rafmacalaba/datause-encoder-data huggingface dataset:
commitments embodied in its trade agreements. We identify and present a number of trends, patterns, and puzzles arising from these data. We raise a number of questions for political-economic theory and additional research to address through more rigorous modeling and econometric analysis. Finally, our results highlight some potentially pressing, short-run policy concerns that arise in light of the observed patterns of the data.

Examination of Turkey during the period 2008-11 is a useful starting point for a number of reasons. First, the global economic crisis imposed on Turkey a relatively exogenous economic shock – the worldwide recession was brought on by the U.S.-initiated financial crisis in 2008. Second, major implications for Turkey’s trade continued well beyond the initial shock due to its legacy of dependence on the EU market: e.g., in each of the 10 years prior to the onset of the crisis, Turkey sent nearly 60 percent of its goods exports to the EU, by 2010 this had fallen to 48 percent. Thus, ongoing weak EU import demand associated with the lengthy European debt crisis acts as a persistent negative shock for some of Turkey’s exporters. Third, Turkey is not only a relatively large and important emerging market to examine, but the complexity and richness of its trade policy provide a particularly interesting case study. Prior to the onset of the crisis, Turkey began the period already with substantial variation – some explicit and some more subtle – in its tariff commitments. Turkey’s tariff commitments have been taken on through its preferential trading relationships – most important of which is a customs union with the European Union – and its membership in the World Trade Organization.

Turkey made vigorous and important changes to its trade policy during 2008-11 that impact an economically consequential amount of imports. First, and _despite_ preferential and multilateral commitments that might limit changes to its applied tariffs, Turkey’s policymakers have exercised flexibilities during 2008-11 by making changes to _both_ its applied MFN and preferential trade agreement (PTA) tariffs. For example, increases to applied import tariffs in the textiles and steel industry alone during this period could affect nearly 9 percent of Turkey’s manufacturing imports. Second, during this same period, Turkey continued intensive use of the relatively formalized TTB policies of antidumping, safeguards and countervailing duties – policies that provide another major class of flexibilities in the multilateral trading system. Applying data from the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012) reveals that Turkey’s use of antidumping and safeguards have affected an increasing share of its trade over the first decade of the 2000s; i.e., by 2011 an estimated 4.4 percent of the value of Turkey’s manufacturing imports and 6.4 percent of import product lines were impacted by these trade barriers. Thus, while Turkey’s policymakers largely withstood protectionist pressure to make 3
# **Infrastructure in Conflict-Prone and Fragile Environments: Evidence from the Democratic Republic of Congo**

Rubaba Ali<sup>1</sup> * A. Federico Barra** Claudia N. Berg*** Richard Damania** John D. Nash** Jason Russ*** JEL Codes: O1, I3, L9 Keywords: Transport, Conflict, DRC, nightlight data, kernel density, ethnic fractionalization, natural historical path 1 Authors in alphabetical order *University of Maryland, ** World Bank,*** George Washington University.
# _Leakage rates_

Table 2 presents leakage rates by targeting indicator. The leakage rate when there is no targeting (46.50 in 1994, and 51.15 in 1997) corresponds exactly to the fraction of the population which is not in poverty. Table 2 shows that leakage rates would be minimized with geographic targeting by the infant mortality rate (according to the 1994 LSMS) or the FONCODES index (according to the 1997 LSMS). Bootstrapped standard errors (not reported, but available from the author upon request) suggest that geographic targeting with any of the indicators in question is a significant improvement on the no targeting scenario, while the differences in outcomes across indicators are not significant.

# _Concentration curves_

Initial simulations show that the concentration curves for the FONCODES index, the infant mortality rate, and the imputed poverty rate are so close to each other as to be virtually indistinguishable from each other on a graph. For the sake of clarity, I therefore graph the _difference_ between each of the concentration curves and the no targeting baseline case in Figures 1.1 and 1.2. (The "concentration curve" for the no targeting scenario is a straight, forty-five degree line: every individual receives the same transfer, so the cumulative fraction of the transfer equals the cumulative fraction of the population at every point). Figure 1.1 shows that this difference is positive throughout for all three curves: no matter where we take the cut-off between "poor" and "non-poor" to be, the poor receive a larger share of transfers when these are made on the basis of the infant mortality rate, the FONCODES index, or the measure of imputed poverty than when transfers are not targeted. Figures 1 .1 and 1.2 also show that no single concentration curve lies everywhere above all others, although the FONCODES index curve always transfers more resources to poor households than the imputed poverty measure. This is noteworthy given that much recent effort has gone into developing these kinds of measures, in Peru and elsewhere.

