rafmacalaba/data-use-mentions-extended huggingface dataset:
grown in several countries—e.g., Iraq, Afghanistan, Nigeria, Pakistan, and the Syrian Arab Republic. These groups have been characterized by their oppression of women and minorities. Previous evidence on violent conflict has focused on analyzing the impact on the targeted areas. A handful of cross-country panel studies have analyzed the economic impact of conflict on areas located far from conflict incidents. Yet, the spatial effects of Islamic extremist groups have not been analyzed before. Given the misogynistic nature of their operations and the strong displacement of the population, a better understanding of the spatial dispersion of their effects on women’s empowerment is needed. This paper has aimed at filling this gap.

We estimated the spatial spillover effects of conflict in Nigeria. In the past decade, the country experienced an increase in violent attacks due to religious extremism and territorial disputes. We estimated spatial spillover impacts of violent events conducted by the Islamist extremist group Boko Haram and, for comparison, militant Fulani herders on and outcomes for women and men.

We found that distant Boko Haram attacks exert positive spillover effects on economic well-being at the household level. This result is driven by an increase in employment prospects among women and men. The effects on the former are concentrated among Christian women and do not generalize well to women of traditionalist and Islamic orientation. Importantly, the improved economic well-being of the household translates into the economic empowerment of women. Specifically, we found a strong increase in their likelihood of owning assets and using modern contraceptive methods. However, even though distant attacks of the extremist group also reduce the propensity of women to regard domestic violence perpetrated by their partner as acceptable, we find a strong surge in the likelihood that a woman reports being pushed, slapped, or punched by her husband. A possible explanation is that for many Nigerian women and men the use violence to sanction women for neglecting children and burning food is justified. When women expand time spent on economic activities it may become increasingly challenging for them to cater to the daily
-0.195|
||[0.550]*|[0.690]***|[0.503]|[0.683]|[0.856]**|[0.522]|
|Log(debt)|-0.0285|-0.0364|-0.0246|-0.0185|-0.00321|-0.0454|
||[0.0208]|[0.0378]|[0.0242]|[0.0193]|[0.0217]|[0.0236]*|
|Female x Log(debt)|-0.0859|-0.169|-0.0188|-0.0867|-0.175|0.0191|
||[0.0458]*|[0.0584]***|[0.0433]|[0.0563]|[0.0674]**|[0.0431]|
|Obs|3,222|1,334|1,888|3,222|1,334|1,888|
|R2|0.264|0.339|0.27|0.238|0.307|0.249|

Sources: World Bank Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports, whether it has financial statements certified by external auditors, the percentage of the firm that is privately owned, and industry, country, and year fixed effects. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

# **3.3 Robustness Checks**

Our estimations illustrate
volatility. As such, governments will be larger in terms of consumption in countries experiencing larger amounts of external risk and that controlling for risk, openness will not exert an independent effect on government consumption.

# **4.1. Empirical results**

We now turn to our large cross-country data set to explore more systematically the factors accounting for government size over the period 1964-1993. Our approach is to relate government size measured as government consumption relative to GDP and expressed in 1985 international prices to a set of determining variables. The data are pooled with ten year averages with coverage of 131 countries. We report sequentially results from the OLS and IV estimations. Later we turn to 3SLS estimation.

In the light of the discussion above, we include on the right hand side; a per capita income variable; a relative price term, given as the ratio of the government consumption price deflator to the private consumption deflator; several demographic variables -- the urbanisation rate and the age dependency ratio; an openness variable defined as total trade normalised by GDP; a terms of trade variable to capture the price component of external risk and measured, as we shall see, in a number of ways; a variable summarising the years since a country has been independent -- to test for any nation-building effects associated with independence; a federal dummy to control for the federal form of government; and a measure of political freedom. The latter is the political freedom component of the Gastil index and is used to test for the positive impact of democracy on government size which presumably works through the median voter model. Finally, decade and regional 18
show that girls significantly outperform boys on reading tests—by 0.162 SD in kindergarten—and that this female advantage persists into the fifth grade (Cornwell et al. 2013).

