rafmacalaba/datause-encoder-data huggingface dataset:
Our results highlight the importance of support for migrants during crises as these vulnerable populations could experience more prejudice and receive less assistance from hosting communities. Our findings also suggest that the _impressionable years_ are a period in which treatments to improve prosocial behaviors could be most effective.

Our study highlights multiple avenues for future research. First, it is likely that users of Facebook—our survey platform—have specific characteristics that may not compare directly to the rest of the population. Facebook users, for example, have more exposure to media content in Facebook platforms. They also read and are proficient users of technology, and as such, have higher education levels than non-users. Moreover, our respondents reported an unusually high likelihood of job loss due to COVID-19. This may explain why they had time to take part in our experiment. If these characteristics are comparable across treatment and control groups, it should not affect our results. However, future research initiatives should examine whether our results can be generalized to other groups such as rural residents with limited internet access. Second, Colombia is a country that is relatively new to international migration inflows. As such, future research could also study the effects of the COVID-19 pandemic in countries where international migration is more common, such as those in the Global North. Third, our analysis only addresses the effects of worldwide crises that affect sending and receiving countries equally. It is possible, therefore, that localized crises can trigger different effects that are more directly associated with migrants, as shown by Rozo and Vargas (2021) concerning the voting effects of Venezuelan migration in Colombia. The authors show that voters responded to higher immigration inflows by supporting more right-wing ideologies because they associated migrants with the leftist regime in Venezuela. Fourth, future studies should assess the validity of our results in situations where respondents truly sacrifice something when they report being more altruistic.

# **References**

- Adida, C. L., A. Lo, and M. R. Platas (2018). Perspective taking can promote short-term inclusionary behavior toward syrian refugees. _Proceedings of the National Academy of Sciences 115_ (38), 9521–9526.

- Alesina, A., C. Favero, and F. Giavazzi (2019). Effects of austerity: Expenditure- and tax-based approaches. _Journal of Economic Perspectives 33_ (2), 141–62.

- Alesina, A. and P. Giuliano (2010). Preferences for redistribution. In A. Bisin and J. Benhabib (Eds.), _Handbook of Social Economics_ , pp. 93–132. North Holland.

- Alesina, A., A. Miano, and S. Stantcheva (2022). Immigration and redistribution. Technical report, _Review of Economic Studies_ , _Forthcoming_ .

14
education levels, since these were most likely to yield enterprises with invested capital below the threshold we had set. GNs were also stratified according to the degree of exposure of firms to the December 26, 2004 Indian Ocean tsunami. A door-to-door screening survey of 3361 households in these GNs was then conducted to identify firms whose owners satisfied the criteria listed above. In April 2005, the first wave of the Sri Lanka Microenterprise Survey (SLMS) surveyed the 659 firm owners which the screen identified as meeting these criteria. After reviewing the baseline data, 42 firms were dropped because they exceeded the capital stock threshold, or because a follow-up visit could not verify the existence of the enterprise. This gives a baseline sample of 617 microenterprises. The implications of the sample design for the selectivity of the sample of female and male owned enterprises is discussed in more detail in Appendix A.

In this paper we exclude the firms which suffered damage to business assets as a result of the tsunami, since recovery of assets damaged by the tsunami might affect returns to capital. This leaves 405 firms to be used in this paper, of which 197 are run by males and 190 by females. For the remaining 18 firms both husband and wife claim themselves as owner. Given their small number, we also drop these dual owner firms. The result is a sample of 387 firms almost evenly split by gender and across two broad industry categories: retail sales, and manufacturing/services. Firms in retail trade are typically small grocery stores. The manufacturing/services firms cover a range of common occupations of microenterprises in Sri Lanka, including sewing clothing, making lace products, making bamboo products, repairing bicycles, and making food products such as hoppers and string hoppers.

The SLMS then re-interviewed the owners of these firms at quarterly intervals for two years, and semi-annually for a third year. We use 11 waves of data, with the eleventh wave gathered in April 2008. In each wave, firm owners were asked about profits, revenues and expenses, changes in physical capital stock, and levels of inventories on hand. Profits were obtained through direct elicitation, which we find to be more reliable than constructing profits from detailed questions on revenue and expenses (de Mel, McKenzie and Woodruff, 2008b). Nominal profits were converted into real profits using 6

-  -
# **Table 2: Equivalence of Welfare Results Across Market Structures in the Simplest One Sector Model, with and without balanced trade**<sup>*****</sup>

