rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 (Continued) HOUSEHOLD-LEVEL SCHOOLING REGRESSIONS: PERU, 1985-86

|Independent<br>Equat|ion 1<br>Equation 2|Equation3|
|---|---|---|
|Variables<br>Female|Male<br>Female<br>Male|Female<br>Male|
|Po|pulation Aged26 to 35 years||
|Constant<br>26.685|26.601<br>25.9919 26.117|26.662 26.230|
|(0.99)|(0.93)<br>(0.95) (0.92)|(1.00) (0.97)|
|Industry share<br>-1.384|-1.733<br>-0.542 -1.590|-0.470 -1.384|
|(1.12)|(1.01)<br>(1.07) (1.00)|(1.07) (1.00)|
|Services share<br>-3.845|-2.639<br>-3.233 -2.170|-3.311 -2.124|
|(1.38)|(1.32)<br>(1.30) (1.30)|(1.31) (1.31)|
|Rural dummy<br>1.862|0.525<br>1.130<br>0.305|1.029<br>0.226|
|(0.35)|(0.33)<br>(0.34) (0.33)|(0.34) (0.34)|
|Household income -0.006|-0.008<br>-0.004 -0.007|-0.004 -0.007|
|(0.00)|(0.00)<br>(0.00)<br>(0.00)|(0.00)<br>(0.00)|
|Father's education|-0.538 -0.303|-0.471 -0.272|
||(0.10) (0.09)|(0.10) (0.09)|
|Mother's education|-0.606 -0.262|-0.576 -0.261|
||(0.10)<br>(0.09)|(0.10)<br>(0.09)|
|Father's occupation||-0.245- -0.121|
|||(0.10)<br>(0.09)|
|Mother's occupation||0.031<br>0.093|
|||(0.07) (0.07)|
|2<br>Adjusted R<br>0.404|0.500<br>0.462<br>0.515|0.464<br>0.515|
|Sample size<br>1148<br>|957<br>1148<br>957<br>1|148<br>957|

Note: Dependent variable is schooling shortfall - age - schooling - 5 Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

32
- More concentrated power in the hands of government parties in parliament leads to

- more motor fuel subsidies or lower fuel prices. This effect is particularly strong for middle-income countries, and is stronger for diesel than for gasoline.

When distinguishing effects between high-, middle- and low-income groups of countries, we have found that the significant subsidy-stimulating effects operate most effectively in middleincome countries, and less so in both high- and low-income nations. It is also, generally, in middle-income countries that subsidy levels and rates are most significant.

Many of our conclusions are novel, but clearly tentative and in need of scrutiny in future work, perhaps on improved data sets. Most of our conclusions however seem to fit rather well with economic theoretical and political thinking on re-distributing welfare through either providing public goods or targeted redistribution of public resources; some of this literature was summarized in Section 2 above.

Even though we have applied the best data set of this type that may be available today, our data still have its limitations. Many country/year observations are missing; our gasoline data are for premium grade while we are to a large extent lacking data for regular gasoline (possibly, the more interesting fuel in terms of subsidies); and our fuel data set itself has limitations (only one data point per year; and fuel price observations are typically taken from the country’s capital the representativeness of which is unclear).

In future work, we seek to make improvements to this analysis, on several fronts, among which we here will mention two. First, the data set applied for this study still lacks key variables such as motor vehicle stocks and measures of optimal country-specific fuel pricing. We do however have access to data for motor vehicle stocks by country, for the period in question, but not for all these countries.<sup>22</sup> We intend, in the future, to pursue empirical work where such data are embedded. Secondly, we will seek to correct for possible problems of endogeneity of key variables, which may include public health expenditures, and some of the political variables employed (in particular, “corruption” could be endogenous and simultaneously determined with other features of the political system such as main governance type). These issues are among those we intend, as stated, to address in further work.

> 22 This data set also has some other limitations, in terms of reliability, and  with regard to the definition of motor vehicle classes.

