rafmacalaba/datause-encoder-data huggingface dataset:
and children between 0 and 14 are weighted 0.5. As can be seen from this table, the difference with _per capita_ estimates is in line with what was expected: inequality based on per adult equivalent consumption is indeed found lower. But the difference is not very high, and the gap between household and cell consumption estimates remains of the same order of magnitude as that of Table 2. As for intra-household inequality, appendix Table S3.2 shows the inequality decomposition obtained when using per adult equivalent consumption with three different equivalence scales: Scale A is the same as that employed in Table S3.1, while Scale B puts a reduced weight on very young children (0.2 for children less than age 4). Scale C applies different weights depending on the kind of commodities. For food, weights are chosen in order to hold account of estimated caloric needs for moderately active people, depending on age and gender, and based on tables provided by the U.S. Department of Health and Human Services.<sup>26</sup> Results are hardly impacted by the reduced weight given to children: intra-household inequality still accounts for about 13% of the total (or 43% of the maximal intra-household inequality simulated taking these equivalence scales into account). Finally, so as to ensure that the results are not driven by education expenditures, an important child specific spending unevenly distributed in the population, we replicate the exercise on consumption aggregates net of education expenditures (school fees, furniture, and transportation). Results are shown in the bottom part of Appendix Table S3.2. They are not significantly altered.<sup>27</sup> Poverty measures are likely to be more sensitive to the choice of equivalence scale, if the risk of poverty depends on household or cell size. In table S3.3 we repeat the same exercise as in table S3.2 this time for FGT indices. As we can see the estimated poverty rates are a bit higher when scale C is used due to the higher weight given to children between 5 and 14 in the equivalence scale for food consumption, but the gap between FGT indices based on per adult equivalent cell and household consumption persists.

25
and Joseph Stiglitz (1977), Dale O. Stahl (1989), and Preston McAfee (1995) describe models where the presence of costly search lowers competition and creates an inefficient allocation of goods across markets. There is also a growing empirical literature assessing the effects of improvements in information on both the level and dispersion of prices. For instance, Jeffrey Brown and Austan Goolsbee (2002) show that the growth of online price comparison sites reduced term life insurance premia by 12 percent in the US. In a recent study, Robert Jensen (2007) shows, using micro-level survey data, that the adoption of mobile phones by fishermen and wholesalers in South India is associated with a dramatic reduction in price dispersion, the complete elimination of waste and an increase in both consumer and producer welfare. This paper presents robust empirical evidence that information improves the functioning of rural markets by increasing the competitiveness of buyers.

Second, direct interactions between producers and processors are gaining considerable interest in the developing world. While intermediaries deliver critical services to rural producers, they are also often exploitative and there can be large efficiency gains from their removal (Ammar Siamwalla 1978; Timothy Besley and Robin Burgess 2000). This intervention shows that it requires serious investment to bypass intermediaries, but it is possible, and can be beneficial for both farmers and final buyers. The immediate benefit to ITC Limited of this intervention was the improvement in quality of soybeans procured, from the creation of a direct marketing channel, and a reduction in its transaction costs. The results in this paper suggest that there can be net welfare gains to farmers resulting from a redistribution of surplus away from traders to the farmers and the overall gain of deadweight loss under monopsony.

The rest of the paper is organized as follows. Section I provides background on the marketing of soybean in the state and the details of the intervention. Section II outlines a conceptual framework for understanding the gains from the change. The data are described in section III. The empirical strategy and results are discussed in section IV. Section V addresses alternative explanations and section VI concludes.

