metadata: authors: 0: author: Rozo Villarraga,Sandra Viviana
1: author: Quintana,Alejandra
2: author: Urbina Florez,Maria Jose
count: Venezuela, Republica Bolivariana de,Colombia
docna: 0: docna: Electoral Effects of Integrating Forced
            Migrants : Evidence from a Southern Country
docty: Policy Research Working Paper
abstracts: cdata!: How does easing the economic integration
            of forced migrants affect native voting behaviors in the
            Global South? This study assesses how a regularization
            program, which facilitated the temporal economic integration
            of half a million Venezuelan forced migrants, affected the
            electoral choices of Colombian voters. This is done by
            comparing the election results in municipalities with higher
            and lower take-up rates for the program, before and after
            its implementation. The findings show negligible impacts of
            the program on native voting behavior. A survey experiment
            was also conducted to investigate the lack of
            voter&#x2019;s response. Even after receiving
            information about the program, Colombian voters showed no
            changes in voting intentions or prosocial views toward
            migrants. Hence, voter&#x2019;s indifference did not
            stem from a lack of awareness about the program. Instead,
            theoretical and qualitative evidence suggests that it may be
            potentially explained by (1) the absence of negative
            economic effects of the program, (2) the low media and
            political salience of the program, (3) the cultural
            proximity between Colombia and Venezuela, and (4) the
            stabilization of Venezuelan inflows into Colombia after 2018.
display_title: Electoral Effects of Integrating
            Forced Migrants : Evidence from a Southern Country
listing_relative_url: /research/2023/03/34016055/electoral-effects-integrating-forced-migrants-evidence-southern-country
topicv3: Electoral Support and Monitoring m1327087 2475,Migrant Workers m1504032 2729,Immigration m1504162 2837,Refugee Resettlement m1505718 2665
publishtoextweb_dt: 2023-03-06T00:00:00Z
dois: 10.1596/1813-9450-10342
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 99
author_keys: 0: author_key: 1932755
1: author_key: NA
2: author_key: 2062597
model_extractions: Policy Research Working Paper 10342 Electoral Effects of Integrating Forced Migrants Evidence from a Southern Country _Sandra V. Rozo Alejandra Quintana María José Urbina_ Development Economics Development Research Group March 2023
Policy Research Working Paper 10342

# **Abstract**

How does easing the economic integration of forced migrants affect native voting behaviors in the Global South? This study assesses how a regularization program, which facilitated the temporal economic integration of half a million Venezuelan forced migrants, affected the electoral choices of Colombian voters. This is done by comparing the election results in municipalities with higher and lower take-up rates for the program, before and after its implementation. The findings show negligible impacts of the program on native voting behavior. A survey experiment was also conducted to investigate the lack of voter’s response. Even after receiving information about the program, Colombian voters showed no changes in voting intentions or prosocial views toward migrants. Hence, voter’s indifference did not stem from a lack of awareness about the program. Instead, theoretical and qualitative evidence suggests that it may be potentially explained by (1) the absence of negative economic effects of the program, (2) the low media and political salience of the program, (3) the cultural proximity between Colombia and Venezuela, and (4) the stabilization of Venezuelan inflows into Colombia after 2018.

This paper is a product of the Development Research Group, Development Economics. 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 sandrarozo@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
# **Electoral Effects of Integrating Forced Migrants: Evidence from a Southern Country**<sup>*</sup>

Sandra V. Rozo<sup>†</sup> _⃝_ r Alejandra Quintana<sup>‡</sup> _⃝_ r Mar ́ıa Jos ́e Urbina<sup>§</sup> Originally published in the Policy Research Working Paper Series on _March 2023._ This version is updated on _February 2025._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Refugees, amnesties, electoral outcomes. **JEL Classification:** D72, F02, F22, O15, R23

> *We are grateful to the editor, Pr. Angelika Rettberg, and the two anonymous referees for the constructive comments. We also thank Marco Tabellini, Mauricio Romero, Claudio Ferraz, Mushfiq Mobarak, Anna Maria Mayda, Michela Carlana, and Ekaterina Zhuravskaya for useful suggestions. We also appreciate suggestions from participants at the SEA presidential session on immigration, HUMANS LACEA Migration Meeting, EAFIT economics seminar, and the WALES conference at Georgetown University. This project was approved by the HML’s IRB in July 2022

(study protocol 2021). Pre-registered trial AEARCTR-0009806.

> †World Bank, Development Research Group. Corresponding author, e-mail: sandrarozo@worldbank.org, tel:+13109945081

- ‡Columbia University, e-mail: aq2241@tc.columbia.edu, tel:+12172501400

> §World Bank, e-mail: murbinaflorez@worldbank.org, tel:+971521034492
# **Highlights**

- The opportunity for economic integration offered to half a million Venezuelan forced migrants in Colombia, in the form of a regularization program, had negligible impacts on native electoral outcomes.

- Results from a survey experiment determined that the null political impacts of the program is not explained by lack of information about the program among the Colombian population.

- The null political impacts of the program are possibly attributed to voter’s indifference to the program due to multiple factors including (1) the absence of negative economic or social effects of the program, (2) the low media and political salience of the program, (3) the cultural proximity between Colombia and Venezuela, and (4)

- The results suggest that in developing countries, native political behaviors can remain unaffected by policies facilitating the economic integration of forced migrants already present in the country.

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# **Acknowledgements**

The authors gratefully acknowledge financial support for this study from the Research Support Budget Fund of the World Bank’s Research Department. The authors declare no conflicts of interest. The findings, interpretations, and conclusions presented in this paper are solely those of the authors and do not necessarily reflect the views of the World Bank, its affiliated organizations, the Executive Directors of the World Bank, or the governments they represent. Any errors or omissions are the sole responsibility of the authors.

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“ _Of course I want to help Venezuelan migrants, we are all migrants in some way, you know? I am just worried about the The backlash could response from my people. political be difficult for my party._ ” (Elected Official in Latin America, March 15, 2021)

# **I INTRODUCTION**

Robust evidence supports a correlation between migration inflows and voter support for anti-migrant political parties.<sup>1</sup> This might explain the reticence and cautionary actions of political parties regarding actions to facilitate migrant integration. Yet, migration reforms related to economic have different effects than integration might migration inflows per se. For example, voters may lack information about regulations concerning migrant integration, may believe those regulations do not affect them or change overall migration inflows into the country, or may support migrants now in the country as long as more migrants do not come because of those regulations.

We examine the electoral effects of the _Programa Especial de Permanencia_ (PEP) in Colombia. The PEP was a temporary economic integration program for undocumented forced migrants. It was known regionally as a type of regularization program. It was offered to half a million Venezuelans in Colombia for to two The approximately living up years.<sup>2</sup> PEP granted working rights and access to public programs, such as full health and education services, plus access to financial services. It was offered to every Venezuelan in Colombia who registered in a census of irregular migrants between April and June 2018. As documented by Urbina et al. (2023) and Ib ́a ̃nez et al. (2024), the PEP program affected profoundly the well-being of treated Venezuelan migrants through improvements in their

> 1See, for example, Gerdes and Wadensj ̈o (2008); Otto and Steinhardt (2014); Mendez and Cutillas (2014); Barone et al. (2016); Harmon (2017); Halla et al. (2017); Dustmann et al. (2016). In one notable exception regarding the Global South by Zhou and Grossman (2021), the authors show migration inflows are related to greater support for the incumbent candidate since they elicited significant international aid.

> 2More than 5.6 million refugees have fled Venezuela’s economic and humanitarian crises (UNHCR 2023). Colombia is the most common destination of Venezuelan refugees; by mid-2022, it hosted almost 2.5 million such migrants. Moreover, Colombia has committed to policies that promote the rapid social and economic integration of migrants.

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labor income, consumption, and health. Yet, the program prompted negligible effects on the labor market outcomes of Colombian workers (Bahar et al. 2021). It also reduced crime rates and the fiscal costs of hosting migrants in Colombia (Bahar et al. 2022; Ib ́a ̃nez et al. 2024).<sup>3</sup> Our analysis assesses the impacts of the PEP program on electoral turnout, support for left-wing, center, and right-wing political ideologies, and electoral competition in Colombia.<sup>4</sup> We examine the causal effects of the PEP program by comparing mayoral and firstround presidential election results in municipalities with higher and lower PEP take-up rates, before and after the program’s rollout in 2018.

We employ municipal data from six mayoral elections for 1,098 of the 1,122 municipalities in Colombia (recorded after the year 2000, when information about the electoral roll became available). Each municipal election is conducted independently and mayors are elected at the local level by plurality rule. As such, the analysis exploits data from more than six thousand individual election points. For robustness, we also examine the effects of the PEP program on first-round presidential elections. Presidents are elected nationally in the first round if they receive fifty percent of the votes, plus one. Hence, the presidential election analysis includes six individual election points.

Our analysis controls for municipal and election-year fixed effects, a rich set of baseline municipal characteristics, their interactions with election-year trends, and department election-year trends. The data supports the validity of the empirical strategy as we observe parallel trends for mayoral and presidential elections in municipalities with higher and lower PEP program take-up rates before the program’s onset in 2018. In line with

> 3Particularly, regularized migrants had higher incomes than those who remained undocumented. This prompted higher tax consumption payments. Moreover, regularized migrants also used the health system more efficiently, employing disproportionately preventive instead of emergency services. Finally, AmuedoDorantes et al. (2025) document that fertility rates for regularized households was reduced as they were granted access to better labor market opportunities and family planning services.

> 4We classified this following the methodology proposed by Fergusson et al. (2020) and employed in Rozo and Vargas (2021).

5
new developments in the difference-in-difference methodology, we demonstrate that our results hold even amid potential violations of the parallel trend assumption and are independent of the algorithm and functional form used.

We document negligible effects of the PEP program on all the outcomes for mayoral and presidential elections. The results do not stem from low precision since the coefficients are close to zero; we establish the same results for the impacts of a similar but larger program called the _Estatuto Temporal de Protecci ́on para Migrantes Venezolanos_ (ETPV). The ETPV is a scaled-up version of the PEP that allowed every Venezuelan migrant who arrived in Colombia before January 31, 2021 to apply for a 10-year permit and receive the same rights as did the PEP. We evaluate the electoral impacts of the ETPV using an equivalent difference-in-difference methodology that exploits the variation in the location of applicants (by department, the only publicly available data) and time variation induced by the program’s inception. Despite the large scale of this program—six times bigger than the PEP and representing four percent of the Colombian population—we show that it also had negligible effects on voting behaviors.

Although Colombian voters showed no response to migrant regularization programs, they reacted strongly to Venezuelan migration inflows, as reported in Rozo and Vargas (2021). In fact, the authors conclude that larger Venezuelan inflows increased voter turnout and shifted votes from left-wing to right-wing political ideologies.<sup>5</sup> We update and replicate their estimates with our data and confirm the results.<sup>6</sup> We also establish that even after employing the same cross-sectional variation as Rozo and Vargas (2021), the PEP program had no effect on voting behaviors.

> 5These effects are predominantly driven by voter concerns about the economic effects of migrants as well as by a novel channel we call _strategic electoral misinformation_ , whereby political parties make a migratory shock salient to voters to demonize the political agenda of rivals.

> 6Importantly, although early settlements of Venezuelan migrants and the concentration of PEP applicants are correlated, the correlation is low. Moreover, to evaluate the impacts of the PEP program, we compare electoral outcomes before and after 2018. In contrast, Rozo and Vargas (2021) evaluate the impacts of changes in annual migration inflows to Colombia.

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Why would voters respond strongly to migration inflows but be indifferent to the PEP program? First, they may lack information about the program: perhaps they are not informed about policies that regulate the labor rights of migrants. In fact, “residence and work permits granted by the Colombian government to Venezuelan refugees” are internet search terms Colombians do not often use. Moreover, mainstream newspapers in Colombia did not report on or heavily scrutinize the PEP program.<sup>7</sup> We were only able to find 120 news articles related to the PEP program by the major Colombian news outlets in 2018. All of these articles were informative and had no on purely negative messages the program’s potential impacts (see Appendix E). Under this scenario, voters might react differently if they were fully aware of the program. The second possibility is that voters know about the program but are indifferent to it.

We test the validity of these channels through a survey experiment involving 1,040 Colombians between October and December of 2022. First, we informed the participants about the PEP program and its benefits. We then collected information on their attitudes towards migrants (including a list experiment and a dictator game to measure altruism), voting intentions, political views on migrants, and general knowledge about the PEP program. The experiment enabled us to measure causally how prosocial behaviors and voting intentions change when individuals have information about the PEP program. We also measured the amount of information the control group had about the PEP. Our results suggest that approximately half of the average adults in Colombia are informed about the PEP program. In addition, when participants received information about the program, no changes emerged in their prosocial behaviors toward migrants or their voting intentions. As such, we conclude that lack of information among voters does not explain their lack of response to the PEP.

# Instead, theoretical and qualitative evidence suggest that the null electoral effects of the

> 7This is corroborated by the findings of Santamaria (2022), who exploits geographical variation in the internet search intensity of these keywords to identify where migrants settled in Colombia.

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program may relate more to voter’s indifference to policies that support migrants after their arrival. This indifference is potentially explained by multiple factors including: (1) Colombian voters cultural proximity to Venezuelan migrants, (2) the stabilization of Venezuelan migration inflows into Colombia after 2018, (3) the low political and media saliency of the PEP program, and (4) the fact that the PEP program prompted no economic or social adversities for voters as has been demonstrated in previous studies (Bahar et al. 2021, 2022; Ib ́a ̃nez et al. 2024; Amuedo-Dorantes et al. 2025).

Our results are most relevant for developing countries that host forced migrants, which actually host the lion’s share of forced migration. In fact, more than 70% of forcibly displaced populations live in developing countries (UNHCR, 2025). Our findings suggest that in environments with large informal sectors, where migration inflows are controlled, and there is cultural proximity between migrants and hosts, native’s political behaviors can be unaffected by policies that facilitate the economic integration of migrants.

