metadata: authors: 0: author: Aksoy,Cevat Giray
1: author: Ginn,Thomas Clinton
count: World
docna: 0: docna: Attitudes and Policies toward Refugees :
            Evidence from Low- and Middle-Income Countries
docty: Policy Research Working Paper
abstracts: cdata!: Exclusionary policies, such as limits on
            refugees’ movement and the right to work, are often
            justified as reasons to minimize economic and social
            tensions with host communities. While these policies have a
            negative effect on refugees’ economic outcomes, their
            ability to mitigate frictions with host communities is
            unknown. Inclusionary policies, on the other hand, could
            foster mutual gains and positive relations. This paper
            builds an extensive dataset of attitudes and economic
            outcomes, refugee populations, and policies at the
            sub-national level covering 14 years (2005-2018) and most
            low- and middle-income countries. Using event study and
            difference-in-differences methodologies, it assesses the
            effects of the arrival of large waves of refugees and finds
            little evidence that large refugee arrivals have a negative
            effect on average attitudes or economic outcomes in the
            short-term. There are also no significant differences
            between places with restrictive and inclusive policies,
            including de jure access to the labor market and opening camps.
display_title: Attitudes and Policies toward
            Refugees : Evidence from Low- and Middle-Income Countries
listing_relative_url: /research/2022/03/33766599/attitudes-policies-toward-refugees-evidence-low--middle-income-countries
topicv3: Migration Policies and Jobs m1504161 2836,Diversified Development m1504447 3031,Sustainable Development Goals m1504460 3134
publishtoextweb_dt: 2022-03-24T00:00:00Z
dois: 10.1596/1813-9450-9985
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1992719
1: author_key: 1389637
model_extractions: Policy Research Working Paper 9985 Attitudes and Policies toward Refugees Evidence from Low- and Middle-Income Countries _Cevat Giray Aksoy Thomas Ginn_ Social Sustainability and Inclusion Global Practice March 2022
Policy Research Working Paper 9985

# **Abstract**

Exclusionary policies, such as limits on refugees’ movement and the right to work, are often justified as reasons to minimize economic and social tensions with host communities. While these policies have a negative effect on refugees’ economic outcomes, their ability to mitigate frictions with host communities is unknown. Inclusionary policies, on the other hand, could foster mutual gains and positive relations. This paper builds an extensive dataset of attitudes and economic outcomes, refugee populations, and policies at the sub-national level covering 14 years (2005-2018) and most low- and middle-income countries. Using event study and difference-in-differences methodologies, it assesses the effects of the arrival of large waves of refugees and finds little evidence that large refugee arrivals have a negative effect on average attitudes or economic outcomes in the short-term. There are also no significant differences between places with restrictive and inclusive policies, including de jure access to the labor market and opening camps.

This paper is a product of the Social Sustainability and Inclusion 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 aksoyc@ebrd.com.

_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
Attitudes and Policies toward Refugees: Evidence from Low- and Middle-Income Countries Cevat Giray Aksoy<sup>∗</sup> Thomas Ginn<sup>†</sup>

**Keywords: refugees, integration, social cohesion, refugee policies.**<sup>1</sup> **JEL codes: F22, J15, J24.**

> ∗European Bank for Reconstruction and Development (EBRD), King’s College London, IZA Institute of Labor Economics, ( `aksoyc@ebrd.com` )

> †Research Fellow, Center for Global Development (CGD) ( `tginn@cgdev.org` )

> 1We thank Leon Bost, Saheel Chodavadia, Franco Malpassi, Reva Resstack, and Pablo Zárate for superb research assistance. We are grateful to Christopher Blair, Guy Grossman, and Jeremy Weinstein for sharing part of the Developing World Refugee and Asylum-Seeker Policy dataset and to Edgar Scrase for sharing UNHCR’s sub-national population data. We additionally appreciate comments from four anonymous reviewers at the World Bank. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. 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. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. Financial support from the IKEA Foundation is also gratefully acknowledged. The views presented are those of the authors and not of CGD, EBRD or any other institution. All interpretations, errors, and omissions are our own.
**1 Introduction**

Policies for refugees like the right to work and freedom of movement are decided by the governments of host countries. The policies vary widely and change over time (Blair et al. 2021a); for instance, while Tanzania prevents refugees from working even within the strictly-enforced camp boundaries, Colombia has extended the right to work to 1.7 million Venezuelans. Social cohesion is one of the many factors influencing governments’ decisions and often used to publicly justify restrictions and containment. Despite anecdotal evidence to the contrary, governments are often worried that more inclusive policies will lead to crowd out of host citizens, reduced social cohesion, and a backlash against the politicians who facilitated refugees’ access.<sup>2</sup> Restrictive policies remain despite the significant costs to refugees and, in many cases, the host communities through missed opportunities for exchange and growth.<sup>3</sup> In this paper, we examine how attitudes toward refugees are affected by the arrival and presence of refugee populations. We first ask how attitudes respond to a large arrival of refugees on average across low- and middle-income countries (LMICs). We then decompose the findings by the characteristics of hosting environments to investigate whether social tension and negative perceptions are reduced in restrictive environments or whether interactions in a more welcoming environment facilitates trust and mutual gains from exchange. We focus on LMICs, where 86% of refugees reside but where relatively little quantitative research has been conducted to date on the determinants of social cohesion (UNHCR 2021b).

We build and combine three main datasets to address our research question. The resulting dataset covers a large proportion of LMICs between 2005 and 2018. The first is a dataset on attitudes. Our main outcome comes from the Gallup World Poll (GWP), due to its extensive spatial and temporal coverage, and we

> 2See Verme and Schuettler (2019) for a review on the minimal evidence on economic effects on host communities.

> 3See Clemens et al. (2018) for a discussion of the literature on the economic effects of policies for both refugees and hosts, and Bahar et al. (2021) for an analysis in Colombia of a recent regularization program.

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supplement these data with 12 additional public opinion surveys. The second dataset is on refugee populations at the sub-national level. We obtain preliminary estimates from the United Nations High Commissioner for Refugees (UNHCR), develop a methodology to impute missing totals, and aggregate at the level of sub-national regions as reported in GWP. The third dataset covers policies on camps, which we also obtain from UNHCR, and on _de jure_ access to the labor market, which we obtain from the authors of the Developing World Refugee and Asylum-seeker Policy dataset (Blair et al. 2021a) and supplement by coding the additional LMICs in our sample.

In order to causally identify the effect of refugees’ presence on attitudes, we use large, sudden arrivals of refugees that create clear before and after time windows at the sub-national level. We examine how attitudes changed within affected regions in our first set of specifications using event study designs. We extend the framework to include regions within the same countries that did not experience a similarly large increase for a difference-in-differences design.

Our preliminary findings suggest that the large arrivals of refugees do not have a negative effect on attitudes in the period we study (up to four years after arrival). The coefficients are positive, small, and statistically insignificant but, importantly, can rule out meaningful negative effects. We then find little evidence that the pattern differs across camp and non-camp settings or across liberal and restrictive right-to-work environments, although the analysis is still at a preliminary stage.

Our strategy allows us to estimate the average effect across contexts, which is arguably the parameter of interest to both policymakers and academics.<sup>4</sup> Studies within a single context have important advantages, but the external validity is always a concern. Meta-analyses may also not produce the true average effect if the set of contexts is systematically biased, perhaps due to data availability or potential publication bias. However, Pottie-Sherman and Wilkes (2017) conduct a metaanalysis of immigration attitudes and group size across 55 studies. Among 487

> 4Our strategy identifies the effect of large waves only. The effects of smaller populations of refugees could evolve differently.

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results, they find “more than half of these results show no relationship and the remainder shows both positive and negative relationships”. Our results are consistent with this work and extend it to refugee flows in low- and middle-income countries. Our findings also relate to the meta-analysis by Verme and Schuettler (2019) who find little average effect of refugees on hosts’ labor market outcomes, which could then influence attitudes.

The paper proceeds as follows. Section 2 reviews the context, and Section 3 outlines literature on the effects of refugee movements both on attitudes and political outcomes. Section 4 describes the data and outlines our empirical approach. Section 5 presents the results after which Section 6 concludes.

# **2 Context**

More than 80 million people were forcibly displaced worldwide at the end of 2020. Over 26 million people crossed international borders as refugees, with 68% of all refugees worldwide coming from only five countries – Afghanistan, Myanmar, South – Sudan, Syria, Venezuela and 73% live in countries neighboring their country of origin (UNHCR 2021b).<sup>5</sup> According to UNHCR, 70% of refugees live in countries where their right to work is restricted, and 66% live in countries where their freedom of movement is restricted (UNHCR 2021a).

