rafmacalaba/datause-encoder-data huggingface dataset:
correctly—for the multiple choice question modules in the core subjects.<sup>6</sup> _GSE_ is defined as the year-over-year gain in the school’s enrollment in first through fifth grade captured in the ASC survey. The indicator is expressed in percent terms. _TPR_ is defined as the number of students who took the fifth-grade PEC exam from a school (recorded by PEC) divided by the number of enrolled students in fifth grade in the school (captured in the ASC survey). The indicator is expressed in percent terms. The values for each of the three indicators are bounded by zero percent and 100 percent.

A composite school performance score ( _CS_ ) was formulated as follows: _CS_  0.65 _GTS_  0.25 _GSE_  0.15 _TPR_ .(1) The assigned weights reflect the provincial government’s relative valuation of the three indicators in assessing school performance.

# _Targeting_

The program is targeted at public primary schools with among the lowest mean student exam scores in the province following a three-step process. First, the three districts with the lowest district-level mean scores in the 2010 fifth-grade PEC exam in Punjab’s 36 districts were selected. These districts were Attock, Mandi Bahuaddin, and Rahimyar Khan. Second, within these districts, schools at the primary level that were functional according to the October 2009 ASC survey and had at least ten students who took the 2010 fifth-grade PEC exam were identified. Third, 1,962 such schools were ranked from highest to lowest by their 2010 schoollevel mean fifth-grade PEC exam scores; the lowest ranked 600 schools were selected for the evaluation sample. The range of mean scores for the sample corresponds to that of 12 percent of all public primary schools in the province.

# _Treatment variants_

In principle, the teacher assigned to be (acting) head teacher is authorized by the district education department to manage the school and its teachers. Head teachers can manage teachers by supervising, rewarding, sanctioning, and supporting teachers. If head teachers are a

> 6 Raw scores are used instead of the scores scaled by PEC because of concerns related to the conversion formula that is applied. The scoring of answers to multiple-choice questions is considered more reliable than the scoring of answers to open-ended questions.

9
by asking how laws facilitate transactions. They examine the significance of legal institutions in the transacting process. Their analysis suggests that firms that invest in constructing contracts and have better knowledge of the law have better chances to successfully complete a transaction. To illustrate the potential advances in research on transaction costs, consider the example of credit access. The World Bank's efforts to collect data on this particular type of costs has already allowed researchers to explore more closely the theorized link between credit access and firm performance. Fan, Lee and Schaffer (1996), for example, analyze the impact of the financial sector reform in Russia on firms in the early years of transition. Their data, collected at the end of 1994, shows that a partial reformn of the banking system, with many banks still acting "soft" in their dealings with firms, slowed the enterprise restructuring process amd undermined financial sector reform. Bank loans are not the firm's only source of financing, especially when credit is rationed or the credit market is underdeveloped. A recent study on Zimbabwean manufacturing firms using the RPED data (Fafchamps, 1996) suggests that trade credit is an important, alternative source of short-term liquidity for many firms.

# F. **The Role of the State**

Even in industrialized countries, the state is generally the most importaint single actor in the economy. The policies the government implements can both harm and benefit economic growth. As Stiglitz (1998) notes, "there is a growing consensus that governments can play a vital role in successful development efforts, but we also recognize that the wrong kind of government intervention can be highly detrimental." Surveys of firms can therefore be an important tool in determining which aspects of government involvement are beneficial and which are harmful.

# **Regulation**

Despite its potential importance in explaining different growth rates across nations, little is known empirically about regulation in developing countries. For instance, 'what are the effects of regulation on firm performance, employment, and pricing? What determines the extent and the types of regulation that the government of a developing country adopts? Do regulations change over time? How do regulations affect firms' perception on the likelihood of 16
Figures 2b and 2c further explore the relationship between schooling outcomes and exposure to the genocide by displaying separately, for each grade 1 to 6 in primary school, the proportion of individuals, for each age that completed that grade. For each of the six grades, the proportion completing that grade is higher in 1992 for the young ages, but at older ages, the completion rate is higher in 2000. This is consistent with the overall picture presented in Figure 2a. Note that, as one progresses through the graphs, the difference between the 1992 and 2000 measure at early ages widens and that the crossing point between the 1992 and the 2000 curves moves to the right toward an older age. For first grade, the difference between the two curves is small and the crossing point is at age 12. Being a young child at the time of the genocide had a small, but negligible impact on these children’s likelihood of completing first grade. However, by fourth, fifth and sixth grades, the difference between the two curves for the younger children is more substantial and the crossing point moves towards an older age. This widening gap between the two curves for younger children suggests that the schooling deficit of the cohorts who were of schooling age after 1994 is not so much due to a lack of access to education, since the differences for the first grade are minimal, but rather to difficulties to progress through the grades or continue attending school.