**Impacts of Global Ten Percent Reduction in Iceberg Trade Costs**

## <u>Full Model Results are Hicksian Equivalent Variation as a Percent of Consumption</u>

||<br>1|<br>2|<br>3|<br>4|<br>5|<br>6|<br>7|<br>8|9|
|---|---|---|---|---|---|---|---|---|---|
||domestic||**Zero Trade**|**Balance for a**|**ll Countries**|**Data Based Trade**|**Balances for**|**all Countries**|domestic|
||trade|**Arkolakis et al.**|**Full M**|**odel Calcula**|**tions**|**Full**|**Model Calculat**|**ions**|trade|
||share|**formula*****|**Armington**|**Krugman**|**Melitz**|**Armington**|**Krugman**|**Melitz**|share|
|**Region**|_rr_<sup></sup>|**ε = 4.58****|σ(D,M)= 5.58**|σ = 5.58**|σ=5.0;a=4.58**|σ(D,M)= 5.58**|σ = 5.58**|σ=5.0;a=4.58**|<br>_rr_<sup></sup>|
|Australia-New Zealand|77.2%|3.0%|3.0%|3.0%|3.0%|3.1%|3.1%|3.1%|77.8%|
|Canada|70.1%|4.1%|4.1%|4.1%|4.1%|4.1%|4.1%|4.1%|70.1%|
|China|72.1%|3.7%|3.7%|3.7%|3.7%|4.0%|4.0%|4.0%|72.8%|
|Japan|83.0%|2.1%|2.1%|2.1%|2.1%|2.1%|2.1%|2.1%|83.0%|
|Mexico-Chile-Peru|67.0%|4.6%|4.6%|4.6%|4.6%|4.8%|4.8%|4.8%|67.3%|
|Low Income NEC|55.5%|6.4%|6.4%|6.4%|6.4%|5.8%|5.8%|5.8%|54.4%|
|Middle Income NEC|75.6%|3.2%|3.2%|3.2%|3.2%|3.3%|3.3%|3.3%|76.4%|
|OECD NEC|81.4%|2.3%|2.3%|2.3%|2.3%|2.3%|2.3%|2.3%|81.6%|
|Philippines|62.9%|5.1%|5.1%|5.1%|5.1%|4.6%|4.6%|4.6%|61.2%|
|United States|84.6%|1.9%|1.9%|1.9%|1.9%|1.7%|1.7%|1.7%|82.9%|
|average for the World|79.0%||2.7%|2.7%|2.7%|2.7%|2.7%|2.7%||

* Model with One Sector, One Factor, No Intermediates, No Labor-Leisure Choice, Untruncated Pareto distribution of productivity.

** ε is the elasticity of demand for imports with respect to variable trade costs, taken from gravity (here the absolute value of the gravity estimate is used); σ(D, M) is the Armington elasticity of substitution between imports and domestic goods; σ is the Dixit-Stiglitz elasticity of substitution between varieties in a sector; and "a" is the shape parameter in the Pareto distribution..

*** Arkolakis _et al_ . (2012) derive the proportional change in welfare in this simple model with zero trade balances − 1/    as: _W_ =  _jj_ where <sup>ˆ</sup> _jj_   _jj_  _jj_ , and  _jj_ and  _jj_ are country j’s shares of global expenditure on home goods in the initial and counterfactual equilibria, respectively (ε = the absolute value of the elasticity of demand for imports

− 1/  with respect to variable trade costs from gravity). Since this is a decline in trade costs,  _jj_  1, and the − 1/  percentage gains from trade using the Arkolakis _et al_ . (2012) formula are 100* _G j_ where: _G j_ =  _jj_ − 1 . **Source: Authors’ calculations.** 37
# **Table B6: Baseline Differences by Treatment Status: Self-Employed**

