rafmacalaba/datause-encoder-data huggingface dataset:
good set the stage for Haiti's evolution as an independent but deeply divided society." White and Smucker describe social capital as "truncated", rich at the local level but weak at regional and national levels. Peasant social groupings are local, stable, autonomous, self-governed and based on reciprocity. These social groups feel insecure, fear and actively avoid the state, and establish 'furtive' agricultural units on the margins of society. In the presence of an oppressive state and the absence of state provided services, these informal social relations become substitutes and serve as the primary social safety nets for the poor. The Haitian state, despite recent changes, is marked by political intolerance, patronage, extraction of wealth and lack of protection for the majority of its citizens.

Mauricio Rubio (1997) shows how in Colombia the existence of a large and growing illegal and underground economy run by powerful criminal organizations has resulted in parallel institutional environments that reward and favor opportunistic and criminal behaviors. Rubio shows how high levels of social capital within 'criminal' organizations are directed to extra-legal activities, rent seeking and high returns exclusively for those involved in such activities. The organizations that run in parallel with government institutions actually provide higher returns to those who participate than a regular career would offer. He estimates that between 1980 and 1993, the average remuneration for minor criminal activities, excluding drug trafficking and kidnapping, tripled in real terms, while returns for formal work remained stagnant. Currently, the annual income of a petty criminal in Colombia is around $20,000 per year, about 10 times the per capita GDP. Two surveys, National Survey of Quality of Life, and Quality of Life in Bogota provide some sobering statistics. In addition to the staggering statistics on homicides in Colombia, 54 percent of the barrios reported problems with conflict, drugs, gangs or prostitution (Knaul and Patrinos, 1998).

Based on a study of persistent inter-communal violence in India in particular cities, Varshney (I 998) argues that there is an integral link between the structure of civic life in a multiethnic society and ethnic conflict. Inter-ethnic networks become agents of peace because they build bridges and manage tensions, but if communities are organized only on intra-ethnic lines and the interconnections with other communities do not exist, ethnic violence is quite likely. He divides both inter- and intra-ethnic networks into associational forms of engagement (formal) and everyday forms of engagement. Associational networks include professional organizations, reading clubs, cadre-based political parties, film clubs, NGOs. Everyday forms of engagement consist of simple, routine interactions such as families visiting each other, children playing together, joint participation in festivals, or eating together. He concludes that while both forms of 30
matching algorithm<sup>14</sup> is used to identify corresponding administrative records for more than 2,500 of the 3,305 respondents reporting a score. For 2,273 respondents, I find exactly one test score; for 263 more, I find two scores in different (typically consecutive) years. Using the KLPS2 survey to determine whether matched scores are first or second attempts, I am able to clearly identify 2,167 first test scores.<sup>15</sup> Their distribution is plotted in the upper right panel of Figure 1 and is tested for a density break in the lower right panel. I find no evidence of manipulation of administratively reported first test scores.

# **2.3 Gender-specific discontinuities**

The KCPE cutoff for secondary school admission is well-known in Kenya; national media recently reported that “Out of the over 695,000 candidates who sat the KCPE examination, 350,000 candidates attained over 250 marks, making them eligible to join secondary school.”<sup>16</sup> However, a survey of secondary schools in the area suggests that, though 250 is the modal 2009 cutoff score reported by school administrators, many competitive schools use higher cutoffs.<sup>17</sup> Further, many schools report different cutoffs for boys and girls: seven out of eighteen reporting cutoff scores for girls report a value below 250. As such, 250 may not be the cutoff where the largest fraction of girls are

> 14The procedure is described in Appendix A.1.5.

> 15For some cases where I observer only one test score, it either appears to be a second score, or it is unclear whether it is a first or second score. I exclude these when using only first test score.

> 16Excerpted from Akolo (2008).

> 17Edward Miguel and Matthew Jukes, unpublished data (2009).

11
# **3. Data**

We use household surveys microdata from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), developed by The Center for Distributional, Labor and Social Studies (CEDLAS) of the University of La Plata, in partnership with World Bank Latin America and the Caribbean Poverty and Gender Group (LCSPP).

Based on the availability of key variables related to infrastructure access and infrastructurerelated assets, we build a five-year interval dataset for 14 countries between 1992 and 2012.<sup>3</sup> The final dataset covers 1.6 million unique household observations and includes the surveys shown in table 1. Table 2 shows the coverage by country and variable.