**Contribution to the literature:** This contributes to studies the paper examining political effects of immigration on receiving countries. With few exceptions in developing countries (Bedasso and Pascal 2020, Altindag and Kaushal 2020, Rozo and Vargas 2021, Zhou and Grossman 2021), most work evaluates the role of migration inflows in shaping election outcomes in the developed countries.<sup>8</sup> The main results of this body of work suggest that more exposure to immigration flows is correlated with greater support for antiimmigration parties and less support among natives for redistributive policies.<sup>9</sup> How-

> 8See Schaub et al. (2021), Otto and Steinhardt (2014), and Hennig (2021) for Germany; Schaub et al. (2021) and Otto and Steinhardt (2014) for Italy; Barone et al. (2016) for Austria; Brunner and Kuhn (2018) for Switzerland; Edo et al. (2019) for France; Dustmann et al. (2019) and Gerdes and Wadensj ̈o (2008) for Denmark; Hangartner et al. (2019) for Greece; and Gimpel (2014) and Mayda et al. (2016) for the United States.

> 9See Alesina and Tabellini (2024), Mayda et al. (2016), and Otto and Steinhardt (2014) for seminal examples. The main mechanisms highlighted by the literature are the economic circumstances conditioning voters (Tomberg et al. 2021, Roupakias and Chletsos 2020, Edo et al. 2019, Barone et al. 2016, Halla et al. 2017, Hainmueller and Hopkins 2014a), cultural values (Hainmueller and Hiscox 2007a, Goldstein and Peters 2014), cultural differences between migrants and natives (Hainmueller and Hiscox 2007a, Bansak et al. 2016b, Bursztyn et al. 2024, Tabellini 2019a, Alesina et al. 2021, Alrababa’h et al. 2021), and sociotropic explanations (Hainmueller and Hiscox 2007b, Hainmueller and Hopkins 2014b,Hainmueller and Hopkins 2015, Hainmueller et al. 2015).

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ever, less attention has been given to how migration policies, rather than inflows alone, electoral behavior. Our shifts this focus the conse- shape study by evaluating political quences of policies that facilitate migrant economic integration.

This paper also adds to the literature concerning the impacts of migration policy reforms. Most studies in this area have examined the impacts of amnesties on labor markets in developed countries (Cobb-Clark et al. 1995, Kaushal 2006, Amuedo-Dorantes et al. 2007, Amuedo-Dorantes and Bansak 2011, Chassamboulli and Peri 2015, Amuedo-Dorantes and Antman 2017, Devillanova et al. 2018, Monras et al. 2018, Hainmueller et al. 2019), with others on the impacts of amnesties on crime behaviors (Baker 2015, Mastrobuoni and Pinotti 2015, Pinotti 2017). The work most closely related to ours considers the implications of the PEP program for Colombian hosting communities, including impacts on labor markets (Bahar et al. 2021), inequality (Lombardo et al. 2021), crime (Bahar et al. 2022), firm development (Bahar et al. 2023), and migrant well-being (Urbina et al. 2023, Ib ́a ̃nez et al. 2024, Amuedo-Dorantes et al., 2025). Our contribution relative to this work is the novel evaluation of the electoral implications of this type of program.

Our work also contributes to the relatively new studies on how humanitarian interventions affect voting behaviors and attitudes towards migrants. For example, Baseler et al. (2025) examine how aid influences attitudes towards refugees in Uganda, while Lehmann and Masterson (2020) study how cash transfers to refugees influence anti-immigration violence in Lebanon. Baseler et al. (2025) find that grants tagged to aid sharing significantly increased support for inclusive policies, including the right of refugees to work and the immigration of additional refugees. Similar evidence is provided by Lehmann and Masterson (2020). The authors show that cash transfers to Syrian refugees did not increase anti-refugee violence. Our study contributes to their work by analyzing how large regularization programs with no direct benefits to hosts, such as cash tied to aid or more cash in the economy, affect native voting behavior in a middle-income country.

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we with research on attitudes and con- Additionally, engage immigration intergroup tact theory. While some scholars emphasize labor market competition as a key driver of anti-immigrant sentiment, recent research suggests that sociotropic and cultural concerns often outweigh economic factors (Hainmueller and Hiscox 2007b, Hainmueller and Hopkins 2014b). Studies in high-income settings also find that naturalization programs enhance migrants’ social and political integration without provoking native backlash (Bansak et al. 2016a), suggesting that economic integration policies can improve social cohesion. However, recent evidence challenges the assumption that prolonged exposure to migrants fosters positive attitudes. For example, Zhou and Lyall (2025) find that extended exposure to displaced populations in Afghanistan did not significantly change local attitudes. These findings align with our study, which finds no evidence that largescale regularization triggered a political backlash in host communities.

Beyond direct economic and cultural concerns, external shocks—such as public health crises or refugee-driven development—can also influence migration attitudes and political preferences, often amplifying or mitigating pre-existing biases toward migrants. For example, Adida et al. (2020) show that public health crises can politicize migration issues, while Zhou et al. (2023) find that refugee-driven local development reduces electoral backlash. These findings suggest that migration policies influence attitudes not only through labor market competition but also via public service perceptions. Our study builds on this insight by evaluating how regularization affects social capital and political preferences in a developing country.

Finally, this paper contributes to research on how information provision influences migration policy preferences. Prior studies document high levels of misinformation regarding the size and characteristics of immigrant populations (Alesina et al. 2022, Grigorieff et al. 2020). While some research suggests that information-provision interventions can correct these misperceptions (Hopkins et al. 2019), their effectiveness in shaping policy prefer10
ences remains debated (Haaland and Roth 2020, Williamson 2020). Recent behavioral studies also suggest that perspective-taking interventions may influence native attitudes toward migrants. Simonovits et al. (2018) show that online interventions can reduce ethnic prejudice, while Adida et al. (2018), Williamson et al. (2021), and Chatruc and Rozo (2024) find that empathy-based interventions can increase support for migrants, albeit with short-term effects. Our study contributes to this literature by evaluating how information provision interventions affect native voting behavior, social capital, and attitudes toward migrants in the context of a large-scale regularization program.

# **II CONTEXT: THE PEP PROGRAM**

By mid-2022, more than 5.6 million Venezuelans had fled the humanitarian crisis in their country. Approximately 2.5 million of them had settled in Colombia, currently the primary recipient of Venezuelan migrants (UNHCR 2023). This number represents a shock equivalent to about five percent of Colombia’s total population.<sup>10</sup> Despite the size of the migration flows, the Colombian government granted these migrants free mobility and opportunities to regularize their status. One of the largest initiatives was the PEP program in 2018.

_The RAMV census:_ between April and June of 2018, the Colombian government undertook a countrywide survey to count the number of irregular (undocumented) migrants living in its territory. It was called the RAMV, _Registro Administrativo de Migrantes Venezolanos_ . Colombian authorities administered the survey at 1,109 different stations in 441 of the 1,122 municipalities. The registration points were located in border municipalities, in municipalities with a large population of Venezuelan migrants, and in municipalities where local authorities requested them. In order to register, migrants had to go to the registration point with proof of Venezuelan citizenship through official identification documentation. Registration was voluntary and was only advertised as a statistic exercise.

10Compared to the Colombian population, Venezuelan migrants are younger, more educated, and have lower employment rates (see Table A.1).

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The RAMV identified 442,462 undocumented Venezuelan migrants living in Colombia in 2018.

_The PEP program:_ In July 2018, just days before leaving office, President Juan Manuel Santos unexpectedly offered a regularization program to everyone who had registered in the survey. The PEP eligibility requirements were (i) registration in the survey, (ii) physical presence in Colombia at the time the decree was issued, and (iii) lack of a criminal record or a deportation order. The processing and issuance of a PEP was free, voluntary, and could only be done online. Sixty-four percent of all undocumented migrants who had registered in the RAMV received a PEP visa. Table A.2 describes the main sociodemographic characteristics of the Venezuelans who applied and did not apply for the PEP program. The data shows that PEP migrants are older, more educated, more likely to be employed in the informal sector, and less connected to networks in Colombia, relative to non-PEP applicants.

_PEP benefits:_ The PEP granted Venezuelan migrants the right to work, the possibility of being scored by SISBEN,<sup>11</sup> access to financial services, and a document to prove regular status in Colombia and thus avoid deportation. Table A.3 depicts the benefits granted by the PEP compared to the rights of all migrants in Colombia.

# **III THEORETICAL FRAMEWORK**

Migrant’s integration in the form of regularization programs represent a governmentendorsed effort to integrate migrants into the economy. These programs shift migrants’ legal standing, granting them full access to labor markets, social services and programs, and legal documentation. These changes can influence native voters’ perceptions, economic expectations, and political behavior in distinct ways, which in turn could be reflected in electoral outcomes. Below, we outline the main theoretical mechanisms through which regularization programs can shape electoral preferences and turnout.

> 11The score used to award anti-poverty social programs in Colombia.

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1. _Changes on voter’s perceptions of migrants as fiscal contributors or burdens:_ Theoretical models suggest that voter preferences for redistribution toward migrants depend on whether they perceive migrants as economic contributors or dependents (Alesina et al. 2021; Tabellini 2019b). In contexts where regularization expands tax compliance, voters may support pro-regularization candidates. Conversely, if voters believe that regularization increases competition for public service access, they may shift support toward parties advocating for restrictive migration policies (Clemens and Hunt 2019). For the case of the PEP program, voter’s may perceive, for example, that the regularization program mainly increased the migrant’s access to public services. If this mechanism is strong we will expect to see potential electoral effects against the political parties or ideologies that passed the regularization programs.

2. _Changes on voter’s perceptions of migrants as labor and economic competition:_ regularization programs may be perceived as either: (i) A labor market threat, increasing competition and depressing wages, leading to opposition toward pro-migrant policies (Caruso et al. 2019; Morales-Zurita 2020), or (ii) an economic stabilizer, allowing migrants to move into the formal sector, which could relieve pressures in the informal labor market shifting native support toward inclusive policies (Akg ̈und ̈uz and Torun 2020; Pedrazzi and Pe ̃naloza-Pacheco 2023; Bahar et al. 2024). However, in high-informality settings like the ones commonly observed in Colombia, regularization did not altered from the programs significantly employment dynamics informal to the formal sector, potentially causing weaker electoral effects compared to the ones that could be expected in developed countries (Ib ́a ̃nez et al. 2024).

3. _Cultural proximity:_ The cultural proximity between migrants and natives can also influence whether regularization programs foster acceptance or backlash from native voters. In societies where are as mem- migrants perceived integrated community bers, regularization may prompt no effects or reinforce positive political engage13
ment. In contrast, where migrants are constructed as an out-group, electoral effects may be shaped by concerns over social and cultural displacement. For example, evidence from Europe and the United States shows that anti-migrant parties capitalize on cultural anxieties (Dustmann et al. 2019; Barrera et al. 2020). In South-South migration contexts, like the Venezuelan-Colombian context, cultural barriers between groups may be weaker suggesting that the opposition against regularization programs may be weaker.

4. _Political salience and electoral mobilization:_ Regularization programs can also increase the political salience of migration, shaping electoral outcomes. In certain contexts, politicians have strategically highlight migration policies to mobilize supporters and consolidate political identities (Barrera et al. 2020; Alesina et al. 2022). In Euhave been linked to increased and shifts rope, regularization policies polarization toward right-wing candidates (Tabellini 2019b). Hence, the political effects of regularization are affected with how media and elites con- programs highly political struct its consequences. In Colombia, for instance, the Venezuelan migration wave increased turnout and led to shifts toward right-wing candidates, driven by economic anxieties rather than ethnic divides (Rozo and Vargas 2021). Yet, there has been relatively low coverage of the Venezuelan regularization programs in media outlets in Colombia. In fact, we did a review of major Colombian news outlets in 2018 and found that coverage of PEP was largely neutral and informational, without alarmist framing that could have heightened its salience. In fact, we only found 120 news articles related to the PEP the Colombian news outlets in program by major 2018. The majority of these articles were descriptive and had no negative messages on the program’s potential impacts (Appendix E).

All in all, the PEP program was implemented in a context characterized by strong cultural proximity between forced migrants and host communities, it had limited short-term eco14
nomic effects for Colombians, and had low media and political salience. These factors suggest that the PEP program could have low to negligible effects on electoral outcomes. The would be if voters the as only potential exception perceive regularization program imposing a higher fiscal burden on migrants. However, evidence indicates that the fiscal net cost of a regularized migrant household is actually _lower_ than that of an undocumented migrant household (Ib ́a ̃nez et al. 2024).

# **IV DATA**

Our main empirical analysis employs municipal-election panel data between 2000 and 2022. Our sample is restricted to this period since municipal-level voting roll registries are available from 2000. We also did a survey experiment to clarify the mechanisms underlying our main results. Details on the survey are presented in section VII. A.

. **_PEP take-up_** The number of individuals who applied for the PEP program is available by municipality. Colombian migration authorities provided the data, which is illustrated 1. in Figure 15
<!-- Start of picture text --> PEP Holders (% Pop. in 2018)<br>0.00<br>0.01 - 0.10<br>0.11 - 0.80<br>0.81 - 6.00<br>6.01 - 12.40<br><!-- End of picture text --> Figure 1. Share of PEP holders (% of population)

**_Elections_** . We use data from six mayoral municipal elections (2000, 2003, 2007, 2011, 2015, and 2019). Each municipal election corresponds to an independent race where the elected official was chosen based on plurality rule. We also use data for the six most recent presidential elections (2002, 2006, 2010, 2014, 2018, and 2022). We only employ data for the first-round elections to maintain consistency across election years. The election data comes from the Colombian electoral agency. We use the information to examine the effects of the PEP program on (i) election turnout (measured by the individuals who voted as a share of the electoral roll); (ii) support for left-wing, center, or right-wing political

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# ideologies; and (iii) electoral competition.<sup>12</sup>

We classify the ideology of mayoral and presidential candidates as left, center, or right, following the methodology proposed by Fergusson et al. (2018) and Rozo and Vargas (2021), both published in leading political science and economic journals. The credibility of this classification is supported by Fergusson et al. (2020), who demonstrate that electoral victories of left-wing parties are associated with increased right-wing paramilitary violence, a pattern consistent with theoretical expectations from Acemoglu and Robinson (2008). Similarly, Rozo and Vargas (2021) apply this methodology to classify candidates in Colombian local elections, further validating its relevance.<sup>13</sup> Building on these prior efforts, we extend and refine the classification by manually coding an additional 1,343 candidates from the 2019 local elections. This expansion ensures consistency and comparability with previous work while addressing the complexities posed by Colombia’s highly fragmented party system. Unlike in Europe, Colombia’s electoral framework facilitates the of coalitions and new move- proliferation temporary political ments, complicating ideological classification.<sup>14</sup> Our classification process relies on electoral data from Colombia’s National Civil Registry and follows a structured three-step approach. First, for mayoral elections, we identify the political party or movement that endorsed each candidate. The initial classification considers party names, mottos, and slogans for ideological markers (e.g., terms like “socialist” suggesting a left-wing orientation or “conservative” indicating right-wing alignment). When this step does not yield a definitive classification, we analyze party statutes

> 12Calculated following Chac ́on et al. (2006) as: 1 _−_ (%1st _Candidate −_ %2nd _Candidate_ ). When the margin of victory among candidates is close to zero, the elections were competitive and the variable takes a value close to one.