40
Policy Research Working Paper 10583

# **Abstract**

Given increasing levels of displacement due to conflict and climate change, it is important to establish robust monitoring systems. This paper explores how remote sensing data, particularly geospatial data, can be leveraged to monitor displacement flows. It draws lessons from northeastern Afghanistan, namely the 2018 drought, which is considered one of the worst in decades. The analysis identifies displacement patterns by combining displacement data from the International Organization for Migration Displacement Tracking Matrix with nighttime lights. The results suggest that the cumulated displacement movements from 2018 to 2020 can be proxied by trends in nighttime light imagery. Settlements with higher net inflows of displaced persons between 2018 and 2020 have comparatively larger nighttime light growth. Allowing for nonlinearity suggests decreasing marginal returns of displacement on nighttime lights, as settlements showing the largest expansion of nighttime lights are those with the lowest displacement inflows. The model uses data on nighttime lights to predict whether a settlement was a net receiver of displacement flows during 2018–20 and correctly classifies 63.2 percent of the settlements as net inflow or net outflow. This study provides a proof of concept to test whether population displacements can be proxied using geospatial data trained on administrative records in a data-scarce environment, where real-time insights can inform humanitarian assistance. This work was done before the political crisis of August 2021.

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 authors may be contacted at adahmaniscuitti@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
**Table 13.  Use of Different Financial Services in Kenya**

||**2006**|**2009**||200|9||
|---|---|---|---|---|---|---|
||overall|Overall|female|Male|Urban|rural|
|Transaction|15|45.2|40.7|50.2|71.6|38.1|
|Savings|25.6|27|21.2|33.4|40.7|23.2|
|Insurance|5.9|6.8|6|8.2|14.1|4.8|
|Credit|6.7|7.3|4.7|9.1|10.3|6.2|

Source: Beck (2009). For detailed definitions, see Source.

The entry of M-Pesa into the remittance market has been powerful and has been behind the increase in the use of transaction services. 39.9% of those surveyed claim to have used M- Pesa, more than the users of any other financial institution or product in Kenya. The popularity of M-Pesa is also reflected in Kenyans’ perceptions. While in 2006, a relative (weighted) majority of those surveyed named ‘specialist money transfer operator’ as the least risky and fastest channel to send remittances and friends and family as the least expensive and easiest to obtain, in 2009 it was M-Pesa that was rated the least risky, the fastest and the easiest to obtain, while it came in as second under the category least expensive (friends and family continue to be rated as the least expensive channel).

While many individual characteristics are associated with the use of financial services, many of these characteristics are also correlated with each other. For example, inhabitants of rural areas are typically poorer and less likely to work in formal jobs – which of these characteristics is the decisive one explaining whether an individual has access to financial services or not? In order to determine the decisive factors, we utilize multivariate regression analysis. Specifically, we regress dummy variables that indicate whether an individual has (i) access to banking services, (ii) other formal financial services, (iii) informal financial services, or (iv) is excluded.<sup>18</sup> The regression results in Table 14 suggest that controlling for other characteristics, women are not less likely to use formal banking or other formal financial services, but they are more likely to use informal services than men and are less likely to be excluded. Rural

> 18 Unlike in Tables 10 through 13, individuals can therefore belong to several categories.

27
imputation methods, energy shares can be imputed into the EU-SILC survey. Utilizing the resulting synthetic dataset, one can overlay energy poverty with official monetary poverty. The findings reveal a substantial portion of the energy-poor population (using the LIHC measure) that does not fall under the category of official income poverty. Consistent with the findings in this paper, the results vary significantly depending on the energy measure used. This research contributes to the expanding body of literature exploring the potential of statistical matching to enhance the current data landscape in the European Union.

