rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9444

# **Abstract**

Women’s property ownership matters for their well-being and agency; it can also advance economic prosperity and promote the human development of future generations. Yet, until recently, lack of data has constrained researchers from gaining a comprehensive overview of gender differences in property ownership in the developing world. Using Demographic and Health Survey data from 41 developing countries, this paper seeks to fill this gap, by investigating the extent of gender gaps in the incidence of property ownership (land and housing) and the factors associated with these gaps, focusing on the role of legal systems. The study finds that there is substantial variation in gender gaps across countries, but in almost all countries men are more likely to own property than women. Within countries, gender gaps are most pronounced for groups that are already disadvantaged, that is, the rural population and the poorest quintile. The disadvantage in property ownership experienced by women reflects a variety of factors, including discriminatory norms and laws on inheritance, property ownership, marital regimes, and protection from workplace discrimination. Countries with more gender egalitarian legal regimes generally have higher levels of property ownership by women, especially housing. These results suggest that reforms to establish a more gender-equitable legislative framework could be an important mechanism to increase women’s property ownership.

This paper is a product of the Gender Global Theme. 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 igaddis@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
|Age|0.000|‐0.002|0.001|0.000|
|---|---|---|---|---|
|Age, squared|‐0.006|‐0.011<sup>*</sup>|‐0.003|0.001|
|Urban|‐0.051|‐0.089<sup>**</sup>|‐0.153<sup>***</sup>|‐0.071<sup>***</sup>|
|Household size|‐0.005|‐0.004|‐0.004|‐0.004<sup>***</sup>|
|Married|‐0.128<sup>***</sup>|‐0.007|‐0.017<sup>***</sup>|‐0.008<sup>***</sup>|
|Divorced or separated|‐0.074<sup>***</sup>|‐0.120<sup>***</sup>|‐0.007|‐0.046<sup>***</sup>|
|Widowed|‐0.001|0.000|‐0.000|‐0.000|
|Years of education|‐0.000|0.002|0.000|‐0.000|
|Ethnicity: Sri Lankan Tamil|0.000|‐0.001|0.001|0.002<sup>***</sup>|
|Ethnicity: Indian Tamil|0.000|‐0.002|0.001|0.000|
|Ethnicity: Sri Lankan Moor|‐0.006|‐0.011<sup>*</sup>|‐0.003|0.001|
|N|5,311|5,390|16,215|32,316|

> * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. Dataset: Labor Force Survey 2015. Estimation sample includes population 15 years and older.

The primary finding from this Oaxaca‐Blinder decomposition is that observable differences in the levels of characteristics explain only a tiny fraction of the difference between the employment rate of men and women. In all regions, difference in the “returns” to those characteristics explains over 100 percent of the gender differences. This rules out some obvious explanations for the gender employment gaps, such as the prevalence of particular ethnic groups or differences in educational attainment.

Looking more closely at the estimated returns, the relationship between employment and education differs greatly for men and women, which is why the returns to education explain a large portion of the gender gap in employment in all regions. The same is true for age and being Tamil, and in the East, the returns to marriage. The estimated returns to these characteristics, in terms of explaining the gender gap in employment, differ significantly between regions. This decomposition analysis is restricted to a relatively small set of explanatory variables that are available in the labor force survey and is therefore only suggestive of key factors. We further investigate some of these factors in the next two sections.

# 4.1.2 Cognitive Skills

Lower productivity could stem from a shortage of skills among women in the Northern and Eastern provinces. A main source of information on cognitive skills in Sri Lanka is the Skills Towards Employment and Productivity (STEP) surveys, which asked skills‐related questions in a variety of countries. Respondents are asked to read passages and answer simple questions about them to test their reading comprehension and are also asked to report how frequently they use reading, writing, and numeracy skills.

Analysis from the 2012 Sri Lanka survey suggests that women in the Northern and Eastern provinces have poorer reading skills, measured objectively, as well as lower self‐reported writing and numeracy skills than in the rest of the country. Figures 13‐15 present a comparison of self‐reported and objectively measured skill levels in reading of men and women in Northern, Eastern, Western, and other provinces. The bars in Figure 13a represent the percentage of respondents that report never using a specific skill in the previous 17
|Public or|56|39|52|32|−3|0.9|
|---|---|---|---|---|---|---|
|private facility|(_N_=|(_N_=|(_N_=|(_N_= 81)||(0.4–|
||533)|282)|292)|||2.1)|

_<mark>Source:</mark>_ <mark>Population Council, Household Survey 2010–11</mark> a Based on differences in changes in proportions that paid for delivery services: negative sign means the change was greater in the comparison group.

b Based on multilevel logit models with interaction terms; 95 percent confidence intervals are in parentheses (* _P_ <0.05).