Recent evidence from developing countries also shows gender gaps in educational achievement. Dickerson et al. (2015) find that on average, boys have a 0.1 SD advantage over girls in mathematics in fifth grade in 19 African counties but the magnitude of the gap varies widely from a non-significant 0.06 SD in Mauritius to as much as a 0.34 SD in Tanzania. Similarly, boys in Chile score 0.08 SD higher in mathematics than girls in fourth grade, and this gap increases to 0.2 SD by grade eight (Bhardwaj et al. 2015). In Indonesia, the gender gap in maths scores in primary school is in the opposite direction, with girls scoring 0.08 SD higher than boys at age 11 (Suryadarama 2015). Moreover, analysis using international assessment data suggests significant gender gaps in secondary school among low- and middle-income countries, with boys scoring 0.25 SD higher than girls in mathematics at age 15 (Bhardwaj 2015). A question that emerges from the existing literature from developing counties is whether gender gaps in cognitive skills emerge as early as those observed in high income countries (i.e., the first year of schooling).<sup>6</sup> Researchers have also paid increasing attention to gender gaps in non-cognitive skills given the growing evidence suggesting that non-cognitive skills observed in early childhood affect academic performance and labor market outcomes in later years (Cunha and Heckman 2008, Cunha et al. 2010). In the United States, girls consistently score higher than boys from kindergarten to grade 5 on a teacher-reported measure of children’s socio-emotional development (Cornwell et al. 2013, DiPrete and Jennings 2012). Similarly, results from Australia and Canada show that in kindergarten, girls outperform boys on the Early Development Instrument (EDI)—
started (2013), the difference in student performance in test scores between _Ceará_ and adjacent states is 7 _._ 6 points in Portuguese and 9 _._ 2 points in Mathematics comparing 2013 to 2011.

We complement the empirical analysis by merging additional data-sets. In terms of including school _−_ level controls, a useful source of information is the _Censo Escolar_ , which provides comprehensive information on school infrastructure and unique ids that allow us to merge it with the SAEB data. For municipal-level controls, we include data on GDP _per capita_ and population size 13
>15</sup> Since two satellites are recording in most years, 30 satellite-years worth of data are available for the 17-year period 1992–2008. Each 30-arcsecond pixel in each satellite-year contains a digital number (DN), an integer between 0 and 63, inclusive, that represents an average of lights in all nights after sunlight, moonlight, aurorae, forest fires, and clouds have been removed algorithmically, leaving mostly human settlements.<sup>16</sup> Figure 2 shows the lights data for one satellite-year for Tanzania. No lights

> 13Five other countries in Sub-Saharan Africa fit this criterion but are not included in analysis. Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia are excluded because they lack (at least) roads data. Using the city definitions below, The Gambia has only one city, and therefore it provides no information in the presence of country*year fixed effects.

> 14Some administrative data on economic indicators such as employment are collected for subnational regions in some countries, including Tanzania, but assessing their comparability is a challenge, and they are typically only available for large regions and not for multiple years.

> 15These sensors are designed to collect low light imaging data for the purpose of detecting moonlit clouds, not lights from human settlements.

> 16A 30-arcsecond pixel has an area of approximately 0.86 square km at the equator,

8
profits and output values between small and large producers, suggesting that the war also impaired farmers’ ability to invest in higher value crops (von Cramon-Taubadel and Nivievskyi 2023). Removing technical, financial, or market-related constraints to diversification in locations not directly affected by conflict could thus be a high priority for support to complement or, if active fighting continues, even precede activities such as de-mining, which aim to restore the productive capacity of areas directly affected by the conflict.

We contribute to three strands of literature. First, we show that freely available medium-resolution imagery can be used to construct an indicator of conflict-related damage to agricultural fields at high levels of spatial and temporal resolution. This indicator is particularly suitable if the affected areas are large and damage is visible at least for some period of time. It builds on a body of studies that use imagery with lower resolution to assess the effects of war (Alix-Garcia et al. 2013), land use change (Meyfroidt et al. 2016), grazing intensity (Dara et al. 2020), or changes in crop cultivation <mark>(Munteanu et al. 2017), often o</mark> ver longer periods of time (Matasov et al. 2019). This indicator can also complement data from the commonly used <mark>Armed Conflict Location and Events Data (ACLED) project (</mark> Raleigh et al. 2010), which uses the number of casualties, a measure that may be less meaningful in agricultural settings, as a proxy for conflict intensity. In the Ukrainian context, our measure improves on the use of press reports or nighttime light data (Zhukov 2023), which are less relevant for agriculture, and it can be applied consistently at a larger scale without the computational and cost burden that would be associated with scaling up the use of data with higher frequency and resolution (Duncan et al. 2023). Developing an automated classifier for conflict-related damage to rural fields would be a straightforward extension over the manual classification adopted
US$300 million (with a World Bank guarantee) was oversubscribed by 70 percent. The average interest rate on KPLC’s total borrowings, which is a blend of commercial and concession debt, is around 6 percent for the period under analysis.