# 3.2. Consensual Approaches and Outcome-based Metrics

**We next explore consensual and outcome-based measures in Bulgaria and rely on those indicators mentioned in Table 1, which are included in the EU-SILC and available in Bulgaria.** Two aspects of the main EU indicators for material and social deprivation (MSD) and severe material and social deprivation (SMSD) are directly applicable to the monitoring of energy poverty using this approach. They are commonly employed as proxies for assessing energy poverty in the EU: the inability to adequately heat one's home and the incidence of arrears on utility bills.<sup>40</sup> Both factors are integral components of the AROPE headline indicator, which has been fundamental to the EU2020 poverty and social exclusion target. It continues to play a key role in supporting the 2030 EU poverty and social exclusion reduction target.

**<mark>Figure 13 shows</mark> that the energy poverty incidence rate fluctuates between 11.0 percent and 46.5 percent, depending on the indicator used.** Consequently, the overlap between monetary and energy poor differs significantly by <mark>indicator (Figure 14). Not surprisingly, the leakage</mark> and type of dwelling indicators<sup>41</sup> report relatively low overlaps. At the same time, nearly half of the monetary poor are also energy poor in the case of the affordability indicators. A high share of households are only energy poor on the building indicator (40.2 percent). These measures can be disaggregated at the NUTS3 level, as the SILC survey is representative at this level.

and welfare is created. To achieve this, the authors construct a harmonized dataset by combining EU-SILC and household budget survey data. Subsequently, they employ various imputation models and identify the most effective one to impute energy spending shares into the EU-SILC data.

> 40 Arrears on utility bills are captured in the SILC survey question: “During the last 12 months, did your household face some problems with payment on time of the expenditure on the dwelling you are living in? Ability to keep the home warm is captured in the SILC survey question: “Can you afford adequate heating of your dwelling?“ 41 Privately-owned inefficient multi-family apartment blocks with insufficient maintenance may have high energy poverty rates.

26
We also find that progress in educational achievement has been limited, with improvements –in terms of higher coverage (share of students with basic level 2 proficiency) or lower inequality of opportunity (between circumstance groups) – seen for half the countries in mathematics and about two-thirds of the countries in reading and science. The changes that have occurred (positive or negative) are in most cases mainly attributable to changes in achievements within groups, albeit with a few exceptions where changes in the distribution of circumstances have led to improvements in inequality of opportunity.

It is important to keep in mind that a three-year window might be too short a time span to observe discernible improvements, and many of the changes seen here are small enough to qualify as statistical noise as opposed to meaningful changes. If the three-year time window is indeed too short, it would imply that policies focused on reducing inequality of opportunity in educational achievement need to act for a long period of time to effect measurable improvements. The fact that public spending on primary education has a strong association with educational achievement suggests that redistributive policies during childhood years have an important role to play in promoting equality of opportunity in educational achievement in the long-run.

# **References**

- Barros, R., Ferreira, F., Molinas, J., and Saavedra, J. (2009). “Measuring Inequality of Opportunities in Latin American and the Caribbean,” The International Bank for Reconstruction and Development and The World Bank.

- Barros, R., Molinas, J., and Saavedra, J. (2010). “Measuring Progress Toward Basic Opportunities for All,” Brazilian Review of Econometrics, 30(2): 335-367.

- Barro, J., and Lee, W. (2013) “A new data set of educational attainment in the world, 1950–2010,” Journal of Development Economics, 104: 184–198

- Bedard, K., and Ferrall, C. (2003). “Wage and Test Score Dispersion: Some International Evidence,” Economics of Education Review, 22: 31-43.

- Blau, F., and Kahn, L. (2005). “Do Cognitive Test Scores Explain Higher US Wage Inequality?” Review of Economics and Statistics, 87: 184-193.

- Brunori, P., Ferreira, F., and Peragine, V. (2013). “Inequality of Opportunity, Income Inequality and Economic Mobility: Some International Comparisons,” IZA Discussion Paper Series No. 7155.

- Castello, A., and Domenech, R. (2002). “Human Capital Inequality and Economic Growth: Some New Evidence,” Economic Journal, Royal Economic Society, 112(478): C187-C200.

