rafmacalaba/data-use-mentions-extended huggingface dataset:
# Heterogeneity by gender equality in access to divorce

The study investigates whether the observed association differs between countries in which women face more legal, financial, customary or religious barriers in access to divorce than men. It uses the OECD 2019 Social Institutions Gender Index (SIGI) which contains a measure of this. The study estimates the main specifications for subsamples defined by whether or not men and women have the same rights to initiate and finalise divorce (see Table S1.2 for details of these samples, as well as comparison with the UK and US as the study referred to studies of these countries earlier).<sup>25,26</sup>

# **3 Results**

# Baseline specification

The study shows estimates of equation (1) for each indicator of IPV, with controls sequentially added (Table 1). The study first presents the unconditional correlation, which is negative for male unemployment and insignificantly small for female unemployment, mirroring the association observed in the raw data in Figure S1.3. The estimates are not sensitive to inclusion of year fixed effects. However, they are sensitive to inclusion of country fixed effects, consistent with time-invariant country-level characteristics being correlated with both IPV and unemployment rates. Once country fixed effects are in, the estimates are not sensitive to covariates.

> 25 Equality in access to divorce is defined in the SIGI as "Women have both the same rights to initiate divorce and the same requirements to finalise divorce or annulment as men, without negative repercussions on their parental authority. This applies to all groups of women. Customary, religious and traditional laws or practices do not discriminate against women’s rights regarding divorce or parental authority after divorce" (SIGI 2019 )

> 26 Variation in access to divorce may also pick up the variation in social acceptability of divorce. The study cross-checked this using data from the World Values Survey (WVS), available for 19 countries in the sample. In the sample with equal access, the average score for social acceptability is 4
# **III.2. The empirical growth model**

To explore the links between growth, inequality and poverty, our empirical strategy generalizes the standard specification used in the literature concerned with the impact of inequality on growth.<sup>17</sup> Building on the illustrative model presented in Section II, we use a specification adding suitable measures of inequality and poverty to an otherwise standard empirical growth regression: where _y_ is the log of per capita income,  _i_ and  _t_ are country- and time-specific effects, _p_ is a measure of poverty, _g_ is the Gini coefficient, _x_ represents a set of control variables, which we shall discuss shortly, and  is an i.i.d error term.

The parameter  in (12) captures the impact of poverty on growth. Thus, if poverty is a growth deterrent, as predicted by the model in Section II (eq. 10), we should find  <0. In turn, the coefficient  in (12) reflects the direct effect of inequality on growth, for given poverty and per capita income levels. However, the overall impact of inequality on growth also depends on how inequality affects poverty - the indirect effect examined in eq. (11). The reason is that, from theory, as well as the very construction of our poverty data (see the Appendix), poverty is a (nonlinear) function of (log) average income ( _y_ ) and inequality ( _g_ ), i.e., _p=p_ ( _g, y_ ). Thus, which means that, even if the direct effect of inequality on growth is nil (i.e.,  =0), its overall effect on growth could still be nonzero through its correlation with poverty.

Identifiability of  and  in (12) hinges on the relationship between poverty, income and inequality. If poverty were close to an exact linear function of (log)
whereas it results in a 0.02 percentage point deterioration in current account balances of oil (natural gas) importers. The results suggest that due to substitution between commodities, positive coal price shocks may be leading oil and natural gas exporters to export more of their commodity (thus resulting in an improvement of their current account balances).

As shown in the Online Appendix, results from the robustness checks based on the inclusion of real exchange rates are highly consistent with the results of the benchmark model.

**Relative contribution of alternative real energy price shocks to current account balances.** The contributions of oil, natural gas, and coal price shocks to the variance of current account balances of alternative country groups are given in Figure 10. The main observation is that oil price shocks contribute the most among energy price shocks; this contribution is higher for oil exporters (versus importers), countries with smaller service sectors, and with relatively less open capital accounts. The contribution of natural gas price shocks is higher for energy importers relative to exporters, countries with historical current account deficits, and relatively more capital-open countries. Finally, the contribution of coal price shocks is higher for energy exporters relative to importers, countries with historical current account surpluses, smaller service sectors, and relatively less open capital accounts.

Overall, these results imply that alternative energy price shocks have quite different effects on and contributions to the variance of current account balances of countries. As shown in the Online Appendix, robustness checks based on alternative ordering of variables or inclusion of real exchange rates highly support the results of the benchmark model. However, certain results are slightly different when countryspecific real exchange rates are included, which can partly be due to the smaller sample size because of limited data on country-specific real exchange rates.