> 13As part of their classification, they manually assigned ideological labels to 354 independent candidates who were unclassified due to one of the following reasons: (i) they ran unopposed, (ii) they did not represent any formal party, or (iii) they participated only in the 2015 elections.

> 14In 2019 alone, 1,287 political parties competed across 1,122 municipalities, with some municipalities hosting over 20 different parties. Many candidates ran under personalist movements rather than established political parties, further complicating ideology classification based on party affiliation.

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to assess their stances. A is coded as if it policy party left-leaning emphasizes policies such as market regulation, state ownership, and pro-peasant initiatives, whereas rightleaning parties prioritize free-market policies, nationalism, and law-and-order measures. If party statutes are unavailable, we classify candidates based on their government plans submitted to the electoral authority, applying the same policy-based criteria.<sup>15</sup> Candidates from parties that do not fit clearly into the left or right categories are classified as centrist or unaligned.

For presidential elections, we classify candidates running in first-round elections from 2000 to 2022 using the same methodology. Electoral data was obtained from Colombia’s electoral authority, and for the 2022 election, we scraped official results from the registry’s website to compile a complete dataset. A detailed description of the classification proceB. dure is provided in Appendix Descriptive statistics for all variables used in the main specifications are in Table C.1. There is significant variation in the turnout of the mayoral elections (the mean is 65 percent and the standard deviation is 11 percent). The dominance of a center-oriented political ideology is clear: on average, 66 percent of voters supported center parties, while 16 percent supported right-wing parties and only 6 percent supported left-wing candidates. On average, these mayoral elections were competitive (0.63).

. **_Other municipal controls_** We also use several municipal characteristics measured before the 2018 implementation of PEP and interact them with election-year indicator variables to flexibly account for non-parametric trends in observable variables that may bias the main results. Descriptive statistics for all controls are illustrated in Table C.2. The data comes from multiple sources listed in the footnote of Table C.2.

> 15A substantial portion of candidates is classified using individual-level information rather than solely relying on party affiliation: 40% based on their government plans, 20% through their social media posts, and 14% based on coalition composition (see Table A.4). This approach minimizes the risk of over-relying on party ideology and better captures local variations in candidate positioning, particularly in light of the large number of centrist candidates.

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# **V EMPIRICAL STRATEGY**

PEP program impacts cannot be estimated by comparing electoral outcomes in municipalities with different program take-up. This is because migrants “vote with their feet” and consider the characteristics of each place when deciding where to reside. For example, Venezuelan migrants may choose to locate in areas that are more prosperous, less violent, or where locals are more welcoming. As such, a simple mean comparison of areas with different program take-up rates may be biased. For this reason, we employ a difference-in-difference methodology. The main specification uses municipal electionyear variation that exploits the unexpected timing of PEP implementation and the municipal location of PEP holders. Specifically, the following specification was used: where _m_ stands for municipality (the equivalent of a county in the United States), _d_ stands for the department (the equivalent of a state in the United States), and _t_ stands for election-year variation. _Y_ represents the electoral outcomes of interest, _PEP_ corresponds to the standardized values of the number of PEP holders as a share of population, and _I(Post 2018)_ is an indicator variable that takes the value of one after 2018. _C_ is a rich set of pre-determined municipal characteristics measured before the PEP program launched. We included interactions of these variables and year indicator variables in all estimates to flexibly account for potential differential non-parametric trends in a number of municipal characteristics observed prior to each migrant’s regularization. The variables included as baseline controls in _Z_ include (i) _conflict and violence-related variables_ such as homicide rates and number of robberies; (ii) _public-finance-related variables_ including revenue, expenditures, capital expenditures, and central government transfers to the municipalities (as a total and by type); (iii) _poverty and inequality_ measured by the number of people subsidized by the health system and the percentage of the population living in rural areas; 19
(iv) _economic growth_ approximated by night light density; and (v) _previously regularized population_ measured as the number of applicants to past smaller regularization programs. These regularization programs only targeted highly educated migrants with passports in Colombia. These variables are listed in Table C.2 mentioned above. Equation (1) also includes municipal ( _γm_ ), election-year ( _γt_ ), and department election-year ( _γdt_ ) fixed effects. Finally, standard errors were clustered at the municipal level to account for geographic serial correlation.

# **V. A Internal validity**

Considering that there is no staggered treatment and all municipalities are treated at the same time, we begin by illustrating the validity of the parallel trend assumption for different specifications of the treatment variable. We explore potential violations of this assumption in the next section.

The canonical difference-in-difference estimates should be valid as long as municipalities with different PEP take-up rates experienced parallel dynamic behaviors in the outcomes examined before the implementation of the program in 2018 (after controlling for the baseline covariates). We examine the validity of the parallel trend assumption through an event study in Figures 2, 3, 4, and 5. The figures illustrate the coefficients of an event study that excludes the elections closest to 2018 for the five electoral outcomes studied. We estimate the event study for (i) the continuous treatment variable (PEP), defined as the standardized values of the variable PEP holders, and (ii) the discrete treatment variable equal to one if the municipality had positive program take-up. Generally, the figures suggest there are no differential trends in the outcome evolution before the implementation of the PEP program. In any case, we formally tested the sensitivity of our estimates to potential violations of the parallel trend assumption (see details in the next section). Our results are robust to all the tests employed.

20
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 2. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β β<br>0 0<br>-.05 -.05<br>.15 .15<br>.1<br>.1<br>.05<br>.05<br> (95% CI) (95% CI) 0<br>β β<br>0 -.05<br>-.05<br>.15<br>.1<br>β (95% CI) .050<br>-.05<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the 2015 election, the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% 21
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 3. Parallel Trend Assumption – Mayoral Elections<br>Discrete Treatment Variable<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β β<br>0 0<br>-.05 -.05<br>.15 .15<br>.1<br>.1<br>.05<br>.05<br>β (95% CI) β (95% CI) 0<br>0 -.05<br>-.05<br>.15<br>.1<br>.05<br> (95% CI)<br>β<br>0<br>-.05<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the discrete variable PEP, which takes the value of one in municipalities where there are PEP holders, with mayoral electoral years excluding the 2015 election—the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

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<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 4. Parallel Trend Assumption – Presidential Elections<br>Continuous Treatment Variable<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β β<br>0 0<br>-.05 -.05<br>-.1 -.1<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β 0 β 0<br>-.05<br>-.05<br>-.1<br>-.1<br>.2<br>.15<br>.1<br>.05<br> (95% CI)<br>β<br>0<br>-.05<br>-.1<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the standardized values of the variable PEP holders with presidential electoral years excluding the election of 2018, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

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<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 5. Parallel Trend Assumption – Presidential Elections<br>Discrete Treatment Variable<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022<br>.04 .04<br>.02 .02<br>0 0<br> (95% CI) (95% CI)<br>β β<br>-.02 -.02<br>-.04 -.04<br>.04<br>.04<br>.02<br>.02<br>0<br> (95% CI) (95% CI) 0<br>β β<br>-.02<br>-.02<br>-.04 -.04<br>.04<br>.02<br>0<br> (95% CI)<br>β<br>-.02<br>-.04<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the discrete variable PEP, which takes the value of one in municipalities where there are PEP holders, with presidential electoral years excluding the election of 2018, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

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# **VI ELECTORAL IMPACTS OF THE PEP PROGRAM**

Table 1 displays the coefficient estimates of equation (1) using different versions of the treatment variable for the mayoral and presidential elections. Independent of the type of election and the definition of the treatment variable, we do not distinguish significant effects of the PEP program on any electoral outcome we examine. Moreover, we also evaluate potential heterogeneous effects of the PEP program in municipalities with different conflict incidence and state presence, but we do not identify any effects statistically different from zero.

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Table 1. Impacts of PEP Program on Electoral Outcomes

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Election - Di_<br>|_screte Treat_|_ment Varia_|_ble_|||
|I(PEPm)_×_I(Post2018)t|-0.006|-0.008|0.015|-0.013|0.007|
||(0.003)|(0.019)|(0.025)|(0.020)|(0.014)|
|q-values|[0.653]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel B. Mayoral Election - Co_|_ntinuous Tr_|_eatment V_|_ariable_|||
|PEPm_×_I(Post2018)t|-0.004|0.001|0.011|-0.012|0.010|
||(0.002)|(0.007)|(0.009)|(0.008)|(0.006)|
|q-values|[0.283]|[0.490]|[0.357]|[0.283]|[0.283]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel C. Presidential Election -_|_Discrete Tr_|_eatment V_|_ariable_|||
|I(PEPm)_×_I(Post2018)t|0.002|-0.009|0.004|-0.001|-0.004|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|_Panel D. Presidential Election_|_- Continuou_|_s Treatmen_|_t Variable_|||
|PEPm_×_I(Post2018)t|-0.019|-0.038|0.008|0.022|0.050|
||(0.015)|(0.016)|(0.016)|(0.021)|(0.026)|
|q-values|[0.250]|[0.105]|[0.500]|[0.280]|[0.129]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1. I(PEPm) is a discrete variable that takes the value of one if the municipality had positive take-up rates and PEPm is the standardized value of the ratio of PEP migrants to population. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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# **VI. A Robustness tests**

We evaluated the robustness of our main findings to a series of empirical exercises outlined below. Our main results remain unchanged.

_Algorithm choice:_ We inspected overall time trends of municipalities with and without PEP program take-up using the raw data in Figures D.1 and D.2. Although we generally see parallel trends in the raw data, a few exceptions prompted us to test for potential static differences in the municipal baseline outcomes (illustrated in Table D.1). Since we found statistical differences in some of these static baseline outcomes, we tested the validity of our main results to a matching difference-in-difference and produced similar results (see Table D.2).<sup>16</sup> We also implemented the non-inferiority test that allows for the existence of potential pre-treatment linear trends between the treatment and control groups (as proposed by Bilinski and Hatfield 2018) in Table D.3; the results remain unchanged.

_Electoral response to larger migrant regularization programs_ : In Table D.4, we further test the robustness of our main results by exploring the effects of a similar but larger regularization program offered by the Colombian government in 2021. The _Estatuto Temporal de Permanencia_ scaled PEP benefits to any Venezuelan migrant who arrived in Colombia before January 2021. The program grants the same benefits as PEP but is more than six times larger. We employ a similar empirical strategy that exploits geographic variation in the program take-up rate by department (the only information available) and time variation in the onset of the program after 2021. The results confirm that voters had no reaction to migrant regularization programs in Colombia. Moreover, this second finding suggests that the negligible effects observed for the PEP program were not driven by power issues.

_Do voter reactions to migration flows differ?_ : So far we have found negligible effects of the PEP program on the voting behaviors of Colombian natives. Yet, Rozo and Vargas (2021)

> 16We did this by predicting the probability of PEP take-up based on the significant covariates and restricting the sample to the common support in the treatment and control groups based on the overlap of those probabilities (see Figures D.3 and D.4).

27
have shown that Venezuelan migration inflows change voter behaviors. The authors exploit geographical variation in the early settlements of migrants (before the onset of the crisis) and annual variation in the aggregate migration flows. They demonstrate that larger migration shocks increase voter turnout and shift votes from left-wing to rightwing political ideologies. We replicate Rozo and Vargas (2021)’s estimates, adding data for the last elections (2019 for mayoral elections and 2022 for presidential elections), and observe similar effects. Particularly, we see that larger migration inflows translate into higher voter turnout and a shift of votes from left-wing to right-wing ideologies (see Table D.5). We illustrate in Figure D.5 that both estimates use different sources of crosssectional variation. Furthermore, we confirm that even when using the cross-sectional variation from Rozo and Vargas (2021), we do not find significant effects of the PEP program (see Table D.6).

_Are the impacts in municipalities with lower/higher take-up?_ We also explore whether the program induced different impacts in municipalities that had higher or lower program take-up. For this purpose, we divide the municipalities in the sample in three terciles for program take-up and a control group with no take-up. We first show that the parallel trend assumption is satisfied for municipalities in tercile groups 1, 2, or 3 relative to the control group (Figures D.6-D.8). We also confirm that there are negligible effects of the program for all outcomes and groups in Table D.7, except for the case of political competition where we document a positive impact for municipalities in Tercile 3.

_External validation using Parliamentary Elites in Latin American surveys:_ To validate our classification, we leverage data from the Parliamentary Elites in Latin America surveys (PELA-USAL), which captures legislators’ perceptions of presidential candidates’ ideologies. In this survey, legislators rank candidates on a 1–10 Left-Right scale, where 1 represents the most left-wing position and 10 the most right-wing. We categorize candidates as right-wing if their score exceeds 7, left-wing if below 4, and centrist if between 4 and 7.

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Our classification aligns with 70% of the surveyed candidates (18 out of 26), while the remaining 30% are classified differently (see Table D.8). As a robustness test, we re-estimate our results using only the 26 presidential candidates included in the Parliamentary Elites survey. The findings remain consistent, reinforcing the robustness of our classification and main results (see Table D.9).

In sum, our results suggest that Colombian voters did not change their voting behaviors in response to migrant regularization programs.

# **VII DISENTANGLING POTENTIAL EXPLANATIONS**

There are two potential explanations for the null effects of migrant’s regularization programs in electoral outcomes: i) voter’s lack of information about the program, or ii) voter’s political indifference toward the program. In this section, we describe the theoretical framework that supports each of these potential explanations and test their validity with a survey experiment.