||In-|**means**<br>|||**p-valu**<br>|**es**<br>vs|
|---|---|---|---|---|---|---|
||person<br>Weekly|Phone<br>Weekly|Phone<br>Triweekly|vs in-p<br>we|erson<br>ekly|phone<br>weekly|
||(1)|(2)|(3)|(2=1)|(3=1)|(3=2)|
|**A. Variables used in rand**|**omization**||||||
|Male|0.37|0.35|0.39|0.74|0.73|0.54|
|Age|36.57|38.22|37.36|0.18|0.49|0.48|
|Education|7.83|8.09|8.56|0.62|0.16|0.40|
|Married|0.57|0.61|0.60|0.60|0.68|0.89|
|Self-employed|1.00|1.00|1.00||||
|Wage-employed|0.00|0.00|0.00||||
|Accra|0.45|0.53|0.61|0.40|0.06|0.32|
|Cape Coast|0.09|0.04|0.05|0.29|0.46|0.66|
|Dependency ratio|0.37|0.41|0.43|0.29|0.08|0.49|
|Log hh assets|7.40|7.31|7.62|0.68|0.27|0.16|
|p-valuejoint F test all vari|ables used i|n A||0.72|0.36|0.88|
|**B. Variables not targeted**|**in the rand**|**omization**|||||
|Days worked per week|5.69|5.57|5.58|0.43|0.44|0.97|
|Hours worked per week|53.82|46.92|49.30|0.03|0.13|0.44|
|Manufacturing|0.10|0.06|0.07|0.30|0.49|0.76|
|Trade|0.69|0.67|0.56|0.81|0.04|0.09|
|Services|0.21|0.26|0.36|0.37|0.01|0.11|
|Public sector|0.00|0.00|0.00||||
|Pay per week|116.87|103.60|135.61|0.57|0.49|0.24|
|p-value joint F test all vari|ables used i|n A and B||0.27|0.10|9,88|
|Total number of|150|110|135||||
|individuals<br>Note: *** p<0.01, ** p<|<br>0.05, * p<0|<br>.10. Stand<br>(Househ|<br>ard errors are<br>old).|clustered a|t treatm|ent level|

35
t. .' !\pt. i t. F>x3tic if les tnd iN q XBuo;.arz.

ixe,\elni? Flastic it o f **Pu1** i . _E\:pe_ lidtulI I __________________________ Sf,ort rin lon, ru, u .r, * m,.t .c mts I 0.31 (0.54 l 1 + .V;.b,1 . lieI' d ; turz (aG) 0. 5 4 ( .01) Tax Buoyancy (with respect to) - 0mo -r , its 1.44 (personral inicome) I n.£r 3'it _ie_ 0.88 (import value) r.tluSi ,..>S Taxes 1 .1 0 (domestic absorptiorJ, .1~., <Th\ (s 1.33 (domestic absorption) S lur1't e; Cals u1lated fcom Tables 23 and 24, BOT, Monthly Bulletin.

42
||**services (2011)**|**facilities (2011)**|**(2011)**|
|---|---|---|---|
|**ICC**|0.466 [0.328, 0.659]|0.385 [0.258, 0.582]|0.137 [0.076, 0.269]|

**Table 2:** Intra-class correlation coefficients for each outcome variable by council area

The high (and statistically greater than zero) intra-class correlation coefficients for each of our three dependent variables confirm that public service provision is indeed highly clustered by council area. This accurately reflects our assumptions about the extent to which decentralization has empowered local councils to affect council-wide public service provision since their creation in 2005. The fact that we observe higher levels of correlation by council area for government health services rather than education also reflects the timing of decentralization, as health was the first public service devolved to the local councils. The high level of councillevel correlation for each of our three outcome variables also corroborates our intuition that multi-level models are indeed the most appropriate specification for analyzing council-level characteristics on EA-level outcomes.

In order to directly model within council dependence, we use random intercepts at the council level in the multi-level specifications that follow. This allows us to properly account for the nested structure of our data while maintaining the assumption that the effect _size_ within any particular council area, controlling for other observable explanatory variables, is interchangeable. That is, in contrast to models that employ random coefficients, we have no _a priori_ expectations about how the _magnitude_ of our key independent variables (the coefficient slope) might depend on any particular council. Rather, our theoretical expectations suggest that the effect size of councilor-chief relations should be generalizable across council areas. However, we test this assumption further in Section 6.

By design, multi-level models allow us to include explanatory variables at the both EA and council levels. The EA-level explanatory variables can be interpreted as capturing both within and across council variation. Each EA-level regression coefficient has both a within group and a between group component. The reported coefficients fall between the two level-specific coefficients, and are weighted by the number of EA observations within each council area and the amount of variance accounted for between councils (the ICC). If we were interested in disentangling the within council from the between council components of the EA-level 20
education and labor markets seem to be the main sources of racial exclusion in Brazil. An important first step is to address racial issues as early as in primary school and remove racial caricatures from textbooks and other school material.