# **4.3. Oil demand versus oil supply shocks and current account balances**

As described in Section 3.3, there is overwhelming evidence in the literature that the effect of oil price shocks on macroeconomic aggregates depends
time periods, and data collected from weather stations. Station data has the advantage of being based only on observation and not including a modeled component. However, station networks often lack complete geographic coverage of a given area and station records may be incomplete, introducing temporal gaps in coverage as well. Re-analysis solves these problems but relies on models to do so. Despite the reliance on models, re-analysis data is broadly believed to provide a reasonable best estimate of weather variables (Auffhammer et al., 2013). As a result, it is widely used in both environmental and development economics (Schlenker and Lobell, 2010; Hsiang, 2016; Emerick, 2018). I chose re-analysis data for this project because comprehensive station data is not available. The ERA5 Land hourly product provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution. I collect data on cloud cover, temperature, and precipitation over the full sample from 2003 to 2014. I aggregate these weather variables to the district level in the primary analysis.

**Agricultural Data:** I collect data on Indian agriculture from a number of sources. The first is the ICRISAT Meso data (Rao et al., 2012). This data is collected by the International Crops Research Institute for the Semi-Arid Tropics and measures the performance, structure, and behavior of the agricultural economy at the district level in India since 1966. I use data from 2003 to 2014. Because Indian district boundaries have changed over time, they provide both apportioned, where they adjust data for boundary changes, and unapportioned data in which data is not re-apportioned based on changes. I use the unapportioned data and manually re-apportion data to the district boundaries as they were recorded in the 2001 census to align with my other data sources. I use data on district level cropping patterns and land holdings from ICRISAT
Policy Research Working Paper 9718

# **Abstract**

This paper estimates a model of household-level demand for electricity services and electricity demand in the Indian state of Rajasthan using a combination of household-level survey and administrative data. The model incorporates customer-level demographic characteristics, billing cyclelevel weather variables, and the fact that households face increasing block prices of electricity. The model allows estimating consumer response to price changes by four categories of energy services demand, namely, heating and cooling, lighting, and for domestic and business end-uses. The knowledge of demand response across different end-use helps in differentiating the impact of price changes along the income distribution. The model finds that the demand for heating and cooling energy is the most price inelastic and income elastic service, whereas the demand for domestic end-use is the most price elastic and income inelastic service of all four categories. The structural demand model also helps in comparing the welfare implications of current energy tariffs to those based on normative principles of efficient retail electricity pricing. For this analysis, first, the social marginal cost of electricity is calculated using publicly available data on generation, transmission, and distribution losses and emissions. The social marginal cost estimate, in combination with observable household characteristics, is then used to examine alternative tariff structures that are more affordable, equitable, and revenue sufficient for the utility than current price structure. An alternative tariff design, comprising of an energy price set to the social marginal cost of electricity and a fixed cost component determined by proxy indicators of household willingness to pay, performs better on the above parameters than the current schedule. Other sources of technical losses, related to transmission or distribution, are not studied in this paper.

This paper is a product of the Poverty and Equity 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
# **Figure A.10. Probabilities of Female-Headed Households Falling in Poverty in Second Year Conditional on Being Non-poor in First Year (percentage)**

<!-- Start of picture text --> Egypt 2017-2020 Iraq 2007-2012<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Jordan 2010-2013 Mauritania 2014-2019<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>Palestine 2011-2017 Tunisia 2015-2021<br>Self-reported Potential Self-reported Potential<br>Females>0.5 Educated fem. Females>0.5 Educated fem.<br>FHH Non-FHH<br>17 14<br>16<br>13<br>15<br>12<br>14<br>11<br>13<br>Percentage (%) Percentage (%)<br>12 10<br>13 16<br>12 15<br>14<br>11<br>13<br>10<br>12<br>Percentage (%) 9 Percentage (%)<br>11<br>50 8.5<br>45 8<br>40 7.5<br>7<br>Percentage (%) Percentage (%)<br>35<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that enters poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

69
a second best option would be the village-specific harvest month). In the Rwanda EICV, we have and use the direct reporting of the household’s harvest month for maize and sorghum. In Malawi and Kenya, and for coffee in Rwanda, we have no information on the month of the harvest (or reported harvest window) for either households or villages in the surveys. In these cases, we assume it is based on other sources. The harvest date is constructed based on input from local agronomists on the normal month of harvest by region to account for spatial variation in production seasons. The harvest month for maize in Malawi is assumed to be April for the southern region, May for the central region, and July for the more arid northern region. The harvest months for tobacco are assumed to be one month earlier. In

> 5 In Kenya, the design of the survey instrument makes the exclusion of irrigated plots more difficult. The incidence of irrigation in the Rift Valley, however, is quite low, with less than 5 percent of farms using irrigation on maize crops. These observations remain in the dataset, although their exclusion does not markedly change the results.