One common characteristic of host countries’ policies is camps. While some camps restrict movement, others do not, with Uganda and Iraqi Kurdistan as two examples of the more open “settlement” model.<sup>6</sup> We find that countries with more restrictive labor market policies for refugees are also more likely to build camps (Table A11), but we treat camps and restrictive employment policy as distinct in our discussion and analysis.

> 5The three most common countries of asylum hosted people almost exclusively from one single country: Turkey (3.7 million Syrians); Colombia (1.7 million Venezuelans); and Pakistan (1.4 million Afghans) (UNHCR 2021b).

> 6We define camps “planned camp”s in UNHCR’s dataset, which includes open camps like in Uganda and Kurdistan.

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The effects of camps on host communities on different outcomes – which may in turn affect attitudes – have been evaluated in a limited number of studies. Maystadt et al. (2020) investigate the refugee-driven landscape changes in Africa. The authors find that refugees cause a small increase in vegetation condition, while contributing to increased deforestation. In a related study, using satellite data on forest cover and loss, Salemi (2021) shows that refugee camp openings are associated with a small reduction in the extensive margin of forest loss (i.e., land clearing) and a small increase in intensive margin forest loss (i.e., gradual reductions in canopy cover). Using global data from 1990 to 2018 on locations of refugee communities and civil conflict (including but not limited to camps), Zhou and Shaver (2021) find no evidence that hosting refugees increases the likelihood of new violent conflict, prolongs existing conflict, or raises the number of violent events or casualties in the area. Ginn (2021) examines camps in Jordan and Iraqi Kurdistan and argues they could play an important role for both refugee and host communities in expanding the stock of housing, which could be a key driver of social cohesion in displacement settings.

Another important dimension is _de jure_ policies. Blair et al. (2021a) significantly advance the study of policies toward refugees in low- and middle-income countries by coding _de jure_ policies in more than 90 countries. They show that policies towards refugees are liberalizing over time in LMICS, unlike in high-income countries. They furthermore argue that policy changes have occurred when neighboring states are in civil war, and hence the country is likely to receive refugees. These policy changes are more likely to be liberal (reductions in restrictions) when the political elites are ethnic kin with a victimized group. In related work, the authors argue that more liberal hosting policies attract additional refugees (Blair et al. 2021b).

These _de jure_ policies relate to attitudes. Figure 1 plots an index of these data on policies against the Gallup World Poll measure of attitudes towards immigrants. The positive relationship indicates that countries with more positive attitudes also allow refugees more legal access to the labor market. Causal relationships could run 4
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# **3 Related Literature**

Our analysis relates to several literatures. First, we contribute to the literature on social cohesion and interactions with host communities.<sup>7</sup> Alrababa’h et al. (2021) conduct a large-scale representative survey of public attitudes toward migration in Jordan. The authors find that while economic concerns do not drive Jordanians’ attitudes toward Syrian refugees, humanitarian and cultural factors matter. In particular, Jordanians who are more exposed to refugees’ challenging living conditions and who are less sensitive to cultural threat demonstrate more positive attitudes toward refugees. Ghosn et al. (2019) explores how an individual’s contact with refugees influence their attitudes about hosting refugees. They find that attitudes towards refugees are associated with whether individual respondents have had contact with Syrians in Lebanon —those with such interactions are significantly more likely to support hosting refugees, to consider hiring a refugee, or to allow one of their children to marry a refugee.<sup>8</sup> Alan et al. (2021) presents an experimental evidence from an educational program in southeastern Turkey that aims to build social cohesion in schools by developing perspective-taking ability in children. The authors find that the intervention increased the likelihood of forming inter-ethnic friendship ties as well as reducing ethnic segregation in the classroom and lowering victimization in school grounds.<sup>9</sup> Second, there is a recent literature on the impact of refugee movements and

> 7De Berry and Roberts (2018) argue that several factors mediate social relations in the context of forced displacement. These factors are: (i) perceptions of identity; (ii) pre-existing relationships between displaced and host communities; (iii) capacity/readiness of communities to host displaced people; (iv) duration of displacement; (v) perceived/real disparities between different groups affected by forced displacement; and (vi) patterns of settlement.

> 8In a conjoint survey experiment, Allen et al. (2021) study preferred policy responses of Colombians in response to the large inflow of Venezuelans into their country. They find that those who have less contact with Venezuelans, those who put more weight on economic priorities, and those who see the situation in Venezuela as mainly an economic problem, tend to support policies that are more restrictive.

> 9There is also the literature on the effectiveness of cash transfers in refugee-hosting settings. Valli et al. (2019) examine whether a short-term transfer programme targeted to Colombian refugees and poor Ecuadorians led to changes in social cohesion measures. They find that the programme contributed to reported improvements in social cohesion among Colombian refugees in the hosting community but had no impact on social cohesion among Ecuadorians. See also Devereux et al. (2017) for review article on the targeting effectiveness of social protection programmes.

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political polarization. Steinmayr (2021) investigates how exposure to refugees in Upper Austria affected voting for the far right Freedom Party. He finds that while hosting refugees in a municipality lowers the support for the Freedom Party, municipalities that experienced the transit of refugees exhibit the opposite pattern. These findings are in line with the predictions of the intergroup contact theory, which suggests that contact can improve attitudes towards refugees provided certain conditions are met. Hangartner et al. (2019) find that residents of Greek islands that experience large and sudden inflowes of refugees become more hostile toward refugees, immigrants, and Muslim minorities, and are more likely to support and lobby for more restrictive asylum policies than natives in similar islands that receive fewer or no refugees. Dustmann et al. (2019) find that allocation of larger refugee shares between electoral cycles leads to an increase in the vote share for right-leaning parties with an anti-immigration agenda in Denmark.<sup>10</sup> Y. Zhou et al. (2021) find no evidence that proximity to refugee settlements in Uganda is associated with more negative (or positive) attitudes towards migrants or migration policy.<sup>11</sup> Third, our study relates to work on how policies shape refugees’ integration outcomes. Two recent studies focused on the impact of employment bans that prevent asylum seekers from entering the local labor market upon arrival. Fasani et al. (2021) find that exposure to a ban at arrival reduces refugee employment probability in subsequent years by about 15 percent —an impact driven primarily by lower labor market participation. Marbach et al. (2018) leverage a natural experiment in Germany, where a court ruling prompted a reduction in the length of the employment ban. They find that longer employment bans considerably slowed down the economic integration of refugees. Slotwinski et al. (2019) evaluates whether inclusive labor market policies increase the labor market participation of asylum seekers, by exploiting the variation in asylum policies in Swiss cantons to

> 10Several other papers also examine the impact of forced displacement on political outcomes in different contexts. See Rozo and Vargas (2021) for evidence from Colombia, Vertier et al. (2020) for France, Dinas et al. (2019) for Greece, Gessler et al. (2021) for Hungary, Gamalerio (2018) for Italy, and Ajzenman et al. (2020) for transit European countries.

> 11Y. Zhou et al. (2021) also show that after the 2014 arrival of 1 million South Sudanese refugees to Uganda, host communities with the greatest exposure to refugee settlements experienced substantial improvements in local development and public goods provision.

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which asylum seekers are randomly allocated. They find that inclusive labor market access regulations substantially increase the employment chances of asylum seekers, in particular if the language distance is short. Zetter and Ruaudel (2018) and Aiyar et al. (2016) argue that, for refugees, the right to work and access to labour markets are key for becoming self-reliant and maximizing their net contribution to the public finances in the longer term.

The literature on the link between policies and public preferences is relatively scarce. Bansak et al. (2016) find that public preferences over asylum seekers are shaped by sociotropic evaluations of their potential economic contributions as well as humanitarian concerns about the deservingness of their claims. Zhou (2018) finds that citizens who live near refugees in their country are substantially more likely to support restrictions on citizenship access compared to fellow citizens farther away. She finds that the effect is stronger for more recent arrivals but does not address whether the effect varies by government and humanitarian policies. Blair et al. (2021a) construct an original dataset of _de jure_ asylum and refugee policies covering more than 90 developing countries that are presently excluded from existing indices of migration policy. They find that unlike in the Global North, forced displacement policies in the Global South have become more liberal over time. Betts et al. (2021) explores the role of inter-group interaction in shaping social cohesion between refugees and host communities in East Africa. The authors find mixed results: host community attitudes towards refugees (and vice versa) are likely to be shaped by a combination of intra-group attitude formation at the neighbourhood level, and inter-group interaction, with different mechanisms of interaction likely to be more salient for attitude formation in particular contexts (e.g. urban versus camp-based).