1. _Lack of Information:_ Voters may fail to respond to a policy if they are unaware of its existence or implications. Research on information and political accountability consistently finds that voters respond to policies when they receive clear, accessible information about them. For instance, evidence shows that providing information about politicians’ corruption records reduces support for corrupt candidates (Ferraz and Finan 2008), and increasing awareness about public policies strengthens political engagement (Besley and Burgess 2002; Bjorkman and Svensson 2009). More generally, research suggests that individuals update their beliefs and behaviors when exposed to credible information, influencing decisions in areas such as education (Campos 2024; Cohodes et al. 2022), social norms (Bursztyn et al. 2020), and technology adoption (Duflo et al. 2011; Beaman et al. 2021).

In the context of migration, studies show that factual information about immigration influences voters’ attitudes and policy preferences (Alesina et al. 2022; Barrera 29
et al. 2020; Grigorieff et al. 2020). If Colombian voters lacked sufficient awareness of the PEP regularization program, it is plausible that they did not incorporate it into their political decision-making.

2. _Voter’s indifference:_ Even when people are aware of a policy, voters may not respond if they perceive it as irrelevant to their welfare. Political science research suggests that voters prioritize policies with direct, visible, and immediate impacts on their lives (Adams et al. 2011; Healy and Malhotra 2009). For example, voters reward politicians for short-term responses to natural disasters but often ignore long-term policy initiatives with less tangible effects (Healy and Malhotra 2009). Similarly, voters may react more strongly to policies perceived as economic threats (e.g., tax increases) than to those with uncertain or diffuse impacts. In Colombia, voter indifference toward the PEP program may stem from several key factors:

- Limited perceived impacts: most Venezuelan migrants in Colombia remain in the informal sector despite regularization (Ib ́a ̃nez et al. 2024). Consequently, the forced migrant’s legal status did not significantly alter labor market competition for Colombian native workers (Bahar et al. 2021). A such the PEP program may not have triggered the economic anxieties typically linked to migration policies. In contrast, studies from high-income countries suggest that when regularization affects formal employment opportunities, it generates stronger political reactions (Halla et al. 2017; Mayda et al. 2022). Additionally, public concern about migration policies often stems from perceptions of crime and security. However, research indicates that the PEP program did not lead to significant increments in crime rates against Colombians (Bahar et al. 2022). In fact, the program lead to an actual reduction in crime rates.

- Limited political saliency: In many contexts, political entrepreneurs and media narratives play a key role in amplifying the salience of migration policies.

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When as a threat—whether economic or politicians emphasize immigration cultural—voters respond by shifting toward anti-migrant parties (Fouka and Tabellini 2022; Rozo and Vargas 2021; Zhou and Grossman 2021). However, in Colombia, the absence of strong anti-immigrant rhetoric surrounding PEP may explain why voters remained indifferent. Unlike migration crises that trigger nationalist backlash (e.g., in Europe and the U.S.), the Colombian case lacked a strong political discourse linking regularization to economic instability, labor problems or crime (Bahar et al. 2022; Ib ́a ̃nez et al. 2024). This lack of political framing likely reduced public concern and contributed to the null effects observed (see Appendix E).

- Cultural proximity: it is also possible, as discussed earlier in the theoretical framework that the cultural proximity between Colombia and Venezuela affected also the perceptions of voter’s in Colombia. As a matter of fact both countries were once part of the same nation called ”La Gran Colombia” and they also speak the same language.

- Stabilization of migration inflows: the last possible reason why voter’s may be indifferent to the PEP program is because in practice migration inflows into the country actually stabilized after 2018 (Figure 6).

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<!-- Start of picture text --> 8,000<br>1.00<br>6,000 0.60<br>0.20<br>4,000<br>-0.20<br>2,000<br>-0.60<br>0 -1.00<br>2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>PEP<br>started<br>Year<br>Venezuelans Growth Rate (Right Axis)<br>(Thousands) Growth Rate<br>Venezuelans Inflows<br><!-- End of picture text --> Figure 6. Venezuelan Inflows to Colombia _Notes:_ The data comes from the Colombian migration agency, _Migraci ́on Colombia_ , and includes transitory migration. There is no data available before 2003.

# **VII. A Testing the lack of information hypothesis**

We designed a survey experiment to evaluate whether Colombian natives: (i) are informed about the PEP program and (ii) conditional on receiving information on the PEP program, change their prosocial views and voting behaviors.

For this we conducted an in the Colom- purpose, in-person survey experiment Bogot ́a, bian city with the highest number of Venezuelan migrants.<sup>17</sup> The randomized trial offered treated individuals information about the PEP program in order to later collect measures on their prosocial views and voting intentions. Particularly, everyone read the following information (translated into English here): _“4.6 million Venezuelans have been forcibly displaced and 4 of every 10 live in Colombia (1.7 million).”_ In addition, the treatment group read 17According to the last population census (2018), 166,566 Venezuelans live in Bogot ́a; this corresponds to 2.32 percent of its population.

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the following statement (translated into English here): _”281,000 irregular Venezuelan migrants have been legalized through the Permiso Especial de Permanencia, which grants them a work permit, access to social programs (such as a subsidized health regime), and access to financial services.”_ Figure F.1 illustrates how the information was presented. The survey was executed between October and December of 2022. The sample size was 1,040 individuals and included Colombian residents who were older than 21 years old and living in Bogot ́a.

The survey followed a structured sequence. First, respondents provided demographic and socioeconomic information, including age, sex, marital status, economic stratum, occupation, formal employment status, education level, student status, and news consumption related to national politics. Second, we included the social desirability bias scale from Crowne and Marlowe (1960) to standardize and control for potential social desirability effects. Third, participants took part in the experiment. They were given five minutes to read informational flyers without any verbal explanation from the enumerators. Both control and treatment groups received flyers with general migration statistics. However, the treatment group also received additional information about the PEP program, detailing work permits, social benefits, and access to financial services.

After reading, participants answered questions on voting intentions, social capital, and attitudes toward Venezuelan migrants. The final section of the survey assessed knowledge of the PEP program. Respondents who reported familiarity with the PEP were asked: (i) Who is eligible for the PEP? (ii) How many Venezuelans currently hold the PEP? (iii) What are the benefits of the PEP? (iv) What rights does the PEP grant to migrants?

Those who stated they were unfamiliar with the PEP were instead asked about the Estatuto Temporal de Protecci ́on (PTT), the most recent regularization policy for Venezuelan migrants. If respondents were unaware of both the PEP and PPT, they were asked: (i) Can Venezuelan migrants regularize their status in Colombia? (ii) What rights do regu33
larized Venezuelan migrants have? Appendix F provides further details on the survey’s structure and design.

Table F.1 that the randomization was successful as there are no dif- confirms significant ferences in any of the 15 sociodemographic variables we collected. Moreover, the test of joint significance also confirms the experiment’s success in maintaining balance across the treatment and control groups.

# **VII. A.1 Are Colombians informed about the PEP program?**

Table 2 examines the control group’s knowledge about the PEP program. Importantly, we asked these questions at the end of the survey to prevent priming the control group. We found that roughly half of the control group knew about the PEP program and understood who was eligible to apply (44.6 and 52.1 percent, respectively). When characterizing the individuals with correct information on the PEP program (panel B), we found they were mature in age, employed, educated, interested in politics, and active voters in the last elections.

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Table 2. Knowledge about the PEP Program Control Group Only

||Average<br>(1)|STD<br>(2)|Observations<br>(3)|
|---|---|---|---|
|_Panel A. People with correct knowledge about PEP_||||
|Knowledge about PEP [=1]|0.446|0.497|543|
|Knowledge of who can access the PEP [=1]|0.521|0.501|261|
|_Panel B. Demographic Characteristics of People with_|_knowledge_|_about PE_|_P_|
|Male [=1]|0.54|0.50|242|
|Age|48.12|15.24|242|
|Education: Primary school or less [=1]|0.08|0.28|242|
|Education: Secondary school [=1]|0.30|0.46|242|
|Education: Technician, university or more [=1]|0.62|0.49|242|
|Married or Cohabitating [=1]|0.52|0.50|242|
|Economic Strata: Low [=1]|0.43|0.50|242|
|Economic Strata: Medium [=1]|0.33|0.47|242|
|Economic Strata: High [=1]|0.23|0.42|242|
|Employed [=1]|0.89|0.31|151|
|Labor Contract [=1]|0.44|0.50|124|
|Student [=1]|0.13|0.34|242|
|Political Interest [=1]|0.86|0.34|242|
|Voted in 2019 Mayoral Elections [=1]|0.75|0.43|242|
|Voted in 2002 Presidential Elections [=1]|0.81|0.39|242|

# **VII. A.2 Once informed, do natives change their voting intentions?**

To evaluate the effects of the program, we estimate the following specification: where _i_ represents the individual and _Y_ denotes the primary outcomes of interest as preregistered. These outcomes include: (i) voting intentions; (ii) self-reported measures of social capital (positive reciprocity, negative reciprocity, altruism, and trust);<sup>18</sup> (iii) experimental measures of altruism toward migrants through a dictator game; and (iv) measures of political attitudes toward migrants. Finally, _X_ includes controls for the stratification 18All the questions have been validated in Colombia and taken from Gallup surveys as reported in Falk et al. (2018).

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# variables.

Tables 3, 4, 5, and 6 illustrate the effects of the program on the primary outcomes of interest. They show that even after treated participants received information about the program, there were no changes in prosocial behaviors or voting intentions in any of our outcomes. Although the coefficients are small, they are generally bigger than two percent. This suggests that the lack of significance is not due to statistical imprecision since our sample size was designed to identify minimum effects of at least two percent.

Table 3. Treatment Effect on Voting Intentions

|Vote intention in 2023<br>Mayoral<br>elections|Vote intention in 2026<br>Presidential<br>elections|
|---|---|
|(1)|(2)|
|I(Treatment)<br>-0.024|-0.025|
|(0.023)|(0.023)|
|R-squared<br>0.018|0.013|
|Observations<br>1,040|1,040|
|Mean values (Control Group)<br>0.839|0.855|

_Notes:_ This table reports an OLS estimate. Dependent variables in columns (i)–(ii) are indicator variables [=1] if the respondent has the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table 4. Treatment Effect on Social Capital

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|I(Treatment)|0.019|-0.023|-0.017|-0.036|
||(0.061)|(0.063)|(0.061)|(0.061)|
|R-squared|0.029|0.000|0.022|0.013|
|Observations|1,040|1,040|1,040|1,040|
|Mean (Control Group)|0.000|0.000|0.000|0.000|

_Notes:_ This table reports an OLS estimate. The variable in Column (i) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer of the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing are you to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

Table 5. Treatment Effect on Altruism Towards Migrants

||Money will<br>kept by<br>their-self (log)|Money will<br>share with<br>vulnerable<br>Venezuelan (log)|Money will<br>share with<br>vulnerable<br>Colombian (log)|
|---|---|---|---|
||(1)|(2)|(3)|
|I(Treatment)|0.011|-0.024|-0.043|
||(0.063)|(0.044)|(0.032)|
|R-squared|0.028|0.010|0.017|
|Observations|332|434|796|
|Mean values (Control Group)|8.077|7.737|8.131|

_Notes:_ This table reports an OLS estimate. Dependent variables are the logarithm of the answer of the respondent about the distribution of 5,000 Colombian pesos between him (column (i)), a vulnerable Venezuelan migrant (column (ii)), and a vulnerable Colombian (column (iii)). All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table 6. Treatment Effect on Political Attitudes towards Migrants

||Colombian<br>government<br>has to help|In favor to<br>a law that<br>helps|Venezuelans<br>compete with<br>Colombians|Venezuelans<br>increase<br>crime|Venezuelans<br>improve<br>Colombian|Positive<br>effect<br>of Venezuelans|
|---|---|---|---|---|---|---|
||Venezuelans|Venezuelans|jobs||culture|in Colombia|
||(1)|(2)|(3)|(4)|(5)|(6)|
|I(Treatment)|0.026|0.025|0.065|-0.066|-0.051|-0.028|
||(0.060)|(0.061)|(0.061)|(0.063)|(0.062)|(0.031)|
|R-squared|0.032|0.036|0.007|0.021|0.027|0.027|
|Observations|1,040|1,040|1,040|1,040|1,040|1,040|
|Mean (Control Group)|0.000|0.000|0.000|0.000|0.000|0.566|

_Notes:_ This table reports an OLS estimate. Dependent variables in columns (i)–(vi) are the standardized reported answer on a 1 to 5 scale of the approval of the statements:(i) _The Colombian government is obliged to help Venezuelan migrants_ ; (ii) _Would vote for a policy to increase government spending to help Venezuelan migrants_ ; (iii) _Venezuelan migrants come to compete for our jobs_ ; (iv) _Venezuelan migrants increase crime_ ; and (v) _Venezuelan migrants improve Colombian society by bringing new ideas and cultures._ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

To assess whether participants engaged with the information and to evaluate the effectiveness of the information treatment, we examined its impact on knowledge of the PEP The treatment led to a increase in of the PEP program. 7-percentage-point knowledge program (approximately 15% of the control group mean), a 4-percentage-point increase in of PEP a increase in of the knowledge eligibility, 9-percentage-point knowledge program’s benefits, and a 0.22-point increase in the total number of correct responses about the PEP program (see Table 7).

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Table 7. Treatment Effect on Knowledge

|Indicator Variables|PEP<br>Knowlede|Number<br>Venezuelans|Who has<br>Access|Benefts<br>Granted|Number<br>Venezuelans|Number of<br>Correct|
|---|---|---|---|---|---|---|
||g|with PEP|to PEP|by PEP|who Fled|Answers|
||(1)|(2)|(3)|(4)|(5)|(6)|
|I(Treatment)|0.070**|-0.000|0.045*|0.091***|0.019|0.225**|
||(0.031)|(0.017)|(0.027)|(0.030)|(0.022)|(0.095)|
|R-squared|0.023|0.012|0.014|0.027|0.019|0.030|
|Observations|1,040|1,040|1,040|1,040|1,040|1,040|
|Mean (Control Group)|0.443|0.0842|0.234|0.339|0.134|1.234|

_Notes:_ This table reports OLS estimates. The dependent variables in columns (1–5) are binary indicators equal to one if the respondent correctly identifies: what the PEP is (Column 1), the number of Venezuelans with PEP (Column 2), who is eligible for the PEP (Column 3), the benefits granted by the PEP (Column 4), and the total number of Venezuelans who have fled their country (Column 5). Finally, Column 6 represents the total number of correct answers across these five questions about the PEP.

