rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. School attendance of adolescent boys and girls over the course of the pandemic**

Table 1 shows for each country-round the share of children aged 10-18 attending school and the status of school closures. The data document that the COVID-19 pandemic caused unprecedented disruptions in school attendance for adolescents in many Sub-Saharan African countries. For example, in Kenya, only 16 percent of children aged 10-17 years attended school between September and November 2020. Similarly, in Uganda, only 32 percent of children aged 10-18 years were reported to attend school between March and April 2021. In both situations, schools were closed due to COVID-19, with some concessions for exam candidates. Pre-COVID-19 household surveys conducted in both countries suggest that prior to the pandemic between 85 and 92 percent of children in these age groups were attending school, indicating a massive disruption in education for millions of adolescent girls and boys.

At the same time, school attendance rates fluctuated significantly over the course of the pandemic, as schools cycled through periods of closures and re-openings. Encouragingly, the data suggest that many children did return to school once they reopened. For example, in Kenya, 99 percent of children aged 1017 were attending school between January and March 2021, at a time when schools had fully reopened – a significant recovery from 16 percent just four months earlier. Likewise, in Nigeria attendance of children aged 10-18 years increased from 59 percent in October 2020, when schools were partially closed due to COVID-19, to 82 percent in March 2021, when schools had reopened. These are early and preliminary results – but they do give rise to the hope that the COVID-19 crisis did not necessarily lead to the massive dropout rates that were initially feared. However, it will be necessary to closely monitor these trends going forward, as there is a risk that children, who regressed in learning during COVID-19, may struggle with the new curriculum and drop out in the months or years that follow.

These sharp fluctuations also imply that routine data collections are necessary during the pandemic
Infrastructure Quality Index (1-7)<br>3<br>2 Roads<br>1<br>0<br>-1<br>-2<br>1 2 3 4 5 6 7<br>WEF Infrastructure Quality Index (1-7)<br>Governance index<br>(Control of Corruption)<br>Governance index<br>(Control of Corruption)<br>Governance index<br>(Control of Corruption)<br><!-- End of picture text --> 4
_Figure 11: Sensitivity Analysis of Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Constant GDP Pov. Baseline 2003 Constant GDP Pov. Baseline 2007<br>Constant GDP Pov. Baseline 2009 GDP current prices - CPI Pov. Baseline 2003<br>GDP current prices - CPI Pov. Baseline 2007 GDP current prices - CPI Pov. Baseline 2009<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data _Figure 12: Sensitivity Analysis of Poverty at LMIC international poverty line ($3.65, 2017 PPP)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Constant GDP Pov. Baseline 2003 Constant GDP Pov. Baseline 2007<br>Constant GDP Pov. Baseline 2009 GDP current prices - CPI Pov. Baseline 2003<br>GDP current prices - CPI Pov. Baseline 2007 GDP current prices - CPI Pov. Baseline 2009<br>Poverty Rate<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on WDI and CBS data
levels. Although 99% of the adult population is literate, the quality of education is low, as shown by Albania‟s performance in the 2009 Program for International Student Assessment (PISA). More than 50% of 15 years old students found it difficult “to use continuous texts unless the texts are short and clearly sign-posted; and even with such texts, they are unlikely to be able to do more than identify a main idea or find explicitly stated information.”<sup>14</sup> The Ministry of Education and Science (MoES) is the central government body responsible for implementing education policies and managing the education system. This responsibility is exercised by staff in the MoES and in twelve administration entities (REDs) functioning at the county level. The MoES is directly responsible for the development of curricula, the selection of textbooks, the structure of the academic year, setting the pay and workloads of teachers, the allocation of resources among local education institutions, and teacher training (World Bank, 2005). The 12 REDs are responsible for the delivery of primary and secondary education in their respective districts, but they are not decentralized government units. They are responsible for the appointment and transfer of teachers, the distribution of administrative materials to schools, inspections of schools, and planning and supervising the construction of new facilities and the rehabilitation of existing ones. However, the RED directors are appointed by the MoES and have no authority with respect to the amount of funding centrally allocated to their region.