Our data and empirical setting provide some unique advantages that allow us to provide new evidence in several dimensions. First, we use sub-national data on refugee populations covering almost all low- and middle-income countries, which enables us to provide large-scale, cross-country evidence on forced migration and attitudes towards immigrants but at a more local level. Second, we study the role of national-level policies in shaping attitudes towards refugees which is facilitated 8
by comparisons across multiple contexts.

# **4 Research Design**

## **4.1 Data**

### **4.1.1 Refugee Populations**

We use refugee population data provided by UNHCR’s Global Data Service on refugee populations at a sub-national level. These data cover 2001 to 2019 for low- and middle-income countries.<sup>12</sup> We code these locations, which are a mix of regions, cities, camps, and geo-locations, to match the sub-national regions in GWP. We include populations who are displaced outside of their country of birth, which captures people who UNHCR classifies as refugees, asylum-seekers, Venezuelans displaced abroad, and others of concern (all of whom are referred to as “refugees” throughout this paper).<sup>13</sup> While populations are estimated at the country level for every year and displaced nationality, sub-national data is missing for 33% of the total refugee population in LMICs between 2005 and 2018.<sup>14</sup> We therefore develop a methodology to impute the missing population totals at the region-origin-year level. We use the regional proportions of the population for country-origin-years where at least 70% of the refugee population’s location is known and combine this measure with the country-origin-year totals to impute the region-origin’s total population over time.<sup>15</sup> This methodology yields a balanced panel of refugee populations by nationality at the sub-national level. For the analysis presented here, refugee populations are aggregated at the region-year level, combining refugees from all countries of origin.

> 12The population totals are as of December of the reporting year.

> 13This excludes internally displaced people, returnees, and stateleses populations who are also under UNHCR’s mandate. It also excludes Palestinians who are under the mandate of United Nations Relief and Works Agency.

> 14Sub-national data is also missing for almost all of the high-income countries, which also drives our choice to focus on LMICs here.

> 15Further details are available upon request.

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The population totals for 2018 and geographic aggregations are mapped in Figure A2.

# **4.1.2 Gallup World Polls**

Our primary outcome, attitudes towards immigrants, comes from the Gallup World Polls (GWP). We use the GWP because it offers significantly more coverage across locations and time than other opinion polls. The GWP aims to conduct annual, nationally representative surveys of approximately 1,000 individuals in each country on a wide range of topics.<sup>16</sup> We use data from 2005 to 2018. This covers 168 countries with at least one survey, 1,732 survey-years, and 2,017,774 observations in total to select sub-samples for analysis.

Since refugees are often geographically concentrated within a host country, we use the lowest sub-national level that is reported in the GWP data. There are multiple considerations when using the data at this level. First, sub-national locations are not reported for all country-years and the geographic divisions vary by country. For instance, respondents’ locations in Kenya are reported at the province level, which divides the country into 47 sub-national units. Locations in Uganda, in contrast, are reported as one of four sub-national regions.<sup>17</sup> Second, the data is not representative of specific sub-national regions, since the multi-stage stratification at the country level may select only a few sampling units within the region. However, analyzing the aggregatate of enough regions (according to their refugee presence and policy) mitigates most concerns about representativeness. Third, some subnational regions were not included in the GWP. Some were excluded randomly during sampling, while others’ exclusion was an intentional decision by Gallup due to security or sparse populations.<sup>18</sup>

> 16We exclude those who were not born in the country of interview from the sample.

> 17The number of reported geographic divisions could affect the statistical power, precision, and accuracy of our estimates. Countries with fewer geographic units likely capture respondents who are further away from the refugee presence. If so, and if effects decline with distance, then the effects we measure are attenuated towards zero by including more respondents in the affected regions who are largely unaffected. We are currently exploring geographic spillovers and levels of aggregation.

> 18The country coverage, sampling strategy, and more details can be found here: `https://www. gallup.com/file/services/177797/World_Poll_Dataset_Details_052920.pdf`

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# **_Measurement of attitudes_**

Our main outcome is the response to the question “is the city or area where you live a good place to live for immigrants from other countries?”. We use this question because it is the only question on immigrants in most GWP (and therefore global) country-years. However, it is not a direct question about perceptions towards immigrants, and the interpretation is potentially ambiguous. We therefore assess our measure with extensive supplementary data.

First, we examine the individual-level correlations between our main outcome and other questions that are asked in a subset of GWP country-years which capture views about immigrants more directly. Table 1 follows the measurement methodology in Asher et al. (2021). It reports coefficients from regressions of the outcome listed in the left column as the dependent variable, our main outcome as the independent variable, and additional fixed effects in some specifications. The results show that respondents who say their area is a good place to live for immigrants are significantly less likely to agree with statements expressing negative views of immigrants. This is consistent across all nine measure and within region-years.

Second, we compare the region-year averages of our main outcome with questions on immigrant perceptions asked in other surveys. We compile 12 additional cross-country surveys repeated over time by reputable organizations.<sup>19</sup> We merge these with GWP and with each other at the lowest possible sub-national level. We then synthesize common questions across different surveys to further populate country-year and region-year measures on attitudes toward immigrants.<sup>20</sup> The synthesized questions and their sources are listed in Table A2.

Table A3 follows the same methodology as Table 1 but uses region-year level averages instead of individual level correlations. The results tell the same story; our main outcome, whether the respondent believes the current area is a good place for

> 19The datasets are the Afrobarometer, Arab Barometer, Asian Barometer, European Election Study, European Social Survey, Eurobarometer, International Social Survey, Latinobarometer, Pew Global Attitudes, Transatlantic Trends, World Bank Country Opinion Survey, and the World Values Survey.

> 20The most common question asked in the different surveys is whether the respondent would be “ok with an immigrant neighbor”, for instance.

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Table 1: Correlating Main and Additional Outcomes: Individual Level

|Outcome|OLS|Year FE|Region FE|Region-<br>Year FE|Obs|Years|Regions|
|---|---|---|---|---|---|---|---|
|Immigration should be decreased|-0.145***|-0.145***|-0.105***|-0.099***|192,813|11|1,712|
||(0.008)|(0.008)|(0.005)|(0.005)||||
|Immigrants take jobs|-0.048***|-0.047***|-0.025***|-0.025***|135,629|9|1,602|
||(0.006)|(0.006)|(0.005)|(0.005)||||
|Immigrant neighbors is “bad thing”|-0.274***|-0.272***|-0.192***|-0.191***|116,902|3|1,917|
||(0.006)|(0.006)|(0.005)|(0.005)||||
|Immigrants in country is “bad thing”|-0.290***<br>(0.006)|-0.287***<br>(0.006)|-0.206***<br>(0.005)|-0.206***<br>(0.005)|116,285|2|1,918|
|Immigrant marrying relatives is “bad thing”|-0.261***<br>(0.007)|-0.258***<br>(0.007)|-0.168***<br>(0.005)|-0.168***<br>(0.005)|114,426|2|1,918|
|Too many immigrants|-0.045***|-0.048***|-0.017*|-0.013|43,346|3|285|
||(0.013)|(0.013)|(0.010)|(0.009)||||
|Oppose citizenship for immigrants|-0.047***<br>(0.014)|-0.046***<br>(0.014)|-0.035***<br>(0.012)|-0.034***<br>(0.013)|20,047|5|172|
|Immigration is a serious problem|-0.207***<br>(0.016)|-0.207***<br>(0.016)|-0.156***<br>(0.013)|-0.156***<br>(0.013)|11,247|1|135|
|Oppose taking Syrian refugees|-0.213***<br>(0.027)|-0.213***<br>(0.027)|-0.195***<br>(0.019)|-0.195***<br>(0.019)|11,152|1|223|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. Each cell is a separate regression at the individual level with the binary dependent variable listed in the left-most column and the independent variable is our main outcome: 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?” and 0 otherwise. _Years_ refers to the number of survey-years the outcome variable is included in GWP. _Regions_ refers to the number of sub-national regions observed in GWP. Results use the Gallup sampling weights and standard errors are clustered at the region (sub-national) level.

immigrants to live, correlates strongly with perceptions toward immigrants. Regionyears where more respondents report their area is a good place for immigrants have a smaller percentage of the population reporting that they would not want immigrants as neighbors, believe immigrants take jobs, oppose further immigration, etc. This holds across regions and within-region changes over time.

