rafmacalaba/datause-encoder-data huggingface dataset:
FUNDAUNGO (2013). Atlas de la violencia en el salvador (2009-2012). _San Salvador_ .

FUSADES (2015). El Salvador Youth Survey Database.

Fuster, J. M. (2013). _The neuroscience of freedom and creativity: Our predictive brain_ . Cambridge University Press.

- Glasswing International (2012a). Estudio Interno sobre impacto de programa extracurricular de seis centros escolares. Technical report, Fundacin Crislida. Glasswing International.

- Glasswing International (2012b). Mi Escuela, Mi espacio. un programa educativo extracurricular para el desarrollo integral de la niez, la adolescencia y la juventud de El Salvador. Technical report, Fundacin Crislida. Glasswing International.

- Goldschmidt, P., D. Huang, and M. Chinen (2007). The long-term effects of after-school programming on educational adjustment and juvenile crime: A study of the LA’s BEST After-School Program. _Los Angeles: UCLA/CRESST. Retrieved September 8_ , 2008.

- Goleman, D. (2010). Emotional intelligence: Why it can matter more than iq. _Bantam Books_ .

- Gottfredson, D. C., A. Cross, and D. A. Soul ́e (2007). Distinguishing characteristics of effective and ineffective after-school programs to prevent delinquency and victimization. _Criminology & Public Policy 6_ (2), 289–318.

- Gottfredson, D. C., S. A. Gerstenblith, D. A. Soul ́e, S. C. Womer, and S. Lu (2004). Do After-School Programs reduce delinquency? _Prevention Science 5_ (4), 253–266.

- Harmon-Jones, E., P. A. Gable, and C. K. Peterson (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. _Biological psychology 84_ (3), 451– 462.

- Haushofer, J. and E. Fehr (2014). On the psychology of poverty. _Science 344_ (6186), 862–867.

- Heckman, J. J. (1979). Sample selection bias as a specification error. _Econometrica 47_ (1), 153–161.

- Heckman, J. J. and T. Kautz (2012). Hard evidence on soft skills. _Labour economics 19_ (4), 451–464.

- Heckman, J. J., J. Stixrud, and S. Urzua (2006). The effects of cognitive and non-cognitive abilities on labor market outcomes and social behavior. _Journal of Labor economics 24_ (3), 411–482.

- Heller, S. B., A. K. Shah, J. Guryan, J. Ludwig, S. Mullainathan, and H. A. Pollack (2017). Thinking, fast and slow? Some field experiments to reduce crime and dropout in Chicago. _The Quarterly Journal of Economics 132_ (1), 1–54.

- Herrenkohl, T. I., C. Sousa, E. A. Tajima, R. C. Herrenkohl, and C. A. Moylan (2008). Intersection of child abuse and children’s exposure to domestic violence. _Trauma, Violence, & Abuse 9_ (2), 84–99.

- Hirsch, B. J., L. V. Hedges, J. Stawicki, and M. A. Mekinda (2011). After-School Programs for high school students: An evaluation of After School Matters. _Evanston, IL: Northwestern University_ .

- Hughes, D., M. Yates, E. Morton, and L. Smillie (2014, December). Asymmetric frontal cortical activity predicts effort expenditure for reward. _Soc Cogn Affect Neurosci 10_ (7).

37
- Deaton, A., Zaidi, S. (2002) Guidelines for Constructing Consumption Aggregates for Welfare Analysis. World Bank, Washington, USA.

- Duflo, E. (2003) Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa. _The World Bank Economic Review_ 17(1), 1–25.

- Evans, D., Popova, A. (2014) Cash Transfers and Temptation Goods: a Review of Global Evidence. World Bank Policy Research Working Paper, no. 6886.

- Fafchamps, M., McKenzie, D., Quinn, S., Woodruff, C. (2011) When is Capital Enough to Get Female Microenterprises Growing? Evidence from a Randomized Experiment in Ghana. National Bureau of Economic Research, no. 17207.

- Filipski, M., Taylor, J., Thome, K., Davis, B. (2015) Effects of treatment beyond the treated: a general equilibrium impact evaluation of lesotho’s cash grants program. _Agricultural Economics_ 46(2), 227–243.

- Fitzgerald, J., Gottschalk, P., Moffitt, R., Gottschalk, P., Moffitt, R. (1998) An Analysis of Sample Attrition in Panel Data The Michigan Panel Study of Income Dynamics. _The Journal of Human Resources_ 33(2), 251–299.

- Gertler, P., Martinez, S., Rubio-Codina, M. (2012) Investing Cash Transfers to Taise Long-Term Living Standards. _American Economic Journal: Applied Economics_ 4(1), 164–192.