Table F.2 tests our results for social desirability. In other words, respondents may have guessed our hypothesis and changed their responses to match what they thought we wanted to hear. Particularly, the table reports the results of a list experiment in which we gave everyone in the survey a list of things they could dislike. The list includes individuals who mistreat others, are poor, and are of a different religion. In the experiment, we randomized the whole sample again. Next, the treatment group for the list experiment was given an additional choice: “Venezuelan migrants.” We then asked respondents to tell us the number of things on that list they disliked but not the specific things they disliked. We tested for the interaction effects of both treatments on the number of things respondents reported they disliked (see Table F.2). These results confirm that our findings were not biased by social desirability as there are no significant differences between groups. Furthermore, we also tested for heterogeneous effects of the program among individuals with higher social desirability as measured by the 13-item, Marlowe-Crowne social desirability scale (see Crowne and Marlowe (1960) for details on the scale). Tables F.3–F.6 show no significant heterogeneous effects of the program among respondents who scored higher.<sup>19</sup> Consequently, we conclude that the lack of response to the PEP program 19We also evaluated them for heterogeneous effects of the treatment on the stratification variables but 39
among voters in Colombia is not due to _lack of information_ about it.

# **VII. B WHAT OTHER FACTORS MAY BE DRIVING THE RESULTS?**

So far we have shown that there was a electoral from Colombian negligible response natives toward the PEP program. Moreover, this results are not explained by a lack of awareness about the program. In this section, we combine our results with previous research on the impacts of the PEP program to offer a plausible explanation. Particularly, our main results are consistent with findings from previous work on the impacts of the PEP program in labor markets, crime behaviors, fiscal accounts, and migrant’s welfare (Bahar et al. 2021, 2022, 2023; Ib ́a ̃nez et al. 2024; Amuedo-Dorantes et al. 2025). In contrast to what is typically documented for developed countries, these studies showed that the PEP did not induced econoic effects for native Colombians in the program negative short term (Bahar et al. 2021, 2022). This occurred since the program only induced a small change in the formalization rates of regularized migrants, with most migrants remaining in the informal sector and in their same jobs but improving their access to public programs and labor conditions in the same jobs (Ib ́a ̃nez et al. 2024). Moreover, by facilitating proper documentation the program also increased the potential costs of crime for migrants and was reflected in reduction in total crime in Colombia (Bahar et al., 2022). Additionally, the actual fiscal costs of a regularized migrant household was actually _lower_ than that of an undocumented migrant (Ib ́a ̃nez et al. 2024). Since most migrants already had access to education and emergency health services, the program in fact enabled savings by granting migrants access to prevention health services. Consequently, in developing countries with large informality, migration reforms most likely have negligible effects on native’s electoral behaviors because most migrants who are eligible for this reforms remain in the informal sector, reduce the probability of committing crimes, and employ a better use of public services.

Further supporting this interpretation, we examine whether individuals who were alfound no effects on any of the groups of interest. The results are available upon request.

40
ready aware of PEP’s role in formalization exhibit different responses to the treatment. Tables F.7–F.10 present these results. The findings indicate that prior knowledge of PEP’s impact on formalization does not lead to significantly different responses across key outcome measures, including voting intentions, social capital, attitudes toward Venezuelan migrants, and altruism toward migrants. This suggests that awareness of PEP’s benefits does not meaningfully amplify the treatment effect, reinforcing our argument that concerns about the program’s economic impact on individual welfare are not the primary drivers of attitudes toward Venezuelan migrants. Consistent with this interpretation, evidence from the 2021 Venezuela Migration Project Observatory Perception Survey shows that most Colombians do not oppose migrant integration (Semana 2023). The survey found that 80% of Colombians support granting nationality to children of migrants born in Colombia, while 72% agree that migrants should have access to basic rights such as health and education. Support for granting legal documentation to migrants increased from 59% to 66% within a few months. Additionally, over 70% of respondents favor policies that facilitate migrant integration into the labor market and entrepreneurship. This broader of further with our un- public acceptance migrant integration aligns findings, derscoring that economic concerns are not the primary determinant of attitudes toward Venezuelan migrants.

All in all, our findings suggest that conditional on having a large informal sector and controlled inflows of migrants, native’s political behaviors can be unaffected by policies that ease the economic integration of migrants. This could be mainly explained by the fact that these programs do not have negative economic effects for natives in the short-run. As discussed earlier other potential reasons explaining voter’s indifference to the program may also be related to the cultural proximity between migrants, the low political and media saliency of the PEP program in Colombia, and the fact that the migration flows into Colombia stabilized after 2018.

41
# **VIII CONCLUDING REMARKS**

This study provides evidence that the large-scale regularization of Venezuelan forced migrants in Colombia, granting work permits and social benefits to half a million undocumented individuals, had negligible effects on native electoral behavior. Notably, this lack of voter does not stem from a lack of information from voter’s about the PEP response program.

Instead, qualitative and theoretical evidence suggests that voter’s electoral indifference toward the program may be related to: (1) the absence of negative economic effects of the program on Colombian voters, (2) the low media and political salience of the program, (3) the cultural proximity between Colombia and Venezuela, and (4) the stabilization of The economic integration of forced migrants is politically sensitive. Although many natives may sympathize with the difficult circumstances faced by migrants, they may also worry about its impact on their communities, particularly regarding job displacement, crime, and the fiscal costs of providing social benefits. In this context, governments often fear that policies supporting migrant integration could come at a political cost. However, our findings provide rigorous evidence that one of the largest regularization programs implemented in a developing country in recent history had no measurable effects on native voting behaviors.

This result has important policy implications. While migration itself can be politically contentious, our findings suggest that policies aimed at economic integration after arrival do not necessarily translate into electoral costs for politicians. Although prior research has documented significant political consequences following mass forced migration flows from Venezuela, our study establishes that the same is not true for policies that facilitate the integration of migrants once they have settled. This distinction underscores an important insight for migration governance: while voters may resist new inflows, they may 42
be far less reactive to policies designed to manage existing migrant populations.

An remains whether this extends Colombia. Do these open question dynamic beyond results generalize to other countries with large informal labor markets and controlled migration inflows? Could similar findings emerge in contexts with different economic structures, political institutions, or migrant populations? Additionally, while we find no short-term electoral effects, future research should explore whether long-term integration policies—such as pathways to citizenship—eventually shape voting behaviors.

Overall, this study underscores the distinction between voter responses to migration inand reactions to While the former can flows post-arrival integration policies. generate political backlash, the latter appears to elicit indifference rather than opposition. This inis critical for the of as sight policymakers navigating challenges migration governance, it suggests that the political costs of integration policies may be lower than commonly assumed.

43
# **CRediT authorship contribution statement**

Sandra V. Rozo: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing- original draft, Writing - review and editing, Resources, Project administration, Supervision. Alejandra Quintana: Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing. Mar ́ıa Jos ́e Urbina: Data curation, Formal analysis, Methodology, Visualization, Writing - review and editing.

# **Declaration of Competing Interest**

The authors declare that have no known interests or they competing financial personal relationships that could have appeared to influence the work reported in this paper.

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- of Legalizing Undocumented Immigrants,” _IZA Discussion Paper_

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- Otto, Alkis Henri and Max Friedrich Steinhardt (2014) “Immigration and election outcomes. Evidence from city districts in Hamburg,” _Regional Science and Urban Economics_ , 45, 67–79.

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Migration on the Female Labor Market: The Venezuelan Exodus in Colombia,” _The Journal of Development Studies_ , 59 (3), 324–341, 10.1080/00220388.2022.2139609.

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55
# **A VENEZUELAN POPULATION, RAMV, AND PEP BENEFITS**

Table A.1. Characterizing Venezuelan Migrants vs. Colombian population

||Colombians|Venezuelans|Mean Diff. (p-value)|
|---|---|---|---|
||(1)|(2)|(3)|
|Female [=1]|0.513|0.491|0.000|
||(0.500)|(0.500)||
|Age: 19-|0.312|0.401|0.000|
||(0.463)|(0.490)||
|Age: 20-39|0.318|0.451|0.000|
||(0.466)|(0.498)||
|Age: 40-59|0.235|0.116|0.000|
||(0.424)|(0.320)||
|Age: 60+|0.135|0.032|0.000|
||(0.342)|(0.175)||
|Married or Cohabitating [=1]|0.472<br>(0.499)|0.468<br>(0.499)|0.056|
|Literate [=1]|0.939|0.957|0.000|
||(0.239)|(0.203)||
|Student [=1]|0.284|0.222|0.000|
||(0.451)|(0.416)||
|Education: Nothing or Preschool|0.068|0.062|0.000|
||(0.251)|(0.241)||
|Education: Elementary School|0.299|0.199|0.000|
||(0.458)|(0.399)||
|Education: Middle or High School|0.419<br>(0.493)|0.489<br>(0.500)|0.000|
|Education: University or More|0.214<br>(0.410)|0.250<br>(0.433)|0.000|
|Employed [=1]|0.897<br>(0.304)|0.803<br>(0.398)|0.000|
|Observations|4,263,965|95,356|4,359,321|

_Source:_ Colombian census of 2018.

56
Table A.2. Characterizing Documented vs. Undocumented Migrants

||PEP|Non-PEP<br>M|ean Diff. (P-value)|
|---|---|---|---|
||(1)|(2)|(3)|
|Female [=1]|0.496<br>|0.499<br>|0.135|
||(0.500)|(0.500)||
|Age (Years)|26.986<br>|23.988<br>|0.000|
||(14.141)|(15.121)||
|Primary or Less [=1]|0.294<br>|0.423<br>|0.000|
||(0.456)|(0.494)||
|Secondary [=1]|0.592|0.506|0.000|
||(0.492)|(0.500)||
|University or more [=1]|0.115|0.071|0.000|
||(0.318)|(0.257)||
|Married or cohabitating [=1]|0.377|0.294|0.000|
||(0.485)|(0.456)||
|Pregnant [=1]|0.037|0.038|0.196|
||(0.188)|(0.191)||
|Sector: Services and Sales|0.815|0.822|0.002|
||(0.388)|(0.382)||
|Sector: Manufacturing|0.060|0.054|0.000|
||(0.237)|(0.226)||
|Sector: Extraction and Transp.|0.078|0.077|0.226|
||(0.269)|(0.266)||
|Sector: Finance and Adm.|0.009<br>|0.009<br>|0.417|
||(0.093)|(0.095)||
|Sector: Other|0.038<br>|0.038<br>|0.758|
||(0.190)|(0.191)||
|Occupation: Formally Employed (% of Pop.)|0.008<br>|0.007<br>|0.582|
||(0.087)|(0.086)||
|Occupation: Informally Employed (% of Pop.)|0.264<br>|0.188<br>|0.000|
||(0.441)|(0.391)||
|Occupation: Self-Employed (% of Pop.)|0.225|0.207|0.000|
||(0.418)|(0.405)||
|Occupation: Unemployed (% of Pop.)|0.186|0.174|0.000|
||(0.389)|(0.379)||
|Occupation: Student (% of Pop.)|0.071|0.088|0.000|
||(0.257)|(0.283)||
|Occupation: Housework (% of Pop.)|0.093|0.104|0.000|
||(0.291)|(0.305)||
|Family size|3.507|3.517|0.134|
||(2.042)|(2.092)||
|Family in Colombia [=1]|0.413|0.473|0.000|
||(0.492)|(0.499)||
|Family in Venezuela [=1]|0.666|0.652|0.000|
||(0.472)|(0.476)||
|Migration Intent: Stay in Colombia|0.889<br>|0.903<br>|0.000|
||(0.315)|(0.296)||
|Migration Intent: Move to another country|0.019<br>|0.021<br>|0.000|
||(0.136)|(0.143)||
|Migration Intent: Return to Venezuela|0.092<br>|0.076<br>|0.000|
||(0.290)|(0.265)||
|Observations|281,307|161,310|442,617|

_Source:_ RAMV census.

57
Table A.3.

||**All Refugees**|**Refugees with RAMV**|**Refugees with PEP**|
|---|---|---|---|
|**Education**|Nursery, pr. and sec.<br>Food and school bus|Nursery, pr. and sec.<br>Food and school bus|Nursery, pr. and sec.<br>Food and school bus|
||No|No|Promotion across levels|
||No|No|Degree recognition|
|**SISBEN**|No|No|Yes|
|**Health**|Emergency care<br>Public health programs<br>Vaccines|Emergency care<br>Public health programs<br>Vaccines|Emergency care<br>Public health programs<br>Vaccines|
||Prenatal care|Prenatal care|Prenatal care|
||Prevention campaigns|Prevention campaigns|Prevention campaigns|
||No|No|Subsidized regime|
|**ICBF****|No|No|Childcare|
||No|No|Earlychildhood service|
|**Formal Labor**|No|No|Jobpermit|
|**Financial Services**|No|No|bankingaccess|

_Source:_ Ib ́a ̃nez et al. (2024). *SISBEN: score used to target social safety net programs in Colombia, and ** ICBF: Colombian Family Welfare Institute.

58
# **B DATA BASE CONSTRUCTION**

# **B. A Mayoral elections**

The database was constructed with original data from the Colombian electoral authority, . _Registradur ́ıa Nacional del Estado Civil_ Our database begins after the 2000 elections, since after that year the electoral data has information on the electoral roll and the total votes received for all candidates. To begin, we identified the political party of each candidate or the political movement that endorsed the candidate’s campaign<sup>20</sup> and then classified its ideology as left, center, or right following the methodology proposed by Fergusson et al. (2020).<sup>21</sup> The classification for each candidate’s ideology includes three steps.

1. Check party names, mottos, and slogans for words that identify the candidate’s party clearly as left-leaning or right-leaning (e.g., communist or socialist for leftwing, and conservative or Christian for right-wing).

2. In the event that the previous step did not work, we checked the party statutes (when available) for policy stances that clearly leaned either to left or right. We coded a party as left-wing if the party statutes included at least three of the following five leftist policy positions: (1) pro-peasant, (2) advocates greater market regulation, (3) thinks workers should be defended against exploitation, (4) advocates state-owned or communal property rights, and (5) anti-imperialist. We coded a party as right-wing if its statutes included at least three of the following five rightwing policy positions: (1) economic growth is emphasized over redistribution; (2) advocates free market, orthodox policies, and privatization; (3) believes that family and religion are the moral pillars of society; (4) appeals to patriotism and national-

> 20In Colombia, a mayoral candidate may register for the elections with the endorsement of a political movement or party, with legal status recognized by the National Electoral Council, or with the support of a significant group of citizens, in which case it must provide the total of the corresponding signatures.

