, it also shows that about 90% of the students across Brazil has access to television and some states in Brazil, namely Amazonas, Piaui, Parana, Distrito Federal and _Maranhao_ have already begun to broadcast educational content through television. According to the student vulnerability index, published by World Bank, Roraima is one of the most vulnerable states in Brazil, where students face high dropout, less family support, higher incidence of working and lower technology. Since most of the Venezuelan children are living in Roraima, they are more susceptible to disrupted learning due to the pandemic.

School closures also mean that the social safety nets through schools are also a↵ected. Many children depended on the school meals as their only source of nutritious meals. Consequently, Law 13,987/2020 has been enacted by the federal government, allowing the resources originally allocated to providing school meals in all public schools (under the national School Meals Program—PNAE) to be used to buy basic food baskets for disadvantaged families. Before this change, many municipalities were already distributing food baskets to the families of vulnerable students (World Bank, 2020).

E↵ect of the COVID-19 pandemic on the labor market is heterogenous, a↵ecting some types of jobs more than others. Unfortunately, the risk of employment disruptions are higher in service based jobs and Venezuelans, as discussed previously, are heavily concentrated in those service sector 28
jobs. In addition, most Venezuelans work in the informal sector, which before the pandemic has no access to job protections and unemployment insurance. In line with Mazza (2020), Venezuelans in Brazil appear to be disproportionately working in tourism, retail and personal care, the informal sectors that are heavily impacted by the pandemic.

<!-- Start of picture text --> 48350<br>78281.2 76538.34 74498.1 77291<br>30517<br>26734.565 27408.134 28090.177 18347<br>16470<br>591 2774 12172 5712 4893<br>840 396 665 1335<br>Time<br>Time<br>Brazilian Families Brazilian Persons<br>Venezuelan Families Venezuelans Persons Venezuelan Families Venezuelans Persons<br>(a) Cadastro Unico Registration (b) Bolsa Familia Access<br>80000 80000 50000<br>40000<br>60000 60000<br>30000<br>40000 40000<br>Venezuela<br>Brazilians(000)<br>20000<br>PBF Beneficiancies<br>20000 20000<br>10000<br>0<br>0<br>2017m1 2017m2 2017m3 2017m4 2017m5 2017m6 2017m7 2017m8 2017m9 2017m10 2017m11 2017m12 2018m1 2018m2 2018m3 2018m4 2018m5 2018m6 2018m7 2018m8 2018m9 2018m10 2018m11 2018m12 2019m1 2019m2 2019m3 2019m4 2019m5 2019m6 2019m7 2019m8 2019m9 2019m10 2019m11 2019m12 2020m1 2020m2 2020m3 2020m4 2020m5 2020m6 2020m7 2020m8 2020m9<br>2017m1 2017m2 2017m3 2017m4 2017m5 2017m6 2017m7 2017m8 2017m9 2017m10 2017m11 2017m12 2018m1 2018m2 2018m3 2018m4 2018m5 2018m6 2018m7 2018m8 2018m9 2018m10 2018m11 2018m12 2019m1 2019m2 2019m3 2019m4 2019m5 2019m6 2019m7 2019m8 2019m9 2019m10 2019m11 2019m12 2020m1 2020m2 2020m3 2020m4 2020m5 2020m6 2020m7 2020m8 2020m9 2020m10 2020m11 2020m12 2021m1<br><!-- End of picture text --> Figure 10: E↵ect of Covid-19 pandemic on access to _Bolsa Familia_ In response to the Coronavirus pandemic, the federal government, initiated two main policies to alleviate the impact of the pandemic. They expanded the PBF to include eligible families and granted a financial benefit of R$ 600 (USD 117), _Auxilio Emergencial_ , to those, whose occupations were seriously a↵ected by the crisis, including informal workers, individual microentrepreneurs, self-employed and unemployed. Eligibility depends on having a per capita household income of less than R$ 522.5 (USD 101) monthly or a total household income of R$ 3,135 (USD 609). As long as one is registered in the Single Registry or receives _Bolsa Familia_ benefit, the receipt of _Auxilio Emergencial_ is automatic, but one can also register for the benefit online. Rough estimates suggest that about 277,197 Venezuelans benefited from the program between April and November 2020. Figure 10 shows that Venezuelan PBF beneficiaries increased from about 15,000 in February 2020 to little more than 48,000 in April 2020 and have remained all most constant till January 2021. World Bank (2020) points out that although these measures may ameliorate the impact of the pandemic, fiscal constraints and high inequality may prevent Brazil from negating all the adverse e↵ects of the crisis in the short-run.

Although Brazil is the only country in the LAC region to provide social benefits during the pandemic without regard to legal status, the benefits are administered through local governments, which are overloaded with new clients and their demands. Institutional capacity is the most limited in Roraima, which is the main gateway for Venezuelan refugees and migrants and with most Venezuelans having no access to digital platform, registering online in _Cadastro Unico_<sup>_ ́_</sup> becomes a challenge, which is reflected in the gradual increase of Venezuelans registered in Figure 10. Experiences in the pandemic point to the importance of a↵ordable and accessible internet, and digital literacy, which will not only benefit Venezuelans but also Brazilians.

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# **5 Results**

This section provides an overview of results of the estimation strategies described in section 3.

## **5.1 Education**

### **5.1.1 Integration**

Venezuelans are less likely to be enrolled in school compared to their Brazilian peers. Overall, the F-Index measure shown as the red line in Figure 11 is about 32, suggesting that it is 0.47 times as likely for Venezuelans to be in school as Brazilians. However, the extent of integration varies across states. Roraima, which hosts the highest number of Venezuelans, has one of the lowest F-Index and a relative probability<sup>27</sup> of 0.25, suggesting that it is 0.25 times as likely for Venezuelan children to enroll as Brazilian children. In the fundamental level of schooling, average F- Index is about 41 while in the high school, it is about 33, suggesting that integration is lower as school level progresses. In Roraima, the F-index at the fundamental level is 26, while at the high school level, it is about 6. This may suggest that learning a new language becomes more difficult as one grows older supporting evidence in Bleakly and Chin (2010). Overall, the diagram also seems to suggest that integration is higher in provinces that have lower number of Venezuelan residents.

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>F−Index − Overall 0<br>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<br>100<br>80<br>60<br>40<br>20<br>0<br>F−Index − Fundamental 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<br>60<br>40<br>20<br>0<br>F−Index − High School 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<br><!-- End of picture text --> Figure 11: Measures of Integration in Education overall and by di↵erent levels and states

> 27Relative probability at each province is given in the appendix.