> 6 In Malawi, the data pertain to the 2002/03 and 2003/04 rainy seasons. In Kenya, it is the 2003/2004 and 2004/2005 rainy seasons. In Rwanda, it is the 2000/2001 season.

10
|(1.000)|(0.026)|(0.031)|(0.028)||
|||[1.000]|[0.462]|[1.000]||
|(3) Illness/injury in HH|0.000|_−_0.005|_−_0.018|0.007|5053|
||(1.000)|(0.033)|(0.037)|(0.039)||
|||[1.000]|[0.462]|[1.000]||
|(4) Child weight for length|0.000|0.079|0.093|0.065|1991|
||(1.000)|(0.048)|(0.055)<sup>_∗_</sup>|(0.055)||
|||[0.544]|[0.252]|[1.000]||
|(5) Vaccine completion index (Under 2)|0.000|0.012|0.118|_−_0.082|1457|
||(1.000)|(0.059)|(0.072)|(0.064)||
|||[1.000]|[0.252]|[1.000]||
|(6) Child birth complication index|0.000|_−_0.026|_−_0.126|0.061|856|
||(1.000)|(0.077)|(0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|
**Learning Outcomes and School Cost-Effectiveness in Mexico: The Pare Program**

# Gladys Lopez-Acevedo

# **1. Introduction**

To understand better the qualitative dimension of basic education, it is necessary to analyze student learning outcomes and school effectiveness. What factors influence them? How responsive is student learning to these factors? What impact can learning improvement interventions have? This paper attempts to address some of these questions.

Empirical studies of student learning achievement in Mexico are scarce. Interest, however, regarding its determinants and the impact of interventions to improve it is increasing. In January 1995, the Ministry of Education presented the Programa de Desarrollo Educativo 1995-2000 (PED), which contains a series of targets and general guidelines in order to improve the coverage, efficiency and equity of the Mexican educational system. In fact, the PED recognizes the importance of research and evaluation in its strategy to improve quality of education. In view of this policy, the Ministry of Education has collected databases that can be useful for this purpose. These include Carrera Magisterial, PARE, and TIMSS (Third International Mathematics and Science Study) databases among others.

Due to data constraints, this paper is unable to do a comprehensive and in-depth analysis of the learning achievement issues in Mexico. The following analysis, therefore, should be regarded as an exploratory rather than as a conclusive study. Available data are used to highlight certain ideas about learning improvement interventions. As mentioned, there has been very little study in Mexico that examined this issue. There are, however, many international studies that looked at this question. An excellent summary of this literature can be found in Fuller and Clarke (1994), and Hanushek (1995).

The early studies on learning outcomes showed that the student's socio-economic and cultural background predominantly determines differences in test scores. These led to the conclusion that there was little that government can do by way of direct educational policy and government interventions to improve learning outcomes. More recent results and experience, however, indicate
Somoni)|5,592|20,353|516,832|0.00|20,000,000|
|Number of refrigerators in the HH.|5,592|1.06|0.57|0.00|11.00|
|<br>Number of washing machine in the HH.|5,592|1.03|0.55|0.00|10.00|
|<br>Number of AC in the HH.|5,592|0.62|1.20|0.00|40.00|
|Number of fan in the HH.|5,592|1.63|1.25|0.00|10.00|
|Number of stoves in the HH.|5,592|1.46|1.13|0.00|15.00|
|Number of bread oven in the HH.|5,592|0.78|0.64|0.00|8.00|
|Number of gas heaters in the HH.|5,592|0.78|0.93|0.00|19.00|
|<br>Number of microwaves in the HH.|5,592|0.38|0.59|0.00|8.00|
|Number of radios in the HH.|5,592|0.48|0.80|0.00|22.00|
|Number of TVs in the HH.|5,592|1.66|1.00|0.00|15.00|
|Number of computers in the HH.|5,592|1.07|0.94|0.00|12.00|
|Number of bicycles in the HH.|5,592|0.80|0.91|0.00|6.00
||(0.147)|(0.150)|(0.151)|(0.151)|(0.151)|
|log maintenance|–0.06|–0.09|–0.09|–0.09|–0.09|
||(0.112)|(0.114)|(0.113)|(0.112)|(0.112)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on export intensity (exports/sales). The instruments are the weighted average the real exchange rate of a firm export partners and the weighted average of the real gdp of a firm export destinations. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

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# 1 Introduction

Household surveys are needed to measure poverty and design distributional policies, yet in several countries they are not conducted due to low statistical capacity, conflict, or lack of resources. In other cases, household surveys are collected but not shared with researchers and policy makers (Dang et al. 2019, Ekhator-Mobayode and Hoogeveen 2022). Lack of data disproportionally affects poorer countries, meaning that ignoring or not properly accounting for such countries in global analyses biases results.