Being able to read and write is important in determining the likelihood of being in poverty. The P0 is 31 percent for household heads that are literate, and 54 percent for those that are not. However, while a negative relationship between years of education completed and poverty is typical, and is evident in most of the Northeast states, it is less marked in Paraíba, where there appears to be only a relative little difference in P0 between household heads with no education (51 percent) and household heads with completed primary education (40 percent).<sup>6</sup> Nevertheless, household heads that have completed secondary education are much better off (20 percent are poor) than those with only primary education (40 percent are poor). Only 3 percent household heads with more than 12 years of schooling were extremely poor. Hence, these figures show that education is an important poverty-reducing factor (see also section 4). Policies to increase school attendance and improve quality of supplied education are important elements in Paraíba’s poverty reduction effort.

In the last half decade, the gap in P0 between the educated and less educated has widened: the more educated are experiencing less poverty, while the less educated are being left behind. For each level of education (lower primary only, upper primary only, secondary only, and tertiary) the probabilities of being poor are estimated for Paraíba.<sup>7</sup> Figure 2.5 shows that there are very large differences in poverty levels by education, and that they have increased over time: since 1995, the P0 for people with some primary education appears to have increased, while the P0 for people with some university education has stayed at its already very low level. In Paraíba, as elsewhere, there is a great deal of debate about the causes of these changes: skill-biased technological change, changes in the relative supply of and demand for workers with different characteristics, and trade liberalization have all been mentioned as possible explanations (Blom and Velez 2001; Blom, Pavcnik, and Schady 2001).

> 6 This picture might be clouded by the relatively small sample size of these groupings in the PNAD datasets for Paraíba.

> 7 The estimations show that the _differences_ in the probabilities of being poor between men and women, by race, and by place of residence have all basically remained constant throughout the 1990s. For these categories, therefore, analysis of the poverty profile at one point in time is sufficient.

10
# **Figure 4: Shares of Commonly Asked Screening Questions by Occupational Type**

|||Managers<br>Professionals<br>Clerical support workers|
|---|---|---|
|**Skill**|Certificate, education, skill<br>|s<br>15.7<br>22.0<br>20.4|
||Computer|8.1<br>8.9<br>3.9|
||Language|10.8<br>2.8<br>5.4|
||Occupational experience|16.1<br>19.3<br>28.1|
|**Personal**|Personal|27.8<br>22.0<br>11.2|
|**Condition**|Location|4.1<br>3.3<br>6.0|
||Salary|2.8<br>6.4<br>6.4|
||Work condition|0.0<br>1.0<br>0.0|
|**Others**|Others|14.6<br>14.6<br>18.6|
|||0<br>20<br>40<br>60<br>0<br>20<br>40<br>60<br>0<br>20<br>40<br>60|
|||Percentage<br>Percentage<br>Percentage|
|||Service workers<br>Sales workers<br>Skilled agricultural workers|
|**Skill**|Certificate, education, skill|s<br>9.6<br>21.6<br>13.5|
||Computer<br>Language|0.3<br>0<br>6.9<br>0.2<br>5.8<br>0|
||Occupational experience|38.5<br>22<br>19.2|
|**Personal**|<br>Personal|15<br>14.1<br>36.5|
|**Condition**|Location|5.9<br>4.5<br>3.8|
||Sl|113<br>94<br>173|
||aary|.<br>.<br>.|
||Work condition|0.3<br>0<br>0|
|**Others**|Others|19.1<br>21.3<br>3.8|
|||0<br>20<br>40<br>60<br>0<br>20<br>40<br>60<br>0<br>20<br>40<br>60|
|||Percentage<br>Percentage<br>Percentage|
|||Construction and craft workers<br>Plant operators, assemblers<br>Elementary occupations|
|**Skill**|Certificate, education, skill|s<br>10.1<br>18.7<br>5.6|
||Computer|0<br>0<br>0|
||L|21<br>07<br>26|
||anguage|.<br>.<br>.|
||Occupational experience|55.4<br>17.6<br>25.4|
|**Personal**|<br>Personal|17.2<br>15.7<br>31.7|
|**Condition**|Location|6.5<br>6.9<br>5|
||Salary|0<br>9.5<br>7|
||Work condition|0<br>5.6<br>1.9|
|**Others**|Others|8.6<br>25.4<br>20.8|
|||0<br>20<br>40<br>60<br>0<br>20<br>40<br>60<br>0<br>20<br>40<br>60|
|||Percentage<br>Percentage<br>Percentage|

Source: Authors’ analysis using Babajob data (Jan – Oct, 2015).