# **3. Empirical Identification Strategy**

The identification strategy can be further illustrated by using a two-by-two difference-indifferences table, which follows a similar approach to the previously discussed Figures 2a, 2b, and 2c. Panel A of Table 2 shows the average years of schooling for younger (age 6 to 15) and older (age 16 to 35) cohorts of children measured in the 2000 and 1992 datasets.<sup>5</sup> If we compare

> 5 Results in this table and the subsequent regressions are consistent if we use alternative cutoff ages for the young and old cohorts, defining the young cohort as aged 6 to 12, 6 to 15, 6 to 18, 6 to 21, and 6 to 24. We also estimate

7
6 Research indicates that young women in partnerships have generally less autonomy and are at greater risk for both physical and sexual violence than older women. In Nicaragua, for example, not only did women between 15-19 report more physical and sexual violence within the last 12 months than older women, but they also experienced more severe acts of violence (Rosales, et al., 1999).

International research within the last decade has revealed that sexual abuse of women and girls by non-partners is also much more common than previously thought. Between 8 and 26% of women and girls reported having been sexually abused, either as children or adults (World Health Organization 2002). These figures are likely to be under-estimates of the true prevalence of abuse, as many women are reluctant to disclose violence due to shame and fear of reprisals (Koss 1993).

One of the main challenges for addressing GBV stems from the lack of evidence on the magnitude and characteristics of violence in different settings. There are many inconsistencies in the methods used by different researchers that make comparisons difficult across countries or even among studies within a given country. These include the reference period used (lifetime prevalence, 12 month prevalence or ongoing violence), the number and wording of questions asked, the age cut-off for abuse (before and after 12 years, before and after 15).

During the last decade, however, there have been several efforts to improve the measurement of GBV. The World Health Organization has conducted a multi-country study on domestic violence and women’s health in 15 sites and 10 countries. This study has produced comprehensive set of research tools, including instruments, field manuals and data entry programs (Garcia Moreno, Watts et al. 2003). The United Nations Interregional Crime and Justice Research Institute (UNICRI) has also initiated surveys in several countries on violence against women. Both the Demographic and Health Surveys conducted by Macro International and the Centers for Disease Control’s Reproductive Health Surveys have begun to incorporate questions on violence in their surveys (Kishor and Johnson 2004).

There is some evidence that large-scale surveys designed primarily for other purposes such as the Demographic and Health Surveys are more likely to under-estimate the prevalence of violence than studies that focus specifically on violence. Characteristics of violence-specific surveys such as specialized training of interviewers, greater emphasis on privacy and safety of respondents, and multiple opportunities to disclose violence have been found to have a positive effect on women’s reporting of violence (Ellsberg, Heise et al. 2001; Jansen, Watts et al. 2004).

# **1.3 A Causal Framework for Understanding Gender-Based Violence**

GBV is a complex phenomenon, shaped by forces that operate at different levels. An ecological framework that combines factors which operate at the individual, relationship, community and societal levels thus provides an appropriate lens through which to examine
# **1. Introduction**

The issue of the increase in food prices has received renewed attention in recent months as the increase in prices worldwide has had large negative impacts on households (e.g., Ivanic and Martin, 2007; World Bank, 2008a and 2008b; IMF, 2008; Wodon and Zaman, 2008). In West and Central Africa, prices for rice, maize and other cereals have increased substantially since the end of 2007. This has led the authorities as well as development partners in many countries to consider a range of compensatory measures that could help offset part of the negative impact on the poor of this increase in prices. However, at least from a conceptual point of view, the net impact of an increase in food prices on the poor is not obvious. Indeed, when discussing the link between rice and other cereal prices and poverty, a key issue is to assess the double and opposite impact that a change in prices can have for producers who are poor or near the poverty line (who benefit from an increase in prices) and consumers who are poor or near the poverty line (who lose out when the price increases).

The techniques for the analysis of the short term producer and consumer impacts of food commodity price changes are well developed in the literature. Early work in this area was conducted by Deaton (1989) using data from Thailand (see also Singh et al., 1986). Similar methods have been used in sub-Saharan Africa among others by Barrett and Dorosh (1996) for Madagascar, Budd (1993) for Cote d’Ivoire, and Loening and Oseni (2007) for Ethiopia. These are also the methods that we use in this paper, which summarizes the evidence on the impact of higher food prices on poverty obtained from a dozen country case studies for West and Central Africa. In all of these country case studies, we find that food price increases tend to lead to an increase in poverty because the consumption effects dominate the production effects as many countries are net importers of food.