The institutional framework currently in place relies on a system that assigns the task of monitoring teachers and imposing penalties for under-performance to administrative units that are not directly held to account by service recipients, i.e., it relies on top-down accountability.<sup>15</sup> However, the MoES has recently been promoting community engagement in school governance structures through parent committees and school boards. Parent committees are composed of elected parent class representatives, and school boards are composed of two or more
## **3.5 Forecasts of Program Impacts**

The forecasters were mainly researchers working on topics in migration and development economics, or those with expertise in humanitarian program implementation, and were asked to predict effects on the program’s primary outcomes. For round 1, a total of 61 individuals, comprising 37 researchers and 23 non-researchers, responded to surveys about the program’s effects on refugee well-being. For round 2, 63 respondents, including 53 researchers and 10 non-researchers, provided predictions on the effects on neighbors’ social cohesion responses. Gathering forecast data allows us to assess whether estimated impacts were in line with the prior beliefs of research and policy experts. Table 2 presents the mean predicted effects, as well as the interval from the 10th to 90th percentiles of predictions. Generally, forecasters anticipated modest improvements in refugee well-being, particularly regarding housing outcomes, while predicting no average impact on the social cohesion measures collected among neighbors.

# **4 Empirical Strategy**

## **4.1 First stage compliance with program assignment**

Table 3, Panel B presents the first stage analysis and the HSP take-up rate of 33%. While statistically significant, the compliance rate is lower than expected, especially given the substantial funding offered by the program. It is likely attributable to several factors, including implementation challenges during the COVID-19 pandemic, uncooperative or reluctant landlords (who may have feared being locked into long-term contracts with refugee tenants, as noted above), and the implementing partner’s policies that led some homes to be excluded due to their condition.

17
|4.956|5.009|3.807|4.363|4.137|4.074|3.942|4.239|3.928|4.245|

Source of raw data: SVS Whereas the dispersion of MWRs declined after the submission of the draft Pensions Law to Congress in 2000, the effects of the actual approval and implementation of the Law in 2004 are less clear. As shown in Table 13, the coefficient of variation declined further in the bottom third of the market in March of 2005, relative to March 2004, but increased slightly for the whole market in the same period. This is somewhat surprising, as the actual implementation of the Law in mid-2004 seems to have generated further efficiency gains, as indicated by a further decline in broker’s commissions from 2.5 to 2

> 10 See Walker (2005) and Rocha and Thorburn (2006).

> 11 Walker (2005) and Rocha, Morales and Thorburn (2006) provide econometric analyses of the annuity rate with company data and show that there were structural shifts in the annuities market after submission of the draft Pensions Law to Congress in 2000.
|(0.023)|(0.033)|
|p-value UCT vs. CCT|0.616|0.237|0.980|0.068|
|p-value Treatment|0.112|0.249|0.996|0.122|
|Mean in Control Group|0.026|0.035|0.055|0.243|
|Sample Size|1,192|2,002|1,977|1,979|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. An individual is considered anemic if her hemoglobin count is less than or equal to 11g/dL if pregnant and less than or equal to 12d/dL if non-pregnant based on WHO guidelines to define mild anemia. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
This paper uses individual-level data on 41,873 respondents from 17 Sub-Saharan African countries to examine gender differences in ten socio-emotional skills and their relationship with education and earnings. It makes four main contributions to the literature.

To our knowledge, ours is the first paper to analyze gender differences in several dimensions of socioemotional skills drawing on a large sample of data from multiple African settings, where limited evidence exists. Only a few key studies have extended analysis of gender differences to developing countries, including some in Sub-Saharan Africa (Lynn & Martin, 1997; Costa et al., 2001; Schmitt et al., 2008), and these studies are mostly confined to measures of the Big Five personality traits<sup>3</sup> . Unexpectedly, these studies find that a higher human development index ranking is associated with larger gender differences in personality traits; the magnitude of gender differences is higher in European and American contexts and is small to nonexistent in Asian and African countries. Based on Cohen’s D, the overall magnitude of the gender difference in our results is similar to results for personality differences in Sub-Saharan Africa (Costa et al., 2001; Schmitt et al., 2008). Our results indicate that men on average have an aggregate socioemotional skills level that is 0.151 standard deviations higher than the average level among women, equivalent to 5.6 years of education or a 5 percentile increase in socio-emotional skills levels. This male advantage persists after controlling for education, age, marital status and even measures associated with confidence, such as self-esteem and self-efficacy. However, the range of gender differences across skills and studies is considerable, and most would be categorized as _small_ or _moderate_ based on a meta-analysis of gender differences in skills (Hyde, 2005).