# _Changes in poverty_

Figures 2.1 and 2.2 consider the impact of alternative geographic targeting regimes on poverty at various budget levels between 10 million soles and 5 billion soles (for a similar approach see Chaudhuri and Ravallion 1994, and Jalan and Ravallion 1998). As a point of reference, the Ministry of the Presidency, the single largest implementing agency of social in the survey-weighted, once again, by the appropriate expansion factors. Because concentration curves are mean-normalized in this way, the results are budget-independent.

11
From Table A2 it can be seen that if we correct the data for outliers the fit is quite acceptable for P0–at least for households in the rural sector and households in the informal urban sector. However, for the other three groups, the deviations are significant. One explanation for this outcome is that the population size is smaller for these groups, and thus the estimated shape parameters are less reliable. The poverty gap ratio is much more difficult to fit. Here the predicted indicators lie more than 40 percent above the observed values. It is interesting to see that in our case the predictions of the Beta distributions lead always to an overestimation of P0 and P1 (except for capitalists and rentiers), compared to the indicators that are directly measured from the survey data. Figures A1 and A2 show that the quality of the prediction depends also strongly on where the poverty line is drawn. The fitted curve crosses the observed curve for the first time around a cumulated population of 40 to 50 percent. If the poverty line is close to this intersection, than the prediction of P1 would be of course quite good.

# 4 The Macro-RHG Framework

Mini-IMMPA is a simplified version of the Integrated Macroeconomic Model for Poverty Analysis developed by Agénor, Izquierdo and Fofack (2003), and Agénor, Fernandes, and Haddad (2003). Although Mini-IMMPA focuses only on the “real” side, it offers a more detailed treatment of the labor market, accounting for features such as employment subsidies and job security provisions. Given its focus on the real side, the building blocks of the “structural” component of Mini-IMMPA consist of the production side, employment, the demand side, external trade, sectoral and aggregate prices, income formation, consumption and savings, private investment, and the public sector. As noted earlier, households are defined according to the skills composition of the workforce and the sector of employment. There is one rural household, comprising all workers employed in the rural sector. In the urban sector there are two types of unskilled households, those working in the informal sector and those employed in the formal sector. The fourth type of households consists of skilled workers employed in the formal urban economy (in both the private and public sectors). Finally, there are capitalists-rentiers whose income comes from firms’ earnings in the urban private sector. The following description of the model is kept quite short; for a more detailed presentation the reader is referred to Agénor (2003, 2004 ).

15
than one year of tenure. The estimated odds ratios imply that the larger is the average tenure in a firm, the more likely it is that this firm is subject to nominal wage rigidity. This result is also in accordance with the implications of the theoretical models on wage rigidity that were reviewed in section 3.<sup>16</sup>

**Table 5: Downward nominal and real wage rigidity** – **additional firm characteristics**

||Tenure|structure|Bonus|payment|
|---|---|---|---|---|
||DNWR/FW|DRWR/FW|DNWR/FW|DRWR/FW|
|Tenure 1–5 years (%)|2.593***|0.822|||
||(0.003)|(0.508)|||
|Tenure above 5 years (%)|2.719***|1.032|||
||(0.000)|(0.899)|||
|Bonus|||1.015|1.098|
||||(0.883)|(0.196)|
|Observations|6|449|10|298|

Notes: The table presents the estimated odds ratios for nominal wage rigidity vs flexible wage and real wage rigidity vs flexible wage. Worker skill groups, % permanent workers, % labour cost, dummy variables for different types of union contracts and sector, size and country fixed effects are added in all specifications. Robust P-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

Table 5 also presents the estimated odds ratio for the dummy variable indicating the payment of performance-related bonuses in addition to the base wage. The estimated odds ratios for nominal and real wage rigidity vs flexible wage category were both insignificantly different from one. This result prevailed when we used the share of bonuses in total pay instead of the above-described dummy variable. This is a surprising finding at least when it comes to DNWR, where we would expect firms having more flexible wage components to be able to afford higher rigidity in base wages at a little cost. It is at odds with evidence for 4 European countries reported by Messina et al. (2009), who find lower wage rigidity in those sectors with a higher share of bonuses and other flexible wage components in total compensation. One possible explanation is that some of our survey respondents confused base wages with total wages at the time of assessing wage freezes and wage cuts, hence answering for the total degree of wage rigidity among the main occupation group employed by the firm.

