rafmacalaba/datause-encoder-data huggingface dataset:
these improvements. We conclude that even if living near a large number of refugees fleeing conflict can make individuals feel less safe (and may be associated with other negative externalities not captured by our study), resource allocation policies that benefit nearby communities can reduce potential backlash against refugees and improve social cohesion between host communities and refugees.

Our paper makes several contributions to the existing scholarship on the impact of refugees’ presence on local communities. First, we add to the literature on social cohesion by demonstrating that, in contrast to prior findings in studies of Global North contexts, the presence of large numbers of refugees does not necessarily generally lead to a backlash within host communities; their reactions are highly dependent on the context and the level of refugee presence. Second, we advance the emerging literature that examines the local economic consequences of hosting refugees, which has generally found a mix of negative and positive effects. We highlight the importance of refugee policies—such as Uganda’s integrative approach to social services—that seek to decrease the burden of hosting refugees on relatively marginalized populations that live near refugee settlements. Third, our project builds upon research that conceptualizes and operationalizes migrant exposure as geographic proximity. This literature argues that changes in social geography with respect to ethnic diversity can shape political attitudes and behaviors, such as hostility towards migrants (Enos, 2014; Ferwerda, Flynn and Horiuchi, 2017; Hangartner et al., 2019). With our data, we are not only able to operationalize exposure as geographic proximity, but we are also able to incorporate the sizes of nearby refugee populations and multiple refugee settlements for a more comprehensive measure of exposure.

# **2 Theoretical Motivation**

Forced migration is an increasing global challenge. The number of people affected by displacement events more than doubled from 34 million in 1997 to 82 million in 2020 (UNHCR, 2020): 1 percent of the world’s current population (1 in 95 people) has been forcibly displaced. Over a third of these people end up as refugees in a foreign country. Understanding the conditions under which host countries are successful (or not) at integrating those who are forced to flee their homes is a humanitarian and economic priority that has important theoretical and policy implications.

Our study builds on three relatively separate strands of research on how the presence of migrants and refugees affects host communities. The first uses public opinion data and election returns 5
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32
# **I. Introduction**

The República Bolivariana de Venezuela is experiencing a multifaceted humanitarian, economic, and social crisis, which has led to an exodus without precedent in the region. By September 2019, approximately 4.3 million people had left the República Bolivariana de Venezuela. This exodus also resembles the refugee crisis experiences in other conflict states, such as the Syrian Arab Republic, Afghanistan, Somalia, and Southern Sudan. Eighty percent of the Venezuelan emigrants migrated to Latin America, with the main recipient countries being Colombia, Peru, and Ecuador. At the beginning of the Venezuelan exodus, Ecuador was characterized as a country of transit to Peru or other Southern Cone countries, such as Chile and Argentina.

Since 2015, more than 1.2 million Venezuelans have passed through Ecuador in a " _humanitarian corridor_ ".<sup>4</sup> However, between 2015 and September 2019, almost 400,000 Venezuelans decided to settle in Ecuador .<sup>5</sup> The hardening of migration policies in other countries of the region, combined mainly with reasons of family reunification and the search for better economic opportunities, helped persuade many migrants to consider Ecuador as a destination country. The migration stock in Ecuador tripled between 2017 and 2018. By August 2019, it had almost doubled again when compared to the same period the previous year (Figure I-1).

