rafmacalaba/data-use-mentions-extended huggingface dataset:
Preventing Violence in the Most Violent Contexts: Behavioral and Neurophysiological Evidence<sup>_∗_</sup> Lelys Dinarte<sup>_†_</sup> Pablo Egana-delSol<sup>_‡_</sup>

**Keywords:** After-School Programs, Emotional Regulation, Violence, Behavioral Economics.

# **JEL Classification:** I29, K42, Z13, I25, D87

> _∗_ We are very grateful for comments from Claudia Martinez, Jeanne Lafortune, Rodrigo Soares, Sara Heller, Francisco Gallego, Jishnu Das, Jed Friedman, Adam Wagstaff, Jose Tessada, Nicolas Figueroa, and Tomas Rau. We also appreciate the comments of participants at the World Bank SESI CoP Seminar, 2019 Texas Economics of Crime Workshop, LACEA-LAMES 2018, 10th Maastricht Behavioral and Experimental Economics Symposium, 7th Annual Interdisciplinary Symposium on Decision Neuroscience, 2017 ESA World Meeting, 2017 SECHI Annual Meeting, 5th Antigua Experimental Economics Conference, and seminar at Pontificia Universidad Catolica de Chile, The World Bank-DEC, IADB-RES, Universidad Javeriana, Universidad de Los Andes, and Universidad del Rosario. We are truly grateful for the incredible support of Glasswing International as an implementer partner, especially to Stephanie Martinez and the education sector team. We are also grateful to principals, teachers, students, and instructors of the public schools in El Salvador that participated in this project. This study was funded by CEDLAS-IDRC (Canada) in the context of the project “Fostering capacities in Impact Evaluation in Latin America,” CONICYT Chile “Soporte Operacional para Becas Nacionales,” and the Centre for Social Conflict and Cohesion Studies (CONICYT/FONDAP/15130009). The intervention was funded by USAID El Salvador Office, HANES, and One Link. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. This study was registered in the AEA RCT Registry with unique identifying number AEARCTR-0001602

> _†_ The World Bank. Development Research Group
their relative role in overall inequality has increased over time until 2001 (from 70.5 percent to 72.3 percent) although it appears to have declined back to 70 percent in 2002.<sup>11</sup> The combination of increased inequality and the growing size of the component attributable to labor

> 10 This index, as Ravallion and Chen (2003) note, has an additional desirable property of being based on an Π ( _h_ )

> “axiomatically appropriate” poverty measure, namely, the Watts index: _W_ = ∫ ln( _H_ / _c_ ( _p_ )) _dp_ . 0

> “axiomatically appropriate” poverty measure, namely, the Watts index: _W_

> 11 The nature of a decline in 2002 is not clear.

10
within-week variation**<br>**(CDR-W)**|||||
|Random Forest Weekly Ensemble|0.616|<br>0.614|0.630|0.641|
|OLS FE, stepwise BIC|0.633|<br>0.634|0.631|0.593|
|**Combined**|||||
|**Credit bureau and phone indicators**|||||
|Random Forest|0.711|<br>0.708|-|0.642|
|Logistic, stepwise BIC|0.772|<br>0.770|-|0.616|
|**Credit bureau and phone indicators,**<br>**within-week variation**|||||
|Random Forest Weekly Ensemble|0.618|<br>0.616|-|0.639|
|OLS FE, stepwise BIC|0.645|<br>0.645|-|0.586|
|Default Rate (%)|11|<br>12|10||
|N|7,068|6,043|1,025|6,975|

Standard indicators evaluate out of sample performance using 5-fold cross validation, averaged over fold draws. Offset indicators are derived from only half of the data (the first half for early transitions; the last half for late transitions); the out of time model is estimated on the early half of transitions and tested on the late half. AUC represents the area under the receiver operating characteristic curve, and BIC the Bayesian Information Criterion. For middle two columns, model is trained on all individuals except the omitted fold, and performance is reported for the given subsample within the omitted fold. Source: authors’ analysis from telecom data.