**Table 6. Summary of financial indicators against global comparators**

||Kenya|Global<br>comparator*|
|---|---|---|
|Net profit (loss) margin|7%|–12%|
|EBITDA margin|11%|6%|
|Current ratio|0.98|0.79|
|Debt service coverage ratio|0.85|–4.56|
|External Financing Index Ratio<sup>14</sup>|0.02|6.92|
|Investment as a percent of revenues|45%|18%|
|Average borrowing rate|6%|7%|
|Government transfers as a share of utility revenue|3%|6%|
|Net capital cost recovery ratio<sup>15</sup>|44%|16%|

Note: Sample average from the Rethinking Power Sector Reform Project data observatory and includes data from Tanzania, Senegal, Peru, Colombia, Pakistan, the Philippines, Vietnam, Kenya, Uganda, Tajikistan, the Indian states of Rajasthan, Odisha, and Andhra Pradesh, and Egypt

# _3.3.2 Institutions_

The structural reforms initiated in the 1990s left Kenya’s power sector partially vertically unbundled, with private sector participation in generation and distribution (table 7). The government has made commitments to further unbundle transmission and distribution, but this seems unlikely in the short-term.

**Table 7. Comparing the extent of utility restructuring in Kenya and comparators, 2015**<sup>**16**</sup>

||**Kenya**|**Senegal **|**Tanzania **|**
Policy Research Working Paper 10844

# **Abstract**

Teachers are one of the most important inputs for learning, but in many low-income countries they are poorly distributed between schools. This paper discusses the case of Malawi, which has introduced new evidence-based policies and procedures to improve the equity and efficiency of the allocation of teachers to schools. The analysis finds that adherence to these policies has been highly variable between the country’s districts, with the most successful deploying 75 percent of teachers according to the rules and the least successful just 22 percent. Using administrative data, the paper identifies the impacts on student repetition rates of reductions in pupil–qualified teacher ratios as a result of the new teachers. The findings show that schools that moved from having more than 90 pupils per qualified teacher to a lower ratio experienced reductions in lower primary school repetition rates of 2–3 percentage points. However, similar impacts on dropout are not observed.

This paper is a product of the Education Global Practice. 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 sasim@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
# **E.20 Bounding Exercise: Unintended Increase**

Data on Ebola incidence in Sierra Leone is incomplete. As such, we cannot directly rule out an increase in exposure by comparing the total number of cases in treatment and control areas. To be a confirmed case in the available Ebola data, an individual must be infected with Ebola and known to health workers through self-reporting or surveillance. CM and NFA could theoretically affect case counts by unintentionally increasing either exposure rates, reporting propensities, or both. We use our empirical results and a simple model to clarify what must be assumed to attribute our results to changes in exposure.

## **Sequence and Information**

Each individual _i_ observes whether they are symptomatic, _s ∈{_ 0 _,_ 1 _}_ . The CDC lists the following as Ebola symptoms: fever, severe headache, muscle pain, weakness or fatigue, diarrhea, vomiting abdominal pain, or unexplained hemorrhage. They also observe the treatment status of their local health facility, _T ∈{_ 0 _,_ 1 _}_ .

_i_ knows that Pr[ _s_ = 1 _| I_ = 1] = 1: if you have Ebola, you will show symptoms. They also know that Pr[ _s_ = 1 _| I_ = 0] = _p ∈_ (0 _,_ 1), i.e., that symptoms like fevers and diarrhea happen to those that are not infected.<sup>A7</sup> The infection rates within control and treatment communities, _eT_ = E( _I | T_ ), are also common knowledge.

_i_ must to decide whether to report their symptoms and be tested, _R ∈{_ 0 _,_ 1 _}_ . They cannot, however, condition this decision on their actual infection status, because this is not known to _i_ prior to testing.

## **Notation**

- Reporting among Symptomatic in Control: Pr( _R | s_ = 1
imperfect proxy.<sup>12</sup> Various indicator variables are used in relation to DPOs. In the sample, 49 percent of the projects were in countries that had DPOs relating to any sector in the preceding five years, 42 percent of the projects were in countries that had public policy reform, and only 13 percent of the projects were in countries that had transport-related DPOs (table 1).<sup>13</sup>

# **3.4. Wider Economic Benefits**

We use remotely sensed measurements of nighttime lights emission intensity to measure the WEBs. Nighttime lights are considered a relatively good proxy for human and economic activity. The assumption is that the amount of light emitted at night is highly correlated with consumption, which increases with higher incomes. Following Henderson, Storeygard, and Weil (2012), who find a strong correlation between the growth of nighttime light intensity and GDP at the national level, multiple studies have expanded this approach and used nighttime lights to estimate economic development at much smaller geographic units. For example, Storeygard (2016) utilizes nighttime lights to study intercity transport costs and their impact on population income in Sub-Saharan African cities, and Harari (2017) uses nightlights to measure the shapes of urban areas in India. Because persistent light emitted at night is often associated with manmade structures, it is assumed that if the intensity of a pixel exceeds a given threshold, this pixel represents a populated location or some level of economic activity.