A large literature from developed countries has examined whether gender stereotypes are reflected in specific socio-emotional skills gender differences, which we summarize in Appendix Table A1. Eagly and Wood’s (2012) cross-disciplinary social role theory predicts that men have an
captures the degree of competition due to the mass of French firms selling the same HS6 product in the liberalized market.<sup>18</sup> Using French Customs export data at the firm-product-country of destination level, we construct an Herfindahl index that this time captures the concentration of export shares of other French firms exporting same product to same country. The inverse of this index captures the degree of competitive pressures generated by French firms selling in that country. Similar to the pre13
**Figure 19: Indicators of general business environment**

<!-- Start of picture text --> ECA countries with no projects<br>ECA countries with projects<br>South Asia<br>Latin America & Caribbean<br>Private foreign ownership<br>East Asia & Pacific<br>Quality certification<br>Line of credit<br>Court system is fair<br>Audited financial statement<br>Corruption is a problem<br>0 20 40 60<br>Percentage of responding firms<br><!-- End of picture text --> The table draws on enterprise surveys in Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, the Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Montenegro, Poland, Romania, the Russian Federation, Serbia, Slovak Republic, Slovenia, Tajikistan, and the Ukraine. (World Bank 2011) 72
data from 1997 to maintain consistency in the number of districts used in the analysis.<sup>1</sup> We use this data to construct an aggregate district-level measure for annual farm productivity. Specifically, we define _FarmProd_ for district _d_ and year _t_ as: In (1), _c_ refers to the crop while _Prod_ denotes the output, measured in thousands of tons for each crop. _Area_ denotes the area alloted to the crop in the district for that year, measured in thousand of hectares. As we are aggregating across 14 crops, we use the federally administered minimum support price (MSP) as the crop-specific weight to convert the cropspecific yields into an uniform monetary value (scaled by area). The MSP provides the minimum price for 22 major crops in India, including both food and non-food crops. The prices are set by the federal government each year prior to the cropping season, allowing producers to know in advance the minimum price guaranteed by the government for their output. As the MSP is binding across all states and districts in India, it provides a national measure of prices which we use to convert the crop-specific yield measure into an uniform monetary value. As the MSP prices are measured as rupees per quintal, our productivity measure is scaled accordingly and measured as rupees per quintal.

Monthly data on district rainfall is also provided by ICRISAT. We aggregate the monthly data into annual data by summing monthly rainfall across all months. We use the two-decade long rainfall data for each district to obtain district-specific rainfall distributions, which we subsequently use to define positive and negative shocks for each district-year combination in Section 3.

Table 1A shows the summary statistics from the ICRISAT data. We see that 54% of district area is used for agricultural activities while 20% of the area in the average district is unsuited for agricultural production. On average, almost 50% of a district’s area under cultivation is irrigated. Over this period, a district’s probability of receiving a positive rainfall shock was 0
characteristics as efficiency of end-use devices, the natural resource stock and income or expenditure level; and

- they are one-shot affairs which don't allow for comparison of results over time, especially regarding seasonality of fuel use.

So, micro-surveys are generally not appropriate for yielding a database that will allow for understanding and addressing energy problems at the national level. If carefully designed, they can be useful for assessing the situation in a pre-defined area and, as such, may have important applications in regions which have special problems.

# Additional Techniques for GQthering Specific Household Energy Information

Specialized investigatory techniques can be used to get important, detailed information on particular<sup>subjects,</sup> e.g. cooking efficiency, household decisionmaking and precise biomass fuel consumption. Laboratory and<sup>field</sup> tests should be conducted to determine theoretical and actual energy efficiencies of cookstoves and ovens.<sup>This</sup> will be essential for calculating the cost of useful energy in cooking. Sociological studies can be made<sup>to</sup> document how family decisionmaking occurs regarding inter alia purchase of fuel, ccioking practices and procurement of cooking equipment. For fuel consumption, a small, representative sample of households<sup>can be</sup> chosen for intensive investigation. Surveyors can visit residences on a daily basis over a period of several weeks to determine exact amounts of fuel used through weighing, volumetric measurements and detailed questioning. For example, FAO has been undertaking countrywide wood consumption surveys since the 1950s. These<sup>attempt</sup> to measure use of all types of wood products such as woodfiel, poles, sawnwood, panel products<sup>and paper.</sup> This has resulted in estimates of wood consumption for several countries, e.g. Sudan (1958), East<sup>Africa (1960),</sup> Tanzania (1970), Thailand (1970) and Zambia (1986). All
**Figure 11: Difference (gap) between regulatory capital over RWA and capital over total assets Panel A: Top 20% banks Panel B: Bottom 80% banks**