> 16 The complete regression results for the regressions investigating the effects of tenure, bonuses and competition are presented in Appendix 8.

21
results provide returns to height ranging from 1.4 percent to 4.5 percent per centimeter. In what follows, the Weil (2007) preferred value of 3.4 percent is used as the baseline. A reasonable range of estimates has 6.8 percent as the upper bound (corresponding to the mean estimated return to height across the 5 studies with estimates greater the Weil (2007) benchmark), and a lower bound of 1 percent as the lower bound (corresponding to the mean estimated return to height in the remaining 13 studies with estimated returns below 3.4 percent).<sup>22</sup>

# **A3.3  The Relationship Between Adult Height and Adult Survival Rates**

The second key ingredient in the calculation is the estimated relationship between height and adult survival, β������,��� . Weil (2007) estimates this using long‐run historical data on stature and survival rates for 10 advanced economies over the 20<sup>th</sup> century, where there is considerable variation within countries over time in adult height. In his sample, average height varies from around 164 cm to 180 cm, and he obtains an estimate of β������,��� �19.2 . To assess the robustness of this finding, the same relationship is estimated using data on female height collected in 172 DHS surveys covering 65 developing countries between 1991 and 2014.<sup>23</sup> In this sample, female height exhibits comparable variation to the historical dataset in Weil (2007), ranging from 148 cm to 163 cm. In the roughly half of the sample corresponding to non‐Sub‐Saharan African countries, a country‐fixed effects regression of height on adult survival results in a slope coefficient of 19 and a standard error of 3.6, which is extremely close to the Weil (2007) baseline estimate of 19.2. In Sub‐Saharan Africa, the slope coefficient is close to zero and very imprecisely estimated. This likely reflects the large swings in adult mortality rates due to the AIDS epidemic. To assess this, the relationship is re‐estimated for all countries, but excluding observations above the Sub‐Saharan Africa median for aids‐related death rates. This gives a very similar estimate of 18.3 with a standard error of 3.4. These estimates lie in the same vicinity as the baseline estimate in Weil (2007). This evidence suggests that the Weil (2007) estimate of β������,��� �19.2 is reasonable to use in the baseline “return” to ASR of γ��� �γ������ � β������,��� �0.034 �19.2 �0.65 .

> 22 All of the discussion here relies on the literature on the returns to height.  While less extensive, there are well‐ identified econometric estimates of the economic returns to a number of other measures of health, including iron deficiency anemia, malaria, and hookworm infestation.  Subsequent updates of this background paper will investigate how the estimates of overall health to productivity change when these other estimates of individual health conditions and their returns are used in this framework.

> 23 The data come from the Health Equity and Financial Protection (HEFPI) Project at the World Bank.  Patrick Eozenou and Adam Wagstaff kindly made this data available.

42
# **C Appendix: Pre-WTO Average Tariffs**

Table C.5: Replication of Main Results using Pre-WTO Average Tariffs

||P|roduct Mark|et|L|abor Mark|et|
|---|---|---|---|---|---|---|
|VARIABLES|_μ_<sup>_m_</sup><br>_ijt_<br>(1)|_μ_<sup>_m_</sup><br>_ijt_<br>(2)|_μ_<sup>_m_</sup><br>_ijt_<br>(3)|_γijt_<br>(4)|_γijt_<br>(5)|_γijt_<br>(6)|
|_τ_91_−_95_× Post_1995|-0.0133**|-0.0268***|-0.0271***|0.00786*|0.00871*|0.00856*|
||(0.00549)|(0.00688)|(0.00671)|(0.00368)|(0.00428)|(0.00455)|
|_ωit_||1.572***|1.556***||0.0708**|0.0702**|
|||(0.170)|(0.172)||(0.0280)|(0.0268)|
|Skill Ratio||0.0699|0.0852||0.101|0.105|
|||(0.153)|(0.152)||(0.148)|(0.152)|
|Small size frms||0.104|-0.0906*||0.350***|0.307***|
|||(0.0857)|(0.0429)||(0.0783)|(0.0844)|
|Medium size frms||0.0570|-0.0883**||0.0934**|0.0745***|
|||(0.0662)|(0.0352)||(0.0396)|(0.0177)|
|_ωit×_ small size frms|||0.0238***|||0.00478|
||||(0.00356)|||(0.00781)|
|_ωit×_ medium size frms|||0.0176*|||0.00228|
||||(0.00819)|||(0.00362)|
|Inverse Mills Ratio||||0.0826|-0.181**|-0.181**|
|||||(0.0451)|(0.0635)|(0.0637)|
|Constant|1.524***|-12.46***|-12.32***|0.441***|-0.103|-0.0976|
||(0.0711)|(1.506)|(1.522)|(0.0370)|(0.314)|(0.305)|
|Observations|1,579|1,574|1,574|601|601|601|
|_R_<sup>2</sup>|0.024|0.485|0.486|0.045|0.115|0.115|
|Number of frm|223|223|223|152|152|152|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