# **I. Background**

5
<!-- Start of picture text --> Sons' Primary Completion Estimates by Birth Year<br>Intercept Linear Coefficient Qudratic Coefficient<br>4 .05 .006<br>.004<br>2 $ Oe ok aee<br>’ > o? % ><br>? .002 le<br>><br>$ ’ ? bd ¢ + 4<br>opttgte |e? ~~ 05 > > a<br>> oF rrHoao4Himare<br>><br>-.002<br>-.2 -.1<br>59 60 61 62 63 64 65 66 67 68 69 7071 72 59 60 61 6263 64 65 66 67 6869 7071 72 596061 62636465 666768697071 72<br>Birth Years<br>Omitted Birth Year 1957-58<br>© Comparison Cohort © Partially Exposed Cohort @ Fullly Exposed Cohort<br><!-- End of picture text -->
# **Table 3:  Predicted changes in Covid-19 infection rates [Out-of-sample predictions]**

|United States|||||||Predicted|
|---|---|---|---|---|---|---|---|
|Total Areas(Counties)|3,084||||||Top<br>|
|Prediction Date|Apri|l 28(14|Days)|May|12(28 D|ays)|Areas|
|Actual Change Rank(in 3,084)|≤50|≤100|≤150|≤50|≤100|≤150||
|Percent of All Areas|1.6|3.2|4.8|1.6|3.2|4.8||
|Prediction Method|||Number|Predicted||||
|Model|14|28|42|11|21|33|80|
|Prior 14-DayChange|0|0|2||||80|
|Random Selection|1|3|4|1|3|4|80|
|Philippines||||||||
|Total Areas(Municipalities)|1,531|||||||
|Prediction Date|Apri|l 28(14|Days)|May|12(28 D|ays)||
|Actual Change Rank(in 1,531)|≤30|≤60|≤90|≤30|≤60|≤90||
|Percent of All Reas|2.0|3.9|5.9|2.0|3.9|5.9||
|Prediction Method<br>|||Number<br>|Predicted<br>|<br>|||
|Model|8|10|17|5|6|9|40|
|Prior 14-DayChange|0|5|10||||40|
|Random Selection|1|2|3|1|2|3|40|
|Western Cape Province,South Africa||||||||
|Total Areas(Sub-Districts)|32|||||||
|Prediction Date|May|28(14|Days)|June|12(28 D|ays)||
|Actual Change Rank(in 32)||≤10|||≤10|||
|Percent of All Areas||31.3|||31.3|||
|Prediction Method|||Number|Predicted||||
|Model||7|||5||10|
|Prior 14-DayChange||3|||||10|
|Random Selection||3|||3||10|

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Section 5 discusses the results and provides evidence of the most plausible mechanisms. Finally, the preliminary conclusions appear in Section 6. All appendix tables are at the end of this paper.

# **2. The Tracking-by-Violence Experiment**

## **2.1 Intervention**

### **After-School Clubs**

This study was conducted within the context of an ASP in El Salvador that was implemented by an NGO, Glasswing International. I partnered with Glasswing to design and implement an experimental evaluation that (i) measures the impact of this ASP as is and (ii) identifies the best targeting approach to improve its effectiveness.

In Dinarte and Egana (2019), we address the first objective. Our main estimations indicate that this ASP not only improved attitudes toward school but also enhanced learning and lessened misbehavior at school. These two effects translate into an improvement in participants’ academic performance. We also estimated neurophysiologial measures of emotional regulation and stress using electroencephalogram recordings. We find evidence that control of emotions and automatic responses to stimulus is a potential mechanism for the effects on behaviors and academic attainment.

By implementing a tracking-by-violence experiment I describe below, this paper addresses the second objective. Therefore, I highlight in this section only the curricula structure and enrollment process that will help readers to better understand the experimental design and its results. The ASP implementation is part of the NGO’s program _Community Schools_ in five public schools in highly vulnerable communities in El Salvador. According to the intervention approach, its main objective is to modify children’s violence and attitudes through the acquisition of life skills, 8
<!-- Start of picture text --> Figure 4a: Concentration of Direct Taxes by Figure 4b: Concentration of Direct Taxes by<br>Income Groups, Men Income Groups, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Pers. Income tax Market income Pers. Income tax<br>Firm income tax Agri. Income tax Firm income tax Agri. Income tax<br>Housing tax Other income tax Housing tax Other income tax<br>Direct tax Population share Direct tax Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct taxes Cumulative proportion of direct taxes<br><!-- End of picture text -->

# _Direct Transfers_

Direct transfers can be classified as PSNP-based and non–PSNP. Both types of transfers are progressive. While both PSNP and non-PSNP transfers are pro-poor (progressive in absolute terms) for women (Figure 5b), non-PSNP transfers appear to be regressive for men (Figure 5a).