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# **5.1.2 Barriers and Facilitators of Integration in Education**

Table 6 shows the estimates of the school selection model shown in equation (6), where the standard errors are clustered at the province level. Columns 1 to 3 report results from the OLS estimation, while columns 4 to 6 report the marginal e↵ect of the Probit model. It should be noted here that the results here are at best correlations and not evidence of causation due to endogeneity of many variables in our model and lack of proper instruments. Venezuelan across Brazil are more likely to be present in public schools, which seems to be not the case in Roraima and Amazonas. This may be due to higher presence of Philantropic schools set up to accommodate the Venezuelan refugees and migrants children. Venezuelans are more likely to be found in schools that have bigger class sizes and in schools which lower proportion of Spanish language teachers, suggesting overcrowding of students and language barriers to be paramount in the lack of participation of Venezuelan children in the education system in Brazil. To help understand the di↵erence in the interpretation of the OLS and Probit models, let’s focus on column (1) and column (4), column 1 suggests that holding everything else constant, an increase in class size by 1 student increase likelihood of Venezuelans to increase by 0.0015 percentage points on average, while column (4) suggests that the predicted likelihood of having Venezuelan students increase by about 0.04 percentage points if class sizes increase by an additional student, holding all variables at the mean reported in Table 2. The schools, where Venezuelans enroll, have better infrastructure or better access to public services and higher educated teachers, suggesting that the quality of education are adequate but with lower number of teachers fluent in Spanish and capacity constraints, Venezuelan children may not be very eager to access education. The concentration in public schools may signify that Venezuelans are opting for it because it is free. Venezuelans are also likely to be present in schools that have higher number of Brazilian students although the magnitude of the e↵ect is very small and close to zero. These results seems to be robust across all the provinces of Brazil and across the di↵erent model specifications.

31
Table 6: Characteristics of schools, where Venezuelans are present

||(Brazil)<br>OLS|(RR&AM)<br>OLS|(Rest)<br>OLS|(Brazil)<br>Probit|(RR&AM)<br>Probit|(Rest)<br>Probit|
|---|---|---|---|---|---|---|
|Female Students|-0.0316<sup>_⇤_</sup>|-0.155|-0.0160<sup>_⇤_</sup>|-0.275|-1.459|-0.0732|
||(0.0137)|(0.0281)|(0.00656)|(0.185)|(0.969)|(0.112)|
|White Students|-0.00925|0.432|-0.0172|-0.0860|1.514|-0.124|
||(0.0119)|(0.0577)|(0.00923)|(0.0978)|(0.921)|(0.0879)|
|Average Age|-0.00638<sup>_⇤⇤⇤_</sup>|-0.00584|-0.00570<sup>_⇤⇤⇤_</sup>|-0.0914<sup>_⇤⇤⇤_</sup>|-0.0705|-0.0899<sup>_⇤⇤⇤_</sup>|
||(0.00143)|(0.0137)|(0.00102)|(0.00782)|(0.0699)|(0.00508)|
|Public|0.0249<sup>_⇤_</sup>|-0.0964|0.0245<sup>_⇤⇤_</sup>|0.235<sup>_⇤⇤⇤_</sup>|-0.0944<sup>_⇤_</sup>|0.250<sup>_⇤⇤⇤_</sup>|
||(0.00919)|(0.0535)|(0.00725)|(0.0403)|(0.0405)|(0.0345)|
|Class size|0.00152<sup>_⇤_</sup>|0.0123<sup>_⇤⇤_</sup>|0.00115<sup>_⇤_</sup>|0.0409<sup>_⇤⇤⇤_</sup>|0.0596<sup>_⇤⇤_</sup>|0.0379<sup>_⇤⇤⇤_</sup>|
||(0.000559)|(0.000121)|(0.000498)|(0.00381)|(0.0224)|(0.00275)|
|Teacher-Student Ratio|0.000338|-0.0141|-0.000967|0.128<sup>_⇤⇤_</sup>|0.212|0.120<sup>_⇤⇤_</sup>|
||(0.00440)|(0.0470)|(0.00448)|(0.0412)|(0.127)|(0.0426)|
|Public Services|0.0137<br>(0.00677)|0.0641<br>(0.0182)|0.0157<sup>_⇤_</sup><br>(0.00596)|0.290<sup>_⇤⇤⇤_</sup><br>(0.0478)|0.135<sup>_⇤⇤⇤_</sup><br>(0.00582)|0.293<sup>_⇤⇤⇤_</sup><br>(0.0486)|
|Amenities|-0.0147<sup>_⇤⇤_</sup>|-0.0192|-0.0148<sup>_⇤_</sup>|-0.113<sup>_⇤⇤⇤_</sup>|-0.141|-0.111<sup>_⇤⇤⇤_</sup>|
||(0.00515)|(0.0178)|(0.00556)|(0.0295)|(0.130)|(0.0300)|
|Spanish Profcient Teachers|-0.000841<sup>_⇤_</sup><br>(0.000408)|-0.000246<sup>_⇤_</sup><br>(0.0000159)|-0.000207<br>(0.000150)|-0.0113<sup>_⇤_</sup><br>(0.00494)|-0.0100<br>(0.00929)|-0.00735<sup>_⇤_</sup><br>(0.00334)|
|Teachers with Undergraduate (%)|0.000186<br>(0.000163)|0.00142<br>(0.000482)|-0.0000244<br>(0.0000307)|0.00688<sup>_⇤⇤⇤_</sup><br>(0.00203)|0.0152<sup>_⇤⇤⇤_</sup><br>(0.00359)|0.00394<sup>_⇤⇤⇤_</sup><br>(0.000751)|
|Teachers with MA (%)|0.000662<sup>_⇤⇤_</sup><br>(0.000202)|0.00168<br>(0.000217)|0.000734<sup>_⇤⇤⇤_</sup><br>(0.000192)|0.00584<sup>_⇤⇤_</sup><br>(0.00186)|0.00372<br>(0.00792)|0.00649<sup>_⇤⇤⇤_</sup><br>(0.00187)|
|Brazilians Overaged|0.0319<sup>_⇤_</sup><br>(0.0124)|-0.0233<br>(0.0446)|0.0408<sup>_⇤⇤_</sup><br>(0.0119)|0.426<sup>_⇤⇤⇤_</sup><br>(0.0987)|0.0280<br>(0.162)|0.511<sup>_⇤⇤⇤_</sup><br>(0.0979)|
|Total Brazilians|0.000147<sup>_⇤⇤⇤_</sup><br>(0.0000255)|0.000375<sup>_⇤_</sup><br>(0.0000164)|0.000129<sup>_⇤⇤⇤_</sup><br>(0.0000200)|0.000676<sup>_⇤⇤⇤_</sup><br>(0.000132)|0.00113<sup>_⇤⇤⇤_</sup><br>(0.000278)|0.000639<sup>_⇤⇤⇤_</sup><br>(0.000130)|
|Ln(Concentration)|1.010<sup>_⇤⇤⇤_</sup><br>(0.188)|0.0825<sup>_⇤_</sup><br>(0.00363)|0.00867<sup>_⇤⇤_</sup><br>(0.00236)|0.0745<sup>_⇤⇤⇤_</sup><br>(0.0163)|0.378<sup>_⇤⇤⇤_</sup><br>(0.0343)|0.0829<sup>_⇤⇤⇤_</sup><br>(0.0138)|
|Observations<br>_R_<sup>2</sup>|172354<br>0.110|5877<br>0.421|166477<br>0.077|172354|5877|166477|

> _⇤ p <_ 0 _._ 05, _⇤⇤ p <_ 0 _._ 01, _⇤⇤⇤ p <_ 0 _._ 001. The standard error is in parentheses and is clustered at the province level. The Probit models (columns 3 to 6) report marginal e↵ects at the means. All regressions include province fixed e↵ect. Rest refers to the Brazil provinces outside RR and AM. Brazilians Overaged is the fraction of Brazilians who are studying at a lower grade than that his age would suggest.