The main variables of interest are the per capita household income adjusted by purchasing power parity, in 2005 US dollars, and infrastructure. Income here includes labor and non-labor income as well as the imputed rent of owner-occupied housing. Infrastructure is measured through a set of dummies related to access to water, sewerage, electricity, fixed and mobile phones, and internet access. Another set of dummies indicates households ́ ownership of infrastructure-related appliances and assets. We focus on the following variables: refrigerators, washing machine, television, computer, car, and motorcycle.

The use of access / ownership dummies means that we are bound to concentrate on the extensive margin, i.e., on whether households are connected or use a service or not, but are unable to offer insights on the intensive margin due to the absence of specific expenditure data.

Finally, we add a number of standard country-level variables such as GDP per capita, income inequality, population density, and urbanization, all from the World Bank Indicators database. To exploit the panel dimension of the data, we split it into income quantiles, alternatively percentiles or deciles, defined by reference to the regional income distribution.<sup>4</sup> We define quantiles corresponding to the region-wide income distribution at each date. In this way, a household at say, the 11<sup>th</sup> percentile or the 6<sup>th</sup> decile of the distribution in 2012 is at similar

> 3 We allow for +/-1 year when the survey for the exact year is not available.

> 4 Following SEDLAC practice when computing inequality measures, we drop observations reporting zero income (see CEDLAS and The World Bank, 2014).

5
**TABLE 3: MEAN WILLINGNESS TO PAY & VSL**

|**Deltarisk**<br>4/ 100,000|**N Obs**<br>600|**Scenario**<br>Helmet<br>**ALL OBSER**|**Mean**<br>**Willingness**<br>**to Pay**<br>**(Rupees)**<br>30.76<br>**VATIONS**|<br>**Value of a**<br>**Statistical Life**<br>**(PPP$, based**<br>**on Mean WTP)***<br>87,393|**% Zeroes**<br>25.67|
|---|---|---|---|---|---|
|7/100,000|600|Pedestrian|36.48|59,218|51.00|
|8/ 100,000|600|Helmet|30.13|42,791|27.33|
|15/100,000|1200|Pedestrian& CityA/B|116.77|88,462|43.25|
|30/ 100,000|600|City A/B|186.34|70,581|38.17|
|4/100,000|256<br>**ONLY P**|Helmet<br>**ERSONS WITH HIGH S**|31.00<br>**CHOOL DIPL**|88,068<br>**OMA & ABOVE**|22.66|
|7/ 100,000|256|Pedestrian|29.61|48,068|51.95|
|8/ 100,000|324|Helmet|35.00|49,716|25.62|
|15/ 100,000|580|Pedestrian & CityA/B|117.15|88,750|43.45|
|30/ 100,000|324|City A/B|241.40|91,439|34.57|
|4/ 100,000<br>**ON**|121<br>**LY PER**|Helmet<br>**SONS WITH AN UNDER**|31.20<br>**GRADUATE**|88,636<br>**DEGREE & ABOV**|24.79<br>**E**|
|7/ 100,000|121|Pedestrian|23.72|38,506|48.76|
|8/100,000|214|Helmet|37.38|53,097|27.57|
|15/ 100,000|335|Pedestrian & CityA/B|89.97|68,159|43.58|
|30/ 100,000|214|City A/B|293.15|111,042|32.71|

- *: VSL is calculated by dividing Mean WTP by the risk reduction.

PPP$ used in this analysis for converting from Rupees is 8.8 (Source: WDI).