<!-- Start of picture text --> FIGURE I-1: THEY ARE NO LONGER IN TRANSIT; MORE AND MORE ARE DECIDING TO STAY<br> 400,000 389,103<br> MONTHLY ARRIVALS<br> 350,000<br> ACCUMULATED MIGRATORY BALANCE (from JANUARY 2015)<br> 300,000 248,480<br> 250,000<br> 200,000<br>94,694<br> 150,000 33,942<br>10,058<br> 100,000<br> 50,000<br> -<br>2015 2016 2017 2018 2019<br>Source: Own estimates based data from the Ministry of the Government of Ecuador, August 2019.<br>62,037 70,950 73,284 76,469 89,015 82,117 93,303 91,356 70,842 97,688 67,434 81,142 72,556 87,828 86,924 90,828<br>30,397 26,988 26,839 30,345 39,178 47,071 48,758 30,073 29,631 52,166<br>6,205 5,659 7,456 9,540 8,361 7,278 6,710 7,869 9,991 10,862 13,726 12,767 8,760 8,012 7,938 6,186 5,982 6,102 7,161 8,890 9,652 11,344 12,082 10,801 14,716 13,071 13,130 12,400 14,647 16,869<br>JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL SEP NOV JAN MAR MAY JUL<br><!-- End of picture text --> The Venezuelan exodus coincided with the Ecuadoran economic crisis, but it was not the cause. Oil prices have been falling since mid-2014, and, as a result, the Ecuadoran government had to shrink spending, which was the main engine of growth. The economy stagnated in 2015 and contracted by 1.2 percent in 2016.<sup>6</sup> Due to fiscal pressures, the coverage of Ecuador's main conditional cash transfer program was also reduced. Working conditions deteriorated. Unemployment, underemployment, and informality rates increased to levels not seen in 10 years. Between 2013 and 2018, approximately 66,000 good jobs were lost. This

> 4 http://reporting.unhcr.org/node/2543.

> 5 Ministry of Government of Ecuador. Available at https://www.ministeriodegobierno.gob.ec/migracion/. 6 World Bank (2018).

2
Table 1: Descriptive statistics of analytical sample

|Country|Pooled|Bolivia|Colombia|Ghana|Kenya|Vietnam|
|---|---|---|---|---|---|---|
|**Panel A**<br>|**Population**<br>|**with 1 to 12ye**<br>|**ars of schoolin**<br>|**g**<br>|||
|Average age|32.9|31.4|34.8|31.4|28.5|38.1|
|Averageyears of schooling|8.9|8.9|8.5|8.2|9.6|9.4|
|Still at school (%)|21.2|35.1|16.0|19.7|17.8|17.6|
|Literacyscores(0‐500)|187|181|223|126|170|234|
|Female(%)|58.9|60.4|59.9|58.1|56.3|59.6|
|Observations|10,665|1,558|1,888|2,227|2,489|2,503|
|**Panel B**|**Workforce**|**aged 25‐64**|||||
|Hourlywage (PPP 2011 $)|3.8|4.6|4.5|2.8|3.1|3.8|
|Employment rate (includingself‐employed) (%)|92.1|94.3|89.7|94.5|83.1|98.0|
|Average age|39.3|39.8|40.3|39.1|35.4|41.7|
|Averageyears of schooling|9.9|11.1|9.8|7.8|10.1|10.9|
|Literacyscores (0‐500)|188|185|227|121|170|237|
|Female (%)|56.3|60.1|55.4|59.3|48.7|57.2|
|Observations|8,156|1,228|1,450|1,717|1,948|1,813|

Note: Pooled specification gives same weight to each country. Source: World Bank (2016a) 21
these criteria alone could cause vast differences between district councils‟ allocations, 25% of the National Village Fund (NVF, TASAF IIs main spending vehicle) was first distributed equally to all councils. The remaining amount was then distributed using the composite index. We now turn to more details of the data sources used in the analysis.

# **3. Data**

# _3.1. Institutional Data from TASAF_

This paper utilizes two main databases from TASAF II. The first of these datasets documents every application received by TASAF II between May of 2004 and October of 2007, for a total of 102,606 applications. More than 95% of the 2,542 wards in mainland Tanzania submitted at least one application, with the median ward submitting 14. The second institutional database describes every TASAF II project funded through August 2008, and gives details of the beneficiaries, project type, and budgets for each of the 4,037 projects funded. This database also shows the composition of funding provided by the NVF, local government authorities, and the community itself. NVF spending typically makes up about 80% of total project costs, and is never below 50%. We merge these datasets at the ward level to calculate the number of applications, the percentage of applications funded, and the total amount spent from each source per ward.