<!-- Start of picture text --> 11.7<br>10.9<br>10.4<br>10.1<br>9.6<br>9.2<br>8.7 8.7<br>8.2<br>7.6 7.6<br>7.3<br>7.0<br>6.7<br>5.9 5.7 6.3 6.0 6.1 6.2<br>5.3 5.1<br>4.0 4.6 4.3 4.4 4.8 4.6 4.6 4.3 4.3<br>3.1 3.2<br>2.8 2.5<br>1.9<br>EU CE & BC WB SC CA Russia Turkey EE RoW EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017 2007 2017<br>13 13<br>12 12<br>11 11<br>10 10<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>Capital Gap - Top 20% (percentage points)<br>1 Capital Gap - bottom 80% (percentage points) 1<br>0 0<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC
# **1. Introduction**

Intimate partner violence (IPV) is pervasive, with about one in three women in richer as well as poorer countries reporting abuse over their lifetime (Devries et. al. 2013). It is increasingly being recognized as an important part of the development agenda (WHO 2014, Michau et al. 2015, UN Women 2015a). While there were no targets for violence against women in the Millennium Development Goals, the new Sustainable Development Goals explicitly call for the “elimination of all forms of violence against women and girls” (UN Women, 2015a). IPV tends to be higher in low income countries, being 65.64% in Central Sub-Saharan Africa and 41.73% in South Asia, compared to 27.85% and 21.32% in Central Europe and North America, respectively (Devries et. al. 2013). It not only harms the physical and mental health of women, it also harms children (Ellsberg et al. 2008, Aizer, 2011, Rawlings and Siddique, 2019). In the last decade, many countries have implemented laws criminalizing IPV, but the extent to which such laws are upheld varies hugely (OECD Development Centre, 2014).

This paper considers how IPV varies with macroeconomic conditions, in particular, male and female unemployment rates, using comparable data for 31 developing countries. It considers male and female unemployment shocks, each conditional upon the other. Since these are correlated with one another but may have different impacts on violence, omitting either one will tend to bias the coefficient on the other and this study shows that this is the case.<sup>2</sup> Of particular interest is how violence against individual women is associated with improved aggregate employment prospects for women. This is important because many interventions in developing countries, for example, microcredit schemes and skills training programmes, are designed to increase women’s employment. The direction

> 2 Independent variation in male relative to female unemployment rates
Policy Research Working Paper 5718

# **Abstract**

Socioeconomic segregation is often decried for denying poorer children the benefits of positive ‘peer effects’. Yet standard, linear-in-means models of peer effects (a) implicitly assume that segregation is zero sum, with gains and losses to rich and poor perfectly offsetting, and (b) rule out theories of ‘social distance’ whereby peer effects are strongest among similar pairings. The paper exploits the random assignment of pupils between classes to identify more general peer effects in Argentine test-score data. Estimates violate both assumptions (a) and (b), and provide micro foundations for the correlations between school segregation, average test-scores, and test-score inequality in municipality-level data.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mlugo1@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
combine GDP data from the World Development Indicators (WDI) with data from the Energy Information Administration (EIA) on primary energy consumption and with data on GHG emissions from Climatewatch/CAIT, all for 2010 to 2019 (the latest year the data are available).

We again use a random slope regression to model the relationship between GDP and energy consumption, which also allows for efficiency gains over time. The random slope model exploits both variation between countries and variation within countries, and in this case allows for countries to convert GDP to energy needs at different rates and improve (or deteriorate) in energy efficiency at different rates. We cap the distribution of country-level coefficients at the 10<sup>th</sup> and 90<sup>th</sup> percentiles as the extreme values likely reflect measurement error or historical patterns that are unlikely to continue in the future.

3