- 25 -
<!-- Start of picture text --> Migrants can Move Freely Migrants as Neighbors Born Non−Ugandan Able to Naturalize<br>0.3<br>0.2 0.041 0.092<br>0.071 0.032<br>−0.006<br>0.1<br>−0.019<br>0.0 −0.098<br>−0.1<br>−0.2<br>Baseline Pres x Pres x Baseline Pres x Baseline Baseline<br>Presence 2016 2020 Presence 2020 Presence Presence<br>Felt Unsafe in Community Feared Crime Any Violent Event (ACLED)<br>0.3 0.175 0.01<br>0.02 0.013 0.011<br>0.2<br>0.062<br>0.045 0.01<br>0.1<br>−0.016 −0.005<br>−0.059<br>0.0 −0.087 −0.109 −0.126 0.00<br>−0.016<br>−0.1 −0.01<br>−0.2 −0.02<br>Baseline Pres x Pres x Baseline Pres x Pres x Pres x Pres x Baseline Pres x Pres x Pres x Pres x<br>Presence 2016 2020 Presence 2006 2011 2016 2020 Presence 2006 2011 2016 2020<br>Estimate<br><!-- End of picture text --> Figure 6: Effect of refugee presence on public opinion and insecurity. The figure shows that a greater refugee presence does not lead to backlash against migrants, but does increase fears of crime (Afrobarometer), and slightly more experience with violence (ACLED). OLS models for Afrobarometer respondents within 150km with Nearest + 20km exposure measure interacting demographic controls with year, and including year and regional fixed effects. For ACLED, standard errors are clustered at the parish level. Estimates include 95% CIs.

When asked in Afrobarometer Round 6 about support for migrants’ free movement across borders (as opposed to restricting cross-border movement to protect own citizens), plot 2 shows that Ugandans experiencing greater presence of refugees responded no differently to those experiencing less. With respect to citizenship policy (plots 3 and 4), which was only included in Afrobarometer Round 5, citizens experiencing greater refugee presence were less supportive of granting citizenship based on birthplace alone, but more supportive of allowing migrants who have lived and worked in Uganda for many years (e.g., refugees) to be eligible for naturalization.

In the bottom row of Figure 6, _higher values_ correspond to _greater feelings of insecurity_ or _greater violence_ . More exposed citizens did not say they felt more unsafe in their community post-2014 (plot 1). In 2020, the effect was negative at -0.11 sd, which means that more exposed citizens felt _safer_ in 2020 than in 2011.

22
**Table 1: Balance at Baseline and Endline**

|||Full Sample||Normalized|Pre|sent in Round 12||Normalized|
|---|---|---|---|---|---|---|---|---|
||Control|Treatment|p‐value|Difference|Control|Treatment|p‐value|Difference|
|_Re‐randomized Variables_|||||||||
|Number of Paid Workers|0.19|0.16|0.498|‐0.059|0.18|0.13|0.255|‐0.092|
|Education (Years)|10.35|10.25|0.641|‐0.040|10.35|10.23|0.604|‐0.050|
|Raven Test Score|3.34|3.34|0.983|0.002|3.31|3.28|0.874|‐0.015|
|Digitspan Recall Score|6.42|6.36|0.628|‐0.042|6.43|6.39|0.786|‐0.031|
|Total Assets|239893|250563|0.768|0.026|246815|248026|0.771|0.003|
|Total Assets<1500LKR|0.06|0.02|0.062|‐0.164|0.06|0.02|0.022|‐0.218|
|Total Assets>935000LKR|0.05|0.06|0.864|0.015|0.06|0.06|0.733|0.005|
|Monthly Profits|13862|14552|0.509|0.057|13916|14485|0.448|0.047|
|Profit Data Missing|0.03|0.02|0.348|‐0.082|0.03|0.02|0.486|‐0.058|
|Monthly Profits<2000LKR|0.06|0.03|0.179|‐0.118|0.06|0.03|0.240|‐0.099|
|Monthly Profits>30000LKR|0.04|0.06|0.257|0.098|0.04|0.06|0.342|0.074|
|Business Practices Score|8.27|8.76|0.343|0.082|8.32|8.78|0.429|0.079|
|From booster sample|0.52|0.53|0.841|0.017|0.53|0.54|0.906|0.032|
|_Stratification Variables_|||||||||
|Retail Sector|0.39|0.38|0.857|‐0.016|0.39|0.39|0.974|‐0.001|
|Colombo|0.47|0.44|0.436|‐0.068|0.46|0.41|0.317|‐0.105|
|Kandy|0.47|0.48|0.893|0.012|0.48|0.50|0.736|0.041|
|_Additional Variables_|||||||||
|Any paid worker at baseline|0.12|0.10|0.388|‐0.075|0.12|0.08|0.119|‐0.129|
|Monthly Sales|41175|52435|0.048|0.171|42079|52142|0.070|0.152|
|Owner's Age|35.43|35.16|0.644|‐0.040|35.34|35.37|0.995|0.006|
|Business is Registered for Taxes|0.31|0.32|0.827|0.019|0.31|0.32|0.916|0.008|
|Weekly hours worked|57.94|59.31|0.379|0.076|58.23|58.55|0.940|0.018|
|Sample Size|286|250||||262|230||
|Joint orthogonality p‐value|||0.734||||0.649||

Note: present in round 12 denotes information on whether they have a paid worker is available in the last survey round.