# _3.2.Poverty Maps_

The institutional data on applications, approvals, and funding are overlaid with poverty maps, also at the ward level, created by the authors using the method described in Elbers, Lanjouw, and Lanjouw (2003). This exercise uses the household surveys from Tanzania‟s 2000/01 Household and Budget Survey (HBS) and the 2002 Population and Housing Census, both conducted by the National Bureau of Statistics (NBS). The HBS is a nationally representative sample of 22,178 households sampled between May 2000 and June 2001. The HBS is a much richer survey than the census, containing information on a wide range of outcomes including demographics, education, health status, and ownership of durable assets. The detail of the HBS allows the construction of a rigorous consumption aggregate, but the survey is not representative at the district, let alone ward, level. Log per capita household consumption can be modeled in the HBS sample using variables that exist in both datasets and then the parameter estimates can be used to simulate per capita consumption for each household in the census.

7
in Grossman and Krueger, 1991).<sup>14</sup> The comparison between per capita city GDP and the city annual mean concentration of PM 2.5 results in an inverted U shape curve.

We find evidence of an Environmental Kuznets Curve at the city level, for our sample of 769 cities. Figure 3 shows a cubic relationship: a positive slope at lower levels of per capita GDP, a maximum point (red dot) beyond which the slope turns negative, and a minimum point (green dot) at the end where the curve starts turning positive again. This incipient positive change at the end of the curve may indicate a certain point of development where pollution levels will start to rise again. However, since the sample of cities beyond that point is relatively small (17 cities), this result cannot be viewed as strong evidence of a positive relationship beyond that point.<sup>15</sup> _Figure 3: Environmental Kuznets Curve at the City Level_ <!-- Start of picture text --> City PM 2.5 and GDP per Capita<br>Environmental Kuznets Curve<br>●<br>30<br>20<br>●<br>10<br>0 2 4<br>City GDP per capita (log)<br>PM 2.5 Annual Mean<br><!-- End of picture text --> Cities with low per capita GDP (first section of the curve, with a positive slope) are in the first phase of development, and pollution starts to rise as a consequence of growth. These growing economies are often transitioning from agriculture activities to more industrialized and (often) “dirty” sectors. As a natural consequence of this structural change in their economies these cities see their pollution levels raise. On the contrary, cities on the second section of the curve (negative slope), and particularly those at the end,

> 14 We use cubic splines to allow for a more flexible estimation (Friedman, 1991; Durrleman & Simon, 1989).

> 15 This is also evident when looking at the regression results: the coefficient for the cubic term is not statistically significantly different from zero.

17
- 8 -

**Figure 1. Information Provision Practices in Japanese ESCO Procurement**

<!-- Start of picture text -->
1.0 0.99 0.98<br>0.92<br>0.9 0.83 0.85<br>0.8<br>0.68<br>0.7<br>0.6<br>0.5<br>0.4<br>0.30<br>0.3<br>0.2<br>0.1<br>0.0<br>Source : Author’s survey data.<br>provided<br>Share of ESCO projects with such information<br><!-- End of picture text -->

One empirical challenge in estimating Equation (1) is that the number of bidders is potentially endogenous (e.g., McAfee and McMillan, 1987; Levin and Smith, 1994; Li and Perrigne, 2003; Li and Zheng, 2009; Ohashi, 2009).<sup>9</sup> According to data collected for this study, the firms’ entry decision indeed looks to be a dynamic process. On average five firms showed initial interest, but only 3.5 firms actually applied for the formal process. Some of them decided not to remain in the competition. As the result, only about three firms submitted final proposals (Figure 2).

To deal with the endogeneity problem, the three-stage least squares (3SLS) model (Zellner and Theil, 1962) is used, instead of the ordinary least square techniques, such as seemingly unrelated regression (SUR). The instrumental variable is the number of firms that submitted an initial expression of interest, denoted by _NEOI_ . This is the maximum number of contenders that can participate in each competition. It is clearly related to the actual number of bidders _N_

> 9 Endogenous entry auction theory suggests that given a fixed positive cost of participating in an auction, bidders will enter until their expected profits are driven to the entry cost. At this level no more firms can expect nonnegative profits from new entry.
# **Figure 11a: Impact of gender for Rio Grande do Norte**

<!-- Start of picture text --> Rio Grande do Norte: Female<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for female dummy.