> 21Fergusson et al. (2020) demonstrate the credibility of their classification by linking electoral victories of left-wing parties to increases in right-wing paramilitary violence, a relationship that aligns with theoretical expectations from Acemoglu and Robinson (2008). Similarly, Rozo and Vargas (2021) applied this method to classify candidates in Colombian local elections, further validating its relevance.

59
ism, and accepts the suspension of some freedoms in order to guarantee security; and (5) prioritizes law and order. We classified parties that did not include at least three of the stances from either list in their statutes as neither nor policy left-wing right-wing.

3. For parties for which official statutes were not available, we checked the government plan that candidates submit to the electoral authority before elections and— when available—searched them for the same policy stances as in the second step.<sup>22</sup> The majority of candidates are classified using individual-level information, rather than party affiliation: 40% are classified based on their government plans, 20% using social media posts, and 14% based on coalition composition (See Table A.4). This approach reduces the risk of over-relying on party ideology and helps capture local variations in candidate positioning, addressing the concern regarding the high number of centrist candidates.

Table A.4. Distribution of the Type of Source Used to Classified Candidates

|**Type of Source**|**Candidates**|**Percent**|
|---|---|---|
|Government Programs|562|41.82|
|Social Media|275|20.46|
|Other|243|18.08|
|Coalition Composition|192|14.29|
|News/Media|44|3.27|
|Blogs/Opinions|11|0.82|
|Unclassifed|10|0.74|
|Academic|7|0.52|
|**Total**|1,344|100.00|

_Note:_ This table provides an overview of the primary sources used to determine candidates’ political ideology.

Finally, we used electoral roll information to calculate the municipality’s turnout and the

> 22For example: for the 2019 elections, Colombia’s electoral authority official website gathered information on all government plans https://wapp.registraduria.gov.co/electoral/Elecciones2019/infocandidatos2019.php

60
share of votes obtained in each election by left-wing parties, center parties (the residual of neither left-wing nor right-wing parties), and right-wing parties.

# **B. B Presidential elections**

Our analysis of presidential elections focuses on the first-round elections that took place between 2000 and 2022. The 2022 presidential elections took place May 29; the results are publicly available on Colombia’s electoral agency web page<sup>23</sup> but are not compiled in a single document, so we scraped the web page to gather all the information. As in the mayoral elections, we classified all candidates according to their apparent political ideology, following Fergusson et al. (2020).

To ensure consistency in classifying presidential candidates, we provide Table A.5, which outlines the criteria used. Candidates are categorized as left, center, or right based on and Candidates as self-identification, policy platforms, party ideology. classified Right met at least two conservative criteria (8 candidates), those classified as Left met at least two left-leaning criteria (7 candidates), and those classified as Center met fewer than two criteria (40 candidates).

23https://resultados.registraduria.gov.co/presidente/0/co 61
Table A.5. Summary of Source Types and Criteria for Classifying Presidential Candidates’ Ideology

|**Political Ideology**|**Source**|**Criteria**|**Num. of Candidates**|
|---|---|---|---|
|Right|Policy proposals of political party|Did satisfy more than 2 criteria|8|
|Right|Self-declared|Declared|3|
|Center|Candidate’s policy proposals|Did not satisfy more than 2 criteria|14|
|Center|Policy proposals of political party|Did not satisfy more than 2 criteria|26|
|Left|Candidate’s policy proposals|Did satisfy more than 2 criteria|2|
|Left|Policy proposals of political party|Did satisfy more than 2 criteria|2|
|Left|Party declares its ideology|Declared|3|
|Left|Self-declared|Declared|4|
|Missing information|No information||15|

_Note:_ This table details how presidential candidates were classified based on self-identification and policy alignment. We follow the classification criteria outlined by Fergusson et al. (2020). In particular, we use five criteria to classify a candidate as left-wing: (1) pro-peasant, (2) advocates greater market regulation, (3) believes workers should be defended against exploitation, (4) supports state-owned or communal property rights, and (5) is anti-imperialist. In contrast, we use the following five criteria to classify a candidate as right-wing: (1) emphasizes economic growth over redistribution; (2) advocates free market, orthodox policies, and privatization; (3) believes family and religion are the moral pillars of society; (4) appeals to patriotism and nationalism, and accepts the suspension of some freedoms to guarantee security; and (5) prioritizes law and order.

62
# **C DESCRIPTIVE STATISTICS**

Table C.1. Descriptive Statistics – Electoral Outcomes

||Year<br>(1)|Observations<br>(2)|Average<br>(3)|St. Deviation<br>(4)|
|---|---|---|---|---|
|_Panel A. PEP Take-up_|||||
|PEP holders*|2018|1,098|0.003|0.043|
|Municipalitywith PEP Holders|2018|1,098|0.740|0.439|
|_Panel B. Mayoral Elections (2000, 2003, 2007, 20_|_11, 2015 and_|_2019)_|||
|Registered voters|2000-2019|6,212|27,701.96|166,629.00|
|Total votes|2000-2019|6,212|15,677.75|82,668.48|
|Votes of left|2000-2019|6,212|1,724.70|26,780.75|
|Votes of center|2000-2019|6,212|8,813.67|39,424.01|
|Votes of right|2000-2019|6,212|2,212.02|10,870.86|
|Election turnout (Votes / Registered Voters)|2000-2019|6,212|0.65|0.11|
|Left (% of Votes)|2000-2019|6,212|0.06|0.15|
|Center (% of Votes)|2000-2019|6,212|0.66|0.26|
|Right (% of Votes)|2000-2019|6,212|0.16|0.21|
|Electoral competition|2000-2019|5,992|0.63|0.19|
|_Panel C. Presidential Elections (2002, 2006,2010,_|_2014, 2018,_|_and 2022)_|||
|Registered voters|2002-2022|6,564|23,532.14|80,693.46|
|Total votes|2002-2022|6,564|11,145.94|41,003.64|
|Votes of left|2002-2022|6,564|2,375.6|11,788.09|
|Votes of center|2002-2022|6,564|3,059.43|13,397.85|
|Votes of right|2002-2022|6,564|5,327.39|20,775.21|
|Election turnout (Votes/Registered voters)|2002-2022|6,564|0.46|0.49|
|Left (% of Votes)|2002-2022|6,564|0.17|0.18|
|Center (% of Votes)|2002-2022|6,564|0.29|0.22|
|Right (% of Votes)|2002-2022|6,564|0.50|0.22|
|Electoral competition|2002-2022|6,557|0.54|0.19|

_Notes:_ PEP holders is the share of migrants who reported having PEP divided by the total population in 2018 in each municipality. Electoral competition among candidates and political ideologies at municipal level is estimated following Chac ́on et al. (2006): 1 - (% winning candidate - % second-place candidate).

Furthermore, Figures C.1 and C.2 illustrate the geographic distribution of outcomes in the mayoral and presidential elections. The figure illustrates several trends of political behavior in Colombia. First, there is a large geographical variation among political outcomes across municipalities. Second, the majority of political participation in Colombia is concentrated in the center and northern areas of the country. Finally, there is more 63
support for right-wing political ideologies in the eastern and center regions of Colombia, whereas left-wing ideologies attract greater support in the west and south.

64
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Table C.2. Descriptive Statistics – Municipal Baseline Controls

||Year|Observations|Average|SD|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Panel A. Confict and Violence_|||||
|Homicide rates (per 100,000 inh.)|2017|1,098|10.07|46.91|
|Number of robberies|2017|1,098|200.24|1,218.37|
|_Panel B. Public Finance_|||||
|Revenue|2017|1,098|49,932.28|218,740.95|
|Expenditure|2017|1,098|49,655.99|220,118.23|
|Capital Expenditures|2017|1,098|42,716.56|188,503.99|
|Central Government Transfers (SPG)*|2017|1,098|19,218.23|62,347.32|
|SPG in education|2017|1,098|7,769.44|41,083.67|
|SPG in health|2017|1,098|6,138.15|15,432.11|
|SPG in sewage and water|2017|1,098|1,298.5|2.304.58|
|SPG in child nutrition programs|2017|1,098|151.73|269.86|
|SPG in children|2017|1,098|133.75|246.87|
|_Panel C. Poverty and inequality_|||||
|Subsidized Regime Affliates|2016|1,098|14,330.32|86,453.46|
|Rural index (% Ruralpopulation)|2017|1,098|0.55|0.24|
|_Panel D. Economic Growth_|||||
|Night Light Density|2009|1,098|3.85|7.21|
|_Panel E. Previous Regularized Population_|||||
|Number of Applicants PEP 1 (August 2017-Oct. 2017)|2017|1,098|36.63|293.95|
|Number of Applicants PEP 2 (Feb. 2018-June 2018)|2018|1,098|58.3|454.21|

_Notes:_ *SPG stands for _Sistema General de Participaciones_ and represents central government transfers to municipalities. Variables are expressed in millions of Colombian pesos, except for expenditures, which are expressed in thousands of Colombian pesos. _Data source:_ (i) homicide rates (per 100,000 inh.) and number of robberies come from the Colombian National Police; (ii) revenue, expenditure, and capital expenditure come from the municipal panel of the Center for Economic Studies at Universidad de los Andes (CEDE); (iii) central government transfers (SPG), and SGP in education, health sewage and water, child nutrition programs, and children come from the Colombian National Planning Department; (iv) subsidized regime affiliates come from the Colombian Ministry of Health; (v) rural index (% rural population) comes from the municipal panel of CEDE; (vi) night light density comes from the National Oceanic and Atmospheric Administration (NOAA); and (vii) number of applicants PEP1 and PEP2 come from Migracio ́n Colombia 67
# **D APPENDIX D: ROBUSTNESS TESTS**

# **D. A Raw Data Trends**

<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Share of Votes for Center Share of Votes for Right<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Electoral Competition<br>2000 2003 2007 2011 2015 2019<br>Election Year<br>PEP No-PEP<br>.75<br>.3<br>.25<br>.7<br>.2<br>.65<br>.15<br>.1<br>.6<br>.8 .4<br>.75 .3<br>.7 .2<br>.65 .1<br>.6 0<br>.7<br>.65<br>.6<br>.55<br>.5<br><!-- End of picture text --> Figure D.1. Evolution of Outcomes by PEP and Non-PEP Status – Mayoral Elections _Notes:_ The figure illustrates the yearly evolution of the electoral outcomes in municipalities with and without PEP take-up. We changed the variables with zero for missing values.

68
<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Share of Votes for Center Share of Votes for Right<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>Election Year Election Year<br>PEP No-PEP PEP No-PEP<br>Electoral Competition<br>2002 2006 2010 2014 2018 2022<br>Election Year<br>PEP No-PEP<br>.55 .4<br>.5 .3<br>.45 .2<br>.4 .1<br>.35 0<br>.5 .7<br>.4<br>.6<br>.3<br>.5<br>.2<br>.4<br>.1<br>0 .3<br>.65<br>.6<br>.55<br>.5<br>.45<br>.4<br><!-- End of picture text --> Figure D.2. Presidential Elections’ Evolution of Outcomes by PEP and Non-PEP Status _Notes:_ The figure illustrates the yearly evolution of the electoral outcomes in municipalities with and without PEP take-up.

69
# **D. B Algorithm Choice**

Table D.1. Descriptive Statistics – Electoral and Municipal Baseline Controls

||PEP<br>(1)|No-PEP<br>(2)|Mean diff. (p-value)<br>(3)|
|---|---|---|---|
|_Panel A. Confict and Violence_||||
|Homicide rates (per 100,000 inh.)|14.190|2.388|0.003|
||(67.448)|(3.972)||
|Number of robberies|385.816<br>(3,653)|11.175<br>(14.634)|0.083|
|_Panel B. Public Finance_||||
|Revenue|81.554|13.620|0.051|
||(588.875)|(7.113)||
|Expenditure|32.850|2.899|0.146|
||(347.791)|(1.676)||
|Capital Expenditures|82.252|13.515|0.060|
||(616.664)|(7.173)||
|Central Government Transfers (SPG)*|27,070<br>(123,000)|6,878<br>(3,726)|0.005|
|SPG in education|12,728|425|0.011|
||(82,056)|(391)||
|SPG in health|8,127|2,320|0.000|
||(25,355)|(1,969)||
|SPG in sewage and water|1,641<br>(4,889)|727<br>(450)|0.002|
|SPG in child nutrition programs|188.248<br>(403.757)|73.683<br>(83.888)|0.000|
|SPG in children|162.116<br>(348.286)|72.740<br>(87.952)|0.000|
|_Panel C. Poverty and inequality_||||
|Rural index (% Rural population)|0.505<br>(0.248)|0.678<br>(0.172)|0.000|
|_Panel D. Economic Growth_||||
|Night Light Density|4.755<br>(8.126)|1.346<br>(2.130)|0.000|
|_Panel E. Previous Regularized Population_||||
|PEP1 (August 2017-October 2017)|83.540<br>(1,029)|0.640<br>(3.94)|0.174|
|PEP2 (February 2018-June 2018)|136.957|1.168|0.188|
||(1,743)|(4.866)||
|Observations|811|286|1,097|

_Notes:_ *SPG stands for _Sistema General de Participaciones_ and represents central government transfers to municipalities. Variables are expressed in millions of Colombian pesos, except for expenditures, which are expressed in thousands of Colombian pesos.