Table 16 shows how school and municipal level characteristics relate to the level of integration and report the results of equation (7). The OLS estimates in columns 1 to 3 suggest that integration is higher in school, where teachers have a MA degree. For example, schools with one additional Brazilian student relate to a decrease F index by 0.008 percentage points. Similarly, schools that demote Venezuelans more than Brazilians is associated with a 6 point lower F-index in Roraima and Amazonas. Controlling for selection with (column 7) and without (column 4) exclusion restriction, the direction of the relationship holds although the magnitude di↵er and is not statistically significant across all specifications. The significance improves once the school fixed e↵ect is taken into account in a panel estimation framework as shown in Table 16 in the Appendix. Overall, this paper finds that grade demotion or being overaged in a class is a major barrier to integration. Integration is higher in schools, where Venezuelans less likely to be demoted to a class, compared to their age. It also finds that integration is higher in municipalities that have lower concentration of Venezuelans, suggesting that spatial segregation of refugee and migrants may not be conducive to integration in the host country, a result that is supported in past studies like Carneiro 32
et al. (2020). Better amenities of schools promote integration as well as superior education of teachers.

The first stage regressions used to estimate the inverse mills ratio ( _λ_ ) for the Heckman model is given in the appendix (Table 17). The standard errors are clustered at the province level and it shows that likelihood of Venezuelans being enrolled in the school is about 0.42 percentage points higher if the school has an additional percentage of overaged Brazilians. Overaged Brazilian 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 16). 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, except column (5), _λ_ is not significant, suggesting that except Roraima and Amazonas, selection bias is not a problem or that our exclusion restriction is weak.

Table 7: Determinants of Integration in School

||(All)|(RR&AM)|(Rest)|(All)|(RR&AM)|(Rest)|(All)|(RR&AM)|(Rest)|
|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|Heckman|Heckman|Heckman|Heckman|Heckman|Heckman|
|Female Students|-12.17|-37.50|-2.423|-12.67|-40.92|-1.322|-12.68|-38.80|-2.059|
||(6.561)|(12.96)|(1.322)|(6.476)|(14.97)|(1.407)|(6.493)|(12.22)|(1.220)|
|White Students|0.0264|28.37|-0.716|-0.211|25.38|-0.293|-0.226|26.74|-0.712|
||(0.799)|(15.96)|(0.474)|(0.972)|(14.79)|(0.776)|(0.922)|(16.97)|(0.650)|
|Average age|-0.257<sup>_⇤⇤_</sup>|-0.634|-0.198<sup>_⇤_</sup>|-0.419|-1.719|0.178|-0.441<sup>_⇤_</sup>|-1.171|-0.352|
||(0.0872)|(0.260)|(0.0759)|(0.321)|(0.140)|(0.490)|(0.190)|(0.534)|(0.243)|
|Public|0.572|2.810|0.429|0.988|5.362|-0.541|1.036|4.214<sup>_⇤_</sup>|0.733|
||(0.373)|(0.870)|(0.244)|(0.825)|(0.831)|(1.419)|(0.566)|(0.191)|(0.819)|
|Class Size|-0.0185|-0.161|0.00154|0.0576|0.421|-0.168|0.0652|0.230|0.0403|
||(0.0485)|(0.379)|(0.0168)|(0.158)|(0.283)|(0.202)|(0.113)|(0.432)|(0.112)|
|TS ratio|-0.808|-7.894|-0.0882|-0.568|-6.576|-0.595|-0.543|-7.030|0.0532|
||(0.834)|(6.143)|(0.239)|(1.001)|(6.027)|(0.705)|(0.909)|(6.409)|(0.474)|
|Infrastructure|0.264|1.602|-0.0670|0.804|4.835|-1.303|0.857|3.688|0.205|
||(0.516)|(1.439)|(0.308)|(0.796)|(2.150)|(1.374)|(0.539)|(1.176)|(0.665)|
|Amenities|-0.708|-2.475|-0.639|-0.901|-3.312|-0.176|-0.913|-3.358|-0.663|
||(0.458)|(0.269)|(0.508)|(0.506)|(0.309)|(0.667)|(0.452)|(0.294)|(0.478)|
|Spanish Profcient Teachers|-0.180<sup>_⇤⇤_</sup>|-0.199|-0.0533|-0.200<sup>_⇤⇤_</sup>|-0.325|-0.000386|-0.201<sup>_⇤⇤_</sup>|-0.293|-0.0575|
||(0.0581)|(0.186)|(0.0526)|(0.0623)|(0.164)|(0.0808)|(0.0587)|(0.155)|(0.0675)|
|Teachers with Undergraduate Degree|0.0287|0.0565|0.00354|0.0419|0.144|-0.0277|0.0431|0.114|0.00911|
||(0.0188)|(0.0718)|(0.0103)|(0.0281)|(0.0812)|(0.0418)|(0.0231)|(0.0600)|(0.0262)|
|Teachers with MA|0.0121|0.0502|0.0104|0.0225|0.119|-0.0132|0.0239|0.0744<sup>_⇤_</sup>|0.0197|
||(0.0122)|(0.0227)|(0.0136)|(0.0217)|(0.0360)|(0.0358)|(0.0139)|(0.00537)|(0.0203)|
|Brazilian Overaged|-1.007|-1.193|-1.518|-0.165|4.466|-3.395||||
||(0.604)|(2.890)|(0.912)|(1.194)|(3.362)|(1.763)||||
|Total Brazilian|-0.00889<sup>_⇤⇤⇤_</sup>|-0.00415|-0.00920<sup>_⇤⇤⇤_</sup>|-0.00785<sup>_⇤⇤⇤_</sup>|0.00119|-0.0118<sup>_⇤⇤_</sup>|-0.00776<sup>_⇤⇤⇤_</sup>|-0.000602|-0.00877<sup>_⇤⇤⇤_</sup>|
||(0.000845)|(0.00327)|(0.000760)|(0.00168)|(0.00344)|(0.00362)|(0.00120)|(0.00218)|(0.00215)|
|Overaged|-0.709|-5.875<sup>_⇤⇤⇤_</sup>|-0.314|-0.732|-6.274<sup>_⇤_</sup>|-0.310|-0.295|-1.859|-0.119|
||(0.825)|(0.178)|(0.525)|(0.844)|(0.437)|(0.509)|(0.763)|(5.016)|(0.700)|
|Ln(Concentration)|-105.5<sup>_⇤⇤⇤_</sup>|-14.02<sup>_⇤_</sup>|-97.80<sup>_⇤⇤⇤_</sup>|-10.86<sup>_⇤⇤⇤_</sup>|-10.86<sup>_⇤_</sup>|-7.088<sup>_⇤⇤⇤_</sup>|-10.81<sup>_⇤⇤⇤_</sup>|-11.95|-5.302<sup>_⇤⇤⇤_</sup>|
||(7.809)|(0.779)|(7.374)|(1.173)|(0.534)|(1.882)|(0.880)|(1.324)|(1.061)|
|_λ_||||2.310|||2.533|||
|||||(4.156)|||(2.725)|||
|_λ_|||||19.16<sup>_⇤_</sup>|||12.81||
||||||(0.951)|||(3.004)||
|_λ_||||||-5.075|||1.081|
|||||||(6.454)|||(3.577)|
|Observations|8175|1203|6972|8175|1203|6972|8175|1203|6972|
|_R_<sup>2</sup>|0.839|0.696|0.621|0.839|0.697|0.622|0.839|0.697|0.621|

> _⇤ 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. Overaged is a dummy if the rate of grade mismatch at school is higher for Venezuelans than Brazilians. Brazilians Overaged is the fraction of Brazilians who are studying at a lower grade than that his age would suggest and is used as the exclusion restriction in columns 7 to 9. Columns 4, 5 and 6 are estimated without using exclusion restrictions. Spanish, college and MA refers to qualifications of teachers in the schools. _λ_ refers to the Inverse Mill’s ratio and the first stage regressions are shown in the appendix.