The poorest women, those in the bottom income decile, capture about 1.9 percent of market income but 16.2 percent of the PSNP transfer; men in the bottom decile receive about 1.8 percent of market income but 14.8 percent of the PSNP transfer. Similarly, for women the bottom 40 percent account for 14.6 percent of market income and 57.5 percent of the PSNP benefit. For men, about 52.4 percent of the PSNP benefit goes to the bottom 40 percent, who also capture 14.3 percent of market income. Women in the top decile (the wealthiest 10 percent) capture 33 percent of market income but only 7.5 percent of PSNP transfers; men in that decile receive 34.9 percent of market income but only 4.8 percent of PSNP transfers.

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# **Figure 13. Methodological quality of the studies of prisoners, by country. Andean Region; 2009**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>Limited<br>Intermediate<br>3<br>High<br>2<br>1<br>0<br>Peru n= 4 Ecuador n= 6 Bolivia n= 1<br>Number of documents<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** In Peru and Ecuador—although the number of studies involved is small—the proportion of methodologically sound studies appears to have the same tendency as other population groups analyzed in both countries.

# **People living with HIV**

It should be noted that the databases only included studies that were related to the HIV epidemic and that were deemed useful to decision-making and policy design. Documents having only clinical content were excluded: for example, documents were excluded that related to treatments, side effects of medication, and studies on lymphocyte function.

In **Peru’s** database, there were six studies done on people living with HIV. Four of them had high methodological quality; two had intermediate quality; and two had limited quality. In **Ecuador** , there were five studies done on people living with HIV. None of them had high methodological quality; one had intermediate quality; and the other four had limited quality. In **Bolivia** , there were two studies done on people living with HIV. Both had intermediate methodological quality (Figure 14).

29
relatively large increases in depth for countries with explicit schemes should, perhaps, be attributed to a host of other factors. Section III offers a more formal attempt to disentangle the relationship between the circumstances surrounding deposit insurance decisions and their eventual impact on financial depth.

Table **2: Characteristics of Countries with Explicit and Implicit Deposit Insurance Programs**

|**Variable**|Exp|licitProgra s||I|**plicit Progra**s||
|---|---|---|---|---|---|---|
||Mean|St Dev|Obs|Mean|St Dev|Obs|
|PerCap Inc*|7268|4406|131|4042|4377|45|
|Inflation|14.3%|16.8%|33|19.0%|20.4%|31|
|3yr Deficits|3.0%|3.4%|32|3.9%|4.6%|39|
|M2 Instabil*|6.9%|6.3%|35|15.3%|9.5%|23|
|Law Index*|3.92|1.79|43|2.74|1.31|43|
|Bank Crisis|25.8%||31|14.3%||56|

Notes: Per capita income measured in 1985 $US. For explicit countries, income and inflation figures are for the year of the program's adoption, deficits and M2 stability are for the three years prior to adoption, and bank crises are for the five years prior to adoption. For implicit countries. income, inflation, deficit, and M2 stability are averaged over all years for which data were available. Bank crisis data are for the five years leading up to the last decision to retain an implicit program (i.e., 1990-94). For both implicit and explicit countries, the ICRG Law Index (a six-point scale with higher values indicating a stronger legal tradition) is averaged over 1985-91. M2 instability is the coefficient of variation in M2/GDP (standard deviation/mean). Bank crisis is a dummy variable equal to one if a bank crisis occurred.

* Means for explicit and implicit are different from one another at the t = 0.05 level. (results similar when variance for each sample assumed equal or unequal).

