rafmacalaba/datause-encoder-data huggingface dataset:
# **I INTRODUCTION**

The popular perception about crime and immigration is that they often move in the same direction (Butcher and Piehl 1998a; Bianchi et al. 2012; Spenkuch 2014a; Fasani et al. 2019; Bahar et al. 2020; Pinotti and Rozo 2022).<sup>1</sup> Additionally, research suggests that during episodes of forced immigration, migrants themselves tend to be victims of crime.<sup>2</sup> In these contexts, it is likely that the most vulnerable migrants, e.g., those who are undocumented and female, might not report crimes against them for fear that interactions with local authorities could result in punishment, discrimination, or deportation.<sup>3</sup> As such, a relevant question points to the effects of facilitating proper documentation and economic integration on undocumented forced migrant’s propensity to commit and report crimes.

We address this question by studying whether a large regularization program of undocumented forced migrants prompted a change of migrant’s behaviors in _reporting_ or _committing_ crimes. Our case study is Colombia, the current largest host of Venezuelan forced migrants.<sup>4</sup> The vast majority of them arrived in Colombia after 2016 as a result of Venezuela’s political, economic, and humanitarian crises. In July 2018, Colombian President Juan Manuel Santos ordered the issuance of a regular migratory status known as Permiso Especial de Permanencia (henceforth PEP for brevity).

The PEP granted regular migratory status for two years to about 281,000 undocumented Venezuelan forced migrants in Colombia. It not only provided these immigrants with proper documentation and a job permit but also gave them the opportunity to receive a score on SISBEN, the mean proxy test used to award social support in Colombia. As such, the PEP enabled undocumented migrants to work in the formal sector and to access social services (including complete health services through the subsidized health regime).

> 1For instance, at least 74 percent of Colombian natives believes that Venezuelan forced immigration exacerbates crime and insecurity (LAPOP, 2021; OXFAM, 2023).

> 2See recent evidence from Colombia by Knight and Tribin (2020) and Mexico by Rozo et al. (2021). 3See evidence by Horn (2010); Calder ́on et al. (2011); Wirtz et al. (2014); Kelly et al. (2199).

> 4According to the latest data from Colombian migration authorities, by 2022 the number of Venezuelans there numbered approximately 2.5 million people, or about 5% of Colombia’s population.

2
However, that has not been the case in the past; Chen and Ravallion (2010) show important changes when moving away from extrapolated 1993 PPP factors toward 2005 PPPs. To overcome this limitation, Jolliffe and Prydz (2015) base their estimates on more up-to-date national poverty lines (i.e., on average from 2008). However, their results are obtained using an estimation of the _implicit_ national poverty lines from combining official poverty headcounts at national poverty lines, as reported by the World Development Indicators (WDI), with the corresponding consumption and income distributions from the World Bank’s PovcalNet database. One caveat of this exercise is that the combination of official poverty rates from the WDI with _unofficial_ harmonized micro-data sets from PovcalNet may result in poverty lines that deviate from the official ones. The poverty lines we use in this paper as inputs for updating comparable regional poverty lines allow for the replication of countries’ official poverty estimates in LAC and is one of the main contributions of this paper. In addition, by using more up-to-date information, our estimates are less sensitive to errors in the CPI than the ones that arise from updating the $2.5 and $4 per person per day poverty lines at 2005 PPPs using national CPIs.

# **3 Data**

We use three different data sets in this paper: (i) country-specific official extreme and moderate poverty lines from NSOs in the region, (ii) household per capita income distribution from harmonized household surveys, and (iii) the most up-to-date PPPs from the 2011 ICP round.

# _3.1 Country-specific official poverty lines_

In order to control for geographical discrepancies in the cost of living within countries when computing the regional poverty lines, we make use of the most up-to-date data on _subnational_ official poverty lines obtained from NSOs or governmental agencies from 18 countries in LAC.<sup>13</sup> The countries considered in this paper include: Argentina, Bolivia, Brazil,<sup>14</sup> Chile, Colombia,

> 13  The level of subnational disaggregation varies from country to country. For some countries poverty lines are determined at the level of cities (e.g., Colombia), while for others they are determined at the regional level (e.g., Brazil) or there is just one line at the national level (e.g., Nicaragua). Almost all poverty lines are in per capita terms in LAC; the only exceptions are Argentina where poverty lines also consider household composition and Uruguay where the non-food component of the poverty line accounts for economies of scale.