However, the robustness of the results across the di↵erent specifications give evidence of grade demotion being a major obstacles for integration in school. This may happen due to either not being fluent in Portuguese or not having the correct documents and accreditation, suggesting that policy formulation needs to focus on giving language training and promoting credential verification and validation.

33
# **5.2 Labor**

## **5.2.1 Integration**

<!-- Start of picture text --> Male Female<br>Male Female<br>80<br>60<br>40<br>F−index<br>20<br>0<br>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<br>30<br>F−Index 20<br>10<br>0<br><!-- End of picture text --> Figure 12: Measure of Integration in Formal Labor Market by Regions and gender It is harder for Venezuelans to access the formal labor market than the Brazilians. The overall share of formal workers in Brazil is about 45%, but as Figure 12 shows that the Venezuelans are 0.36 times as likely to enter the formal sector as Brazilians, with a corresponding F-index of 26.5. The level of integration varies across states, with lowest in Roraima, which also have the highest concentration of Venezuelan formal workers, with about Venezuelans being 0.08 times as likely as Brazilian to be a formal worker. Integration is higher in states that has higher job opportunities like Rio de Janeiro, Sao Paulo and Minas Gerais. These states also receive the highest number of Venezuelans, who were relocated through the ”interiorization” strategy,<sup>28</sup> run by the federal government with the help of UNHCR and other civil society organizations, which involves the voluntary relocation of Venezuelans from Roraima to other cities within Brazil to ensure e↵ective socioeconomic integration. Similarly, Parana (PR), Santa Catarina (SC) and Rio Grande do Sul (RS) may have high integration as a result of receiving high number of Venezuelans relocated through the ”interiorization” strategy, suggesting the key role played by the ”interiorization” strategy in helping Venezuelan refugees and migrants to be included in the more productive sector of the economy. It

> 28It is one of the strategies under Operation Welcome, which is discussed in the introduction.

34
also seems to be harder for women to access formal sector jobs with an F-index of 20, compared to the F-index of 29 for Venezuelan males, highlighting that women have to face additional constraints to enter the formal labor market. These constraints may be related to lower returns to education and higher penalty for being minority women as Table 22 shows in addition to the common stereotype of the male being the primary earner of the family.

# **5.2.2 Determinants of Integration and the Wage Gap**

Table 8 shows the estimation results of equation (8),<sup>29</sup> where the standard errors are clustered at the province level and indicates that Venezuelans are more likely to work in firms that hire more temporary and female workers in Brazil, except Rorarima and Amazonas. Column (1) suggests that likelihood of Venezuelans being present in the formal sector firm increases by 0.15 percentage points on average if the firm incurs a percentage point increment in the proportion of female employed, holding everything else constant. Similarly, for column (4), it implies that predicted likelihood of Venezuelans being present increases by about 0.73 percentage points if the firm incurs a percentage point increment in the proportion of female employed, holding all the variables at their mean reported in Table 3. These firms are also less likely to hire college educated personnel and are more likely to operate in construction and trade industry and are less likely to work in agriculture and manufacturing industry. Venezuelans are less likely to work for formal sector firms, where Brazilian workers are more likely to be occupationally downgraded. Occupational downgrading seems to be a stronger deterrent in rest of Brazil than in Roraima and Amazonas. Outside Roraima and Amazonas, firms with additional number of non-Venezuelan refugees and migrants can be related to about 0.78 point increase in the predicted likelihood of Venezuelans being present in a formal sector firm on average, suggesting that the overcrowding e↵ect is less than the positive e↵ect of higher networks, but in Roraima and Amazonas, the information gathered through network has no significant e↵ect on the likelihood of getting into a formal sector firm.

> 29All results can be interpreted as correlation and not causation due to potential biases due to endogeneity issues.