Cross-country regressions for the level of financial depth in 1992 indicate that the positive link between explicit deposit insurance and financial development suggested by the summary statistics in Table I is closely associated with income levels. Controlling for income and the average level of inflation from 1990-92, the partial correlation between explicit deposit insurance and financial depth is actually _negative._<sup>_9_</sup> The estimated coefficients for the explicit deposit insurance variable imply large reductions in financial

- 9 Similar qualitative results apply when deficits as a percentage of GDP are included in the regression and when the coefficient of variation in M2/GDP replaces inflation as a measure of instability. The instability measures enter the regressions with the expected negative sign, but they never approach conventionally accepted levels of significance, perhaps because they are averaged over a relatively brief time period (1990-92).

8
The above analysis of country spreads can be extended to capital flows with the aid of a suitable econornetric model of their determinants, a task undertaken in Fiess (2002). In brief, the model describes the simultaneous determination of flows and spreads making use of the decomposition of the latter into their global and local components shown above. Empirical implementation of this framework shows that capital flows are negatively affected by both global and local risk. In turn, local risk rises with the total volume of debt and the primary fiscal deficit, both expressed as ratios to GDP.

Using this framework, we can assess the roles of local and global factors in the observed time path of capital flows to Argentina. This is done in Figure 2.7, which plots the variation in flows to Argentina attributable to local factors, as a fraction of the total variation explained by ithe model.<sup>9</sup> The figure shows that Argentina-specific factors played a negligible role until 1998, but became increasingly important following the Russian crisis, and especially after October 2000. Indeed, from the latter date up to September 2001, local factors account for two-thirds of the total variation in capital flows to Argentina explained by the model.

**Figure 2.7. Capital Inflows to Argentina: Contribution of Local Factors (Percent of Total Variance Explained)**

<!-- Start of picture text --> 70%-<br>60% -<br>50%-<br>40%<br>30% -A l<br>20% -<br>10%<br>0%<br>a0 40 40 4 ,0 0 _0 0 1=. i... 4... 4.. 40 40 4 to 4 0 4 40 2 40 4 40 40 40 40 a, 40 4 a0, 0 9 0 0 0 ' 0 - 9 9t0<br>C -s ; -C C co C L C -<br>X < g nzt 7g n n °W <1 7 °U <en g <1 7) g<br>Source: Fiess (2002)<br><!-- End of picture text -->

**9** Like in the previous figures, we use a 48-month moving window for these exercises. Alternative window lengths result in qualitatively similar results.

15
The main variables in this analysis come from the woman’s questionnaire where a number of anthropometric measures are collected.

We make use of adult height as our main health variable. The micro‐econometric literature often uses adult height as a proxy indicator for overall health and long‐term adult well‐being since it reflects the accumulation of shocks to health through childhood and adolescence. A rough consensus drawn from this literature is that an improvement in health associated with a 1‐centimeter increase in adult height raises productivity by 3.4 percent (Kraay, 2018). Respondent’s height is reported in centimeters in the DHS data.

The DHS also records how long the individual has resided in the current location. We utilize this information to restrict the sample to only those women whose birth‐place coincides with the current location. This allows us to guard against the possibility of mis‐measuring exposure to nitrogen pollution if the woman was born in a different location than the location in which she currently resides. We trace the birth‐year histories of all adult women ranging from 1966 to 1999, a period when the effect of the Green Revolution was already in force yet nitrogen fertilizers were still increasing in use.

# 2.2 Water Quality Data

The Central Water Commission (CWC) within the Ministry of Water Resources monitors data on ambient water quality throughout the country. We compile and harmonize a rich data set of water pollution measurements between the years 1963‐2017 along a network of 375 river monitoring stations throughout India. Many gaps in the data exist since the pollution measures are not consistently recorded over the entire sample time frame and the panel is unbalanced. Over the 1963‐2017 period, 75% of water quality data are missing (60% between 1986‐2017). To circumvent these problems, we build on a new class of spatial statistical network model for stream data to interpolate and fill in missing observations across monitor‐year pairs (ver Hoef and Peterson, 2010). The model takes advantage of the fact that water quality in a station downstream depends on environmental conditions and human activities upstream of the station, and on the water quality “received” from upstream (directed network). Therefore, spatial covariates in a well‐defined upstream area and the spatial dependence between observations based on stream distance allow to model water quality and predict it in unsampled locations. We train such model to fill‐in missing observations. We then collapse monitor level observations to the district level. A more complete description of the model is provided in Appendix 1.