37
Figure 8: Political Influence Concentration Index and Poverty Headcount Index <!-- Start of picture text --> EAS ECS LCN<br>MEA SAS SSF<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Poverty Headcount Index<br>1<br>)Π<br>.9<br>.8<br>.7<br>.6<br>Political Influence Concentration Index, I(<br>.5<br><!-- End of picture text -->

# **5.3 Linking Arithmetics with Politics**

Figure 9 shows the relationship between the Arithmetics of DRM, as measured by _I_ ( _P_<sup>_δ_</sup> ) (with _δ_ = _∼p_ in the left panel and _δ_ = _r_ in the right panel), and the Politics of DMR, as measured by _I_ (Π). In particular, the vertical and horizontal lines correspond to the average value of the two indices among the analyzed countries, dividing the space in four quadrants. The West quadrants in each figure correspond to the set of countries where there is lower capacity to mobilize resources for poverty eradication. The South quadrants in both figures, on the other hand, define the set of countries which are more likely to enact some redistribution, thanks to a lower concentration of political influence.

Among those countries with a poverty eradication capacity close to one, where a small transfer from the non-poor or the rich would be enough to solve poverty, we expect those in the South-East quadrant to have more difficulties in actually mobilizing resources than those in the North-East quadrant, due to the higher concentration of political power. This should be even more striking when, under redistribution rule (ii), only the rich are responsible for eradicating poverty, since the optimal marginal tax rate would be higher, and this would foment stronger opposition. When we check the fiscal effort of countries in our sample, we find evidence supporting our theoretical hypothesis. Figure 10 shows the share of tax revenue on GDP for countries in our sample, which we group according to the 19
The first (top) panel in Table 4 focuses on enrollment. The probability that a child in the control group is enrolled at the time of the follow-up survey is 0.709. This is about 6 percentage points lower than the comparable number at the time of the baseline survey, 0.770, reported in Table 2. On average, children in the control group were one-and-a-half years older at follow-up than at baseline (12.9 years old, compared to 11.4). Enrollment is negatively correlated with age, and the decrease in enrollment can be accounted for by the ageing of the study population.<sup>11</sup> Table 4 also shows that substantial fractions of children in the control group dropped out of school or became new enrollments during the study period. These tabulations suggest that there is considerable scope for transfers to affect enrollment.

Turning next to the estimated effect of winning the BDH lottery, the first row in Table 4 shows that the probability that a lottery winner is enrolled in school at the time of the follow-up survey is 3.4 to 3.7 percentage points higher than that for a lottery loser. The next two rows in the table show that higher school enrollment among lottery winners appears to be driven both by lower drop-out rates and higher new enrollment rates. Drop-outs are approximately 3.1 to 3.6 percentage points lower and new enrollments 3.8 to 4.4 percentage points higher among lottery winners. The estimates of δ in regressions of enrollment and drop-outs are significant at conventional levels, while those for new enrollments are not significantly different from zero— presumably, because of smaller sample sizes.

The results in Table 4 suggest that children in households who won the BDH lottery had significantly higher enrollment rates than those who lost the lottery. This suggests that households who won the lottery may also have spent more on schooling, and children in these households may have higher school attainment for their age. There is some evidence that this is the case. In a regression of changes in household spending on schooling between the baseline and follow-up surveys on the dummy for lottery winners, the coefficient is 1.141 (with a standard error of 0.592); in a regression of changes in the highest grade completed by a child on the dummy variable for lottery winners, after discarding implausible values for these changes (changes smaller than zero or larger than two), the coefficient on lottery winners is 0.036 (with a standard error of 0.028).<sup>12</sup> Although the coefficients on lottery winners in these regressions are

> 11 In a simple regression of enrollment at baseline on age in years, with the sample limited to children age 6-17 assigned to the control group, the coefficient is -0.063, with a robust standard error of 0.003.

> 12 Both regressions include the extended set of controls and canton fixed effects.