35
Table 8: Characteristics of Firms, where Venezuelans work

||(Brazil)<br>OLS|(RR & AM)<br>OLS|(Rest)<br>OLS|(Brazil)<br>Probit|(RR & AM)<br>Probit|(Rest)<br>Probit|
|---|---|---|---|---|---|---|
|Female Proportion|0.151<sup>_⇤⇤⇤_</sup>|-0.0288|0.165<sup>_⇤⇤⇤_</sup>|0.726<sup>_⇤⇤⇤_</sup>|-0.0974|0.844<sup>_⇤⇤⇤_</sup>|
||(0.0285)|(0.194)|(0.0273)|(0.113)|(0.488)|(0.0758)|
|White Proportion|-0.0683<sup>_⇤⇤⇤_</sup><br>(0.0175)|-0.303<sup>_⇤⇤_</sup><br>(0.00132)|-0.0513<sup>_⇤⇤⇤_</sup><br>(0.0130)|-0.428<sup>_⇤⇤⇤_</sup><br>(0.0976)|-0.897<sup>_⇤⇤⇤_</sup><br>(0.0430)|-0.345<sup>_⇤⇤⇤_</sup><br>(0.0864)|
|Temporary Proportions|0.612<sup>_⇤⇤⇤_</sup>|0.718|0.599<sup>_⇤⇤⇤_</sup>|2.148<sup>_⇤⇤⇤_</sup>|4.577|2.088<sup>_⇤⇤⇤_</sup>|
||(0.125)|(0.137)|(0.136)|(0.550)|(2.792)|(0.573)|
|High School|-0.0323|0.201|-0.0537<sup>_⇤_</sup>|-0.0592|0.553<sup>_⇤⇤_</sup>|-0.172|
||(0.0275)|(0.0623)|(0.0249)|(0.147)|(0.212)|(0.145)|
|College|-0.231<sup>_⇤⇤⇤_</sup>|-0.183<sup>_⇤_</sup>|-0.236<sup>_⇤⇤⇤_</sup>|-1.135<sup>_⇤⇤⇤_</sup>|-0.510<sup>_⇤⇤⇤_</sup>|-1.241<sup>_⇤⇤⇤_</sup>|
||(0.0436)|(0.00532)|(0.0466)|(0.164)|(0.0000541)|(0.158)|
|Agriculture|-0.0827<sup>_⇤⇤_</sup>|-0.138|-0.0810<sup>_⇤⇤_</sup>|-0.435<sup>_⇤⇤⇤_</sup>|-0.387|-0.453<sup>_⇤⇤⇤_</sup>|
||(0.0229)|(0.166)|(0.0225)|(0.104)|(0.476)|(0.107)|
|Manufacturing|-0.0529<sup>_⇤⇤⇤_</sup>|-0.0128|-0.0556<sup>_⇤⇤⇤_</sup>|-0.252<sup>_⇤⇤⇤_</sup>|-0.0352|-0.277<sup>_⇤⇤⇤_</sup>|
||(0.00823)|(0.0165)|(0.00844)|(0.0438)|(0.0477)|(0.0456)|
|Constructions|0.114<sup>_⇤⇤⇤_</sup>|0.0735|0.119<sup>_⇤⇤⇤_</sup>|0.449<sup>_⇤⇤⇤_</sup>|0.185|0.479<sup>_⇤⇤⇤_</sup>|
||(0.0274)|(0.0274)|(0.0292)|(0.0851)|(0.106)|(0.0895)|
|Trade|0.0500<sup>_⇤⇤⇤_</sup>|0.125|0.0446<sup>_⇤⇤⇤_</sup>|0.220<sup>_⇤⇤⇤_</sup>|0.345<sup>_⇤⇤⇤_</sup>|0.210<sup>_⇤⇤⇤_</sup>|
||(0.0103)|(0.0117)|(0.0103)|(0.0339)|(0.0131)|(0.0373)|
|Firm Size_>_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<sup>_⇤⇤_</sup>|-0.114<sup>_⇤⇤⇤_</sup>|-0.373<sup>_⇤⇤_</sup>|
||(0.0208)|(0.00802)|(0.0225)|(0.125)|(0.0211)|(0.142)|
|Non-Venezuelan Migrants|0.729<sup>_⇤⇤_</sup>|0.393|0.780<sup>_⇤_</sup>|2.598<sup>_⇤⇤_</sup>|1.303|2.762<sup>_⇤⇤_</sup>|
||(0.253)|(0.331)|(0.298)|(0.846)|(0.992)|(0.983)|
|Ln(Concentration)|0.0511|0.0262|0.0314|-0.0842<sup>_⇤⇤⇤_</sup>|0.0700<sup>_⇤⇤⇤_</sup>|-0.0891<sup>_⇤⇤⇤_</sup>|
||(0.0257)|(0.00293)|(0.0224)|(0.00609)|(0.00709)|(0.00578)|
|Observations|14955|895|14060|14955|895|14060|
|_R_<sup>2</sup>|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<sup>_⇤⇤⇤_</sup>|-6.421|-12.33<sup>_⇤⇤⇤_</sup>|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<sup>_⇤_</sup><br>|8.222<br>|10.84<sup>_⇤_</sup><br>|0.443<br>|34.76<br>|-0.644<br>|0.188<br>|25.03<br>|0.627<br>|
||(4.312)|(5.397)|(4.720)|(6.606)|(14.95)|(8.360)|(5.957)|(27.95)|(7.810)|
|Temporary Proportion|10.71<sup>_⇤_</sup>|11.78|8.904<sup>_⇤_</sup>|39.07|-77.04|45.92|39.45<sup>_⇤_</sup>|-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<sup>_⇤_</sup>|2.556|20.97<sup>_⇤⇤⇤_</sup>|17.04|18.85|13.84|15.58<sup>_⇤_</sup>|0.797|22.64<sup>_⇤⇤⇤_</sup>|
||(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<sup>_⇤⇤_</sup>|-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<sup>_⇤⇤⇤_</sup>|-2.755|-4.658<sup>_⇤⇤_</sup>|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<sup>_⇤_</sup>|3.097|-4.786<sup>_⇤⇤⇤_</sup>|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<sup>_⇤⇤_</sup>|-6.132|-27.83<sup>_⇤⇤⇤_</sup>|-28.46<sup>_⇤⇤_</sup>|2.331|-29.43<sup>_⇤⇤⇤_</sup>|-28.56<sup>_⇤⇤_</sup>|-2.017|-28.94<sup>_⇤⇤⇤_</sup>|
||(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<sup>_⇤_</sup>|-2.232|-29.07|12.48<sup>_⇤_</sup>||||
||(13.66)|(39.42)|(4.515)|(14.60)|(39.17)|(5.075)||||
|Non-Venezuelan Migrants|367.8<sup>_⇤⇤⇤_</sup>|123.7|428.8<sup>_⇤⇤⇤_</sup>|414.3<sup>_⇤⇤⇤_</sup>|2.481|493.3<sup>_⇤⇤⇤_</sup>|416.8<sup>_⇤⇤⇤_</sup>|66.18|479.3<sup>_⇤⇤⇤_</sup>|
||(61.68)|(130.5)|(55.71)|(83.42)|(69.02)|(61.57)|(68.53)|(12.58)|(57.42)|
|Ln(Concentration)|-102.4<sup>_⇤⇤⇤_</sup>|-8.579<sup>_⇤_</sup>|-102.9<sup>_⇤⇤⇤_</sup>|-3.945|-12.27<sup>_⇤_</sup>|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<sup>_⇤⇤⇤_</sup>|-28.05|-3.290|-37.26<sup>_⇤⇤⇤_</sup>|-28.01|-1.581|-41.16<sup>_⇤⇤⇤_</sup>|-34.43|0.353|
||(4.381)|(17.44)|(3.147)|(4.535)|(17.23)|(3.117)|(1.784)|(8.210)|(3.098)|
|_λ_||||19.68|||10.76|||
|||||(15.58)|||(21.63)|||
|_λ_|||||-31.36|||-154.0||
||||||(15.30)|||(161.5)||
|_λ_||||||21.06|||24.08|
|||||||(14.77)|||(15.03)|
|Observations|2743|475|2268|2743|475|2268|2743|475|2268|
|_R_<sup>2</sup>|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. _λ_ 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 ( _λ_ ) 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, _λ_ 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 ( _λ_ ) 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. _λ_ 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<sup>_⇤⇤_</sup>|-0.059<sup>_⇤⇤⇤_</sup>|-0.088|-0.048<sup>_⇤⇤⇤_</sup>|-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<sup>_⇤⇤⇤_</sup>|0.003<sup>_⇤_</sup>|0.002<sup>_⇤⇤⇤_</sup>|0.020<sup>_⇤⇤⇤_</sup>|0.027|0.020<sup>_⇤⇤⇤_</sup>|0.019<sup>_⇤⇤⇤_</sup>|0.023<sup>_⇤_</sup>|0.019<sup>_⇤⇤⇤_</sup>|
||(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<sup>_⇤⇤⇤_</sup>|0.200<sup>_⇤_</sup>|0.311<sup>_⇤⇤⇤_</sup>|0.298<sup>_⇤⇤⇤_</sup>|0.183<sup>_⇤_</sup>|0.300<sup>_⇤⇤⇤_</sup>|0.299<sup>_⇤⇤⇤_</sup>|0.185<sup>_⇤_</sup>|0.301<sup>_⇤⇤⇤_</sup>|
||(0.014)|(0.004)|(0.014)|(0.013)|(0.005)|(0.013)|(0.013)|(0.004)|(0.013)|
|Female|-0.016<sup>_⇤⇤⇤_</sup>|-0.009|-0.016<sup>_⇤⇤⇤_</sup>|-0.071<sup>_⇤⇤⇤_</sup>|-0.046|-0.071<sup>_⇤⇤⇤_</sup>|-0.059<sup>_⇤⇤⇤_</sup>|-0.015|-0.059<sup>_⇤⇤⇤_</sup>|
||(0.003)|(0.002)|(0.003)|(0.005)|(0.005)|(0.005)|(0.015)|(0.004)|(0.014)|
|High School|0.021<sup>_⇤⇤⇤_</sup>|0.018|0.021<sup>_⇤⇤⇤_</sup>|0.023<sup>_⇤⇤⇤_</sup>|0.035|0.023<sup>_⇤⇤⇤_</sup>|0.023<sup>_⇤⇤⇤_</sup>|0.014|0.023<sup>_⇤⇤⇤_</sup>|
||(0.002)|(0.009)|(0.003)|(0.002)|(0.012)|(0.002)|(0.003)|(0.006)|(0.003)|
|College|0.058<sup>_⇤⇤⇤_</sup>|0.059|0.058<sup>_⇤⇤⇤_</sup>|0.135<sup>_⇤⇤⇤_</sup>|0.173|0.135<sup>_⇤⇤⇤_</sup>|0.113<sup>_⇤⇤⇤_</sup>|0.059|0.113<sup>_⇤⇤⇤_</sup>|
||(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<sup>_⇤⇤⇤_</sup>|0.009|0.052<sup>_⇤⇤⇤_</sup>|0.047<sup>_⇤⇤⇤_</sup>|0.004|0.048<sup>_⇤⇤⇤_</sup>|0.046<sup>_⇤⇤⇤_</sup>|0.003|0.047<sup>_⇤⇤⇤_</sup>|
||(0.011)|(0.008)|(0.011)|(0.011)|(0.009)|(0.011)|(0.011)|(0.008)|(0.011)|
|Administration|0.033<sup>_⇤_</sup>|0.020|0.033<sup>_⇤_</sup>|0.032<sup>_⇤_</sup>|0.016|0.032<sup>_⇤_</sup>|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<sup>_⇤_</sup>|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<sup>_⇤⇤_</sup>|0.017|0.016|0.036<sup>_⇤⇤_</sup>|0.016|0.015|0.038<sup>_⇤_</sup>|0.015|
||(0.009)|(0.000)|(0.009)|(0.009)|(0.000)|(0.009)|(0.009)|(0.001)|(0.009)|
|Firm_>_10|-0.029<sup>_⇤⇤⇤_</sup>|-0.004|-0.029<sup>_⇤⇤⇤_</sup>|-0.030<sup>_⇤⇤⇤_</sup>|-0.009|-0.030<sup>_⇤⇤⇤_</sup>|-0.029<sup>_⇤⇤⇤_</sup>|-0.008|-0.029<sup>_⇤⇤⇤_</sup>|
||(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)||||
|_λ_||||-0.344<sup>_⇤⇤⇤_</sup><br>(0.037)|||-0.254<sup>_⇤_</sup><br>(0.101)|||
|_λ_|||||-0.395|||-0.039||
||||||(0.081)|||(0.022)||
|_λ_||||||-0.343<sup>_⇤⇤⇤_</sup>|||-0.256<sup>_⇤_</sup>|
|||||||(0.038)|||(0.101)|
|Observations|15832423|238293|15594130|15832423|238293|15594130|15832423|238293|15594130|
|_R_<sup>2</sup>|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)<br>OLS|(RR & AM)<br>OLS|(Rest)<br>OLS|(Brazil)<br>Heckman|(RR & AM)<br>Heckman|(Rest)<br>Heckman|
|---|---|---|---|---|---|---|
|Di↵erential|||||||
|Brazilian|11.682<sup>_⇤⇤⇤_</sup>|11.663<sup>_⇤⇤⇤_</sup>|11.682<sup>_⇤⇤⇤_</sup>|11.682<sup>_⇤⇤⇤_</sup>|11.663<sup>_⇤⇤⇤_</sup>|11.682<sup>_⇤⇤⇤_</sup>|
||(0.012)|(0.001)|(0.012)|(0.012)|(0.001)|(0.012)|
|Venezuelan|11.715<sup>_⇤⇤⇤_</sup>|11.658<sup>_⇤⇤⇤_</sup>|11.746<sup>_⇤⇤⇤_</sup>|11.715<sup>_⇤⇤⇤_</sup>|11.658<sup>_⇤⇤⇤_</sup>|11.746<sup>_⇤⇤⇤_</sup>|
||(0.022)|(0.019)|(0.015)|(0.022)|(0.019)|(0.015)|
|Di↵erence|-0.033|0.006|-0.064<sup>_⇤⇤⇤_</sup>|-0.033|0.006|-0.064<sup>_⇤⇤⇤_</sup>|
||(0.020)|(0.021)|(0.012)|(0.020)|(0.021)|(0.012)|
|Decomposition|||||||
|Explained|-0.029<sup>_⇤⇤_</sup>|-0.017<sup>_⇤⇤_</sup>|-0.044<sup>_⇤⇤⇤_</sup>|-0.077<sup>_⇤⇤⇤_</sup>|-0.023<sup>_⇤⇤_</sup>|-0.098<sup>_⇤⇤⇤_</sup>|
||(0.011)|(0.007)|(0.008)|(0.021)|(0.008)|(0.022)|
|Unexplained|-0.004|0.023|-0.020<sup>_⇤⇤_</sup>|0.044<sup>_⇤_</sup>|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_<sup>_ ́_</sup> , 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 13<sup>_th_</sup> 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.