# **Figure 11b: Impact of gender for Northeast**

<!-- Start of picture text --> North East: Female<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for female dummy.

# **4.3.3  Experience and family characteristics**

In the labor market literature, skills and experience are often proxied by the age of a worker. When we include the age and age squared, the latter to capture nonlinearities in the data, both turn out statistically significant correlates of poverty for some years while for other years age is only marginally significant. This indicates that the older the household head the lower probability the household will be poor, albeit at a decreasing rate for the old age. These findings reveal that the household combination is more important than age of the household head, since other included family characteristics are strongly 31
# **Table 4: Robustness Check - Power Outages and Sector of Activity**

|**Dependent Variable**||**Labor Pro**|**ductivity (logs)**||
|---|---|---|---|---|
|**Sample**|**Firms that u**<br>**business**|**se power for**<br>**activities**|**Manufacturing**<br>**Firms**<br>**(manufactures**<br>**product by itself)**|**Retail and**<br>**Other**<br>**Service**<br>**Firms**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.063**|-0.063**|-0.038**|-0.059|
||(0.025)|(0.025)|(0.018)|(0.050)|
|Total number of power outage incidents per day over the last month|0.082||||
||(0.133)||||
|Average length of power outage, 0 if no outage experienced|0.005||||
||(0.004)||||
|Total duration of power outages last month (in days)||0.025|||
|||(0.018)|||
|Firm uses generator Y/N|-0.196|-0.201|||
||(0.136)|(0.129)|||
|Firm uses electricity for activity Y/N|||0.134|0.028|
||||(0.141)|(0.179)|
|Log of total number of employees in regular month|-0.513***|-0.512***|-0.609***|-0.549***|
||(0.123)|(0.123)|(0.189)|(0.133)|
|Largest owner has secondary education or higher: Y/N|0.161|0.155|0.142|0.528**|
||(0.219)|(0.219)|(0.174)|(0.218)|
|Manager experience (years)|0.008|0.008|0.020|0.009|
||(0.007)|(0.007)|(0.012)|(0.008)|
|Largest owner has a job in formal sector or looked for one over last 2<br>years Y/N|-0.164|-0.152|-0.052|0.143|
||(0.205)|(0.196)|(0.271)|(0.280)|
|Largest owner started another business in the last three years Y/N|-0.042|-0.033|-0.151|-0.074|
||(0.089)|(0.095)|(0.125)|(0.182)|
|Firm uses machinery in its current operations Y/N|0.064|0.068|0.095|-0.023|
||(0.136)|(0.131)|(0.180)|(0.179)|
|Firm uses vehicles in its current operations Y/N|0.366***|0.378***|0.370**|0.498**|
||(0.099)|(0.101)|(0.171)|(0.218)|
|Manufacturing firm Y/N|0.056|0.058|||
||(0.106)|(0.108)|||
|Produces or sells under contract from another business or person<br>Y/N|0.316*|0.314*|0.250*|0.752***|
||(0.167)|(0.168)|(0.128)|(0.223)|
|Firm uses bank account to run business Y/N|0.257|0.263|0.326*|0.047|
||(0.165)|(0.172)|(0.171)|(0.207)|
|Firm has a loan at this time Y/N|-0.073|-0.074|-0.100|-0.062|
||(0.081)|(0.079)|(0.119)|(0.194)|
|Firm pays informal payment to remain unregistered Y/N|-0.249**|-0.251*|0.064|-0.509|
||(0.126)|(0.129)|(0.258)|(0.418)|
|Firm experienced crime last month Y/N|0.199|0.201|-0.210|0.291|