Based on the strong individual- and regional-level relationships, we argue that our main outcome serves as a useful proxy measure for a general perception toward immigrants and refugees. We believe it is likely that many respondents approximated the question as a version of “are immigrants good or bad”. Where possible, however, we support our main findings with the additional related outcomes.

# **4.1.3 Policies**

For data on refugees’ right to work, we utilize the data and methodology from the Developing World Refugee and Asylum-Seeker Policy (DWRAP) dataset by Blair 12
et al. (2021a). This index lists five questions on the laws related to the employment of refugees at the country-year level:<sup>21</sup>

1. Does the law or policy contain a provision guaranteeing the right to work?

2. Does the law or policy contain a provision guaranteeing the right to selfemployment and/or to start a business?

3. Does the law or policy contain a provision guaranteeing the right to work in professional fields provided an individual holds the requisite training or certification?

4. Does the law or policy oblige individuals to hold a work permit?

5. Does the law or policy place additional restrictions on individuals in terms of work, including restrictions on which industries they may work in, or where they may work?

We are grateful to the DWRAP authors for sharing these data for their sample in Africa and much of Asia. We supplement these data by coding laws in 20052018 for 33 additional LMIC countries that are not present in the original DWRAP dataset but have GWP data on attitudes. These laws are listed in Table A4, which complements Table A1 in Blair et al. (2021a). We follow the DWRAP methodology and create a 0 to 1 index that combines the five measures according to Anderson (2008). Higher DWRAP indices indicate laws that afford refugees more access to the labor market.

While the _de jure_ environment is important, it does not provide the complete picture. Laws, both liberal and restrictive, are sometimes ignored in practice. Our hypotheses are based on de facto access – what the refugees and host populations actually experience. We are therefore working to create an additional dataset, the 21Questions 1-3 are assigned 0 if no; 1 if yes, only for recognized individuals; 2 if yes, for all individuals. Question 4 is coded 0 if yes, and permits cost a fee; 1 if yes, but permits are free; 2 if no, work permits are not required. Question 5 is coded 0 if yes, at least two work restrictions are in place, in addition to any work permit requirement; 1 if yes, at least one work restriction is in place, in addition to any work permit requirement; 2 if no work restrictions are in place, in addition to any work permit requirement.

13
Refugee and Asylum-seeker Work Rights dataset (RAWR), with partners at Asylum Access, a non-profit focused on refugees’ work rights.<sup>22</sup> The goal is to measure de facto labor market access, along with freedom of movement, access to public services, and the extent to which host communities benefit from refugee response programming, through a structured survey of practitioners in each country. Data collection is ongoing.

The other policy we evaluate is the existence of camps. These data come from the same UNHCR database as the sub-national populations, described above. We code camp presence in a region-year if there is the existence of a planned camp in the UNHCR data.<sup>23</sup> We again impute across unexplained discontinuities in camp existence and check the data against secondary sources. We measure camp presence as a binary variable, as we do not have a reliable measure of the proportion of the refugee population within a region-year living in the camps.

## **4.1.4 Additional Data**

We further supplement the main datasets with region-year variables from AidData’s GeoQuery (population, GDP based on nightlights, distance to the border and urban centers, and ACLED conflict data), country-year data from the World Bank (income ranking and population), and country-year data from the Polity5 project on institutions (Systemic Peace 2018).

# **4.2 Empirical Strategy**

Our overall question is how the arrival and presence of refugees affect host community attitudes. First, we address how refugees affect attitudes on average across locations. Then we examine the heterogeneity of the effects by host country policy, in terms of the _de jure_ right to work and the existence of camps.

> 22This project builds on Asylum Access’ earlier efforts to create global scorecards for refugees’ work rights, available here: `https://www.refugeeworkrights.org/scorecard/` .

> 23This excludes self-settled camps, transit centers, and collective centers, which are either temporary or not initated by policymakers

14
There are multiple challenges to identifying the causal effects of the refugee presence on host community attitudes. First, refugees do not randomly select locations; they may settle – or stay longer – in places with more favorable attitudes. The host governments also may select the locations where refugees are required to live that are less costly politically for the host government. Overall, the areas where refugees live potentially differ from areas where refugees do not live within each country. We therefore adopt strategies that examine changes in attitudes _within_ a region over time. These region-level fixed effects capture characteristics that are fixed over time like distance to the border and the presence of major cities.

Second, attitudes towards refugees, and factors that potentially affect attitudes like income and education levels, likely change over time, independent of the presence of refugees. Therefore, even within regions, relating the change in the number of refugees with changes in attitudes could capture pre-existing trends that would have happened independently of the refugee presence.

To identify the effects of the refugee presence, we therefore exploit large waves of refugee arrivals to a region over a short period of time. These shocks define “pre” and “post” periods within regions, and the assumption is that the timing of the shocks are indepedent of region-level trends. The specifications capture the immediate and short-term effects under additional assumptions which vary by specification and are discussed below. However, although displacement is often a long-term situation, we find signifcant variation in the duration of refugee presence by regions.<sup>24</sup> We therefore limit the time windows to four years before and after the events.

We define an “event” in multiple ways. Our main definition is an increase of at least 10,000 refugees in one calendar year. We vary the 10,000 cutoff in alternative specifications. We further explore percentage increases (i.e. a 50% increase that represents an absolute gain of at least 5,000 refugees) and gains in

> 24The length of displacement is potentially affected by attitudes and policies. This threatens the validity of a two-way fixed effects model that includes all regions and years and further motivates our event design. We are currently exploring how pre-existing attitudes, policies, and other characteristics relate to the duration of displacement situations to evaluate this further.

15
per capita measures (i.e. an increase of 100 refugees per 100,000 residents). We look at the largest increase within eight-year windows. Therefore, if a region has consecutive years of growth of at least 10,000 refugees, we select the year with largest absolute change as the event.<sup>25</sup> Since we study a period of 14 years, a few regions are included twice that have events more than four years apart. The empirical strategy is therefore at the region-event level.

We present multiple specifications that allow for different threats to identification. Our first model compares the periods before and after events, within regions with an event. We report the specification that pools the four years in the post period (which mirrors a regression discontinuity design) in the tables and the event study specification that includes binary variables for leads and lags (each of the years until or since the event) in the figures. In most specifications we include year fixed effects that control for trends across the sample.<sup>26</sup> Formally, we estimate the following equation using only eventually-treated regions: where _i_ denotes individuals, _r_ sub-national regions, and _t_ years. Our outcome, _Attitudesirt_ , comes from questions asked of all GWP respondents about their views on whether _"the city or area where they live a good place or not a good place to live for immigrants from other countries"_ . Responses are coded as dummy variables, with one representing a positive answer and zero otherwise.

_Wavert_ denotes our treatment variable, which takes a value of 1 if the region _r_ received a large wave of refugees in period _t_ or at any earlier period as discussed above. _Rrt_ is a vector containing two control variables at the region-year level: the Inverse Hyperbolic Sine (IHS) transformed total population of region _r_ at period _t_ , and the IHS transformed refugee population in the same region and period. _Xirt_

> 25We are working on additional definitions, including examining the first event in a window, as well as defining an event across consecutive years, to better accurately capture the pre-post spirit of the idea.

> 26We also include specifications without year fixed effects given the recent concerns raised by De Chaisemartin and d’Haultfoeuille (2020) and others.

16
includes individual-level control variables: age, age squared, and indicator variables indicating whether the respondent is male, has completed secondary or tertiary education, and lives in a small town, suburb, or a large city. Finally, _γr_ is a sub-national region fixed effect, which controls for time-invariant variation in the outcome variable caused by factors that vary cross-sub-national regions. Year fixed effects, _τt_ , capture the impact of global shocks that affect all sub-national regions simultaneously. Our standard errors, in this specification and the ones below, are clustered at the region-event level, to account for correlation over time.