- Gilligan, M., Mvukiyehe, E., Samii, C. (2013) Reintegrating Rebels into Civilian Life: QuasiExperimental Evidence from Burundi. _Journal of Conflict Resolution_ 57(4), 598–626.

- Glennerster, R., Takavarasha, K. (2013) Running Randomized Evaluations: A Practical Guide. Princeton University Press, Princeton, USA.

- Haggblade, S., Hammer, J., Hazell, P. (1991) Modeling agricultural growth multipliers. _American Journal of Agricultural Economics_ 73(2), 361–374.

- Harvest Choice (2011) Total livestock population (TLU) (2005). HarvestChoice, International Food Policy Research Institute, Washington, USA.

- Haushofer, J., Shapiro, J. (2014) Household Response to Income Changes: Evidence from an Unconditional Cash Transfer Program in Kenya. Mimeo.

- Hausman, J. (2001) Mismeasured Variables in Econometric Analysis : Problems from the Right and Problems from the Left. _Journal of Economic Perspectives_ 15(4), 57–67.

30
|th<br>Use Modern<br>Use Hidden<br>Contraception<br>Contraception|(7)<br>(8)|-0.006<br>-0.011<br>(0.030)<br>(0.025)<br>[0.830]<br>[0.655]|0.162<br>0.127|6.91<br>6.91|24,898<br>24,898||s an indicator that equals 1 if true; in|domestic violence (going out without|g, instrumented with the free primary<br>e measures for MTI regions without|birth cohorts from 1970 to 1988. All|sion. Standard errors are clustered at||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|and Heal<br>ns for<br>e (of 5)|||||||, and 8 it i|easons for|of schoolin<br> denote th|women in|que regres||
|ng on Knowledge <br>Acceptable Reaso<br>Domestic Violenc|(6)|-0.254<br>(0.124)<br>[0.041]|2.681|8.42|24,052|05, 2011, and 2016).|ns. In columns 1–3, 7|0 to 5 of acceptable r|s the predicted level <br>I-T<br>and _I_MTI<br>_zy_<br>, which|All samples include|estimate is from a uni||
|s of Schooli<br>Height<br>(z-score)|(5)|0.132<br>(0.104)<br>[0.204]|-0.168|5.01|19,879|y (in years 20|e eight colum|he count from <br>|�<br> Schooling_izy_ i<br> measures _I_MT<br>_zy_|ions occurred.|for age. Each||
|ect of Year<br>BMI<br>(z-score)|(4)|0.206<br>(0.096)<br>[0.031]|-0.020|6.13|19,491|Health Surve|of each of th|umn 6 it is t<br>|d).<br><br>Years of <br>I) intensity|wo intervent|and a cubic||
|Table C.10: National Estimates of Ef<br>Read about<br>Know about<br>Literacy<br>Fam. Planning<br>Fam. Planning|(1)<br>(2)<br>(3)|0.122<br>0.053<br>0.023<br>(0.019)<br>(0.018)<br>(0.020)<br>[0.000]<br>[0.004]<br>[0.255]|0.115<br>0.046<br>0.935|7.73<br>6.91<br>6.91|24,480<br>24,885<br>24,898|based on data from the Ethiopian Demographic and|ndex. The dependent variable is described at the top|ndardized value of the described outcome; and in col|dren, arguing with husband, refusing sex, burning foo<br> measure _I_FPE<br>_zy_<br>, two mother tongue instruction (MT|spectively, and the interactions for regions in which t|ear and zone fxed efects, zone-specifc linear trends,|n parentheses; _p_-values are shown in square brackets.|
|||�<br>Years of Schoolingizy|Mean of Dependent<br>(Pre-Reform Cohorts)|First Stage F-Statistic|N|_Source_: Author’s analysis|_Note_: BMI = body mass i|columns 4 and 5 it is a sta|permission, neglecting chil<br>education (FPE) intensity|and with script change, re|regressions include birth y|the zone level and shown i|

A.26
**Table 1: Hypothetical tradeoff between Family State Benefits and a targeted cash transfer**

||_With family_|_allowance_|_If family a_<br>_is with_|_llowance_<br>_drawn_|_If family_<br>_allowance is_<br>_withdrawn and_<br>_replaced by_<br>_TOP-UP GMI_|
|---|---|---|---|---|---|
||2008|2009*|2008|2009*|2009*|
|Income|% of<br>population|% of<br>population|% of<br>population|% of<br>population|% of<br>population|
|less than 50 LVL|5.04|8.23|6.70|10.13|0|
|>=50 & <75 LVL|5.70|7.87|5.98|8.08|18.21|
|>=75 & <90 LVL|3.63|4.29|3.43|4.58|4.58|
|**Poverty Rate (<90**|**14.36**|**20.39**|**16.11**|**22.79**|**22.79**|
|**LVL)**||||||
|**Poverty GAP**|**5.86**|**8.27**|**7.236**|**10.30**|**6.49**|