The proportion of women who paid for delivery of the most recent birth in any health facility (public or private) declined by 17 percentage points in villages with a voucher client and by 20 percentage points in villages with no voucher client at follow-up between 2008 and 2010– 11 (table 5). The difference-in-differences estimate from the multilevel logit model was not statistically significant.

# **<<B>>** Socioeconomic Inequities

A difference of 11 percentage points was seen between births to poor mothers and births to nonpoor mothers who made four or more ANC visits in villages with a voucher client at follow-up (table 6). In villages with no voucher client, a difference of 15 percentage points was seen. Although the gap was greater between poor and nonpoor women in villages without a voucher client compared with villages with such a client, the difference-in-differences estimate from the multilevel logit model was not statistically significant.

The results further show that the proportion of births to poor women who were delivered at a private facility was greater in villages with a voucher client compared with villages with no voucher client (table 6). The inclusion of wealth quintiles in the multivariate model was one attempt to control for different distributions in poverty levels in voucher and nonvoucher villages. Nonetheless, the difference in the proportion of births delivered by poor and nonpoor women at a private facility was greater by 7 percentage points in villages with a voucher client compared with villages with no voucher client. The difference-in-differences estimate from the multilevel logit model was, however, not statistically significant. A similar pattern was noted for births to mothers who received PNC.

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Secondly, by using high resolution population layers to identify areas with economic activity some noise from adjacent non‐economic areas is removed. Hopefully providing a better data set to pinpoint where a change in light value would be due to a change in economic output and not due to stray light or other light sources such as forest fires. One potential downside of this is that some agricultural land will not be included in the analysis. Finally, instead of aggregating up to a provincial or municipality level, the data are kept at a cell level.

More specifically with regards to the methodology, one started with localized damage data modeled from actual intensity measures. These values were matched with any intersecting VIIRS cell and assigned to the corresponding month. For light cells that intersected several damage cells an average value was used. As earthquake damage estimates were modeled based on centroids, only the centroid intersection was used.

Once the nightlight and damage data had been merged, population data was included. The population data sets were aggregated up to the same cell size as the VIIRS data and then matched to find the total population in each nightlight cell. Due to slight differences between the HRSL and WorldPop data sets, two different cutoffs for populated cells were utilized. As HRSL specifically provide settled areas and set other areas to having 0 population, any VIIRS cell with no settled areas in it, would be excluded from the final data set. As for the WorldPop data, any VIIRS cell with less than 5 people in it was cut.

Finally, for potential future analysis, the VIIRS cells were assigned to administrative subdivisions such as provinces and municipalities. In cases where a cell crossed province borders, the province that contained the centroid was used.

# _B. Case Study Methodology_

Once the data sets were constructed, one case study for each natural hazard type was chosen to graph the effect the hazard would have on the local nightlight values. The choice of hazards was based on reported damage and “fame”. In the case of floods, they were also picked based on how “isolated” they were, i.e. some of the largest events were lasting for months on end potentially confounding the effect of floods in different months.

To check for any effect from the disasters, two graphs were constructed for each event; one with the mean of the nightlight values of cells that were struck by the event and one with the cells that were not affected. Furthermore, the analysis focused on the affected regions – state or province level ‐ and not the national averages.

# _C. Regression Methodology_

The second set of analysis consists of regressions which were run to determine any effect that the different natural hazards might have had on the nightlight value. The methodology chosen were that of fixed effects, correcting for time and spatial effects. To correct for potential heteroscedasticity, Driscoll‐Kraay standard errors are used (Driscoll and Kraay 1998).

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sample. The findings suggest that the predictions remain accurate when including household heads older than 65 years of age.

Third, we consider what happens if the model had perfect foresight with respect to the included covariates. Given that we have panel data, we can observe all included covariates in both time periods. Instead of using cross-section controls, we make use of the actual panel structure to include actual variables for the period in which we predict poverty, in our case retrospective variables. Given that we estimate the model for households observed in round 2 and predict their income in round 1, this is feasible in our setup. As already mentioned, all variables are time-variant to some extent. Therefore, this exercise intends to correct for any changes in age, years of education, household size, and migration from households in each of the four countries. In general, using retrospective covariates does not affect performance substantially.

Last, we consider population changes by means of different weighting structures. As mentioned in Section 3, we weight the estimates using the survey weights in each of our data sources. This is common practice in household surveys to carry out inference on the entire population. There are two factors that may affect the performance of LASSO-PMM. On the one hand, statistics institutes in some countries vary their sampling structure from survey to survey. On the other hand, immigration shocks can dramatically alter the population in a country if many people enter or leave the country, which can affect welfare and poverty rates. If either of these events occur, they may affect welfare predictions from LASSO-PMM and other methods because they rely on the assumption that the population is relatively stable between periods.