14
However, neither of these measures necessarily translates into substantially reduced overall earnings inequality. Oaxaca-Blinder decompositions are decompositions at the mean characteristic values for the sample and do not give good insight about the full differential distributions of characteristic values in each subsample.<sup>13</sup> Differences in characteristic distributions within each subsample generate the within subsample earnings differences that we observe in Table 1. Therefore, even if mean characteristics were equalized within each group as well as treatment of those characteristics, substantial overall earnings inequality could still exist in the society because of the spread in characteristics—and potentially in returns to characteristics—within groups. In the next section we consider how to simulate both more equal characteristic distributions and more equal treatment and how these simulations would affect measures of overall earnings inequality.

# **Simulating more equal treatment to assess its effects on overall earnings inequality**

In this section we move beyond the simple Oaxaca-Blinder decomposition framework by simulating wages using firstly the Oaxaca-Blinder framework in which only returns are equalized, and secondly by using an expanded framework in which both returns and the processes generating some of the underlying characteristics are equalized. We then compare the actual earnings inequality indexes to the one calculated for the distributions of simulated wages under both of these frameworks.

Operationally, the expanded framework consists of allowing some of the variables in the earnings equation to be determined by earlier processes that are also estimated separately by group. There is certainly evidence, both in the Latin American context and for other regions and countries, that earlier processes affect outcomes that then affect earnings. For example, Patrinos and Psacharopoulos (1997) find that family background has an impact on educational attainment and grade repetition. And it is certainly the case that observers believe that these processes have severe effects that differ by group; witness Tzannatos (1999, p. 551): “To break the vicious circle of women's low initial human capital endowments and inferior labor market outcomes compared

> who, using Brazilian data, finds the difference between men by race to be mainly driven by characteristics, though among women by race it is driven mainly by differences in treatment.

> 13 Other decompositions can be performed which do consider the variance in outcomes across the earnings distribution; for instance see Yun (2006), who creates a valuable extension on the more widely known Juhn, Murphy, and Pierce (1993) method; quantile regression analysis is another strategy.  We choose to focus the paper

6
it is worth noting that destination countries with implemented projects are larger and have more abundant land for cultivation and land under forests (respectively 6.5 and 13.1 million hectares). On the contrary, origin countries have no such land left, as is most clearly visible for ―exclusive‖ ones, (with 0 and 0.1 million hectares respectively for land for cultivation and land under forest). There is some difference in agro-ecological potential between the country categories: as one would expect if such investments were to follow comparative advantage, potential output values from cultivation of uncultivated land are higher in destination than origin countries, and highest in those with implemented projects. Also, with origin countries obtaining more than half of the attainable yield already compared to about one-third in destinations, there appears to be potential for catch-up growth using existing technology. Interestingly, this potential is even greater in destination countries with implemented projects than in other destination countries. Data on governance suggest that, overall, regulatory quality, protection of investors‘ rights, and land governance are significantly weaker in destination countries. Interestingly, there is no significant difference in governance between destinations with and without implemented projects.

# **4. Econometric results**

Analyzing the determinants of large agricultural investment in unilateral and bilateral models suggests that (i) agro-ecological suitability is indeed a critical factor for both demand and actual implementation; (ii) the difference between potential and actual production (i.e. the ―yield gap‖) affects land demand but not project implementation; and (iii) while conventional governance variables are at most weakly significant, a measure of land governance that incorporates the security of local land rights is highly significant, strongly suggesting that demand for land is significantly higher in settings where such rights are only weakly protected.

# **4.1 Unilateral relationships**

Regressions for the count of projects involving large-scale land acquisition at the country of destination (unilateral case) are reported in Table 5 (where the top panel refers to all projects and the bottom panel to projects with some production only). While we only report results from the Poisson model, qualitatively similar results are obtained if we estimate the equation by OLS instead. Our results are also robust to a correction of the variance-covariance matrix of the ―sandwich‖ estimator to take into account possible problems of omitted variable and intra-group correlation of residuals. In all cases, the potentially cultivable area outside of forests or the potential value of output on suitable non-forest area is highly significant, suggesting that land availability is a primary motivation for such investment. The coefficients of 0.496 and 0.688 for area (in col. 1) or 0.526 and 0.684 for potential output value (in col. 2) suggest that, other things being equal, a 10% increase of potentially suitable area or output value would increase 13