Notes: A household will get 70-percap LVL/child and 50-percap LVL/adult if its per capita income is less 50 LVL *Projections based on the baseline scenario

**Table 2: Cost Projections**

||_Estimated Cost_<br>_in Million LVL_|
|---|---|
|Total amount paid as family benefits in 2008|83.40|
|Total projected amount needed for Top-UP GMI in 2008|65.69|
|Total projected amount needed for Top-UP GMI after crisis in 2009|92.46|

# _c) Increasing household incomes of unemployed people through augmented unemployment benefits or public works provision_

Unemployed people are disproportionately represented among the poor in Latvia. Improving their welfare without creating disincentives to seek regular work is crucial. In this regard, the government took two steps. First, it increased the overall duration of unemployment benefit payments to nine months; a payment of 45 LVL (one-quarter of minimum wage) is made for three months if the unemployed person receives unemployment insurance benefits for six months; or for six months if the unemployed person receives unemployment insurance benefits for three months. Second, the government implemented a public works program that paid a net wage of 100 LVL per month (the gross minimum wage is 180 LVL per month, which leads to a net minimum wage of about 130 LVL per month) to participants. Both measures increase household incomes.

In the aftermath of the crisis, if no unemployment benefits were paid, poverty would have risen dramatically to 23.1 percent. Under the 2008 unemployment insurance plan rules, newly 15
# **1.  Introduction**

Indoor air pollution from burning wood, animal dung and other biofuels is a major cause of acute respiratory infections (ARI), which constitute the most important cause of death for young children in developing countries (Murray and Lopez, 1996). Acute lower respiratory infection (ALRI), the most serious type of ARI, is often associated with pneumonia (Kirkwood et al., 1995). ALRI accounts for 20% of the estimated 12 million annual deaths of children under five, and about 10% of perinatal deaths (WHO, 2001; Bruce, 1999). Nearly all of these deaths occur in developing countries, with the heaviest losses in Asia (42% of total deaths) and Africa (28%) (Murray and Lopez, 1996). Through its effect on respiratory infections, indoor air pollution (IAP) is estimated to cause between 1.6 and 2 million deaths per year in developing countries (Smith, 2000). Most of the dead are in poor households and approximately 1 million are children. The size of IAP’s estimated impact has prompted the World Bank (2001) and other international development institutions to identify reduction of indoor air pollution as a critical objective for the coming decade.

The current scientific consensus is that most respiratory health damage comes from inhalation of respirable particles whose diameter is less than 10 microns (PM10), and recent attention has focused particularly on fine particles (PM2.5). However, the design of cost-effective IAP reduction strategies has been hindered by lack of information about actual PM concentrations in poor households. Data have been scarce because monitoring in village environments has been difficult and costly. Relative small-scale studies of indoor PM10 exposure from woodfuel combustion have been conducted in Kenya (Boleij, et al (1989, 36 households), Guatemala (Smith, et al., 1993, 60 households), 3
financing obstacles. Loans per capita enters negatively and significantly at the 10% level in regression (6), but loses significance once we control for financial development. The loanincome ratio enters positively and significantly when we control for Private Credit/GDP. Deposits per capita does not enter significantly in either regression, while the deposit-income ratio only enters significantly when we control for financial development.

The economic effect of outreach on firms’ financing obstacles varies across the different indicators. A one standard deviation change in outreach indicators is associated with 0.07, 0.11, 0.05 and 0.16 lower financing obstacles in the case of geographic branch penetration, demographic branch penetration, geographic ATM penetration, and demographic ATM penetration, respectively.<sup>23</sup> This compares to a standard deviation of 0.44 in general financing obstacles across countries. Thus, cross-country results suggest that demographic penetration of bank outlets is somewhat more important than geographic penetration.

The results in Table X also suggest that financial intermediary development is not robustly associated with firms’ financing obstacles, once we control for our outreach indicators. While Private Credit/GDP enters significantly and negatively by itself (column 1) and when controlling for indicators of deposit and loan use, it loses significance once we control for branch and ATM penetration indicators. The R<sup>2</sup> statistics suggest that while financial intermediary development and controls for firm characteristics explain 40% of cross-country variation in firms’ financing obstacles, and banking system outreach alone explains 28-50% of variation, together the independent variables explain 49-79% of cross-country variation.<sup>24</sup>

> 23 We multiply one standard deviation of the respective outreach indicator (Table III) by the Table X coefficient in the regression including Private Credit/GDP.  The effect size is larger if we instead use the coefficients from the regressions excluding Private Credit/GDP.