> 14  The Brazilian case is different from the rest; the country does not have an official poverty line and rate. Currently, the government accepts the thresholds of 70 and 140 _reais_ as their national extreme and moderate poverty lines, respectively, based on the benefit provided by its main social programs: _Brasil sem miseria_ and _Bolsa Familia_ . However, for the purpose of this paper, we use older subnational poverty lines calculated by Institute for Applied

7
regions, moving from actual to optimal lending results in increases for Africa (5%), East Asia / Pacific (20%) and South Asia (33%), and decreases for Europe / Central Aisa (-57%), Latin America / Caribbean (-26%) and Middle East / North Africa (-19%).

Table 7.2: % Differences Between Actual and Optimal Lending<sup>a</sup>

|**Region **|**Climate**|**Pollution **|**Land**|**Biodiversity**|**Water **|**Policies**|**Other **|**Total**|
|---|---|---|---|---|---|---|---|---|
|**AFR**|-41|30|64|-44|13|0|-65|5|
|**EAP**|23|21|27|90|-14|57|99|20|
|**ECA**|-15|-46|-85|-70|-61|-61|-26|-57|
|**LCR**|127|-30|-70|17|-9|-40|-16|-26|
|**MNA**|*|-20|138|*|-63|-20|97|-19|
|**SAR**|-62|-4|181|-69|212|14|-12|33|
|**Total**|0|0|0|0|0|0|0||

a Estimates reflect changes from actual to optimal lending

* Division by 0 in growth rate calculation

Tables 7.4 and 7.5 present % differences for AAA under two assumptions. Table 7.4 holds thematic AAA shares constant at their levels for 2000-2003, while Table 7.5 assumes that thematic AAA shares are equal to thematic lending shares for 1998-2003. The differences between the two tables are evident, reflecting the substantial differences between thematic shares for lending and AAA in Table 7.3. Lending shares are higher for climate, pollution, land and water, while AAA shares are higher for biodiversity and environmental policies and institutions. The difference for pollution is particularly striking (31% of lending vs. 10% of AAA).

Table 7.3: Thematic Shares for Lending (1998-2003) and AAA (2000-2003)

|Resource|Climate|Pollution|Land|Biodiversity|Water|Policies|Other|
|---|---|---|---|---|---|---|---|
|Lending|10|31|15|3|23|15|4|
|AAA|4|10|7|10|19|27|21|

At the regional level, Tables 7.4 and 7.5 show that the change in assumptions makes little difference for allocations. Whether or not thematic lending shares are used for 20
|**Table 3b: Robustness of municipa**<br>**_(Robu_**|**l health expe**<br>**political va**<br>**_st std. errors_**<br>(1)<br>Share of<br>health in<br>2008 total|**nditures result**<br>**riables**<br>**_inparentheses)_**<br>(2)<br>Share of<br>health in<br>2008 total|**s to the inclusio**<br><br>(3)<br>Avg. Share<br>of health in<br>2007-2008|**n of alternate**<br>(3)<br>Avg. Share<br>of health in<br>2007-2008|
|---|---|---|---|---|
||spending|spending|spending|spending|
|**_A: Including indicator for incumbe_**|**_nt mayor facin_**<br>|**_g a term limit_**<br>|<sup>~~*~~</sup>|<sup>~~**~~</sup>|
||-0.013|-0.016|-0.019|-0.027|
|Indicator = 1 for 3<sup>rd</sup>term mayor|(0.01)|(0.014)|(0.01)|(0.012)|
|Vote Buying|-0.047<sup>~~**~~</sup>|-0.05<sup>~~*~~</sup>|-0.045<sup>~~**~~</sup>|-0.046<sup>~~*~~</sup>|
|(% respondents reporting it in|(0.021)|(0.027)|(0.022)|(0.028)|
|<br>municipality)<br>**_B: Including average number of ca_**<br>**_2007 elections)_**|<br>**_ndidates conte_**|<br>**_sting in munic_**|<br>**_ipal elections (1_**|<br>**_998, 2004, and_**|
||-0.0004|-0.001|-0.001|-0.0004|
|Average number of candidates|(0.0145)|(0.02)|(0.013)|(0.015)|
|Vote Buying|-0.037<sup>~~***~~</sup>|-0.048<sup>~~*~~</sup>|-0.045<sup>~~**~~</sup>|-0.051<sup>~~*~~</sup>|
|(% respondents reporting it in|(0.013)|(0.024)|(0.012)|(0.029)|
|<br>municipality)<br>**_C:Including number of politicians_**|**_with whom ma_**<br>|**_yor reports pol_**<br>|**_itical affiliation_**<br>||
|Number of politicians affiliated|-0.005<br>|-0.006<br>|-0.004<br>|-0.006<br>|
|<br>with mayor|(0.004)|(0.005)|(0.004)|(0.005)|
|Vote Buying|-0.04<sup>~~***~~</sup>|-0.04<sup>~~*~~</sup>|-0.044<sup>~~**~~</sup>|-0.047<sup>~~*~~</sup>|
|(% respondents reporting it in|(0.014)|(0.025)|(0.02)|(0.03)|
|municipality)<br>**_D: Including margin of victory in 2_**|**_007 municipal_**<br><sup>~~*~~</sup>|**_elections (as p_**<br><sup>~~*~~</sup>|**_roportion of tot_**<br>|**_al votes)_**<br>|
||-0.03|-0.04|-0.024|-0.036|
|Average margin of victory|(0.02)|(0.03)|(0.02)|(0.03)|
|Vote Buying|-0.056<sup>~~***~~</sup>|-0.07<sup>~~*~~</sup>|-0.051<sup>~~**~~</sup>|-0.067<sup>~~*~~</sup>|
|(% respondents reporting it in<br>municipality)|(0.02)|(0.04)|(0.02)|(0.03)|
|District Fixed Effects|No|Yes|No|Yes|