Our second model adds the regions that did not experience an event in order to potentially control for country-level trends. We take the sample of country-years from the first specification - regions with events, and the eight-year windows around those events - and add the regions that did not experience an event. When including year or country by year fixed effects (along with the region-event fixed effects present in all regressions), this respresents a difference-in-differences design, when the years after the event are pooled into one binary variable, or an event study design with controls, when each lead and lag year in treated regions is assigned an indicator variable. The identifying assumption is that attitudes in the regions with an event would be on parallel trends with regions that never have an event if refugees had not arrived. However, this specification requires the assumption that events in one region of the country did not affect attitudes in regions without events. If respondents in other parts of the country also change their attitudes based on the events in other regions, this specification - which compares treated and untreated regions at a given point in time - would not capture those country-level changes.<sup>27</sup> This difference-indifferences specification is very similar to the one above, but includes never-treated regions from countries with at least one treated region. This leads to the following specification:

> 27 We are working on specifications at the country-level that also introduce countries without events as controls.

17
where _i_ denotes individuals, _r_ sub-national regions, and _t_ years as noted above. Other covariates are identical to the ones reported for equation 1. In addition, _c_ denotes countries. _τt ∗ φc_ is a country-by-year fixed effect, which controls for all potentially omitted variables that can vary across countries and years.

These models report the average effects across all treated regions. We then examine the heterogeneity of the effects according to our two dimensions of interest: the _de jure_ right to work policy index (scaled to 0-1) and an indicator for the existence of camps. The question is whether refugee waves in places with camps (or restrictive laws) have the same average effect as places without camps (or liberal laws). This specification now compares across locations, instead of the within-region comparisons across time for the average effects. Places that implement restrictive laws likely differ from places with liberal laws in more dimensions than refugee policy, and these differences could instead explain any differences in the evolution of attitudes across settings. We begin to assess the comparability, as well as scope for reverse causality (i.e. places that had or anticipated more negative effects opened camps), in the results section, with more analysis to follow.

Heterogeneity results are based on the following equations, identical to the ones above but including an interaction term of between our treatment variable and the variable of interest in each case: _Attitudesirt_ = _β_ 1 _Wavert_ + _β_ 2 _Policyr ∗ Wavert_ + _Rrt_<sup>_′θ_+</sup><sup>_X_</sup> _irt_<sup>_′λ_+</sup><sup>_γr_+</sup><sup>_τt_+</sup><sup>_εirt_(3)</sup> _Attitudesirt_ = _β_ 1 _Wavert_ + _β_ 2 _Policyr ∗ Wavert_ + _Rrt_<sup>_′θ_+</sup><sup>_X_</sup> _irt_<sup>_′λ_+</sup><sup>_γr_+</sup><sup>_τt∗φc_+</sup><sup>_εirt_(4)</sup> where, as before, equation (3) uses only treated regions, while specification (4) includes never-treated regions from countries with at least one treated region.

_Policyr_ is either an indicator variable equal to 1 if there was a refugee camp present in region _r_ at any moment in the event window around a large wave of refugees at that region (and 0 if not or if that region is untreated), or the value of the employment policy index from the DWRAP dataset. The policy variable itself 18
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refugee population was increasing in the window preceding the event, then the jump in the year of the event (year 0), and then a slight increase in the year after the event on average.

Figure 3 shows the evolution of attitudes in the same sample (i.e., regions with at least one event in the window around the event) in multiple ways. The top panel shows evolution of attitudes in levels where an event is defined in absolute levels as “10,000 increase or more”, and the bottom panel shows trends in attitudes in terms of percentage changes where the event is defined as “100 per cent increase or more” in the number of refugees. Neither figure shows evidence of a trend in average attitudes before the event. None of the four years is statistically different from 0.

Table 2 provides the regression form of the results. The first three columns, which look only at the regions with events, also reflect the positive effect. Column 1 shows the results only with region-event fixed effects. Column 2 adds controls for region-level characteristics, including the inverse hyperbolic sine of the refugee population and the regional population, and individual-level variables like age, gender, education, and urban or rural location. Column 3 adds year fixed effects to account for trends across these regions. Columns 4 and 5, however, add the regions without events from the sample of 34 countries, in the same year windows. The coefficient on the indicator variable for the post-period is insignificant and very close to 0 in column 4, which adds year fixed effects that control for trends in the full sample of countries, and column 5, which adds country by year fixed effects that control specifically for country-level time effects. Columns 4 and 5 suggest that the small, insignificant before and after change measured in columns 1-3 is also – occurring in the regions without events, so the net effect the difference between the regions with events and without events, controlling for pre-existing differences – is close to 0. The results importantly provide no evidence for negative effects of refugee arrivals over the time horizon we analyze.

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Table 2: Refugee Waves and Attitudes Toward Immigrants

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|||||+ Never treated||
|VARIABLES|Event FE only|+ Controls|+ Year FE|regions|Country*Year FE|
|Post-event: _≥_10,000 increase|0.008|0.014|0.031|-0.005|0.004|
||(0.016)|(0.023)|(0.025)|(0.020)|(0.020)|
|IHS refugee population||-0.001|-0.003|0.006|0.002|
|||(0.005)|(0.005)|(0.004)|(0.004)|
|IHS region population||-0.045|0.016|-0.030|-0.004|
|||(0.209)|(0.266)|(0.128)|(0.176)|
|Age||0.001|0.001|0.000|0.001|
|||(0.001)|(0.001)|(0.001)|(0.001)|
|Age<sup>2</sup>||-0.000|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|(0.000)|
|Male||0.016**|0.017**|0.004|0.004|
|||(0.007)|(0.007)|(0.005)|(0.005)|
|Completed secondary education||0.021**|0.020**|0.019***|0.017***|
|||(0.010)|(0.010)|(0.007)|(0.006)|
|Completed college education||0.060***|0.059***|0.050***|0.048***|
|||(0.013)|(0.013)|(0.011)|(0.011)|
|Lives in small town||0.017|0.013|0.044***|0.044***|
|||(0.015)|(0.014)|(0.011)|(0.010)|
|Lives in suburb of large city||0.066***|0.067***|0.084***|0.081***|
|||(0.017)|(0.017)|(0.015)|(0.014)|
|Lives in large city||0.063***|0.065***|0.069***|0.067***|
|||(0.016)|(0.016)|(0.011)|(0.011)|
|Constant|0.605***|1.214|0.297|0.968|0.610|
||(0.007)|(3.193)|(4.100)|(1.896)|(2.611)|
|Observations|71,313|71,313|71,313|216,051|216,051|
|R-squared|0.092|0.095|0.098|0.130|0.142|
|Event FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|No|Yes|Yes|No|
|Country*Year FE|No|No|No|No|Yes|
|Never treated Regions|No|No|No|Yes|Yes|
|Dep Var Mean|0.609|0.609|0.609|0.614|0.614|
|Events|113|113|113|113|113|
|Years|12|12|12|12|12|
|Regions|101|101|101|600|600|
|Countries|35|35|35|35|35|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the attitudes towards immigrants question, equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals. Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1, 2, and 3) and countryyears (columns 4 and 5) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 10,000 refugees in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

a recent literature has shown that the TWFE estimator problematic in the pres22
ence of heterogeneous<sup>29</sup> and dynamic<sup>30</sup> treatment effects (Sun and Abraham 2021; Borusyak et al. 2021; Goodman-Bacon 2021; De Chaisemartin and d’Haultfoeuille 2020; Callaway and Sant’Anna 2021).

We examine the validity of the pre-trends assumption and the properties of our TWFE regressions as the impact of refugee inflow is likely to vary across subnational regions and over time in Figure A1. In line with the result presented in Figure 3, none of the results reported in all panels of Figure A1 provide evidence of pre-trends.

# **_Refugee waves and attitudes toward immigrants_**

Table 3 replicates the specification from columns 3 and 5 in Table 2 for different definitions of events. Columns 1 and 2 lower the cutoff to 5,000 refugees in one year, while columns 3 and 4 raises the cutoff to 50,000 refugees per year. Columns 5 and 6 define the event as an increase of at least 100%, representing an absolute change of at least 5,000 refugees in the year, and set the population to 10 in region-years before the event with zero refugees in order for the percentage to be defined. The results are mostly consistent with the results in Table 2, with regiononly samples showing positive, insignificant coefficients at multiple cutoffs and the all-region samples showing effects close to 0, except for column 4. Columns 3 and 4 together suggest large waves (more than 50,000 refugees) led to more positive attitudes in the receiving region (column 3), but grew by 4.5 percentage points less on average than the other regions that did not receive the largest wave within the country. This is likely due to the binary nature of the event definition, as other _β_ ˆ _T W F E_ 29In theis a caseweightedof heterogeneousaverage of grouptreatmenttime-leveleffects,averagethetreatmentproblem ariseseffects,becausewhere thetheweightsestimatedare unequal over groups and time, and may be negative. In a general design, weights are more likely to be negative for periods in which many groups are treated and to groups treated for many periods (De Chaisemartin and d’Haultfoeuille 2020). In a staggered adoption design (a setting where units can move into, but not out, of a binary treatment with heterogeneous timing between groups), this implies that weights on later time periods are more probable to be negative (Borusyak et al. 2021).