<!-- Start of picture text --> 2017 2018 2019 2020<br>Time<br>CadUnico PBF CadUnico PBF<br>(a) Over time (b) Over regions<br>60 80<br>50<br>60<br>40<br>F−Index 30 F−Index 40<br>20 20<br>10<br>0<br>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<br><!-- End of picture text --> 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 coefficient 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 official 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<sup>_⇤⇤⇤_</sup><br>(0.0493)|-0.192<br>(0.101)|-0.125<sup>_⇤⇤⇤_</sup><br>(0.0272)|-0.531<sup>_⇤⇤_</sup><br>(0.170)|-0.580<sup>_⇤_</sup><br>(0.293)|-0.566<sup>_⇤⇤_</sup><br>(0.183)|
|Income per capita|-0.000256<sup>_⇤⇤⇤_</sup>|-0.000335|-0.000254<sup>_⇤⇤⇤_</sup>|-0.00683<sup>_⇤⇤⇤_</sup>|-0.00663<sup>_⇤⇤⇤_</sup>|-0.00683<sup>_⇤⇤⇤_</sup>|
||(0.0000161)|(0.0000388)|(0.0000159)|(0.000122)|(0.000280)|(0.000121)|
|Extreme Poverty|0.426<sup>_⇤⇤⇤_</sup>|0.302|0.430<sup>_⇤⇤⇤_</sup>|0.290<sup>_⇤⇤⇤_</sup>|0.174<sup>_⇤⇤⇤_</sup>|0.289<sup>_⇤⇤⇤_</sup>|
||(0.0147)|(0.0308)|(0.0150)|(0.0638)|(0.0142)|(0.0637)|
|Infrastructure|-0.0434<sup>_⇤⇤⇤_</sup>|-0.0301|-0.0439<sup>_⇤⇤⇤_</sup>|-0.162<sup>_⇤⇤⇤_</sup>|-0.113<sup>_⇤⇤_</sup>|-0.165<sup>_⇤⇤⇤_</sup>|
||(0.00429)|(0.00748)|(0.00438)|(0.0164)|(0.0378)|(0.0166)|
|Tertiary|-0.0896<sup>_⇤⇤⇤_</sup>|-0.0886|-0.0895<sup>_⇤⇤⇤_</sup>|-0.490<sup>_⇤⇤⇤_</sup>|-0.318<sup>_⇤⇤⇤_</sup>|-0.492<sup>_⇤⇤⇤_</sup>|
||(0.00456)|(0.0119)|(0.00468)|(0.0183)|(0.0857)|(0.0183)|
|Secondary|-0.0571<sup>_⇤⇤⇤_</sup>|-0.0237|-0.0580<sup>_⇤⇤⇤_</sup>|-0.185<sup>_⇤⇤⇤_</sup>|-0.0791<sup>_⇤⇤_</sup>|-0.184<sup>_⇤⇤⇤_</sup>|
||(0.00287)|(0.00812)|(0.00282)|(0.00922)|(0.0263)|(0.00928)|
|Children - 15_<_Age _<_=17|0.0482<sup>_⇤⇤⇤_</sup>|0.0461<sup>_⇤_</sup>|0.0482<sup>_⇤⇤⇤_</sup>|0.155<sup>_⇤⇤⇤_</sup>|0.128<sup>_⇤⇤⇤_</sup>|0.155<sup>_⇤⇤⇤_</sup>|
||(0.00202)|(0.00249)|(0.00211)|(0.00528)|(0.0140)|(0.00528)|
|Children - 5_<_Age _<_=15|0.113<sup>_⇤⇤⇤_</sup>|0.119<sup>_⇤_</sup>|0.113<sup>_⇤⇤⇤_</sup>|0.427<sup>_⇤⇤⇤_</sup>|0.393<sup>_⇤⇤⇤_</sup>|0.426<sup>_⇤⇤⇤_</sup>|
||(0.00401)|(0.00817)|(0.00417)|(0.00774)|(0.0355)|(0.00777)|
|Children - 0_<_Age _<_=5|0.0755<sup>_⇤⇤⇤_</sup>|0.0549<sup>_⇤_</sup>|0.0765<sup>_⇤⇤⇤_</sup>|0.256<sup>_⇤⇤⇤_</sup>|0.173<sup>_⇤⇤⇤_</sup>|0.256<sup>_⇤⇤⇤_</sup>|
||(0.00419)|(0.00255)|(0.00427)|(0.00902)|(0.0221)|(0.00911)|
|Family Members|0.00554<sup>_⇤⇤_</sup><br>(0.00174)|0.0178<sup>_⇤_</sup><br>(0.000956)|0.00504<sup>_⇤⇤_</sup><br>(0.00177)|0.0322<sup>_⇤⇤_</sup><br>(0.0112)|0.0871<sup>_⇤⇤⇤_</sup><br>(0.00537)|0.0323<sup>_⇤⇤_</sup><br>(0.0112)|
|Head White|-0.00977<sup>_⇤⇤⇤_</sup>|-0.0173<sup>_⇤_</sup>|-0.00963<sup>_⇤⇤⇤_</sup>|-0.0250<sup>_⇤⇤⇤_</sup>|-0.0553<sup>_⇤⇤⇤_</sup>|-0.0258<sup>_⇤⇤⇤_</sup>|
||(0.00225)|(0.000496)|(0.00224)|(0.00640)|(0.00435)|(0.00652)|
|Head Female|0.0609<sup>_⇤⇤⇤_</sup>|0.0604<sup>_⇤⇤⇤_</sup>|0.0609<sup>_⇤⇤⇤_</sup>|0.283<sup>_⇤⇤⇤_</sup>|0.244<sup>_⇤⇤⇤_</sup>|0.283<sup>_⇤⇤⇤_</sup>|
||(0.00291)|(0.00000841)|(0.00298)|(0.0114)|(0.00185)|(0.0114)|
|Head Age|-0.000319<sup>_⇤_</sup>|-0.00144|-0.000300<sup>_⇤_</sup>|0.000447|-0.00294<sup>_⇤⇤⇤_</sup>|0.000419|
||(0.000137)|(0.000271)|(0.000139)|(0.000556)|(0.000492)|(0.000557)|
|Head Employed|-0.0214<sup>_⇤⇤⇤_</sup>|-0.0694|-0.0199<sup>_⇤⇤_</sup>|0.105<sup>_⇤⇤⇤_</sup>|-0.191<sup>_⇤_</sup>|0.108<sup>_⇤⇤⇤_</sup>|
||(0.00576)|(0.0358)|(0.00598)|(0.0165)|(0.0825)|(0.0167)|
|Head -Self Employed|0.0864<sup>_⇤⇤⇤_</sup>|0.0633|0.0867<sup>_⇤⇤⇤_</sup>|0.172<sup>_⇤⇤⇤_</sup>|0.110<sup>_⇤⇤⇤_</sup>|0.172<sup>_⇤⇤⇤_</sup>|
||(0.0117)|(0.00910)|(0.0120)|(0.0315)|(0.0135)|(0.0314)|
|Head Agri|0.0619<sup>_⇤⇤⇤_</sup>|0.0554<sup>_⇤_</sup>|0.0623<sup>_⇤⇤⇤_</sup>|0.251<sup>_⇤⇤⇤_</sup>|0.241<sup>_⇤⇤⇤_</sup>|0.252<sup>_⇤⇤⇤_</sup>|
||(0.00385)|(0.00394)|(0.00397)|(0.0107)|(0.00516)|(0.0108)|
|head - Months Worked|0.000514|0.000128|0.000486|0.0220<sup>_⇤⇤⇤_</sup>|0.0185<sup>_⇤⇤⇤_</sup>|0.0220<sup>_⇤⇤⇤_</sup>|
||(0.000392)|(0.000544)|(0.000402)|(0.00147)|(0.000425)|(0.00146)|
|Ln(Concentration|0.00640<sup>_⇤⇤⇤_</sup>|-0.0151|-0.0257<sup>_⇤⇤⇤_</sup>|-0.297<sup>_⇤⇤⇤_</sup>|-0.0444<sup>_⇤⇤_</sup>|-0.00556|
||(0.00106)|(0.00394)|(0.00117)|(0.0150)|(0.0163)|(0.0136)|
|Observations|8729818|268305|8461513|8729818|268305|8729818|
|_R_<sup>2</sup>|0.478|0.384|0.479||||