_Each panel is a separate regression including all the variables listed in Table 3a, plus each additional political variable listed in the panel_ 34
test score by one and a half points. Column 2 shows that this effect is not constant across the level of education of the recipient student. (A formal test of equality of coefficients can be found in the appendix, table 13). Wealthier pupils are more sensitive to the background of their peers. One implication of this result is that children from more disadvantaged origins ( _Li, LMi_ ) not only perform worse than wealthier students ( _UMi, Ui_ ) due to their own socioeconomic background, but also they lose out in the social interaction game, unable to benefit as much from the positive externalities of having wealthy peers. These results also point to the possibility that when rich and poor pupils interact, the losses incurred by the former are greater than the gains to the latter. All in all, the heterogeneity of the peer-group effect calls into question the validity of the standard, linear-in-means model of estimating the impact of social interactions.

Table 4: Test of heterogenous peer-group effects

||Depen|dent vle:|Te|st score in Spanish|
|---|---|---|---|---|
||A|ll||Restricted sample|
||(1)|(2)||(3)<br>(4)|
|Class mean SES|2.814<br>(.254)<sup>_∗∗∗_</sup>|||2.133<br>(.297)<sup>_∗∗∗_</sup>|
|Class mean SES * Li||1.246<br>(.293)<sup>_∗∗∗_</sup>||.553<br>(.333)<sup>_∗_</sup>|
|Class mean SES * LMi||2.923<br>(.263)<sup>_∗∗∗_</sup>||2.279<br>(.304)<sup>_∗∗∗_</sup>|
|Class mean SES * UMi||3.744<br>(.277)<sup>_∗∗∗_</sup>||3.108<br>(.321)<sup>_∗∗∗_</sup>|
|Class mean SES * Ui||5.332<br>(.287)<sup>_∗∗∗_</sup>||4.694<br>(.329)<sup>_∗∗∗_</sup>|
|Obs.|389,513|389,513||333,276<br>333,276|
|e(N-g)|7,444|7,444||6,463<br>6,463|
|_R_<sup>2</sup>|.068|.069||.066<br>.068|
|Test dif coefcients||✓||✓|

Note: Standard errors are robust and clustered at the school level. Other variables included in the regressions are the student’s number of books at home, gender, repeat, class size and age. ONE, 2000. Argentina.

The next set of results (table 5) estimates equation (3) to examine whether the influence of the peers on a student’s performance is monotonically increasing and linear. The evidence is consistent with the idea 21
# (0.314)

|Expenditure|||4.54e-05**|5.13e-05**|4.25e-05**|
|---|---|---|---|---|---|
||||(2.13e-05)|(2.14e-05)|(2.00e-05)|
|Flood risk perception: Unlikely/Indifferent||||||
|Flood risk perception: Likely||||0.722***<br>(0.211)||
|Tenure: Owner||||||
|Tenure: Own dwelling but not land|||||-0.470|
||||||(0.604)|
|Tenure: Rent from private person|||||-0.0927<br>(0.375)|
|Tenure: Rent from relative|||||0.779<br>(0.633)|
|Tenure: Rent-free with relative|||||-0.248<br>(0.387)|
|Tenure: Other|||||-0.000226<br>(0.440)|
|Constant|-1.563***|-1.025***|-1.857***|-2.037***|-1.728***|
||(0.169)|(0.259)|(0.223)|(0.215)|(0.384)|
|Observations|1,008|1,008|1,008|1,008|1,008|
|Prob > F|3.50e-06|0.908|1.71e-05|2.39e-07|4.06e-05|