30When considering a setting with two time periods and one treatment (treatment status changes by one unit) and one control group (treatment status is unchanged), the possibility of dynamic effects requires one to account for the prior path of treatment and control group. Intuitively, a TWFE difference in differences regression does not control for past treatment history, and is thus not robust to dynamic effects. Similarly, Sun and Abraham (2021) show that the pre- and postevent effect estimates in the canonical event study setting may mix, leading to incorrect estimates of pre-event trends, as well as the instantaneous and dynamic effect of treatment.

23
regions in these countries likely also increased their refugee populations at the same time, but by less than the 50,000 cutoff; we are exploring this and other hypotheses. We use the absolute increase of 10,000 refugees as the main event definition to balance the sample size, in terms of number of region-events and countries, and the potential magnitude of the effects.

Table 3: Varying the Definition of an Event

|VARIABLES|(1)<br>Attitudes<br>Event Regions|(2)<br>Attitudes<br>All Regions|(3)<br>Attitudes<br>Event Regions|(4)<br>Attitudes<br>All Regions|(5)<br>Attitudes<br>Event Regions|(6)<br>Attitudes<br>All Regions|
|---|---|---|---|---|---|---|
|Post-event: _≥_5,000 increase|0.015|-0.033*|||||
||(0.022)|(0.018)|||||
|Post-event: _≥_50,000 increase|||0.080**|0.029|||
||||(0.039)|(0.044)|||
|Post-event: _≥_100% increase|||||0.051*|-0.017|
||||||(0.030)|(0.025)|
|IHS refugee population|-0.002|0.002|-0.002|0.006|-0.011**|-0.002|
||(0.004)|(0.004)|(0.008)|(0.008)|(0.005)|(0.005)|
|IHS region population|0.090|0.063|-0.830**|-0.630**|-0.028|0.096|
||(0.175)|(0.117)|(0.375)|(0.246)|(0.203)|(0.139)|
|Observations|90,496|268,390|28,540|87,366|60,654|195,809|
|R-squared|0.102|0.133|0.099|0.163|0.102|0.147|
|Event FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|Yes|No|Yes|Yes|
|Country*Year FE|No|Yes|No|Yes|No|No|
|Never treated Regions|No|Yes|No|Yes|No|No|
|Dep Var Mean|0.611|0.611|0.597|0.596|0.612|0.618|
|Events|150|150|37|37|108|108|
|Years|12|12|12|12|12|12|
|Regions|132|697|34|232|104|529|
|Countries|41|41|18|18|35|35|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the attitudes towards immigrants question, equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals. Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1, 3, and 5) and countryyears (columns 2 and 6) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 5,000 refugees (row 1), 10,000 refugees (row 2) or 50,000 refugees (row 3) in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

24
Table 4: Refugee Waves on Other Outcomes

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||IHS Income|IHS Income|Divers Index|Divers Index|Satisfaction|Satisfaction|
|VARIABLES|Event Regions|All Regions|Event Regions|All Regions|Event Regions|All Regions|
|Post-event: _≥_10,000 increase|-0.063|-0.044|3.106|0.955|0.010|-0.009|
||(0.103)|(0.064)|(2.249)|(1.708)|(0.021)|(0.016)|
|IHS refugee population|-0.005|-0.003|-0.365|0.156|-0.001|-0.000|
||(0.019)|(0.012)|(0.302)|(0.324)|(0.005)|(0.004)|
|IHS region population|-0.166|0.318|4.006|14.189|-0.076|-0.089|
||(0.901)|(0.638)|(18.036)|(11.865)|(0.220)|(0.137)|
|Age|0.012***|0.013***|-0.043|0.056|0.001|-0.001**|
||(0.004)|(0.003)|(0.081)|(0.047)|(0.001)|(0.001)|
|Age<sup>2</sup>|-0.000**|-0.000***|0.001|-0.001|0.000|0.000***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|
|Male|0.109***|0.187***|0.746|0.733**|-0.015**|-0.009**|
||(0.023)|(0.017)|(0.455)|(0.332)|(0.007)|(0.004)|
|Completed secondary education|0.462***|0.509***|1.445**|1.802***|-0.001|-0.007|
||(0.026)|(0.021)|(0.644)|(0.438)|(0.009)|(0.007)|
|Completed college education|0.954***|1.032***|5.318***|6.002***|-0.011|-0.021**|
||(0.045)|(0.047)|(1.053)|(0.865)|(0.016)|(0.011)|
|Lives in small town|0.053|0.162***|0.735|3.200***|0.007|0.019**|
||(0.056)|(0.030)|(1.076)|(0.774)|(0.014)|(0.008)|
|Lives in suburb of large city|0.121*|0.281***|4.189***|5.045***|-0.007|0.007|
||(0.068)|(0.042)|(1.207)|(1.023)|(0.020)|(0.014)|
|Lives in large city|0.271***|0.357***|3.515***|4.749***|0.012|0.028***|
||(0.065)|(0.032)|(1.246)|(0.847)|(0.018)|(0.010)|
|Constant|9.619|1.917|-14.012|-168.128|1.820|2.006|
||(13.964)|(9.561)|(278.139)|(177.102)|(3.408)|(2.035)|
|Observations|68,008|228,701|53,375|191,628|70,764|230,140|
|R-squared|0.420|0.372|0.146|0.208|0.075|0.108|
|Event FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|Yes|No|Yes|No|
|Country*Year FE|No|Yes|No|Yes|No|Yes|
|Never treated Regions|No|Yes|No|Yes|No|Yes|
|Dep Var Mean|7.690|7.449|47.72|49.17|0.686|0.695|
|Events|110|110|107|107|113|113|
|Years|10|10|12|12|12|12|
|Regions|98|584|96|561|101|601|
|Countries|34|34|33|33|35|35|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variables are the inverse hyperbolic transformation of per capita annual household income in international dollars (columns 1 and 2), diversity index (columns 3 and 4), and a satisfaction question (columns 5 and 6), equal to 1 if the respondant is satisfied with the city or area where he lives, 0 if not, and blank or missing otherwise. Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1, 3, and 5) and country-years (columns 2, 4 and 6) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 10,000 refugees in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

# **_Refugee waves and other outcomes_**

Table 4 looks at three additional GWP outcomes using the same specifications and the main definition of an event. Columns 1 and 2 examine individual-level responses to per capita annual income in international dollars. After transforming using the inverse hyperbolic sine, we find insignificant coefficients in both specifica25
tions. In columns 3 and 4, we examine Gallup’s Diversity Index, which was designed to measure attitudes towards people from different racial, ethnic, or cultural groups. Consistent with Table 2, we find positive but insignificant effects using the eventregions-only specification, which flip to negative and still statistically insignificant when adding regions without an event. Columns 5 and 6 examine responses to the question “are you satisfied or dissatisfied with the city or area where you live?”, a companion question to our main measure on immigrants. Again neither specification is statistically significant.

# **5.2 Heterogeneity by Employment Policies and Camps**

The section above argues that there is little effect on attitudes towards immigrants in the periods immediately following a large wave of refugees on average across affected regions in LMICs with available data. This averages across significant variations in hosting policies, which we hypothesize could affect social cohesion and other outcomes for host communities.

We next decompose the average effects by variations in the policy index and the presence of camps. Instead of looking at changes within regions, this exercise involves comparing effects across different hosting situations. Policies are not randomly assigned and could be endogenous to attitudes in the host region; the estimates can be interpreted as causal under the assumption that attitudes would have evolved similarly if policies had been the same.<sup>31</sup> In order to gauge the similarities across regions, Table 5 looks at a set of observable characterics compiled from many of the sources listed in Section 4, including GWP, UNHCR, DWRAP, and AidData.<sup>32</sup> Regions may still differ on a number of unobservables, but the observables provide a starting point to assess

> 31In the DWRAP dataset, out of 113 events, we identified 30 cases where there were changes in employment policy towards refugees in the event window. For events with a single change, 9 of them registered a "positive" change and 5 of them a "negative" one. There are 16 events (15 from Turkey and 1 from Rwanda) that experience both a positive and a negative change in their event window. Our main results are robust to dropping these events.