> 24 We also experimented with regressions where we include GDP per capita instead of Private Credit/GDP.  While it does not enter significantly by itself, the demographic penetration ratios also turn insignificant, as does loans per capita.  This result can be explained by the high correlation between GDP per capita and demographic branch and

23
_Annex 1 Table 3: Varying trimming threshold on anthropometry data for 6-36-month-olds with fourth_ _<u>quartile of saturation interaction</u>_

||Height-for-<br>Age Z score|Stunting|Severe<br>stunting|Weight-for-<br>Age Z score|Underweight|
|---|---|---|---|---|---|
|_Trimming>-3 and<3 SD_||||||
|Program village|-0.118|0.025|-0.000|-0.012|0.048|
||(0.150)|(0.059)|(0.002)|(0.160)|(0.044)|
||0.181|0.020|-0.002|0.348|-0.097|
|Fourth quartile of saturation|(0.346)|(0.089)|(0.005)|(0.259)|(0.075)|
|Program village*Fourth quartile|-0.774**|0.302**|0.043|-0.939***|0.136|
|of saturation|(0.385)|(0.127)|(0.042)|(0.308)|(0.090)|
|Control Observations|131|131|131|145|145|
|Treated Observations|142|142|142|151|151|
|Control Mean|-1.271|0.341|0.001|-0.821|0.160|
|_Trimming >-4and <4SD _||||||
|Program village|-0.153|0.035|0.011|0.057|0.029|
||(0.174)|(0.062)|(0.028)|(0.174)|(0.047)|
||0.045|0.045|0.026|0.357|-0.098|
|Fourth quartile of saturation|(0.364)|(0.094)|(0.062)|(0.261)|(0.085)|
|Program village*Fourth quartile|-0.836**|0.324**|0.139|-1.157***|0.218**|
|of saturation|(0.410)|(0.129)|(0.096)|(0.353)|(0.109)|
|Control Observations|140|140|140|153|153|
|Treated Observations|156|156|156|172|172|
|Control Mean|-1.371|0.382|0.166|-0.947|0.206|
|_Trimming>-6 and<6 SD_||||||
|Program village|-0.349*|0.058|0.036|0.127|0.018|
||(0.208)|(0.062)|(0.040)|(0.180)|(0.047)|
||0.488|-0.005|-0.033|0.498*|-0.125|
|Fourth quartile of saturation|(0.552)|(0.091)|(0.068)|(0.273)|(0.087)|
|Program village*Fourth quartile|-1.079*|0.329**|0.159|-1.320***|0.245**|
|<br>of saturation|(0.573)|(0.126)|(0.102)|(0.373)|(0.111)|
|Control Observations|147|147|147|156|156|
|Treated Observations|164|164|164|175|175|
|Control Mean|-1.452|0.402|0.106|-0.986|0.217|
|_Trimming>-7 and<7 SD_||||||
|Program village|-0.194|0.058|0.036|0.071|0.018|
||(0.227)|(0.062)|(0.040)|(0.199)|(0.047)|
||<br>0583|<br>-0005|<br>-0033|<br>0543*|<br>-0125|
|Fourth quartile of saturation|.<br>(0.545)|.<br>(0.091)|.<br>(0.068)|.<br>(0.294)|.<br>(0.087)|
|Program village*Fourth quartile|<br>-1.214**|<br>0.329**|<br>0.159|<br>-1.237***|<br>0.245**|
|<br>of saturation|(0.564)|(0.126)|(0.102)|(0.397)|(0.111)|
|Control Observations|149|149|149|156|156|
|Treated Observations|164|164|164|176|176|
|Control Mean|-1.457|0.402|0.106|-1.003|0.216|

*** p<0.01, ** p<0.05, * p<0.1 All specifications include linear controls for child age (in months). Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey

67
however not been successful. Other methods such as stabilization funds, contingent financing and export diversification have also not been successful in stabilizing export revenues.

An alternative approach to stabilizing export revenues is to use market-based risk management tools such as futures hedging. Though futures hedging cannot insulate exporters from a long term secular decline in commodity prices, they are effective in managing short term price risk. Using futures markets for hedging is a notion that is just now beginning to gain acceptability among developing countries. The New York Mercantile Exchange (NYMEX) estimates that developing countries are increasingly holding a higher percentage of the total open interest in crude oil futures. Since the Gulf war countries like Mexico, Brazil and Chile are regular users of the oil derivatives markets (see Claessens and Varangis (1995)).