Standard error in parentheses and are clustered at the province level. Columns (4) to (6) report the marginal e↵ects of the Probit model estimation. _⇤ p <_ 0 _._ 05, _⇤⇤ p <_ 0 _._ 01, _⇤⇤⇤ p <_ 0 _._ 001 All regressions include state fixed e↵ect Another interesting result from Table 12 is that concentration of Venezuelans, which is the 42
number of Venezuelan over total population in a municipality, has a negative e↵ect on the propensity of a household to receive _Bolsa Familia_ benefits. This might suggest that having the same ethnic network may lower the need for Venezuelans to access social protection or it might also suggest congestion. So, the model is re-estimated separately first focusing on Roraima and Amazonas, where the concentration is the highest and then on rest of Brazil and find that the relationship is still significant and slightly higher for rest of Brazil, suggesting that both factors are important. Table 13 supports this finding by showing that the average coverage gap is higher in Roraima and Amazonas than the other states in Brazil. The model also seems to explain more of the variation in coverage rate in rest of Brazil than in Roraima and Amazonas. The high unexplained gap may partially reflect the selection of the kind of Venezuelans, who register into _Cadastro ’Unico_ , which this paper could not control for due to data limitations. It may also reflect that some Venezuelans do not have the sufficient Portuguese language skill or the knowledge about their rights for registration.