> 32For comparison, Table A11 lists the same descriptive statistics for all regions in LMICs in 2018 in countries with at least 5,000 refugees.

26
Table 5: Summary Statistics by Employment Policies and Camp Presence

|||Camp presence|||Median Index||
|---|---|---|---|---|---|---|
||No|Yes|Diference|Below|Above|Diference|
|Good Place for Immigrants (Main Outcome)|0.614|0.608|-0.006|0.602|0.629|0.027|
||(0.180)|(0.228)|(0.903)|(0.210)|(0.183)|(0.559)|
|Refugee population|94,344|101,401|7,056|99,825|92,845|-6,980|
||(109,769.017)|(130,601.280)|(0.797)|(101,545.086)|(146,151.220)|(0.820)|
|Total Population|4,840,615|3,393,306|-1,447,310|3,357,098|5,839,565|2,482,467|
||(7,352,056.533)|(5,010,991.585)|(0.296)|(4,487,745.424)|(8,959,934.481)|(0.177)|
|GDP per capita (USD PPP)|8,891|3,407|-5,485|6,651|6,276|-375|
||(5,573.259)|(4,858.658)|(0.000)|(5,200.356)|(7,173.631)|(0.808)|
|Elementary education (%)|38.4|57.4|19.0|44.8|49.5|4.8|
||(18.658)|(24.189)|(0.000)|(20.553)|(27.078)|(0.423)|
|More than elementary education (%)|61.6|42.6|-19.0|55.2|50.5|-4.8|
||(18.658)|(24.189)|(0.000)|(20.553)|(27.078)|(0.423)|
|Rural (%)|12.3|37.9|25.6|23.6|22.4|-1.2|
||(23.037)|(35.782)|(0.001)|(34.799)|(25.416)|(0.863)|
|Small town (%)|27.9|34.2|6.4|30.1|31.5|1.4|
||(27.690)|(31.610)|(0.359)|(29.950)|(28.860)|(0.836)|
|Suburbs or large city (%)|59.8|27.9|-32.0|46.4|46.1|-0.3|
||(32.926)|(34.962)|(0.000)|(35.998)|(39.795)|(0.978)|
|Minimum distance to border (km)|16|9|-7|13|12|-2|
||(43.180)|(26.389)|(0.345)|(32.135)|(45.091)|(0.843)|
|Travel time to a major city|175|192|17|173|200|27|
||(349.218)|(149.347)|(0.765)|(275.153)|(291.992)|(0.689)|
|Population density|1,103|173|-930|710|684|-26|
||(3,425.058)|(395.252)|(0.074)|(3,089.883)|(1,413.398)|(0.959)|
|Camp presence (%)|0.0|100.0|100.0|45.3|37.9|-7.4|
||(0.000)|(0.000)||(50.253)|(49.380)|(0.524)|
|Employment index|0.3|0.2|-0.1|0.2|0.4|0.2|
||(0.265)|(0.066)|(0.004)|(0.056)|(0.298)|(0.000)|
|Polity index|1.8|1.3|-0.5|0.5|3.5|3.0|
||(4.805)|(4.345)|(0.631)|(4.227)|(4.702)|(0.007)|
|_N_|47|35|82|53|29|82|

Notes: Observations are at the region-event level. The sample consists of the events in the the main specifications for waves of at least 10,000 refugees in a year. Time-varying variables are reported at the year of the event. The first variable is the main dependent variable from GWP in specifications like Table 2, equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement. “Refugee population” is the region-level refugee population described in Section 4.1. “Total population” is the region-level population figure based on the UN Gridded Population Data of the World, version 4. GDP per capita comes from the GWP. Minimum distance to the border, travel time to a major city, and population density are provided by AidData and spatially merged to the boundaries in GWP. “Camp presence” is the percentage of region-events with camps, with the data described in Section 4. The Employment index corresponds to _de jure_ policies for labor market access from the DWRAP data and supplemented by the authors. It is scaled from 0 to 1, with 1 representing no legal barriers to employment. The polity index is the polity2 scores from the Polity Project, scaled from -10 to 10, with 10 representing democracy and -10 representing autocracy.

the necessary assumption and comparability. Encouragingly, the levels of the main outcome on attitudes from GWP is similar across regions with and without camps, and in regions above and below the median of the employment index within the 27
<!-- Start of picture text --> g<br>g<br>§<br>3 =<br>3B<br>a 2<br>ae<br>oo iJc—J<br>a s<br>eo ¢<br>a =<br>[4<br>“4 3 2 -1 0 1 2 3<br>Year Relative to Large Arrival<br>== Below Median LMA “=== Above Median LMA<br><!-- End of picture text -->
Figure 5 plots the mean refugee population levels for four pre-event and four post-event years, for sub-national regions with and without camps.<sup>35</sup> It shows that refugee populations in regions without camps (the blue line) and regions that will get camps in the window (the green line) before the event, followed by the expected jump at the time of the event. The increases in regions with camps is slightly larger than regions without camps in the two years after the event, though not statistically different. Overall, regions with and without camps were roughly on similar population trends both before and after the events.

Figure 6 plots the evolution of attitudes by the _de jure_ policy environment, again in levels, within the sample of regions with events only. Neither the blue line (regions with below median labor market access) or the green line (regions with above median policies) show no significant pre-trends. Overall, the patterns indicate similar pre-trends, which provide suggestive evidence for the identifying assumption. It furthermore shows no evidence that attitudes in regions with above median policy evolve differently than regions with below median policy.

Table 6 shows the regressions, combining post-event time periods into a single indicator. Column 1 shows the specification with only event regions and region-event and year fixed effects, analogous to the average specification in Table 2, Column 3. It reflects the positive but insignificant coefficient on attitudes after the event and finds no statistical difference in the interaction, which represents the difference in the above-median regions from the below-median regions. Column 2 of Table 6 adds the regions without events, analogous to Table 2 Column 5, with region-event and country-year fixed effects. These specifications, as in the Table 2 regressions, show no evidence of a main effect, but also no effect of a differential effect in above-median regions. Column 3 looks at one of the secondary outcomes, the inverse hyperbolic sine of per capita income. Columns 4 through 6 examine the same specifications and change the definition of an event to an increase in 5,000 refugees in a calendar year. The columns are consistent and suggest a preliminary main finding that attitudes and incomes in regions with more inclusive _de jure_ policy evolve similarly in regions

> 35Regions are assigned a 1 if there is a camp in the region at any point in the sample window.

29
<!-- Start of picture text --> g<br>=2<br>g<br>is<br>3<br>= 8s<br>a<br>o3s=<br>E<br>i}<br>~ o<br>-4 3 -2 “1 0 ; 1 2 3<br>Year Relative to Large Arrival<br>=—————= Regions w/oCamp “=== Regions w/ Camp<br><!-- End of picture text --> <!-- Start of picture text --> %<br>o<br>Zoos<br>Ss Fe<br>n<br>[o)<br>a<br>2<br>Za<br>Zzo csS<br>B<br>=<br>S<br>&<br>BS F<br>oa<br>at<br>—<br>k=<br>=<br>-4 3 -2 “1 0 ; 1 2 3<br>Year Relative to Large Arrival<br>=———= Below Median LMA == Above Median LMA<br><!-- End of picture text -->
with less inclusive policies.