Text analysis can provide additional insight into employer preferences beyond minimum qualification requirements, and it can be especially useful in understanding the cognitive and non-cognitive skills that employers demand for each position. Employers rarely ask job seekers about their non-cognitive skills directly, since there are few objective measures for this skill type. While information on demand for these skills may be obtained from interviews with employers and surveys, an analysis of job advertisements may be more accurate, as it reflects the real-world behavior of employers.

Figure 5 illustrates the skill- and qualification-related words that appeared most frequently in the job descriptions posted to Babajob in 2014. Figure 6 shows the proportion of advertisements using the clusters of these keywords. Overall, skill- and qualification-related words appear more often in professional-level job descriptions. For both professional and non-professional occupations, the words “experience” and 14
ulation census of 2018: Barranquilla, Bogot ́a, Medell ́ın (and their metropolitan areas)—three of the largest cities in Colombia—and a fourth “region” of smaller cities.<sup>8</sup> Figure 2 illustrates the geographic distribution of the sample and the number of Venezuelan migrants in the 2018 population census, which can be taken as a proxy of the overall distribution of Venezuelan migrants in the country. Forced migrants in the sample fulfilled the following criteria: (i) were aged 18 or older; (ii) were the household head or partner; (iii) were undocumented at the time of arrival in Colombia; and (iv) arrived in Colombia between January 1, 2017 and December 2018.

We constructed the sampling frame separately for RAMV and non-RAMV forced migrants. For the former, we drew the sample directly from the RAMV census, which had information on 442,462 Venezuelan forced migrants in Colombia. Using the census, we drew a representative sample of 13,083 forced migrant households in the four regions mentioned above, from which we surveyed 1,135 households. For non-RAMV forced migrants, for whom there was no administrative data available, the sampling frame was constructed by combining databases shared by associations of Venezuelan migrants in the four regions above with referrals from forced migrants who were surveyed as part of the RAMV sampling frame. The non-RAMV sampling frame included data from 12,554 non-RAMV households, 81 percent of which were obtained from the organizations. Using this sampling frame, we surveyed a random sample of 1,097 migrant households in the same four regions mentioned above: 527 households referred by the organizations and 570 referred by other irregular migrants. As discussed below, key outcomes were elicited in each household from the household head and partner or another randomly selected adult member. This produced an overall sample of 3,896 forced migrants surveyed in 2,232 households, including 1,947 RAMV and 1,708 non-RAMV individuals.

To assess whether forced migrants in both subsamples were comparable, Table B.1 reports data for migrants in each group according to reasons for migration and pre-migration socioe-

> 8The fourth region includes C ́ucuta, Villa del Rosario, Cali, Cartagena, Riohacha, Maicao, Uribia, Valledupar, Santa Marta, and Arauca.

12
24 crisis in Russia on nutritional status. This could be understood if Russian households were able to fully smooth food consumption during this period of dramatic expenditure shortfalls. However, it would also be consistent with an overstatement of the increase in poverty in 1998. This latter observation could be obtained if inflation between 1994 and 1998 had somehow been overstated. Indeed, Gibson, Stillman and Trinh (2008) find evidence of a substantial overstatement in the CPI for urban Russia. To the extent that this latter finding holds for Russia more generally, and that the price deflators that accompany the RLMS data track the Russian CPI, it is possible that poverty in 1998, estimated from RLMS data, is also overstated. We apply our poverty prediction method to the RLMS data to probe these alternative narratives.

In our second examination of poverty trends in the face of uncertain pricedeflators we consider the case of Kenya. Two recent household expenditure surveys in Kenya are the 1997 Welfare Monitoring Survey (WMS) and the 2005-6 Kenya Integrated Household Budget Survey (KIHBS). These surveys were implemented during different periods of the year and more detailed consumption data was collected during the KIHBS – raising some questions regarding the comparability of the data.<sup>18</sup> However, it is the choice of the appropriate deflator that was generally considered to pose the greatest challenge to tracking the evolution of poverty during this period in Kenya. The official CPI almost doubled between 1997 and 2005-6, while the deflator based on recalculations of the rural and urban poverty lines suggested a much lower price increase (6 percent in

> 18 The 1997 WMS survey was carried out during 3 months (February -May 1997), while the data collection for the 2005 KIHBS spanned May 2005 till May 2006 during which field work was organized in 17 three week cycles with all 69 districts covered in each cycle.  The consumption data collection during the KIHBS was also more detailed.  During the WMS consumption data was collected for broad (aggregated) categories:  79 food (7 day recall) and 48 non-food items compared with the use of more detailed categories during the KIHBS: 140 food items (7 day recall) and 184 non-food items (1 month recall).