**_Source:_** Data for the study comes from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table report further robustness check using the average ex-ante trade liberalization tariff instead of the initial tariff to replicate the main results. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1.

39
Table 5: Accuracy of Population Density Predictions based on LASSO Selected Poisson Regression of Population Density

|Imagery Type|In-sample _R_<sup>2</sup>|Out-of-sample _R_<sup>2</sup>|Number of <br>Candidate|variables<br>Selected|No. of Villages|
|---|---|---|---|---|---|
|A. National Sample||||||
|1. No satellite imagery|0.806|0.650|35|15|13,970|
|2. Low-resolution, public|0.843|0.702|35|18|13,970|
|3. All public|0.888|0.750|39|13|13,437|
|B. 55 sub-district sample||||||
|1. No satellite imagery|0.731|0.589|35|5|1,360|
|2. Low-resolution, public|0.804|0.677|35|7|1,360|
|3. All public|0.858|0.710|39|9|1,275|
|4. All public + propriety texture|0.874|0.755|46|11|1,275|
|5. All|0.918|0.830|67|23|1,178|

Note: The results are based on a Poisson regression model whose dependent variable is the census village population density, and the independent variables are selected based on LASSO regularization among the following: Relevant satellite imagery sources as described in panel B in Table 1, district fixed effects, a binary indicator for urban villages, and log-village area. The models with “No satellite imagery” use only the district fixed effects, an indicator for urban villages, and log village-area as independent variables. “Lowresolution public” indicators refer to the low-resolution and public indicators as defined in Table 1. “All public” indicators refers to all the public imagery based indicators, including the high-resolution imagery based indicators described in Table 1. “Proprietary texture” indicators include very high-resolution texture features based on Digital Globe from Engstrom et al. (2017) as defined in Table 1. “All” imagery refers to all public and proprietary texture indicators, and object identifiers, such as cars, roofs etc., as defined as in Table 1. The out-of sample _R_<sup>2</sup> is obtained from stata’s crossfold command using five-fold cross-validation.

23
# _A. Characteristics of PROSPERA and non-PROSPERA children_

Tables 2a and 2b present descriptive statistics comparing baseline characteristics (in 20072008 for sixth graders) for PROSPERA and non-PROSPERA students for the sample of sixth graders in schools located in communities with middle to very high margination indices and for the sample of sixth graders in indigenous primary schools, which we treat as a proxy for impacts on indigenous children. As expected, Table 2a shows there are clear socioeconomic differences favoring non-PROSPERA children. With respect to school characteristics, the schools that PROSPERA children attend tend to have fewer students per teacher, but teachers have lower education levels and schools have fewer resources, as measured by having a computer.

Table 2a: Descriptive Statistics of Beneficiaries and Non-beneficiaries All 6th Graders in Medium to Very High Poverty Areas: School Year 2007-2008

||Non-PROS|PERA<br>|PROSPE|RA<br>|
|---|---|---|---|---|
|||Std||Std|
|Variables|Mean|.<br>Dev.|Mean|.<br>Dev.|
|_Student and Household Characteristics_|||||
|Age|11.76|0.78|11.93|0.79|
|# siblings|3.14|2.26|4.39|2.53|
|Have health problems|0.08|0.27|0.11|0.31|
|Spanish is first language|0.89|0.31|0.77|0.42|
|Currently works|0.66|0.47|0.53|0.5|
|HH has DVD|0.73|0.44|0.55|0.5|
|HH has TV|0.89|0.31|0.79|0.41|
|HH has MP3 player|0.47|0.5|0.33|0.47|
|HH has cell phone|0.6|0.49|0.39|0.49|
|HH has computer|0.27|0.45|0.09|0.29|
|HH has internet|0.15|0.36|0.05|0.23|
|HH has calculator|0.89|0.32|0.87|0.34|
|Aspires to attend college|0.56|0.5|0.36|0.48|
|Studies English|0.32|0.47|0.13|0.34|
|_School Characteristics_|||||
|Student teacher ratio|28.63|13.44|24.99|13.05|
|Has computer|0.74|0.44|0.63|0.48|
|More than 1 grade per classroom|0.17|0.38|0.28|0.45|
|Proportion of teachers with university|0.75|0.28|0.68|0.35|

Source: Authors calculations: ENLACE 2007-2008 merged with context questionnaires and school characteristics.