28
Policy Research Working Paper 6378

# **Abstract**

Use of temporary trade barriers has proliferated across countries, industries, and even policy instruments. This paper constructs a panel of bilateral, product-level United States steel imports that are matched to a unique data set on trade policy exclusions that are associated with the 2002 United States steel safeguard in order to compare the trade impacts that result from application of various temporary trade barrier policies over 1989–2003.

The analysis finds that the trade effects of an applied safeguard—which is statutorily expected to follow the principle of nondiscriminatory treatment—can nevertheless compare closely with the application of the explicitly discriminatory antidumping policy. The results on trade policy substitutability complement other recent research on these increasingly important forms of import protection.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|0.782|0.457|
|Employee|0.664|0.662|0.646|0.349|0.705|
|PhD degree|-|0.001|-|0.974|0.378|
|Master’s degree|0.013|0.007|0.018|1.878|0.153|
|<br>Postgraduate diploma|0.006|0.009|0.007|0.208|0.812|
|Bachelor’s degree|0.936|0.937|0.941|0.098|0.907|
|Undergraduate diploma|0.015|0.020|0.010|1.125|0.325|
|Dailysurveyresponse rate|0.264|0.270|0.268|0.889|0.411|

# 5. Results

## 5.1 Effects of Stabilization on Migration Intentions

current occupation under scenarios of political and economic stabilization, with and without controls. These estimates are relative to the reference category representing a status quo marked by considerable economic and political instability in the country. Across the three outcome measures of interest, we see that only political stabilization will reduce the value attached to 13
consumption expenditure and financial assets accumulation, the advantages of cadre households over non-cadre households are consistent with what we observe for the income measures. In common with income returns, there are positive correlations between cadre status and expenditures and asset accumulation after controlling for both observables and fixed household unobservables, but results from expenditure and asset accumulation models suggest only modest returns to cadre status.

**_Life-Cycle Effects._** As our favored model does not include age or any other indicator of stage in the lifecycle, one might be concerned that the cadre variable in our regressions is picking up lifecycle effects. For survey data from the period from 1993 to 2003, the RCRE survey enumerated the age of the main household income earner (the definition used for household head) in the following categories: (1) below 31, (2) between 31 and 40, (3) between 41 and 50, (4) between 51 and 60, and (5) above 60. Appendix Table 1 presents the percentages of the households with the age of main laborer in each of the five categories. Specifically, 35 percent of the households have their main laborers with the age between 41 and 50 while 29 percent of the households between 31 and 40 and 20 percent between 51 and 60. Moreover, cadre and non-cadre households appear to differ greatly in the share of households between 41 and 50. While 45 percent of cadre households have their primary income earner between 41 and 50 while only 34 percent of the non-cadre households fall in this age range. When we include indicator variables to control for age of the main income earner, we do not observe appreciable differences in the returns to cadre status (Appendix Tables 2 and 3). First, when including the age variables in the regression, the point estimate of cadre households rises to 102 yuan,

21
Policy Research Working Paper 9962

# **Abstract**

On-chain crypto-assets transaction volumes have grown rapidly, particularly during the COVID-19 pandemic. Crypto-assets activity appears to be a global phenomenon, although it still remains modest relative to gross domestic product for most countries. Panel regressions across more than 130 countries show that the variation in countries’ monthly crypto volumes is mostly driven by globally relevant factors such as real U.S. longer-term inflation expectations, U.S. real Treasury yields, and gold and crypto-asset prices, rather than recent country-level macroeconomic developments. Cross-sectional regressions offer tentative evidence that crypto activity is higher in countries with higher information and communications technology adoption and higher reliance on remittances. Taken together, the findings shed new light on the drivers behind crypto activity and offer support to the notions that crypto-assets are perceived as a risk asset, a potential macro hedge, and a potential tool to support cross-border transactions. However, the results come with caveats: a significant portion of the sample period includes extraordinarily loose global financial conditions; the crypto volume data have a short history, rely on important limiting assumptions, and do not represent all crypto activity; and crypto-assets represent a fast-evolving, increasingly diverse asset class and industry.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Information and Technology Solution Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at efeijen@worldbank.org, ykawashima@worldbankgroup.org, and rmittal1@worldbankgroup.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the
. Surface type_|_as a percent_|_of the total_|
|---|---|---|---|---|---|---|---|
|_etwork_|||||_primary network_|||
||Good|Fair|Bad|No info|Paved|Not Paved|No info|
||%|%|%|%|%|%|%|
|Peru|55|20|16|9|Peru<br>85|8|7|