_Table 35: Propensity to invest in housing by (1) actual flood exposure and amount of rent payed (in cedis/month), (2) exposure, expenditure and amount of rent payed, (3) exposure, expenditure, amount of rent payed and flood risk perception._

||(1)|(2)|(3)|
|---|---|---|---|
|Unaffected|ref.|ref.|ref.|
|Affected|1.089***|1.080***|0.883**|
||(0.324)|(0.323)|(0.387)|
|Monthly rent|-0.00483|-0.00522|-0.00477|
||(0.00308)|(0.00333)|(0.00334)|
|Expenditure||1.99e-05|2.34e-05|
|||(2.41e-05)|(2.42e-05)|
|Flood risk perception: Un-||||
|likely/Indifferent|||ref.|
|Flood risk perception: Likely|||0.445<br>(0.288)|
|Constant|-1.158***|-1.270***|-1.408***|
||(0.201)|(0.231)|(0.266)|

59
We start with a comparison of firm size distribution in Mexico and the United States using our data from the Mexican census and data from the U.S. census of firms. Table 1 presents for different size categories the total number of firms and employees for both Mexico and the United States. The data on the distribution of firms by firm size category are presented in panel A, and the data on the distribution of the number of employees by firm size category are presented in panel B of Table 1. The firm size categories are based on the number of employees. The data for Mexico are for the year 1998, and the data for the United States are for the year 1997. We report data both for all firms and for manufacturing firms only. The source of the U.S. data is the 1997 U.S. Economic Census from the U.S. Department of Commerce.

The average firm size in Mexico is much smaller than in the United States. While over 96 percent of firms in Mexico employ less than 10 employees, only about 78 percent in the United States do so. As a percentage of the total firms, the number of large firms with more than 500 employees is about eight times larger in the United States than in Mexico.

The differences between Mexico and the United States are even more pronounced for manufacturing firms. The total number of manufacturing firms covered is similar in order of magnitude for both countries, about 340,000 firms. However, these firms employ a much larger number of employees in the United States (almost 19 million) than in Mexico (just over 4 million). The reason is that a much larger proportion of Mexican firms has only a small number of employees. While only about half of the manufacturing firms in the U.S. employ less than 10 people, in Mexico this number exceeds 90 percent. The numbers confirm Tybout’s (2000) observation that employment in developing 19
**Table  3.1 Environmental classification of excreta-related diseases important in wastewater-irrigated agriculture**

|**Category**|**Environmental transmission**<br>**features**<sup>_a_</sup>|**Major examples**|**Exposed groups and**<br>**relative infection risks**<sup>_b,c_</sup>|
|---|---|---|---|
|Non-bacterial|Non-latent|_Viral diseases:_Hepatitis A, E|Fieldworkers: +|
|feco-oral diseases|Low to medium persistence<br>Unable to multiply<br>High infectivity|and F Diarrhea due to rotavirus,<br>norovirus and adenovirus<br>_Protozoan diseases:_Amebiasis<br>Cryptosporidiosis Giardiasis<br>Diarrhea due to_Cyclo-spora_<br>_cayetanensis_,_Enterocytozoon_<br>_bienusi_and_Isopora belli_|Consumers: +++|
|Bacterial feco-<br>oral diseases|Non-latent<br>Medium to high persistence<br>Able to multiply<br>Medium to low infectivity|Campylobacteriosis<br>Cholera<br>Pathogenic_Escherichia coli_<br>infections<br>Salmonellosis<br>Shigellosis|Fieldworkers: +<br>Consumers: +++|
|Geohelminthiases|Latent<br>Very high persistence<br>Unable to multiply<br>High infectivity|Ascariasis<br>Hookworm infection<br>Trichuriasis|Fieldworkers: +++<br>Consumers: +++|

_Source:_ Adapted from Feachem _et al_ ., 1983.

> _a_ Latency is the length of time required outside a human host for the pathogen to become infective, and persistence is the length of time the pathogen can survive outside a human host.

> _b_ +++ = high risk, + = low risk. These risks refer to the use of _untreated_ wastewater for crop irrigation; they can be reduced by wastewater treatment and the use of the post-treatment health-protection control measures detailed in Table 4.2.

> _c_ Note that fieldworkers are often also consumers.

_Transmission of microbial disease through wastewater_ The available good-quality epidemiological evidence on the health risks due to wastewater use in agriculture was first reviewed by Shuval _et al_ . (1986). The main findings from their study were:

- Soil-transmitted helminthic infections<sup>12</sup> represented the major actual (as opposed to potential) health risk (Box 3.2) to both those working in wastewater-irrigated fields and those consuming wastewater-irrigated foods uncooked when untreated wastewater was used for crop irrigation, but not when treated wastewater was used.