The objective of this paper is to assess the risk management prospects for hedging Ecuadorian oil. We develop a portfolio model of hedging and use it to evaluate the costs and beneiits of different hedging strategies. Our paper shows that there are effective risk reducing strategies available to Ecuadorian policy makers that would have reduced the variance of Ecuadorian oil revenues over time. While these strategies may necessitate foregoing unexpected gains, they would have prevented unanticipated short term losses. We provide estimates of the costs and benefits of different hedging strategies that may aid in policy formulation.

2
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# E. Achieving Greater Vertical Eauity in Taxation

In light of the limited revenue capacity of income taxes, most developing countries have been forced to rely upon a variety of indirect taxes as their revenue workhorses. Typically, sales and excise taxes, along with taxes on international trade, comprise at least two thirds of the total amount of revenue collected. Only in Zimbabwe and Malawi have income taxes approached anywhere close to one half of total revenue. Many countries have, therefore, been concerned to design their structure of indirect taxes to either reduce or remove the burden of these taxes on low income groups. Exemptions of basic necessities from the value added or other form of sales tax, plus the imposition of differentially higher tax rates on items of luxury consumption, have been the instruments most frequently used to achieve this goal. The effects of these kinds of policies on horizontal equity have ordinarily been neglected.

Some countries, such as Zimbabwe, have discovered that certain kinds of indirect tax ay be highly progressive in their impact. For example, raising import duties in the presence of quantitative restrictions may be an effective way of raising taxes from wealthy holders of import licenses. Nevertheless, almost all of the incidence studies that are cited by the studies in the tax reform project indicate that the bottom 20 percent of taxpayers in these countries typically spend 10 percent or more of their income in satisfying various tax obligations. None of the reforms have achieved notable success in significantly reducing tax burdens on the poor.
investors absorbed 18 percent (Table 3.2). New advances to the government by the Bank of Greece were banned in 1993 when existing balances were converted into a long-term loan.

**Table 3.2: Sources of Financing the Public Sector Borrowing Requirement, 1991-95**

||1991|1992|1993|1994|1995|1991-95|
|---|---|---|---|---|---|---|
|Bank of Greece|5.0|15.6|-0.8|0.6|-18.1|-1.2|
|Banks: Treasury Bills|1.7|-17.8|16.4|18.4|13.5|9.1|
|Banks: Other Loans|12.8|19.9|9.9|3.0|-0.9|7.7|
|Non-banks|68.0|71.2|42.8|66.2|86.4|66.5|
|Foreign Investors|12.5|11.2|31.8|11.8|19.2|18.0|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Total in billion GRD|2015.9|1363.0|2408.1|2450.6|2417.7|10655.3|
|Total % of GDP|12.4|7.3|11.4|10.3|9.1||

Source: Bank of Greece, Annual Report for 1996:144 Several factors may account for the success of government policy. Of major importance was the effort to reduce the public sector borrowing requirement through a multitude of fiscal initiatives. This was effectively halved between 1989 and 1995 from 18 to 9 percent of GDP. Various other measures were taken that related to the yield offered on government securities and their competitiveness vis-à-vis bank deposits or to the easiness with which such securities could be bought and sold.

- Interest rates on government securities became highly positive in real terms. After a long period during which interest rates on treasury bills (and bank deposits) were highly negative (Table 3.3), they were raised to well above the rate of inflation. Interest rates remained highly positive for most of the period between 1985 and 2000. They experienced a significant decline only over the past few years as they converged to euro zone levels.

- The offered instruments addressed the preferences of retail investors for short terms and variable rates. The Greek saving public exhibited a reluctance to invest in fixed-rate medium or long-term bonds. This probably reflected their unhappy experience with government bonds in earlier periods when default and/or hyperinflation wiped out the value of their holdings. Among variable rate instruments, longer maturities earned large spreads over shorter ones to encourage investors to hold them. The spread between 12month and 3-month Treasury bills exceeded 4 percentage points in the early 1990s, while 3- and 5-year floating rate bonds earned an additional 1 to 2 percentage points. In 1994, the real rate of return on medium-term floating rate bonds was close to 10 percent.

- Foreign-currency-indexed (FCI) instruments were offered at attractive fixed coupons but at a substantially lower rate of interest than the nominal rates on domestic currency instruments. The Greek drachma depreciated between 1986 and 1995 at an average annual rate of 9 percent. Thus, the cost of FCI bonds turned out to be close to that of domestic instruments. (The use of FCI bonds was terminated in 1994 when domestic rates were substantially lower.)

28