To measure WEBs, nighttime light data from the U.S. Air Force Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) were used. Pixels in DMSP-OLS have a resolution of 30 arc seconds, or approximately 1 by 1 kilometer at the equator. These data are available for the period between 1992 and 2012. For each pixel, the digital number of calibrated light intensity ranges from 0 to 63, which is often referred to as the nightlight intensity (we use the “stable lights” band, where ephemeral
13 rural areas, much of it presumably representing imputed values of freely collected wood. An analysis of household energy use patterns in Malawi showed that the most common combination of energy sources for household use (not just for cooking but for all purposes) was electricity and charcoal in urban areas (29 percent of all urban households) and firewood only in rural areas (84 percent of all rural households), with no expenditures on kerosene or electricity reported by the latter group.

When confining examination to clean forms of energy, LPG dominated primary cooking energy in Angola, Gabon, and Mauritius, and electricity was dominant in Botswana and South Africa. In South Africa, between 2010 and 2018, the percentage of the total population living in households using LPG as the primary cooking fuel was consistently below 5 percent, fluctuating between 1.9 percent in 2011 to 4.3 percent in 2017 and averaging 3.2 percent. By contrast, the percentage of people living in households using electricity as the primary source of energy for cooking gradually rose from 69 percent in 2010 to 82 percent in 2018 during the same nine-year period. In most countries, either LPG or electricity dominated, leaving Tanzania as the only country where the preference was equally split between the two choices (Figure 5). The same qualitative patterns were seen but in larger percentages in urban areas (Figure 6), and conversely the population shares were much smaller in rural areas (Figure 7).

**Figure 5  Percentage of people living in households that cited LPG or electricity as the primary source of energy for cooking and annual expenditures per capita**

_Source_ : World Bank staff calculations using household survey data. _Note_ : The South African survey did not ask enough questions to enable estimation of the total household expenditures.
85 Gundlach, Erich (1995). “The Role of Human Capital in Economic Growth: New results and Alternative Interpretations.” _Weltwirtschaftliches Archiv_ . Vol. 131, No. 2, pp. 383-402.

Hall, Bronwyn H., Adam B. Jaffe, and Manuel Trajtenberg (2002). “The NBER Patent-Citations Data File: Lessons, Insights, and Methodological Tools.” In _Patents, Citations and Innovation: A Window on the Knowledge Economy._ Jaffe, Adam B. and Manuel Trajtenberg (eds). The MIT Press, Cambridge Massachusetts, USA.

Hall, B. H., A. B. Jaffe, and M. Tratjenberg (2001). "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools." NBER Working Paper 8498.

Hall, Robert E. and Charles I. Jones (1999). “Why Do Some Countries Produce So Much More Output per Worker than Others ?” _Quarterly Journal of Economics_ . Vol. 114, February, pp. 83-116.

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Jorgenson, Dale W. and
I POLICY RESEARCH WORKING PAPER 2229

# **Summary findings**

Since its creation in 1991, the Peruvian Social Fund (FONCODES) has spent about US$570 million funding microprojects throughout Peru. Many of these projecrs have involved building and renovating school facilities.

Paxson and Schady analyze the targeting and impact of FONCODES investments in the education sector, using data from FONCODES, Peru's 1993 population census, Peru's 1994 and 1995 Living Standards Measurement Surveys, and a 1996 household survey conducted by the Peruvian Statistical Institute.

They present their results based on various descriptive and econometric techniques, including nonparametric regressions, differences-in-diff'erences, and instrumental variables estimators.

They show that FONCODEiS projects in the education sector have reached poor districts and, to the extent they live in those districts, poor households. FONCODES has had a positive effect on school attendance rates for young children, but not on the likelihood that childrer. will be at an appropriate school level for t1 eir age.

Among other recommendations, they su,ggest that FONCODES consider random assignment of some education projects for a subsample of the population, to test the robustness of the study's assumptions and results.

Lack of disaggregated data on such measlirn s as the time children spend in school, pupil-teacher ratios, anc scholastic achievement precluded analysis of the impac: of FONCODES education projects on scho )l quality. Collecting such data, and undlerstanding how improvements in school infrastructure inte:ract with other school-level changes to produce more learning, should be a research priority.

- - This paper a product of the Poverty Division, Poverty Reduction and Economic Managernent Network is part oi: a larger effort in the network to understand the functioning and impact of social funds. Copies of the paper art' available fr te from the World Bank, 1818 Hf Street, NW, Washington, DC 20433. Please contact Norbert Schady, rocm MCS-138, telephone 202-458-8247, fax 202-522-1557, email address nschady@worldbank.org. Policy Research Worl:in- Papers are also