- Bacterial feco-oral diseases, such as diarrhea and cholera, can be transmitted to those consuming wastewater-irrigated salad crops and raw vegetables.

> 12 The principal soil-transmitted helminths (also called geohelminths and human intestinal nematode worms) are detailed in Box 3.1. In the context of wastewater use in agriculture, reference is commonly made to ‘helminth eggs’, rather than ‘soil-transmitted helminth eggs’ or ‘human intestinal nematode eggs.’

24
does not lead to clear-cut predictions about the effects of increased availability of information to managers. On the one hand, models of hierarchy as specialization in knowledge acquisition (like Garicano, 2000) suggest that more decisions ought to be taken at lower levels if the amount of information available to all levels is increased. Similarly, a standard agency perspective might also suggest that more decisions would be delegated if new or more accurate performance measures become available, especially if (as in our sample) the directors are under significant time constraints. However, to the extent that the plant managers were initially better informed than their bosses by virtue of being closer to the operations, the availability of the better measures might have reduced their information advantage, favoring the directors’ making more decisions. But while the theoretical literature is large, the empirical literature is very limited.

To measure decentralization we collected data on eight variables: the locus of decisionmaking for weaver hiring, manager hiring, spares purchases, maintenance planning, weaver bonuses, investment, and departmental co-ordination, and the number of days per week the owner spent at the factory. Because firms’ organizational designs change slowly over time, we collected this data at lower frequencies – pre-intervention, in March 2010 and in August 2010. For every decision except investment and days at the factory we scored decentralization on a 1 to 5 scale, where 1 was defined as no authority of the plant manager over the decision and 5 as full authority (see Appendix Table A2 for the survey and Table A4 for descriptive statistics). These questions and scoring were based on the survey methodology in Bloom, Sadun and Van Reenen (2009b), which measured decentralization across countries and found developing countries like India, China and Brazil typically have very centralized decision-making within firms. The measure of the decentralization for investment was in terms of “The largest expenditure (in rupees) a plant manager (or other managers) could typically make without a Director’s signature”, which had an average of 12,608 rupees (about $250). Finally, the number of days the owners spend each week at the factory is a revealed preference measure of decentralization. The owners are usually located either at their head-offices in Mumbai (which they prefer as it dramatically reduces their commute) or at the factory (if it needs direct management from them).

To combine all eight decentralization measures into one index we took the first principal component, which we called the decentralization index. We found changes in this index were strongly and significantly correlated with changes in management across firms, as better management led to more decentralization. Table 3 looks at this in a regression format: 21
<!-- Start of picture text --> A Round 2 v. Round 1 Diff-in-Diff Coeff.<br>2 Training was very useful —e —eo—<br>_ Have enough information about Council budget —O- —e—<br>ral Capacity building sufficient for LGA needs -—— —e—<br>8 Number officials participated in train last year —eo— ——_e—_<br>é Capacity building sufficient for own needs +e —e—<br>Share of staff can use excel oe eo<br>2 Used computer previous week 2 5<br>Ee Share of staff can create ppt eo —e<br>z Share of staff can write email eo —e<br>= Share of staff can write memo : eo eo<br>Share of staff with computer access ~s eo<br>g Employees feel cynical (reverse) a 2<br>q Power and connections matter more than work (rev) oe eo<br>& Pressure from peer to perform high standards eo —e—<br>a Employees share values :—e —eo—<br>S Employees trust one another : eo —o—<br>2 Employees take pride in duties : —_eo— —e—<br>S Better employees are promoted faster —o— —e—<br>g Government does best to keep top performers = —eo—<br>8 Leaders interested in attracting talent a —e—<br>& Senior staff use the right staff for jobs eo eo<br>Leaders act in attracting talent oe —eo—<br>rsrs rs ie<br>-2 -1 0 1 -2 -1 0 1<br><!-- End of picture text -->
Cambodia Expenditure Transaction Profile, Advances, 2017‐ Amount Ranges in Riels.

<!-- Start of picture text --> 97.84 98.83 100.00 100.00<br>100<br>87.21 89.32<br>90 83.98<br>75.02<br>80<br>65.04<br>70<br>57.22<br>60 54.36<br>50 42.47 42.45<br>40<br>30<br>20<br>10.07<br>5.19 7.30<br>10 0.32 0.76 1.28 2.71<br>0<br>Riels<br>Cumulative Percent Transactions Cumulative Percent Amount<br>Percentage<br><!-- End of picture text --> 30