_Source:_ World Bank based on GIS data. _Source:_ World Bank based on GIS data.

The Ministry of Transport and Communication (MTC) provided a georeferenced road network data set that contained data on surface type and road condition for 93 percent of the country’s roads, updated in May 2015. However, it lacked information on terrain, which is an input to HDM-4. Thus, we used a Digital Elevation Model (DEM) to classify the country into 15 geographies and extracted elevation data at fixed intervals along the line of the road network. We calculated terrain class for each link and grouped them into the seven terrain types used by the HDM-4 model. Though we obtained traffic volume data (AADT) from the MTC, it was not georeferenced or included as one of the attributes in the GIS network. Hence, based on the origin and destination pair of the AADT, we identified the location of origin and destination (OD) and attributed the primary roads that can connect the OD with the shortest distance (the simulation is based on the shortest path routing algorithm). Once we had all the needed information to match the physical characteristics of the GIS network with the outputs of HDM-4, we appended the corresponding road user cost and vehicle speed to each link.

We then selected ODs using national and provincial capital cities, population centers of over 25,000 inhabitants, main ports and airports, and
reach into countries with which it is not contiguous. Except for one I in table 1 when it was active in combat in Chad, its activities are coded with an F, but this information does not identify where Libya was intervening. Burkina Faso’s involvement in the Mano River complex is another example of an important player that has thus far avoided internal violence. Except for 1987, when it experienced a coup that seems to be the source of the X, Burkina Faso does not appear in table 1. According to the coding rules, it is at peace as much as any country in the sample. In addition to African countries, other countries that play this role are outside of Africa, including Cuba, France, the United States, and countries acting under the aegis of the United Nations, which have sent ground troops to participate in African conflicts.

Most of the countries that participate in these complexes are involved in reciprocal interactions, however, in which one country supports antagonists in a neighboring country and the neighbor responds in kind. Such pairs include Sudan and Uganda, Angola and CongoKinshasa, and Liberia and Côte d’Ivoire, among many others. Yet, the PRIO/Uppsala data record few if any of the international activities of Burkina Faso, Liberia, Libya, Sierra Leone, Côte d’Ivoire, or Somalia, among others. This lack of information means that econometric studies based on these data cannot incorporate these intercountry relationships even if they use specifications based on neighborhood effects, as in the spatial econometric literature (see below). Côte d’Ivoire has been a long-time member of the Mano River complex, influencing the dynamics of the complex well before it experienced associated violence and, in particular, providing a launching place for Taylor’s invasion of Liberia in 1989. From the coup attempt of September 19, 2002, until at least 2011, it has experienced a violent contest over the monopoly of force within its own borders. This contest is tied to the dynamics of the Mano
representative sample of the population (72.93 percent) believe this to be “likely" or “very likely”. We then ask whether borrowers expect future forbearance to be offered by private banks or the government, and find that 63 percent of respondents believe that banks are likely or very likely to offer forbearance in the future while an overwhelming majority of 84 percent of respondents believe that future forbearance will be granted as the result of a government or regulator initiative.