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<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>Year Year<br>Share of Votes for Center Share of Votes for Right<br>Year Year<br>Electoral competition among candidates<br>Year<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.08 .08<br>.06 .06<br>.04 .04<br>.02 .02<br> (95% CI) 0 (95% CI) 0<br>β β<br>-.02 -.02<br>-.04 -.04<br>-.06 -.06<br>.08<br>.06<br>.08 .04<br>.06 .02<br>.04 0<br> (95% CI) .02 (95% CI) -.02<br>β 0 β -.04<br>-.02<br>-.06<br>-.04<br>-.06<br>.08<br>.06<br>.04<br>.02<br> (95% CI) 0<br>β<br>-.02<br>-.04<br>-.06<br><!-- End of picture text --> Figure D.3. Parallel Trends for the Common Support – Mayoral Elections Discrete Treatment Variable _Notes:_ The figure illustrates the results of an event study estimation for the municipalities in the common support of the predicted pscores. We interacted the discrete variable PEP, which takes the value of one in the municipalities where there are PEP holders, with mayoral electoral years excluding the 2015 election, the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent 71
<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>Year Year<br>Share of Votes for Center Share of Votes for Right<br>Year Year<br>Electoral competition among candidates<br>Year<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022<br>.04 .04<br>.02 .02<br>0 0<br> (95% CI) (95% CI)<br>β β<br>-.02 -.02<br>-.04 -.04<br>.04 .04<br>.02<br>.02<br>0<br>0<br> (95% CI) (95% CI)<br>β -.02 β<br>-.02<br>-.04<br>-.04<br>.04<br>.02<br>0<br> (95% CI)<br>β<br>-.02<br>-.04<br><!-- End of picture text --> Figure D.4. Parallel Trends for the Common Support – Presidential Elections Discrete Treatment Variable _Notes:_ The figure illustrates the results of an event study estimation for the municipalities in the common support of the predicted pscores. We interacted the discrete variable PEP, which takes the value of one in the municipalities where there are PEP holders, with mayor electoral years excluding the 2015 election, the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent 72
Table D.2. Impacts of PEP Program on Electoral Outcomes Matching Diff-in-Diff

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Elections_||||||
|I(PEPm)_×_I(Post2018)t|-0.001|-0.007|0.012|-0.014|0.006|
||(0.004)|(0.020)|(0.027)|(0.022)|(0.016)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.847|0.464|0.359|0.428|0.384|
|Observations|5,035|5,035|5,035|5,035|4,842|
|_Panel B. Presidential Elections_||||||
|I(PEPm)_×_I(Post2018)t|0.002|-0.011|0.008|-0.002|0.001|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.868|0.901|0.843|0.869|0.679|
|Observations|5,439|5,439|5,439|5,439|5,426|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1 using a matching differencein-difference. We predicted the probability of having PEP take-up by municipality using the municipal characteristics that were significant in Table D.1 and restricted the sample to the common support sample in the treatment and control groups where the pscores overlapped. Panel A shows the results for the mayoral elections, and panel B for the presidential elections. The variable PEPm is the standardized value of the share of PEP holders over the population in 2018. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality fixed effects. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

73
74
# **D. C Non-Inferiority Test**

Table D.3. Impacts of PEP Program on Mayoral and Presidential Elections Non-Inferiority Test for Potential Violation of Parallel Trend Assumption

||Election|Shar|e of Vote|s for|Electoral|
|---|---|---|---|---|---|
||Turnout|Left|Center|Right|Competition|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Mayoral Elections - C_|_anonical 2x_|_2 variable_||||
|I(PEPm)_×_I(Post2018)t|-0.006|-0.008|0.015|-0.013|0.007|
||(0.003)|(0.019)|(0.025)|(0.020)|(0.014)|
|q-values|[0.653]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel B. Mayoral Elections - C_|_ontinuos va_|_riable_||||
|PEPm_×_I(Post2018)t|-0.003|0.001|0.010|-0.011|0.010|
||(0.002)|(0.007)|(0.009)|(0.008)|(0.007)|
|q-values|[0.316]|[0.530]|[0.383]|[0.316]|[0.316]|
|R-squared|0.853|0.450|0.363|0.422|0.386|
|Observations|6,174|6,174|6,174|6,174|5,969|
|_Panel C. Presidential Elections_|_- Canonical_|_2x2 varia_|_ble_|||
|I(PEPm)_×_I(Post2018)t|0.002|-0.009|0.004|-0.001|-0.004|
||(0.004)|(0.007)|(0.008)|(0.007)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|_Panel B. Presidential Elections_|_- Continuos_|_variable_||||
|PEPm_×_I(Post2018)t|-0.019|-0.036|0.007|0.022|0.051|
||(0.014)|(0.016)|(0.016)|(0.021)|(0.026)|
|q-values|[0.237]|[0.124]|[0.467]|[0.276]|[0.124]|
|R-squared|0.877|0.898|0.845|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal Controls_×_Year|Yes|Yes|Yes|Yes|Yes|
|<br>Differential Trend|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. It shows the results of our main specification in equation 1, controlling for an indicator variable of differential linear pre-trends between the treatment and control groups. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

75
# **D. D Impacts of a Larger Regularization Program**

Table D.4. Electoral Impacts of ETPV Program – Presidential Elections

||Election<br>Turnout|Shar<br>Left|e of Votes<br>Center|for<br>Right|Electoral<br>Competition|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Presidential Elect_|_ions - Conti_|_nuous Var_|_iable_|||
|RUMVd_×_I(Post2018)t|-0.014<br>(0.006)|-0.047<br>(0.027)|0.019<br>(0.015)|0.027<br>(0.020)|-0.080<br>(0.040)|
|q-values|[0.157]|[0.157]|[0.157]|[0.157]|[0.157]|
|R-squared|0.988|0.959|0.969|0.953|0.879|
|Observations|198|198|198|198|198|
|Department FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of the effect of the ETPV program on electoral outcomes. The variable RUMVd is the total number of migrants who were issued the Registro Unico<sup> ́</sup> de Migrantes Venezolanos (RUMV) over the population in 2020 by department. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include department and electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined department characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

76
# **D. E Impacts of Migration Flows are Different**

Table D.5. Effects of Venezuelan Migration on Mayoral and Presidential Elections

||Election|Shar|e of Votes|for|
|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|
|_Panel A. Mayoral Elections_|||||
|Predicted Venezuelan Infows|0.022*|-0.013*|0.003|0.017*|
||(0.010)|(0.005)|(0.014)|(0.011)|
|q-values|[0.064]|[0.064]|[0.263]|[0.087]|
|R-squared|0.797|0.437|0.441|0.484|
|Observations|4,693|4,693|4,693|4,693|
|_Panel B. Presidential Elections_|||||
|Predicted Venezuelan Infows|0.002|-0.011***|0.003*|0.008***|
||(0.002)|(0.002)|(0.002)|(0.003)|
|q-values|[0.122]|[0.001]|[0.058]|[0.004]|
|R-squared|0.823|0.852|0.942|0.917|
|Observations|6,768|6,768|6,768|6,768|
|Municipality FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|

_Notes:_ This figure replicates the estimate by Rozo and Vargas (2021), adding the last elections for mayors in 2019 and president in 2022. Panel A shows the results for the mayoral elections between 1997 and 2019, and panel B for the presidential elections between 1994 and 2022. Predicted Venezuelan Migration is the interaction between the shift share of early settlements of Venezuelans in 1993 and the cumulative number of individuals arriving in Colombia from Venezuela each year, over the total Colombian population each year. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period of analysis. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

77
<!-- Start of picture text --> re<br>ri ‘ ay<br>an ¢ah A ys Ven|]inMissing 1993/ InformationForeigners in 1993<br>54 ah [__]0.000<br>PCT aa | [HHHE 0.00010.3001 -- 0.3000 0.7000<br>, 2 » = J0.7001- 0.9700<br>? Sp HM 0.9701 - 1.0000<br>wi\, og aeMe‘ eg<br>nS" aay.ekMig: |SAE<br>P s os .<br>RZ } a<br>a<br>Rica lat ,<br>‘4 .ey,a 7 a<br>“ |<br>opAe = |<br><!-- End of picture text -->

<!-- Start of picture text --> sagt. aA [_]0.0000PEP Holders/ Pop. in 2018<br>D>: ae [ly 0.0001- 0.0003<br>aFo oe:aFe HEHM 0.00040.0013-- 0.00120.0037<br>- 1.6024<br>yp ay HMM 0.0038<br>yc “ak<br>Mihaialeae a SEee‘ ales<br>eGee oeSh GE A<br>P a ¥ <=) \<br>Cavabrs eect ‘ &<br>Gere<br>SNe Le LY t<br>xaeWs WA<br><!-- End of picture text -->
Table D.6. Impacts of PEP Program on Mayoral and Presidential Elections Migration Test

||Election|Sha|re of Vote|s for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Mayoral Elections_||||||
|EarlySettlements1993m_×_I(Post2018)t|0.003|0.016|-0.010|-0.012|-0.000|
||(0.002)|(0.011)|(0.015)|(0.012)|(0.008)|
|q-values|[0.557]|[0.557]|[0.673]|[0.557]|[1.00]|
|R-squared|0.861|0.461|0.368|0.430|0.392|
|Observations|4,954|4,954|4,954|4,954|4,815|
|_Panel B. Presidential Elections_||||||
|EarlySettlements1993m_×_I(Post2018)t|-0.005|0.003|-0.006|0.003|0.004|
||(0.002)|(0.004)|(0.005)|(0.005)|(0.007)|
|q-values|[0.227]|[0.863]|[0.780]|[0.863]|[0.863]|
|R-squared|0.894|0.902|0.860|0.876|0.691|
|Observations|5,177|5,177|5,177|5,177|5,173|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|Yes|
|Municipal Controls_×_Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Early Settlements is the geographical variation from Rozo and Vargas (2021), defined as the ratio of Venezuelan migrants by municipality over total foreigners in Colombia in 1993 (before the onset of the Venezuelan crisis). Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

79
# **D. F Heterogeneous Effects**

<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.6. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable – Tercile 1<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.01 .02<br>.01<br>.005<br>0<br>0<br> (95% CI) (95% CI)<br>β β -.01<br>-.005<br>-.02<br>-.01 -.03<br>.04 .04<br>.02<br>.02<br>0<br>0<br> (95% CI) (95% CI)<br>β -.02 β<br>-.02<br>-.04<br>-.06 -.04<br>.04<br>.02<br>0<br> (95% CI)<br>β<br>-.02<br>-.04<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercile 1 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

80
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.7. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable<br>Tercile 2<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.04<br>.01<br>.005 .02<br>0 0<br> (95% CI) (95% CI)<br>β β<br>-.005 -.02<br>-.01 -.04<br>.04<br>.06<br>.02<br>.04<br>β (95% CI) .020 β (95% CI) 0-.02<br>-.02 -.04<br>-.04<br>-.06<br>.06<br>.04<br>.02<br> (95% CI)<br>β<br>0<br>-.02<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercil 2 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

81
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure D.8. Parallel Trend Assumption – Mayoral Elections<br>Continuous Treatment Variable<br>Tercile 3<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.1<br>.04<br>.02 .05<br>0<br> (95% CI) (95% CI)<br>β β 0<br>-.02<br>-.05<br>-.04<br>.2 .05<br>0<br> (95% CI) .1 (95% CI) -.05<br>β β -.1<br>0<br>-.15<br>-.1 -.2<br>.1<br>.05<br>0<br> (95% CI)<br>β -.05<br>-.1<br>-.15<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation for the tercile 3 of the PEP holders intensity. We interacted the standardized values of the variable PEP holders with mayoral electoral years excluding the election of 2015, the year the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

82
Table D.7. Impacts of PEP Program on Electoral Outcomes by Treatment Intensity Mayoral Elections

||Election|Sha|re of Votes|for|Electoral|
|---|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|Competition<br>(5)|
|_Panel A. Tercile 1 - PEP Hold_|_ers Municip_|_alities_||||
|PEPm_×_I(Post2018)t|-0.000|-0.004|0.006|-0.002|0.005|
||(0.002)|(0.011)|(0.016)|(0.013)|(0.009)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.876|0.482|0.382|0.439|0.398|
|Observations|3,107|3,107|3,107|3,107|2,977|
|_Panel B. Tercile 2 - PEP Holde_|_rs Municipa_|_lities_||||
|PEPm_×_I(Post2018)t|-0.002|-0.012|0.026|-0.014|0.001|
||(0.002)|(0.012)|(0.017)|(0.014)|(0.009)|
|q-values|[0.763]|[0.763]|[0.763]|[0.763]|[0.763]|
|R-squared|0.867|0.456|0.375|0.441|0.411|
|Observations|3,134|3,134|3,134|3,134|3,005|
|_Panel C. Tercile 3 - PEP Holde_|_rs Municipa_|_lities_||||
|PEPm_×_I(Post2018)t|-0.017|-0.002|0.048|-0.048|0.064**|
||(0.009)|(0.028)|(0.037)|(0.035)|(0.024)|
|q-values|[0.124]|[0.310]|[0.166]|[0.166]|[0.042]|
|R-squared|0.880|0.451|0.383|0.436|0.420|
|Observations|3,126|3,126|3,126|3,126|3,002|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE x Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls x Year|Yes|Yes|Yes|Yes|Yes|

_Notes:_ This table presents the results of our main specification in equation 1. I(PEPm) is a discrete variable that takes the value of one if the municipality had positive take-up rates and PEPm is the standardized value of the ratio of PEP migrants to population. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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# **D. G External Validation using Parliamentary Elites in Latin American Surveys**

Table D.8. Presidential Candidates with Divergent Ideological Classifications

|**Candidate**|**Election Year**|**Our Classifcation**|**Avg. Parliamentarians’ Perception**|
|---|---|---|---|
|GERMAN VARGAS|2010|Center|Right|
|GERMAN VARGAS|2018|Center|Right|
|JUAN MANUEL SANTOS|2014|Center|Right|
|MARTHA LUCIA RAMIREZ|2014|Center|Right|
|NOEMI SANIN|2002|Center|Right|
|NOEMI SANIN|2010|Center|Right|

_Note:_ This table shows 5 candidates who have run in 8 presidential elections, with ideological classifications based on Fergusson et al. (2020) that differ from how legislators, on average, perceive these candidates’ ideologies when asked to classify them on a 1-10 left-right spectrum.