Table 6: Heterogeneity on Attitudes Response by _de jure_ Policies

|VARIABLES|(1)<br>Attitudes<br>Event Regions|(2)<br>Attitudes<br>All Regions|(3)<br>IHS Income<br>All Regions|(4)<br>Attitudes<br>Event Regions|(5)<br>Attitudes<br>All Regions|(6)<br>IHS Income<br>All Regions|
|---|---|---|---|---|---|---|
|Post-event: _≥_10,000 increase|0.035|0.005|-0.072||||
||(0.030)|(0.031)|(0.093)||||
|Post-event: _≥_10,000 increase*<br>Median Policy|-0.035|-0.002|0.009||||
||(0.036)|(0.037)|(0.129)||||
|Post-event: _≥_5,000 increase||||0.021|-0.030|-0.074|
|||||(0.025)|(0.026)|(0.073)|
|Post-event: _≥_5,000 increase*<br>Median Policy||||-0.041|-0.020|-0.093|
|||||(0.031)|(0.034)|(0.117)|
|IHS refugee population|-0.002|0.000|-0.003|-0.001|0.002|0.002|
||(0.005)|(0.005)|(0.014)|(0.005)|(0.004)|(0.012)|
|IHS region population|0.053|0.001|0.485|0.130|0.077|0.294|
||(0.279)|(0.185)|(0.716)|(0.180)|(0.120)|(0.322)|
|Observations|67,391|186,505|188,663|86,217|238,487|258,194|
|R-squared|0.092|0.129|0.368|0.100|0.122|0.382|
|Event FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|No|Yes|No|No|
|Country*Year FE|No|Yes|Yes|No|Yes|Yes|
|Never treated Regions|No|Yes|Yes|No|Yes|Yes|
|Dep Var Mean|0.620|0.631|7.357|0.619|0.624|7.283|
|Events|106|106|102|143|143|141|
|Years|12|12|10|12|12|10|
|Regions|97|547|531|128|644|628|
|Countries|33|33|32|39|39|38|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variables are the attitudes towards immigrants question (columns 1, 2, 4 and 5), equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals, and inverse hyperbolic transformation of per capita annual household income in international dollars (columns 3 and 5). Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1 and 4) and country-years (columns 2, 3, 5 and 6) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 10,000 refugees (rows 1 and 2) or 5,000 refugees (rows 3 and 4) in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

Figure 7 and Table 7 show heterogeneity on attitudes by the existence of camps. Figure 7 again shows the average attitudes in blue, with nearly flat pre-trends. The green line shows the attitudes in regions with camps with similar levels and lack of pre-trends. After the event, attitudes evolve similarly in regions with and without camps. This result is mirrored in Table 7, where the insignificant average positive effect again manifests in Columns 1 and 4 (for events of 10,000 and 5,000 refugee increases, respectively) with no statistically significant differential effect between regions with and without camps. Including less-affected regions and examining 31
<!-- Start of picture text --> ra<br>@ os<br>Zz °<br>B<br>o<br>a<br>~ 8<br>vo<br>zZ<br>2<br>B<br><<br>=I<br>Bz=Ss_<br>a— 4<br>£<br>a<br>“4 3 -2 “1 0 ; 1 2 3<br>Year Relative to Large Arrival<br>=——————= Regions w/o Camp “=== Regions w/ Camp<br><!-- End of picture text -->
Table 7: Heterogeneity on Attitudes Response by Refugee Camp Presence

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Attitudes<br>Event Regions|Attitudes<br>All Regions|IHS income<br>All Regions|Attitudes<br>Event Regions|Attitudes<br>All Regions|IHS Income<br>All Regions|
|Post-event: _≥_10,000 increase|0.039|-0.010|-0.006||||
||(0.031)|(0.025)|(0.058)||||
|Post-event: _≥_10,000 increase*Camp|-0.015|0.028|-0.074||||
||(0.032)|(0.033)|(0.092)||||
|Post-event: _≥_5,000 increase||||0.006|-0.057**|-0.069|
|||||(0.028)|(0.023)|(0.054)|
|Post-event: _≥_5,000 increase*Camp||||0.019|0.050*|-0.040|
|||||(0.028)|(0.030)|(0.088)|
|IHS refugee population|-0.003|0.002|-0.003|-0.002|0.002|0.002|
||(0.005)|(0.004)|(0.012)|(0.004)|(0.004)|(0.011)|
|IHS region population|0.022|-0.002|0.323|0.089|0.073|0.215|
||(0.265)|(0.177)|(0.639)|(0.178)|(0.119)|(0.308)|
|Observations|71,313|216,051|228,701|90,496|268,390|300,547|
|R-squared|0.098|0.142|0.372|0.102|0.133|0.386|
|Event FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|Yes|Yes|No|No|
|Country*Year FE|No|Yes|No|No|Yes|Yes|
|Never treated Regions|No|Yes|No|No|Yes|Yes|
|Dep Var Mean|0.609|0.614|7.449|0.611|0.611|7.363|
|Events|113|113|110|150|150|147|
|Years|12|12|10|12|12|10|
|Regions|101|600|584|132|697|680|
|Countries|35|35|34|41|41|40|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variables are the attitudes towards immigrants question (columns 1, 2, 4 and 5), equal to 1 if the respondent answers yes to the question “is the city or area where you live a good place to live for immigrants from other countries?”, 0 for disagreement, and missing for blanks or refusals, and inverse hyperbolic transformation of per capita annual household income in international dollars (columns 3 and 5). Regional controls for the inverse hyperbolic sine transformation of region population and refugee population at the year of the event, and individual controls for age, age squared, sex, educational level and city size were used. The sample includes individual respondents to the Gallup World Poll in region-years (columns 1 and 4) and country-years (columns 2, 3, 5 and 6) who were surveyed within 4 years before or after an event. An event is defined as a region with an increase of at least 10,000 refugees (rows 1 and 2) or 5,000 refugees (rows 3 and 4) in a calendar year. Results use the Gallup sampling weights and standard errors are clustered at the event level.

intervals on our estimates are reasonably precise and do allow for the refutation of some important possible effect sizes.

One reason for the null results, and potential for bias overall, is the measurement of our main dependent variable.<sup>36</sup> First, the Gallup World Poll survey question asks about immigrants instead of refugees, and those attitudes likely differ in some cases. However, in the large events we study in heavily impacted regions, refugees are likely to be a large share of the immigrant population and potentially the main

> 36The measures of other variables could also attenuate our results. The region-level data on populations also likely contains measurement error, which could generate events in the data that do not correspond to the setting. We are working to verify these data and the events under study.

33
association with a survey question on “immigrants”.<sup>37</sup> Furthermore, while the responses correlate strongly with mutliple dimensions of attitudes toward immigrants, the literal interpretation of the question could yield a different interpretation of our results. Respondents answering whether it is a good area _for immigrants_ could explain, for example, column 1 of Table A1; places that have more rights for immigrants are likely better places for them to live, all else equal. However, this would not explain columns 3 and 5 of this table showing the same relationship using more precise measures on attitudes. Overall, we believe the measure, although imperfect, would be able to detect substantial shifts in attitudes based on the evidence presented in Section 4.1.

These results are preliminary and further work could amend these findings. We are exploring additional specifications that may provide better identification, for instance defining events across multiple years (instead of choosing the largest event) and evaluating spillovers explicitly at the country level and in neighboring regions to events. We are also working on a number of robustness checks, including to survey timing, weighting, aggregation, other outcomes, clustering of standard errors, outliers, and imputation methods, as well as more summary statistics to better describe the data.

# **6 Policy and Program Implications**

The mass arrival of refugees has been a major concern for a set of low- and middleincome countries. In this paper, we conduct an analysis of the impact of large-scale refugee arrivals on attitudes in these settings and discuss the role of policies on social cohesion and other outcomes for the host communities. Our preliminary findings are two fold: (i) on average across all regions with large, sudden flows, we find statistically insignificant effects on attitudes, but precise enough to rule out most

> 37At the country-level, refugees make up 32% of the immigrants in the countries in our main specification in 2010, when country-level data on immigrant populations is available (United Nations 2019). This share will be substantially higher in the specific years and regions we study.

34
negative responses; (ii) across different hosting situations, we do not find differences between regions with more or less restrictive labor market policies and regions with and without a camp. We additionally find similar minimal differences on income across policy regimes. Overall, while restrictive policies are often justified to benefit the host communities, we find little evidence to support the argument.

Combined with complementary research that demonstrates the harm exclusionary policies have on refugees and hosts, our study adds further evidence that integration of refugees is likely positive-sum in most settings.<sup>38</sup> Couttenier et al. (2019), for instance, show that offering labor market access to asylum seekers and fostering social integration is able to mitigate the detrimental effect of past conflict exposure on criminality. Granting certainty about longer-term legal status, secure living conditions and access to economic opportunities also offers incentives for the displaced to make human capital investmemts (Schuettler 2021).

Our findings also suggest a middle ground in debates over camps, which are restrictive in some settings but not all. The positive and negative effects of concentration on the host communities appear to balance on average. Instead, the design of the camps or settlements, including resources provided to refugees and shared with host communities, is likely key to determining the aggregate benefits.<sup>39</sup>

> 38See Clemens et al. (2018) for an overview and Bahar et al. (2021) for a recent example.

> 39See Y. Zhou et al. (2021) and Ginn (2021) for discussions and other references.

35