The baseline LASSO-PMM approach estimates individual-level poverty using household level data and therefore multiplies household-level weights by household size. We try two different 27
were randomly allocated to tree maintenance groups of five, half of which were offered collective PES contracts with payments that increase linearly in the number of trees still alive at endline, and the other half where payments depended on the highest survival threshold that was met at endline. To ensure balance with respect to climatic as well as socio-economic characteristics, treatment allocation was stratified at the sub-forest level.

Consistent with threshold group payments changing the nature of the strategic interaction from a social dilemma to a coordination game, we find that threshold group payment schemes have positive impacts on a number of coordination indicators, such as the number of maintenance planning meetings, trust in fellow group members, and the extent to which group members contributed equally to the maintenance activities. Despite the positive impact on these intermediate coordination indicators – but consistent with Burkina Faso’s very harsh climatic conditions making tree survival very uncertain – we also find that survival rates are significantly higher with linear (as opposed to threshold) group payments. We propose two possible mechanisms giving rise to these results – cooperation dynamics and uncertainty about the effort-survival relationship – and test their relevance using data from our RCT as well as from a real-effort laboratory experiment, implemented using students from Tilburg University, that closely mimics the key features of the tree maintenance task in the We are not the first to consider the issue of how to overcome the financial tragedy of the commons that standard PES payment schemes give rise to. Narloch _et al._ (2012) implemented a lab-in-the-field experiment in Bolivia and Peru and found that individual rewards were more effective in promoting agrobiodiversity conservation than collective rewards. Using a setup similar to that of Narloch _et al._ (2012), Salk _et al._ (2017) find the opposite result in their study in the Lao People’s Democratic Republic, possibly because they allowed for open communication. Kerr _et al._ (2014) draw attention to the risk of collective financial compensation resulting in crowding-out of community members’ intrinsic motivation to contribute, and suggest that non-monetary compensation may outperform financial payments. Hayes _et al._ (2017) exploit the gradual rollout of a PES program in Ecuador and find that, as predicted by Travers _et al._ (2011), the effectiveness of collective PES payments crucially depends on the (strength of the) community’s governance structure in place. Kaczan _et al._ (2017) report the results of a lab-in-the-field experiment 5
**Figure 3:** Gains to GDPPC from closing the Gender Employment Gap (%): GEGI vs. 2050 LTGM

<!-- Start of picture text --> YEM<br>IRQ<br>SYR<br>JOR<br>PSE<br>IRN DZA<br>EGY<br>LBN<br>MARTUN<br>SAU DJI<br>LBY<br>OMN<br>KWT BHR<br>ARE<br>QAT 45 degree line<br>Fitted values: y=1.56x+0<br>0 10 20 30 40 50 60 70 80<br>2050 LTGM<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>Aggregate capital fully adjusts (GEGI)<br>10<br>0<br><!-- End of picture text --> Note: x axis values are the 2050 gains in output per capita from closing the gender gap by 2050 obtained calibrating the LTGM/LTGM-NR, green lines from figure 2. y axis values are the Gender Employment Gap Index (GEGI) from Pennings (2022) (ARE, BHR, KWT, OMN, QAT and SAU are adjusted for unequal male-female populations), blue lines from figure 2. The regression run to obtained the fitted values equation does not assume no constant (the 0 constant is obtained as a result of the regression).

# GEGI than the LTGM by 2050 (Figure 3).

The time path of the boost to growth in the LTGM is shown in Figure 4 and Figure 5. The adjustment path is only slightly non-linear, so a good approximation of the boost to growth at 2035 is around half of the boost to growth by 2050. These paths also suggest that medium term growth gains of boosting female employment are substantial, even if the implementation takes some time. For example, a steady reform process starting in 2020 would have produced gains of 25% of GDP in Yemen and 18% of GDP in Algeria by 2035.

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# **Taking Stock of Economic Regulation of Power Utilities in the Developing World: A Literature Review**<sup>**1**</sup>

## **Martín Rodríguez Pardina and Julieta Schiro**<sup>*****</sup>

*Macroconsulting S.A. of Argentina

**Keywords** : Power sector regulation; Power sector reform; Power sector market design and regulation; Electricity pricing; Electricity tariffs; Developing countries; Emerging markets.