27
42 Table 9. Average effect of the Venezuelan migration shock on educa– tional outcomes Rural areas (marginal effects)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent Var.|Ln(Enrollment)|Promotion Rate|Dropout Rate|Math Std.<br>Score|Language Std.<br>Score|
||**P**|**anel A: Without **|**Controls**|||
|Foreign Males|0.0077***|-0.0314|-0.0051|0.0016|0.0060|
||(0.0020)|(0.0239)|(0.0184)|(0.0036)|(0.0064)|
|Native Males|0.0110***|-0.0343|0.0033|-0.0006|-0.0003|
||(0.0019)|(0.0220)|(0.0153)|(0.0010)|(0.0011)|
|Foreign Females|0.0073***|-0.0100|-0.0019|0.0046*|0.0014|
||(0.0018)|(0.0239)|(0.0181)|(0.0024)|(0.0041)|
|Native Females|0.0112***|-0.0180|0.0043|0.0005|0.0007|
||(0.0019)|(0.0224)|(0.0156)|(0.0010)|(0.0014)|
|R-Squared|0.8978|0.2908|0.2464|0.6467|0.5996|
|||**Panel B: With C**|**ontrols**|||
|Foreign Males|0.0076***|-0.0302|-0.0065|0.0015|0.0059|
||(0.0020)|(0.0237)|(0.0182)|(0.0036)|(0.0064)|
|Native Males|0.0109***|-0.0331|0.0018|-0.0008|-0.0005|
||(0.0019)|(0.0219)|(0.0152)|(0.0009)|(0.0011)|
|Foreign Females|0.0072***|-0.0088|-0.0033|0.0045*|0.0013|
||(0.0018)|(0.0238)|(0.0178)|(0.0023)|(0.0041)|
|Native Females|0.0111***|-0.0168|0.0028|0.0003|0.0005|
||(0.0018)|(0.0223)|(0.0154)|(0.0010)|(0.0014)|
|R-Squared|0.8979|0.2910|0.2467|0.6477|0.6003|
|Native Males Mean|3.888|93.34|3.400|-0.180|-0.459|
|Native Males SD|1.669|11.23|8.939|0.774|0.752|
|Native Females Mean|3.789|95.28|2.670|-0.492|-0.442|
|Native Females SD|1.708|9.671|8.230|0.667|0.704|
|Foreign Males Mean|0.934|89.25|7.876|-0.339|-0.713|
|Foreign Males SD|1.027|25.33|22.33|1.084|1.384|
|Foreign Females Mean|0.912|90.64|7.175|-0.834|-0.504|
|Foreign Females SD|1.007|24.20|21.67|1.353|1.248|
|Observations|81,049|81,049|81,049|24,612|24,612|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
## **3. PROVINCIAL BANK PRIVATIZATION IN ARGENTINA**

## **3.1. Background**

At the beginning of the decade, each Argentine province owned at least one bank. The performance of these publicly-owned provincial banks in the 1990s has been substantially worse than that of private banks, and the losses they incurred imposed substantial fiscal costs upon the provinces.'<sup>3</sup> Beginning in 1991, when the provincial government of Corrientes passed a law authorizing the privatization of Banco de Corrientes, provincial governments started to consider privatizing the public banks. The trickle of provincial bank privatizations became a flood after the "Tequila Crisis" of December 1994. Of the nearly thirty provincial banks, almost half had been privatized by the end of 1996, and several other privatizations had been authorized but not completed. Although other players, for example the Federal Government of Argentina, the Central Bank of Argentina and international donors, might be able to indirectly influence the privatization decision, the final choice is made by the provincial government. This allows variation along several key dimensions, including fiscal performance of the province, bank performance and the political incentives facing key players, while keeping other institutional details similar.

# 3.2. **Variable Descriptions and Summary Statistics**

In this section, we describe and provide summary statistics for variables that might have affected the incentives and constraints facing decision-makers in the provincial governments. We include variables indicating whether opponents could block privatization in either the executive or legislative branch of government; bank quality variables indicating the cost of continuing to support the public provincial bank; a fiscal deficit variable indicating the province's ability to support money-losing enterprises; and a term-limit variable to capture the governor's incentives to take costly actions.

> 13 See Clarke and Cull (1997) for a description of the performance of public and privatized provincial banks in Argentina.

8