There has also been a literature on assessing whether in the medium to long term, the increase in prices is compensated by an increase in wages, among others for those workers who contribute to the production of food crops (see for example Ravallion, 1990; Boyce and Ravallion, 1991, Rashid, 2002; Christaensen and Demery 2007; and Ivanic and Martin, 2007). The findings from these studies suggest that wage offset compensate only in a limited way for the initial increase in food prices. Finally, there has also been a substantial amount of work looking at the impact of various policies to deal with food production and prices. This can be illustrated with the case of rice. Indonesia is a country that used to import substantial amounts of 2
business owners with less than $200 in capital, with a follow-up experiment finding returns of 20 percent per month or more. Finally, in Ghana, Marcel Fafchamps et al. (2013) also find returns averaging 20 percent per month, but also evidence of no returns for women running very small businesses, and evidence that the impact is higher when capital is given in-kind than as an unrestricted cash grant.

Finally, if all that was holding individuals back from moving from a low-level steady-state like A in Figure 4 to operating at a much higher level out of poverty like B was access to finance, then one would expect microfinance loans to have large impacts on poverty if individuals could borrow enough to finance the lumpy production technology needed to move them out of a trap. However, there are now a number of randomized experiments that randomly offer microfinance to individuals or communities, and none of them have found strong evidence of impacts on business growth or poverty (see Banerjee et al, 2013 for an example, and their last section for an overview of several recent studies).

As a result, in most of the world it seems unlikely that the combination of a limited number of lumpy productive investments, coupled with constraints on borrowing, are causing poverty traps. However, individuals may remain persistently in poverty because their underlying characteristics may be such that their single steady-state level of production is just extremely low (Barrett and Carter, 2013). Regardless of the amount of financing available, individuals with poor skills and facing other constraints may be unable to produce at more than a subsistence level. This has led to a number of policy interventions which provide assets coupled with intensive skills training, and savings promotion. Examples include the BRAC ultra-poor program evaluated by Oriana Bandiera et al. (2013) among rural women in Bangladesh; the Bardhan hard-core program evaluated by Banerjee et al. (2011) in rural West Bengal, India; and the SKS ultra-poor program evaluated by Jonathan Morduch et al. (2012) in rural Andhra Pradesh, India.

These programs were aimed at extremely poor households, with eligibility requiring households to have no male workers, not own productive assets, have limited land, and not being microfinance clients. The Bandiera et al. (2013) study finds this combination intervention has lasting impacts over a four year horizon, with an increase in earnings that is large in relative terms, but small in absolute value (approximately US$0.07 per day higher). Banerjee et al. (2011) find similar results over a 18 month horizon, with program households receiving an approximate US$0.06 per day increase in per capita earnings. In contrast Morduch et al. (2012) find no overall impact on income growth, which they attribute to the presence of opportunities for wage labor that opened up at the same time as the ultrapoor program and enabled the control group to grow at the same rate.

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# **Table 5. R-Squared Shapley-Owen Decomposition for Ecuador** **_Estimations Using Individual-Level Data_**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|_Outcomes:_|**Ever**<br>**employed**<br>**after**<br>**graduation **|**Percent**<br>**of time**<br>**employed**|**Wages**|
|_Percent of explained variation attributable to:_||||
|All quality determinants|35.00|37.64|32.06|
|Infrastructure|-|1.98|-|
|Costs|-|0.84|-|
|Training and curriculum|15.53|12.39|12.45|
|Faculty|9.46|15.81|10.08|
|Link with productive sector|8.78|5.83|8.09|
|Other Practices|1.23|0.80|1.44|
|Student characteristics (PCA Score)|1.71|1.42|1.52|
|Program characteristics (PCA Score)|2.27|3.65|2.49|
|HEI characteristics (PCA Score)|6.52|2.75|6.32|
|Student administrative variables (PCA Score)|19.90|27.60|22.99|
|Peer administrative variables (PCA Score)|20.01|17.48|21.04|
|Field of study fixed effects|14.59|9.47|13.59|
|R-squared|0.265|0.354|0.282|
|Obs.|1,214|1,201|1,206|

_Source_ : Own estimations using data from the WBSCPS and Ecuador individual-level data. For variable definitions, see Section 3 and Appendix 1.

_Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Table A14, estimated with individual- and program-level data for Ecuador. For each regression, the table shows R- squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of students (equal to the number of observations in the underlying regression).