Finally, we also compare prior beliefs in the population to beliefs among experts, elicited through the Social Science Prediction Platform (DellaVigna et al., 2019). The results, reported in the Supplementary Appendix, show that expert predictions differ from those in the population of borrowers. Experts are more optimistic about the effect of forbearance on debt repayment and on average predict positive treatment effects of debt forbearance offers on loan repayment. To match the design of our experiment, we asked experts to predict the effect of forbearance offered by the regulator and forbearance offered by one’s lender. Interestingly, we find that the median expert _overestimates_ the effect of forbearance offered by the regulator, and _underestimates_ the effect of forbearance offered by a private lender on both repayment rates and on the demand for doing business with the lender in the future.

In sum, the results of a survey representative of the borrower population illustrate that respondents overwhelmingly expect repayment deferrals to have negative implications for credit discipline. Moreover, respondents believe that loan repayment could be impacted negatively through two alternative channels: the anticipation of future relief and expectations of more lenient credit enforcement by

> 11 In total, 39.9 percent of respondents think that some beneficiaries needed and deserved relief, and 6.6 percent of respondents think some beneficiaries did not need or deserve debt forbearance.

8
purpose vehicles (SPVs) to finance infrastructure investments, which carry “inherent” risks as they often circumvent borrowing limits and create contingent liabilities.

Regression analysis using time series data through the 1990s for seven large federal countries (Argentina, Australia, Brazil, Canada, Germany, India, and the United States) reveals that subnational government finance is overwhelmingly pro-cyclical, implying that the behavior of state governments in the United States is not an exception but the general rule (Rodden and Wibbels 2010). Only Canada was found to be engaged in harmonized expenditure smoothing in that decade.

In the recent crisis, other countries also harmonized their policies. China, with its combination of high savings and large fiscal space, provides an important example of a countercyclical subnational response (Ter-Minassian and Fedelino 2010; Fardoust, Lin, and Luo 2012). Expansionary subnational government spending played a key role in strengthening the overall impact of the stimulus and sustaining economic growth. A less dramatic example is India, where the central government eased borrowing constraints on state governments in 2009 and 2010.

Global economic growth recovered in 2010 but slowed in 2011. Serious concerns have emerged that the relatively fast-growing economies are also beginning to experience a downturn. In view of these trends, the IMF’s 2012 assessments recommend a gradual fiscal adjustment, cautioning governments against drastic cuts in their deficits and spending, lest they exacerbate the contraction. This advice is based on the conclusion drawn from empirical studies that the “fiscal multiplier” is especially large during times of crisis.

Over the past decade, subnational spending as a share of government spending increased in many federal countries (Ter-Minassian and Fedelino 2010). The change has important implications for the size of fiscal multipliers and their impact on the national economy and for policy/institutional reform for improved coordination across government levels in implementing a harmonized crisis response.

This paper is a follow-up to a research project carried out at the World Bank on the macroeconomic impact of the Great Recession in developing countries. The results of that project,
1,340 (Munishi, 1997). The expansion is dominated numTerically by lower level facilities like dispensaries (from 36 to 1313), but the number of private for profit hospitals also increased from 4 to 20, while the number of government hospitals remained constant at 77.

A review of public spending in the Philippines (World Bank, 1995) using panel data from 1983 to 1990 for 13 regions, found that public spending on health improved infant mortality in regions with lower incomes but had virtually no effect in richer regions. This is consistent with the private and public sectos being close substitutes in the higher income regions with a resulting highly-elastic private supply and small impact on outcomes, and a less-elastic supply response in poorer regions which results in a substantial impact of public spending (Hammer, 1997a).

While it might seem obvious that the private sector is an important part of the health sector that cannot be ignored in analysis of the potential irnpact of public sector interventions, it often has been ignored. A recent review of appraisals of World Bank projects, an institution with a large share of economists, found that out of 180 appraial reports, _none,_ that is, _not one,_ referred to the implications for, anid of, the private sector.