We address these gaps by developing a method that through a simple regression can predict annual income and consumption (henceforth welfare) distributions with a credible distributional shape for each country. The method uses widely available social and economic indicators at the country level and does not require any household survey data for the country of interest. We explicitly search for a simple model that can be easily taken up in applied work and can work for the most data deprived countries. To estimate such a model, we leverage rich information on welfare distributions from more than 2,000 household surveys available in the World Bank’s Poverty and Inequality Platform (PIP), which covers 168 countries, using data between 1991 and 2020. For each of these surveys, we have distributions of daily per-capita household income or consumption expressed in purchasing-power-parity-adjusted dollars.

We sequentially remove one of the 168 countries from the sample and predict the excluded distribution for this country using the remaining 167 countries and various predictors. As potential predictors, we search among more than 1,000 candidate variables spanning multiple databases and including remote sensing indicators. After repeating this leave-one-out cross-validation for each of the 168 countries with available data, we compare the predicted distributions to the survey-based distributions, favoring models that minimize the prediction error.

We find that a model that uses GDP per capita, under-5 mortality, life expectancy, rural population shares, and regional dummies predicts welfare well, and that adding more information does not lead to any relevant gains. National accounts data stand out as the single best predictor of welfare, even,
θθ parameter is directly related to percentage changes in the Gini index, there is no functional relationship δδ between the percentage change in the Gini index, , and the parameters of the linear GIC. We thus use an algorithm that iteratively changes the slope of the GIC until it matches the desired . αα To illustrate how the convex and linear GICs could look in practice, we use the welfare distribution from αα To illustrate how the convex and linear GICs could look in practice, we use the welfare distribution from a survey in Côte d’Ivoire from 2015. From 2015 to 2016 IMF data suggest that GDP per capita in Côte d’Ivoire grew by 5.6%. Figure 1 explores how this growth can be distributed if inequality stays unchanged or if the Gini increases or decreases by 1% (ignoring for the moment that only part of this growth is passed through to the consumption observed in surveys). The initial Gini in Côte d’Ivoire was 41.5, meaning that a 1% drop ( ) would bring the Gini to 41.1, while a 1% increase ( =0.01) would bring it to 41.9. Lowering the Gini by 1% does not have to impose a large cost on the top of the distribution. Because of αα= −0.01 αα Lowering the Gini by 1% does not have to impose a large cost on the top of the distribution. Because of the large income share of the top of the distribution, the reduction in the growth rate of the wealthiest individuals necessary to ensure that the bottom grows substantially faster than the mean is relatively small. For example, in the case of Côte d’Ivoire, a convex growth incidence such that the Gini decreases by 1% means that households at the 10<sup>th</sup> percentile grow 2.5 pp faster than the mean, yet only reduces the growth at the 90<sup>th</sup> percentile by 0.5 pp
making it difficult to draw conclusions. Improvements in cognitive

> 2 The _Subsidios_ program was implemented in the late 2000 ́s in Bogotá and targeted vulnerable population classified as _Sisben_ level 1 and _Sisben_ 2 based on the proxy-means test constructed with information from a census of the poor. The program‘s transfers and conditions are similar to _Familias en Acción_ , which is a nationwide and older program. However, the _Subsidios_ program offered two other treatments with a different structure of benefits. One is the ―savings treatment,‖ which reserves one third of the bimonthly payment to be given to families as a lump sum at the end of the year, just before enrollment into the subsequent grade level. The other is the ―tertiary treatment,‖ which also reserves part of the bimonthly payment, but then pays families a substantially larger amount if students graduate from high school (eleventh grade). Students who continue to tertiary education are eligible to receive that amount one year earlier than those who do not. These two additional treatments may change the way families respond to the program in two ways: one is due to the reduction in the amount of cash families receive on a bimonthly basis, therefore limiting their liquidity; another is through the incentives created by linking cash transfers directly to grade progression and matriculation in tertiary education.

> 3 It is worth noting that the results of these two papers are not generalizable to the entire population covered by _Oportunidades_ given that the samples of analysis only contain non-migrant rural households. Migrants are expected to have higher enrollment in college and better job opportunities, including higher salaries.

6