Table 13: Decomposition of the coverage rates

||(1)<br>Brazil|(2)<br>RR & AM|(3)<br>Brazil - RR &AM|
|---|---|---|---|
|Di↵erential||||
|Brazilian|0.549<sup>_⇤⇤⇤_</sup>|0.699<sup>_⇤⇤⇤_</sup>|0.544<sup>_⇤⇤⇤_</sup>|
||(3258.87)|(780.65)|(3179.49)|
|Venezuelan|0.362<sup>_⇤⇤⇤_</sup>|0.370<sup>_⇤⇤⇤_</sup>|0.335<sup>_⇤⇤⇤_</sup>|
||(65.54)|(58.56)|(29.53)|
|Di↵erence|0.187<sup>_⇤⇤⇤_</sup>|0.329<sup>_⇤⇤⇤_</sup>|0.209<sup>_⇤⇤⇤_</sup>|
||(33.87)|(51.58)|(18.45)|
|Decomposition||||
|Explained|0.0456<sup>_⇤⇤⇤_</sup>|0.137<sup>_⇤⇤⇤_</sup>|0.0857<sup>_⇤⇤⇤_</sup>|
||(13.87)|(38.82)|(11.49)|
|Unexplained|0.142<sup>_⇤⇤⇤_</sup>|0.192<sup>_⇤⇤⇤_</sup>|0.124<sup>_⇤⇤⇤_</sup>|
||(24.85)|(28.90)|(10.59)|
|Observations|8729818|268305|8461513|

_t_ statistics in parentheses _⇤ ⇤⇤ ⇤⇤⇤ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001. Oaxaca decomposition conducted by linear probability model. 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. The analysis is on data from 2019 _Cadastro Unico_ .

Overall, in line with previous results on education and formal labor market, the results on social protection sector suggests that language training programs, provision of information and facilitation of di↵erent document or credential verification and validation may encourage more Venezuelan refugees and migrants to register in _Cadastro ’Unico_ and benefit from receiving _Bolsa Familia_ cash transfers.

43
# **6 Conclusion and Policy Implications**

The inflow of Venezuelan refugees and migrants into Brazil is a humanitarian crisis but it has also created a development challenge. Greater economic and social inclusion of the refugees and migrants will not only build human capital for Venezuelans but will also create a multiplier e↵ect of benefits for the host communities and vice-versa a failure to build human capital will have dire consequences not only the Venezuelans but also on the host communities. This paper is a starting step to investigate the extent of integration of Venezuelans in Brazil and the main challenges that they are facing.

Overall, our results suggest that even though theoretical legal constraint is scant, Venezuelan refugees and migrants still face various challenges to assimilate in Brazil. Congestion, language barrier and mismatch of age and grade attainment are the main impediments for the Venezuelan refugees and migrants children to integrate in schools. Similarly, occupational downgrading is the main barrier for the working age Venezuelan refugees and migrants to access the formal labor market. Venezuelans who register for access to social protection programs are also poorer than their Brazilian counterparts. Another result worth pointing out is the spatial concentration of the Venezuelan refugees and migrants are not helping integration rather it is accelerating congestion and deterring integration, especially in the education sector, stressing the importance of relocation programs in the integration of Venezuelan refugees and migrants.

<!-- Start of picture text --> 127753<br>Primary<br>126049<br>129245<br>Fundamental<br>125084<br>128071<br>High School<br>123858<br>125580<br>College<br>125761<br>0 50,000 100000 150000<br>Predicted Observed<br><!-- End of picture text --> Figure 14: Average predicted<sup>30</sup> and observed monthly wage of Venezuelans by education level 44
Two obvious barriers to integration of Venezuelan refugees and migrants in Brazil, that we could not control for in our estimation due to data limitations, are language barriers and xenophobia. The official language of Brazil is Portuguese, while the mother tongue of Venezuelans is Spanish. Consequently, language barrier may partially explain not only the downgrading in grades and occupations, but also lower enrollment rate in _Cadastro Unico_<sup>_ ́_</sup> . Hostility toward refugees and migrants is another barrier. Brazil ranks 50<sup>_th_</sup> in the migrant acceptance index (Esipova et al., 2017), published by Gallup Poll, below many of the other Latin American host countries, so it is likely that xenophobia against Venezuelans may hinder their integration. However, our data does not provide any information on that. The results of the Venezuelans not being sufficiently rewarded for their human capital provides some evidence of discrimination, but it may also point to productivity loss due to other factors like being fluent in Portuguese that we did not control for in the estimation. However, the results point out that occupational downgrading is higher among Venezuelans suggesting di↵erential treatment. Figure 14 shows that the wage gaps between the observed Venezuelan wage and their predicted wage if they are rewarded equally for their endowments as Brazilians and controlling for selection to waged employment are substantial specially at the fundamental<sup>31</sup> and the high school level.

Our results suggest certain policy implications that may accelerate inclusion of Venezuelan refugees and migrants and their children in Brazil. These policy implications include:

- Facilitation of the process of credential and skill verification and validation - This will subdue downgrading in both schools and the formal labor market.

- Strengthen voluntary relocation to areas within Brazil that have more job opportunities - The federal government, along with UNHCR and other civil society have been implementing a program like this since 2018.

- Provision of language training may help children to enroll in school at the grade, commensurate with their age and also promote employability of Venezuelan adults.

- Development of labor intermediation services focusing on language training, Venezuelan community outreach and specialized counsellors, who know employers where language is less a factor or looking for particular skill sets as suggested in Mazza (2017).

- Increased capacity of schools through holding di↵erent shifts can reduce overcrowding.

- Stronger labor market activation programs to include job intermediation and skills and language training to help in overcoming search barriers and matching friction.

- Continue provision of information assistance for documents issuances and enrollment to education, health and social assistance services and benefits and inform Venezuelan refugees and migrants of their social rights and guarantee access.

It should be noted here that results reported in this paper do not provide evidence of causation rather it provides insights on correlations. Another challenge this paper faces is that the population data, used to estimate the school age and working age population, is not updated regularly, which may lead to overestimation of Venezuelan population in Roraima as it is the main gateway. However, further research is needed to study the impacts of the policy already in place, like the relocation programs. Research is also needed to study how language barrier and xenophobia are a↵ecting

> 31Fundamental level refers here to grades 6 to grade 9 and primary refers to grade 5 completed.

45
integration e↵orts and how best to combat them. With the disruptions of COVID-19, research is also needed to see how it impacted the Venezuelan refugees and migrants in Brazil and how should the livelihood and humanitarian aid be adapted to address its e↵ects.

46