Table 2b presents the same information for the sample of sixth graders enrolled in indigenous schools. Although beneficiary children have lower socioeconomic status in this group as well, it is worth noting that differences are much smaller between beneficiary and non-beneficiary children than was true in the broader sample. The estimations below control for these variables 15
<!-- Start of picture text --> Figure 1<br>Net Migration 2000<br>N<br>W E<br>S<br>Indigena.shp Percentage<br>-5.69 - -2.72<br>-2.72 - -0.58<br>-0.58 - 1.87<br>1.87 - 5.12<br>5.12 - 11.37<br>600 0 60 0 1200 Miles<br><!-- End of picture text -->

# **<u>Figure 2</u>**

<!-- Start of picture text --> Per Capita FDI (From 1994 to 2000)<br>Millions of Dollars<br>1 - 122<br>122 - 452<br>452 - 972<br>972 - 1812<br>1812 - 4315<br>N<br>W E<br>S<br>600 0 600 1200 Miles<br><!-- End of picture text --> 24
When Emotion Regulation Matters: The Efficacy of Socio-Emotional Learning to Address School-Based Violence in Central America<sup>*</sup> Lelys Dinarte-Diaz<sup>†</sup> Pablo Egana-delSol<sup>‡</sup> Claudia Martinez A.<sup>§</sup> Cindy Rojas A.<sup>¶</sup> Originally published in the Policy Research Working Paper Series on _March 2022._ This version is updated on _February 2024._ To obtain the originally published version, please email prwp@worldbank.org.

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

*We appreciate the feedback from Sofia Amaral, Samuel Berlinski, Chris Blattman, Christina Brown, Julian Cristia, Andy De Barros, Andrew Dillon, Taryn Dinkelman, Gemma Dipoppa, Rafael Di Tella, Valentina Duque, Claudio Ferraz, Francisco Gallego, Sebastian Gallegos, Felipe Gonzalez, James Heckman, John Eric Humphries, Caro Lopez, Jeanne Lafortune, David McKenzie, Daniel Mejia, Ernesto Schargrodsky, Manisha Shah, Mica Sviatschi, Roman Zarate, and participants in different seminars at which we presented this work. Egana-delSol acknowledges the support and generosity of Rosalind Picard and the Affective Computing Group at MIT and Affectiva, especially Maksat Amangeldiyev for helping construct the digital platform to collect data in the field. We are truly grateful for the support of Glasswing International as an implementer partner. We thank Carlos Espinosa, Tatiana Mojica, David Quiroz, and Steffanny Romero for their research assistance. Dinarte-Diaz acknowledges support from the Templeton World Charity Foundation, Inc., Grant Proposal TWCF0362 and the Research Support Budget (RSB) from the World Bank. Egana-delSol acknowledges the support provided by the Centre for Social Conflict and Cohesion Studies (COES ANID/FONDAP/1523A0005) and the ANID/NCS2021033 Millennium Nucleus on The Evolution of Work (MNEW). Martínez A. acknowledges the support from Fondecyt 1201305 and ANID/Millennium Nucleus on Intergenerational Mobility: From Modelling to Policy (MMOVI) [NCS2021072]. The research project’s protocol was reviewed and approved by the Institutional Review Board at the PUC Chile under ID Protocol 190129001. 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 a unique identifying number: AEARCTR-0003976

> †The World Bank, IZA, CESifo, HiCN. Email: ldinartediaz@worldbank.org

> ‡Adolfo Ibanez University School of Business, EGAP, MNEW. Email: pablo.egana@uai.cl.

> §Corresponding author. Inter-American Development Bank and Pontificia Universidad Católica de Chile, J-PAL. Email: claudiam@iadb.org

> ¶Inter-American Development Bank. Email: cindyro@iadb.org