# _V) What, then, can be done?_

PHC programs have not had the impact some originally expected and the analysis and implementation of PHC gave insufficient attention to incentives in public sector employment and to market conditions. How can these concerns be more incorporated seriously when setting 37
Table 6: BAU-2030 and base year data aggregated by 5% percentiles of income. GDP per capita as population weighted averages, population in millions, number of poor people in millions, annual tree loss in square kilometers, PM2 _._ 5 in population weighted average concentrations, carbon emissions in million tons. World totals are scaled to world totals using multipliers (1.16 for population and carbon based on the share of population in our data, and tree loss 1.42 based on the share of tree cover in the data).

|Income|GDP p.c.|GDP p.c.|Pop.|Pop.|No. Poor|No. Poor|Treeloss|Treeloss|PM25|PM25|CO2|CO2|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Group|2014|2030|2014|2030|2014|2030|2014|2030|2014|2030|2014|2030|
|1|1_,_094|1_,_548|68|101|41|55|3_,_382|3_,_640|15|20|10|28|
|2|1_,_495|2_,_611|171|262|74|70|3_,_750|3_,_977|17|21|20|109|
|3|1_,_899|2_,_388|46|67|19|24|1_,_415|1_,_684|23|25|22|39|
|4|2_,_305|3_,_308|96|139|36|43|1_,_821|1_,_931|29|31|27|71|
|5|2_,_917|4_,_720|237|305|57|45|
# **Table 6. Survey Firms’ Aggregated Employment Changes by Export Status**

||Survey|Average Firm|Level Employme|nt Change|Total E|mployment Ch|ange|Average Per|cent Employm|ent Change|
|---|---|---|---|---|---|---|---|---|---|---|
|||2013-2016|2016-2020|2013-<br>2020|2013-2016|2016-2020|2013-2020|2013-2016|2016-2020|2013-2020|
|Always|7|-18.4|239.0|220.6|-129|1,673|1,544|-4.0|53.9|47.8|
|New|17|6.2|57.4|63.6|105|977|1,081|10.8|90.5|111.0|
|Never|141|-35.2|20.8|-14.4|-4,957|2,927|-2,026|-45.8|50.1|-18.7|
|Stopped|33|0.3|12.4|12.7|11|409|420|0.1|3.5|3.5|
|Total|198|-25.1|30.2|5.1|-4,971|5,985|1,019|-18.5|27.4|3.8|

Note: The above table reports the changes in employment levels by export status that we see in the 2013-2016-2020 panel data, unweighted. Export status is defined as follows: (i) “always” are firms which exported in all three years (2013, 2016, and 2020); (ii) “new”
was significantly higher than the share of full-time jobs (41 percent). Full-time jobs are significantly more likely to be in wage employment than part-time jobs. Moreover, part-time jobs are more likely to be in agriculture and fishing. Finally, full-time jobs are statistically significantly more likely to be in jobs related to electricity, gas and water supply.

**Table 5: Characteristics of full-time and part-time jobs (percentage)**

||**Full-time**|**Part-time**|**Difference**|
|---|---|---|---|
|Formal|46.4|29.5|16.9***|
|Informal|53.6|70.5|−16.9***|
|Wage|51.1|34.7|16.4***|
|Self-employment|41.4|51.0|−9.5**|
|Agriculture and fishing|3.7|10.3|−6.6***|
|Mining and quarrying|0.0|0.0|0.0|
|Manufacturing|8.0|5.8|2.3|
|Electricity, gas, and water supply|0.6|0.1|0.5**|
|Construction|5.1|8.1|−3.0|
|Commerce|44.4|36.7|7.8*|
|Transportation, storage, and communication|7.0|6.0|0.9|
|Financial, insurance, and real estate|1.4|0.7|0.7|
|Public administration|7.5|6.1|1.4|
|Other services|22.1|26.1|4.0|

_Source_ : Kinshasa Survey 2018. _Note_ : Difference-in-means tests are conducted