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Table D.9. Presidential Candidates with Divergent Ideological Classifications

||Election|Shar|e of Vote|s for|Electoral|
|---|---|---|---|---|---|
||Turnout|Left|Center|Right|Competition|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Presidential Election_|_- Discrete_|_Treatmen_|_t Variable_|||
|I(PEPm)_×_I(Post2018)t|0.002|-0.018|-0.003|0.007|-0.004|
||(0.004)|(0.016)|(0.004)|(0.004)|(0.013)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|R-squared|0.877|0.257|0.785|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|_Panel B. Presidential Election_|_- Continuo_|_s Treatm_|_ent Variab_|_le_||
|PEPm_×_I(Post2018)t|-0.019|-0.051|0.007|-0.006|0.05|
||(0.015)|(0.029)|(0.011)|(0.022)|(0.026)|
|q-values|[0.249]|[0.247]|[0.497]|[0.497]|[0.247]|
|R-squared|0.877|0.257|0.785|0.866|0.682|
|Observations|6,561|6,561|6,561|6,561|6,549|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Department FE x Year|Yes|Yes|Yes|Yes|Yes|
|Municipal controls x Year|Yes|Yes|Yes|Yes|Yes|

_Note:_ This table presents the results of our main specification in equation 1. I(PEPm) is a discrete variable that takes the value of one if the municipality had positive take-up rates and PEPm is the standardized value of the ratio of PEP migrants to population. Electoral competition was estimated following Chac ́on et al. (2006):1 - (% winning candidate - % second-place candidate). The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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# **E MEDIA DISSEMINATION OF THE PEP PROGRAM**

Table E.1. News in National and Regional Newspapers Mentioning the Permiso Especial de Permanencia

|Main topic of the news|Number of the news|
|---|---|
|Information about PEP program and RAMV census|33|
|Border Enforcement and Migration Control|14|
|Causes of Migration and Venezuelan Context|5|
|Consequences of accepting Velenzuelan Migrants in Colombia|22|
|Government benefts for Venezuelans in Colombia|10|
|Situation of Venezuelan Migrants in Colombia|21|
|Statistics of Venezuelan Migration|15|
|Total Number|120|

_Notes:_ This table presents the total number of news articles found when we searched the _Permiso Especial de Permanencia_ or the acronym PEP in the principal newspapers of Colombia in 2018. The newspaper sources used in the analysis were El Tiempo, El Espectador and Publimetro. To complement the analysis, we used the Google news tool to check for regional newspapers.

86
# **F SURVEY EXPERIMENT**

_Survey Details_ The sample size was chosen to guarantee two conditions: (i) that the sample represents the population universe with an estimated margin of error of ± 2.5%, and that (ii) we would be able to detect a difference of at least 2% in the primary outcomes between treatment and control groups. The sampling method was probabilistic and stratified using the geostatistical tool of the Colombian Statistics Agency (DANE), which contains information about the urban blocks, locality, and economic strata in Bogot ́a. It was stratified by gender (female and male), two age groups (21–28 and 29+), and economic strata (high, medium, and low). The number of respondents needed in each subgroup was calculated so that the proportion of respondents in each stratum corresponded to the real proportion of inhabitants in that stratum out of the total sample universe. We only surveyed one member per household. The surveyed individual was the one with the closest birthday date to the day when the survey was carried out. The survey involved face-toface interviews conducted in Spanish. The participants were asked 35 questions, which took approximately 45 minutes and were structured in 6 modules: (i) sociodemographics characteristics, (ii) social desirability measurements, (iii) list experiment, (iv) attitudes toward Venezuelan migrants, (v) political views on migrant integration policies and voting intentions, and (vi) general knowledge about the PEP program. Respondents answered the modules in the order listed above; this order was carefully chosen to prevent priming the control group before asking about their prosocial behaviors and attitudes towards migrants. The last module was used to assess the control group’s knowledge about the PEP program.

87
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Table F.1. Survey Experiment: Successful Covariate Balance

|Variable|Control|Treatment|P- value|
|---|---|---|---|
|Age|51.007|49.362|0.106|
|Male [=1]|0.476|0.526|0.107|
|Ed: Primary school or less [=1]|0.163|0.172|0.696|
|Ed: Secondary school or less [=1]|0.364|0.370|0.842|
|Ed: Tchnician, university or more [=1]|0.473|0.457|0.628|
|Married or Cohabitating [=1]|0.535|0.526|0.785|
|Economic Strata: Low [=1]|0.498|0.500|0.953|
|Economic Strata: Medium [=1]|0.308|0.314|0.833|
|Economic Strata: High [=1]|0.194|0.186|0.746|
|Employed [=1]|0.899|0.872|0.293|
|Labor Contract [=1]|0.404|0.440|0.418|
|Student [=1]|0.132|0.123|0.686|
|Political Interest [=1]|0.771|0.743|0.291|
|Voted in mayoral 2019 elections|0.731|0.717|0.610|
|Voted inpresidential 2022 elections|0.789|0.765|0.349|
|Join F-Test|||0.394|
|Observations|546|494|1,040|

_Notes:_ Column (1) presents the control sample mean and Column (2) the treatment sample mean. Column (3) depicts the p-value of the t-test regression. We performed a joint orthogonality test by running a multinomial logit where the dependent variable is the assigned treatment, the explanatory variables are all the covariates in this table, and the base group is the control group. The joint orthogonality test p-value is 0.217. Definition dependent variables: labor contract is an indicator [=1] if the respondent reported a labor contract in his actual job. Political interest is an indicator [=1] if the respondent reported awareness of the current political situation in the country.

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Table F.2. List Experiment

||List Experiment<br>(1)|
|---|---|
|_β_1=I(Treatment)_×_I(List Treatment)|0.176<br>(0.112)|
|_β_2=I(Treatment)|-0.059<br>(0.080)|
|List Treatment Diff. Effect=_β_1+_β_2|0.117<br>(0.078)|
|R-squared|0.050|
|Observations|1,040|

_Notes:_ The table depicts the results of the listing experiment randomly assigned to all participants. The respondents were asked: how many of these individuals would you not want to have as neighbors?. “[Please respond how many, not which of them].” The options were: “a. Abusive people”; “b. People in poverty”; “c. People who profess a religion different from yours”; “d. Venezuelan migrants.” Option d was the statement assigned randomly so that only half the participants got this statement. The variable List Treatment is an indicator variable [=1] if the respondent received option d on the questionnaire. This table reports OLS estimates from equation xx. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

Table F.3. Heterogeneous Effects on Voting Intentions by Social Desirability Score

||Vote intention<br>in 2023|Vote intention<br>in 2026|
|---|---|---|
||Mayoral<br>elections|Presidential<br>elections|
||(1)|(2)|
|_β_1=I(Treat)_×_I(High Soc. Desirability)|0.057**|0.049**|
||(0.023)|(0.022)|
|_β_2=I(Treatment)|-0.025|-0.025|
||(0.023)|(0.022)|
|Social Desirability Diff. Effect=_β_1+_β_2|0.032|0.024|
||(0.033)|(0.032)|
|R-squared|0.024|0.018|
|Observations|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. Dependent variables in Columns (i)–(ii) are indicator variables [=1] if the respondent had the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table F.4. Heterogeneous Effects on Social Capital by Social Desirability Score

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_β_1=I(Treat)_×_I(High SDS)|-0.054|-0.069|-0.000|0.043|
||(0.061)|(0.061)|(0.061)|(0.061)|
|_β_2=I(Treatment)|0.019|-0.023|-0.017|-0.036|
||(0.061)|(0.061)|(0.061)|(0.061)|
|SDS Diff. Effect=_β_1+_β_2|-0.035|-0.092|-0.017|0.007|
||(0.087)|(0.086)|(0.086)|(0.086)|
|R-squared|0.031|0.066|0.027|0.019|
|Observations|1,040|1,040|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. The variable in column (i) is an index constructed using the methodology of Kling et al. (2007) and the answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer regarding the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing you are to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table F.5. Heterogeneous Effects on Altruism Towards Migrants by Social Desirability Score

||Money will<br>kept by<br>their-self|Money will<br>share with<br>vulnerable<br>Venezuelan|Money will<br>share with<br>vulnerable<br>Colombian|
|---|---|---|---|
||(1)|(2)|(3)|
|_β_1=I(Treat)_×_I(High SDS)|-0.067|0.004|-0.007|
||(0.060)|(0.045)|(0.032)|
|_β_2=I(Treatment)|0.004|-0.029|-0.043|
||(0.063)|(0.044)|(0.032)|
|SDS Diff. Effect=_β_1+_β_2|-0.064|-0.025|-0.050|
||(0.092)|(0.062)|(0.045)|
|R-squared|0.032|0.018|0.022|
|Observations|1,040|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. Dependent variables are the logarithm of the answer of the respondent about the distribution of 5,000 Colombian pesos between him (Column (i)), a vulnerable Venezuelan migrant (Column (ii)), and a vulnerable Colombian (Column (iii)). All the columns control for sex (female and male), two age groups (21– 28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table F.6. Heterogeneous Effects on Political Attitudes towards Migrants by Social Desirability Score

||Colombian<br>government<br>has to help<br>Venezuelans|In favor to<br>a law that<br>helps<br>Venezuelans|Venezuelans<br>compete with<br>Colombians<br>jobs|Venezuelans<br>increase<br>crime|Venezuelans<br>improve<br>Colombian<br>culture|Positive<br>effects of<br>Venezuelans in<br>Colombia|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_β_1=I(Treat)_×_I(High SDS)|-0.018|0.032|0.064|-0.091|0.133**|0.007|
||(0.060)|(0.060)|(0.061)|(0.063)|(0.062)|(0.031)|
|_β_2=I(Treatment)|0.026|0.024|0.064|-0.066|-0.052|-0.028|
||(0.060)|(0.061)|(0.061)|(0.063)|(0.062)|(0.031)|
|SDS Diff. Effect=_β_1+_β_2|0.008|0.056|0.129*|-0.157|0.081|-0.021|
||(0.085)|(0.085)|(0.086)|(0.089)|(0.088)|(0.043)|
|R-squared|0.033|0.036|0.008|0.027|0.032|0.028|
|Observations|1,040|1,040|1,040|1,040|1,040|1,040|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with the Social Desirability Score (SDS). The Social Desirability Score (SDS) is a measure of the individual’s propensity to report socially desirable answers. High SDS refers to having an above-median score among all participants. Dependent variables in columns (i)–(vi) are the standardized reported answer on a 1 to 5 scale of the approval of the statements:(i) _The Colombian government is obliged to help Venezuelan migrants_ , (ii) _Would vote for a policy to increase government spending to help Venezuelan migrants_ , (iii) _Venezuelan migrants come to compete for our jobs_ , (iv) _Venezuelan migrants increase crime_ , and (v) _Venezuelan migrants improve Colombian society by bringing new ideas and cultures_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.
Table F.7. Heterogeneous Effects on Voting Intentions by PEP Benefits Knowledge

|Vote intention<br>in 2023|Vote intention<br>in 2026|
|---|---|
|Mayoral<br>elections<br>(1)|Presidential<br>elections<br>(2)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)<br>0.086<br>(0.053)|0.102**<br>(0.051)|
|_β_2=I(Treatment)<br>-0.087*|-0.100**|
|(0.046)|(0.044)|
|Knowledge PEP Diff. Effect =_β_1+_β_2<br>-0.001|0.002|
|(0.027)|(0.027)|
|R-squared<br>0.024|0.022|
|Observations<br>1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. Dependent variables in Columns (i)–(ii) are indicator variables [=1] if the respondent had the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

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Table F.8. Heterogeneous Effects on Social Capital by PEP Benefits Knowledge

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)|0.095|-0.087|0.032|0.160|
||(0.141)|(0.145)|(0.140)|(0.140)|
|_β_2=I(Treatment)|-0.077|0.034|-0.054|-0.154|
||(0.121)|(0.124)|(0.120)|(0.120)|
|Knowledge PEP Diff. Effect =_β_1+_β_2|0.018|-0.054|-0.022|0.006|
||(0.073)|(0.075)|(0.073)|(0.073)|
|R-squared|0.032|0.003|0.026|0.015|
|Observations|1,002|1,002|1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. The variable in column (i) is an index constructed using the methodology of Kling et al. (2007) and the answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer regarding the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing you are to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

95
Table F.9. Heterogeneous Effects on Altruism Towards Migrants by PEP Benefits Knowledge

||Colombian<br>government<br>has to help<br>Venezuelans|In favor to<br>a law that<br>helps<br>Venezuelans|Venezuelans<br>compete with<br>Colombians<br>jobs|Venezuelans<br>increase<br>crime|Venezuelans<br>improve<br>Colombian<br>culture|Positive<br>effects of<br>Venezuelans in<br>Colombia|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)|-0.047|0.062|0.007|-0.031|-0.003|0.055|
||(0.138)|(0.140)|(0.140)|(0.144)|(0.143)|(0.070)|
|_β_2=I(Treatment)|0.043|-0.044|0.031|-0.056|-0.058|-0.077|
||(0.119)|(0.120)|(0.120)|(0.124)|(0.123)|(0.060)|
|Knowledge PEP Diff. Effect =_β_1+_β_2|-0.003|0.018|0.038|-0.088|-0.061|-0.022|
||(0.072)|(0.072)|(0.073)|(0.075)|(0.074)|(0.036)|
|R-squared|0.043|0.043|0.019|0.031|0.033|0.042|
|Observations|1,002|1,002|1,002|1,002|1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. Dependent variables are the logarithm of the answer of the respondent about the distribution of 5,000 Colombian pesos between him (Column (i)), a vulnerable Venezuelan migrant (Column (ii)), and a vulnerable Colombian (Column (iii)). All the columns control for sex (female and male), two age groups (21– 28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.
Table F.10. Heterogeneous Effects on Political Attitudes towards Migrants by PEP Benefits Knowledge

||Money will<br>kept by<br>their-self|Money will<br>share with<br>vulnerable<br>Venezuelan|Money will<br>share with<br>vulnerable<br>Colombian|
|---|---|---|---|
||(1)|(2)|(3)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)|0.002|-0.069|-0.042|
||(0.140)|(0.109)|(0.074)|
|_β_2=I(Treatment)|-0.003|0.028|-0.008|
||(0.117)|(0.096)|(0.064)|
|Knowledge PEP Diff. Effect =_β_1+_β_2|-0.001|-0.042|-0.049|
||(0.078)|(0.052)|(0.038)|
|R-squared|0.028|0.012|0.018|
|Observations|321|419|764|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. Dependent variables in columns (i)–(vi) are the standardized reported answer on a 1 to 5 scale of the approval of the statements:(i) _The Colombian government is obliged to help Venezuelan migrants_ , (ii) _Would vote for a policy to increase government spending to help Venezuelan migrants_ , (iii) _Venezuelan migrants come to compete for our jobs_ , (iv) _Venezuelan migrants increase crime_ , and (v) _Venezuelan migrants improve Colombian society by bringing new ideas and cultures_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.