**JEL Classification** : L94

> 1 This paper is a product of the “Rethinking Power Sector Reform” knowledge program of the World Bank’s Energy & Extractives Global Practice. Any views presented here are the authors’ alone and should not be attributed to the World Bank or any other person or institution. Financial support from the Energy Services Management Assistance Program (ESMAP) and the Public Private Infrastructure Advisory Facility (PPIAF) is gratefully acknowledged. Peer review comments were provided by Vivien Foster, Katharina Gassner, Joseph Kapika, David Reinstein, and Bernard Tenenbaum. Any shortcomings are the sole responsibility of the authors.
will use an ordinary least squares regression model (OLS) and otherwise ANCOVA where we have the outcome variable measured at baseline. All variables denominated in Ethiopian Birr are winsorized at the 99th percentile to deal with the possibility of sensitivity of the results to outliers.

# **3.2.3** **_Role of the trainer_**

The regression specifications for the analysis of trainer characteristics in the PI/BSED study take the form: StudentPsych1i =∝+β1TrainerChar0tr + β2StudentPsych0i + εit (E3) Where StudentPsych1i are psychological outcomes (e.g. personal initiative, self-efficacy, error competence, entrepreneurial identity and locus of control) of student i measured at follow-up I, 1.5 years after the training. TrainerChar0tr are a set of characteristics for trainer tr such as: age, gender, tenure, experience, cognitive and noncognitive skills and job satisfaction. The coefficients β1 measure the correlation of the trainer characteristics with student psychology among those who participated in the PI or BSED trainings. StudentPsych0i is the baseline value of the student's psychology outcome variable. Standard errors are clustered at the classroom and training round level (the six TVET colleges where the trainings took place had multiple training rounds where the student could have attended a training). εit it is the error term.

Tables 1 and 2 provide a summary of the training interventions and details of the study.
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mitigate the risk of endogeneity, though it is difficult to avoid it completely (e.g., Graham et al., 2010), the lagged values are used to measure thickness of the existing industry. Two types of agglomeration are considered: the number of other agrobusinesses, _Ni_ , and the number of all firms registered, _ALLNi_ . While the former is often referred to as localization economies, the latter is called urbanization economies. _X_ contains other location-specific characteristics, including transport connectivity.

There are at least two important empirical issues to estimate Equation (1). First, spatial autocorrelation matters. Especially, our primary interest is the impacts of transport connectivity on firms’ agglomeration. Agglomeration economies would likely spill over. If firms are concentrated at a particular location, neighboring areas may also benefit from it. In addition, transport infrastructure is by nature a network industry. All firms are somehow connected to each other, but the connectivity must be dependent on the condition of the underlying infrastructure. Therefore, any observation may not be independent of another, i.e., _Cov_ ( _ut_ ,<sup>_u_</sup> _s_ )  0 .

Second, our dependent variable is left-censored at zero because there are many communes where no agrobusiness exists. This is a typical empirical issue with spatial data (e.g., Agarwal et al., 2002; Lee et al., 2016). In our data, 410 agrobusinesses were newly registered for the last three years: 2015-17. To avoid any possible effect of geographically biased events, the total number for three years is used. These firms are located at 142 communes of a total of 1,549 communes in Madagascar. Thus, the dependent variable has an excessive number of zeros.

To deal with these two problems, the spatial Tobit (SPTobit) regression model is used. Denoting the latent variable of the output produced by _ΔN*_ , our censoring mechanism is this:
# **Figure 5 Products offered to SMEs (II)**

The source of this figure is the data collected from banks. The banks that answered the information displayed in Figure 5.A represent around 40% of the private sector loans in each country and the banks that answered the information displayed in Figure 5.B represent between 15% and 20% of the private sector loans in each country. Figure 5.B displays the weighted average of the terms of the main lending products to SMEs, weighted according to the amount each bank lends of each product.

# **A. Share of main products in total lending to SMEs**

<!-- Start of picture text --> 70% 63%<br>60% Argentina Chile<br>50% 38%<br>40% 32%<br>30%<br>17%<br>20%<br>8% 7% 7% 6%<br>10%<br>0%<br>Working capital loans Investment loans Credit letters Leasing<br>B. Average term of main lending products to SMEs<br>Weighted average according to bank lending of each product<br>3,704<br>4,000<br>3,500 Argentina Chile<br>3,000<br>2,500 Working capital loans<br>2,000<br>1,500 1,041 1,171 1,399 1,116<br>745<br>1,000 372<br>500 139 25 99 28 142 98 198 40 176 180 45 88<br>0<br>0<br>Working Credit Over- Credit Other Investment Credit Leasing Factoring Other<br>capital cards drafts lines loans letters<br>loans<br>Share of products in total loans to SMEs<br>Number of days<br><!-- End of picture text -->

# **B. Average term of main lending products to SMEs**