We focus on cumulative exposure to nitrate‐nitrogen when the concentrations exceed safety thresholds of 10mg/l from the year of birth to age 3. Prior work suggests that the first 1,000 5
# **_3.2. Gender Attitudes Composite Indexes_**

The module on gender attitudes consisted of 35 questions on employment, gender-based violence, decision-making, and general attitudes, answered by the main respondent of the household survey (Table 1). These 35 questions, asked to the respondents in Malayalam and English, were structured to elicit pointed answers that were recorded as: “Yes,” “No,” “Depends on the situation,” and “Don’t know.”<sup>7</sup> This analysis took the affirmative responses into account, whereas “No,” “Depends on the context,” and “Don’t know” are classified as 0. Thus, all the answers were coded as a binary variable with 1 for “yes” and 0 for “no.” The employment section had three questions: whether it is shameful for a wife to earn more than her husband, if a woman threatens a man’s job opportunities and livelihood, and if a woman should not work outside her home if economic conditions improved. General attitudes, the second section, included 13 questions related to whether a woman’s capacity to carry out her household chores was important to earn community respect and if it is shameful for a man to carry out household work. It also included paired questions to assist in the comparison of attitudes of male and female respondents on specific issues, such as whether girls and boys had to be brought up to be submissive and modest, and if outspoken and assertive boys and girls should be disciplined. Such a structure is crucial as it allows establishing if girls and women are at a comparative disadvantage than boys and men. The third section, decision-making, consisted of nine questions asked whether women should leave decision-making to men on a range of issues, such as property, family planning, and female participation in political and religious organizations, and self-help groups.

Thirteen questions on GBV, included in the last section, are the following: whether a woman’s dressing style provokes men to behave badly with them, whether a woman should cover up being sexually abused to save family honor, if a woman who gets beaten by her husband loses community respect, and whether a man who beats his wife is respected by the society. It also featured questions to gauge comparative attitudes held by both genders on the same issues. Examples include paired questions on whether they felt men and women had to tolerate aggressive behavior from their spouses to protect their

> 7 The formulation of the responses into these categories was undertaken after a series of pilot tests with respondents from various socioeconomic groups.

12
This paper quantifies the heterogeneous effects of Latin America’s trade regulations for Colombia’s exporting firms, concentrating on specific types and channels through which they operate: firms’ value of exports and their probability of participating in and exiting from export markets. There is a focus on Latin America and the Caribbean (LAC) as the region has become Colombia’s most important export market. This, coupled with the unique data of trade regulations and other non-tariff measures (NTMs) in LAC, provides an ideal setup.

We use a firm-level gravity model with a difference-in-differences research design that identifies heterogeneity in treatments across NTM types and channels. Our econometric approach accounts for selection and omitted variable bias as well as endogeneity in the timing of NTMs with respect to trade flows into destination markets. Moreover, we test for the robustness of results with stringent fixed effects, different measures of firm size and alternative samples to rule out endogeneity concerns.

The impact of trade regulations varies greatly across types and channels. Of all NTM types considered, only technical barriers to trade (TBTs) — a standard-like measure — and quantity controls explain changes in Colombian firm-level exports. On average, TBTs undermine Colombia’s firmlevel exports behavior across all three channels considered. These results are consistent with a model in which trade regulations increase firms’ compliance costs relative to consumer benefits. The effect is even more pronounced for quantity control measures which undermine Colombian firms’ exports and their likelihood of remaining in markets. Intriguingly, new quantity controls in product-destination markets _lower,_ on average, firms’ exit rates.

Looking at the heterogeneous effects of NTMs, we show that this effect is driven by small firms that benefit from quantity controls. Conversely, big firms are more likely to leave export markets when faced with new quantity controls. We argue that quantity control measures constrain big firms 3