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<!-- Start of picture text --> Total reduction<br>Market Income Net Market Income Disposable Income Consumable Income Final Income _ in poverty rate<br>By household type<br>couple without children -19.5%<br>couple with children -5.5%<br>couple w/children and elderly -39.8%<br>elderly -78.2%<br>By type of head:<br>out of labor force -37.3%<br>unemployed -12.3%<br>agriculture 7.8%<br>manufacturing 5.2%<br>services 6.9%<br>By location of household:<br>Ulaanbaatar -16.8%<br>Aimag cent “16.7%<br>Soum center “14.4%<br><!-- End of picture text -->

||MarketIncomeNetMarketIncomeDispo|ableIncomeConsumableIncome<br>FinalIncome_|Total reduction<br>inpovertyrate|
|---|---|---|---|
|Byhousehold type||<br>||
|couple without children|||-19.5%|
|couplewithchildren|||-5.5%|
|<br>couple w/children and elderly|||-39.8%|
|elderly|||-78.2%|
|By type ofhead:||||
|out oflabor force<br>|||-37.3%<br>|
|unemployed<br>|||-12.3%<br>|
|agriculture<br>|||7.8%<br>|
|manufacturing<br>|||5.2%<br>|
|services|||6.9%|
|By locationofhousehold:<br>||||
|Ulaanbaatar|||-16.8%|
|Aimagcent|||“16.7%|
|Soum center<br>|||“14.4%<br>|
|~~Countryside~~|||~~-10.1%~~|

||Mongolia|Armenia|Georgia|Ghana|Indonesia|Jordan|Russian<br>Federation|SouthAfrica|Sri Lanka|
|---|---|---|---|---|---|---|---|---|---|
||2016|2011|2013|2013|2012|2010|2010|2010|2010|
|Directtaxes|0.011|0.021|0.022|-0.006|0.000|0.007|0.014|0.043|0.003|
|Directtransfers|0.068|0.092|0.100|0.001|0.004|0.005|0.020|0.052|0.004|
|Indirecttaxes|-0.008|0.001|-0.014|0.002|-0.002|-0.001|-0.001|0.013|0.006|
|Indirect subsidies|-0.050|0.000|0.000|-0.001|0.001|0.004|0.032||0.005|
|In-kind transfers||||||||||
|Pre-school|0.005|0.001|0.002|0.001||0.000||0.000|0.011|
|Primary|0.012|0.005|0.016|0.013|0.009|0.013||0.030||
|Secondary||||0.002||0.002||0.017||
|Tertiary|0.002|0.000|0.001|-0.008|-0.001|0.000||-0.001|0.000|
|Health|0.005|0.004|0.008|0.007|0.003|-0.009|0.013|0.043|0.006|
# **TABLE 4. PUBLIC EXPENDITURE AND FINANCIAL ACCOUNTABILITY (PEFA)** **<u>INDICATORS RELEVANT TO HEALTH</u>**

|**Predictability and control in**<br>**budget execution**|**Budget credibility**|**Budget comprehensiveness**<br>**and transparency**|
|---|---|---|
|° Predictability in the availability of<br>funds for commitment of<br>expenditures|<br>° Aggregate expenditure outturn<br>compared to original approved<br>budget|° Transparency of inter-<br>governmental fiscal relations|
|° Recording and management of<br>cash balances, debt and<br>guarantees<br>° Effectiveness of payroll controls|° Composition of expenditure out-<br>turn compared to original<br>approved budget|° Public access to key fiscal<br>information|
|° Competition, value for money<br>and controls in procurement<br>° Effectiveness of internal<br>controls for non-salary<br>expenditure<br>° Effectiveness of internal audit|||
|**Policy-based budgeting**|**Accounting, recording and**<br>**reporting**|**External scrutiny and audit**|
|° Orderliness and participation in<br>the annual budget process|° Availability of information on<br>resources received by service<br>deliveryunits|° Scope, nature and follow-up of<br>external audit|

Source: PEFA Secretariat (2005).

The PEFA indicators are useful for identifying where in the budget process governance problems exist. For example, a poor score on the aggregate expenditures compared to original approved budget may be a sign of poor management, inadequate monitoring of processes, poor recordkeeping, and/or weak disbursement systems. In any event, there is a clear absence of accountability in financial management. If a country scores low on the effectiveness of payroll controls indicator, the problem of payroll leakages, or payment to the wrong individual, may be serious. A low score on availability of information on resources received by service delivery units suggests some combination of inadequate transparency, poor recordkeeping, low budget management capacity, and insufficient accountability.

Data for five countries are shown in Figure 2 to illustrate relative performance across these three areas. Bangladesh scores relatively poorly on all three indicators; the Dominican Republic scores the worst on aggregate expenditure compared to original approved budget; Macedonia scores relatively poorly on information on resources received by service delivery units; and Mozambique and Ukraine perform poorly with respect to effectiveness of payroll controls. PEFA indicators can thus be helpful in pinpointing and prioritizing areas where action is needed to strengthen budget processes and help bolster good governance in PFM.

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