18
14 values. This implies that reliable parameter estimation, and good underlying data in particular, are critical for dependable policy recommendations.

The CGE will provide the sectoral pattern of factor employment and the resulting factorial (functional) income distribution. To map these incomes to the socioeconomic groups requires data on the household's factor endowments (the amount of land, capital and human capital owned by the different groups) and its sector of activity. In order to make these models dynamic, factor endowments should be made endogenous. This would require models of investment processes in land, capital and education for each of the socioeconomic groups, a difficult empirical task. A second issue is identifying the sector of activity for the household. Surveys consistently show that households earn income from a variety of sources, for example, agricultural households may supplement their income with non-agricultural activities in the off-season. Over time, the shares of income received from these two sectors can change and the model should incorporate such changes. These two issues are difficult and not addressed in most models; they are often handled by specifically se'ecting socioeconomic groups that are essentially extended functional distributions. Groups are defined according to their function and sector of activity, thus ignoring multiple income sources.

The CGE provides results on the mean income of different socioeconomic groups, but to measure the impact on poverty, some estimate of how this income is distributed within the group is also needed. For example, if the mean real income of small farmers increases, it is not clear that any of the poor small farmers receive the increase. The importance of changes in intergroup variance (the portion explained by the model when it produces mean incomes) varies by country and by the selection and number of socioeconomic groups. Bourguignon, et al (1983) found that in Venezuela, 33% of the total inequality of labor
treatments remains after controlling for various measures of access to credit, wealth, risk aversion, and a rich set of ability measures. The data suggest that women do invest their grants differently than men. Over the three years our surveys span, we find that on average none of the $100 grants remain in female-owned enterprises, while all of the smaller grants remain in male-owned enterprises. Females, on the other hand, invest and retain in the business a somewhat larger portion of the $200 grants. Compared with men, women were more likely to invest in equipment like ovens, sewing machines, or furniture, and less likely to invest in inventories and working capital.

But we find a marked difference between males and females in the returns generated by grants which are invested. As with the returns to the treatments, these differences are not explained by differences in measured ability, differences in entrepreneurial attitudes, the location of the business, or the percentage of sales made outside of the immediate neighborhood. Differences in sector of work may explain some of the difference. The gap in returns is much larger when we compare female-only industries such as lace to male-only industries such as repair services. Nevertheless, we still find women to have returns near zero in industries in which both male- and femaleowned enterprises both comprise at least 25 percent of the sector’s firms.

As with any finding based on data from a single country, we should ask whether the findings are likely to hold in other regions or countries. Using cross sectional data from Brazil and Mexico, we show that similar gender differences appear in urban households in these countries. Women earn a significantly lower return on marginal investments of capital, and the gap is larger in sectors in which either males or females predominate and smaller in sectors in which both males and females are active.

Finally, while the results from the Sri Lankan data show zero average return for women, they do not imply that there are no female-owned enterprises which have high returns. In the final section of the paper we examine the heterogeneity of returns among female-owned enterprises, and show that returns are indeed high for the subset of highability, financially-constrained firms. However, at least in our data, male-owned firms are on average more constrained and have higher returns to marginal capital investments, suggesting an economic efficiency argument for directing more resources towards them.

4

-  -
|1_, X_)_−E_(_Y |T_ = 0_, X_)<br>(4)<br>(5)<br>OLS<br>IV<br>0.055***<br>0.064**<br>(0.020)<br>(0.027)<br>0.041**<br>0.048*<br>(0.020)<br>(0.029)<br>0.036*<br>0.039<br>(0.019)<br>(0.026)<br>0.007<br>0.007<br>(0.013)<br>(0.016)<br>Yes<br>Yes<br>0.1617<br>-|ed by the number of household<br>INs and properly treated ITNs.<br>s in brackets. Columns (3) and<br>mn (5) estimates the difference<br>didn’t by instrumenting program<br>ported in parentheses. Controls<br>formation about access to water,<br>the village). To control for joint<br>e report p-values of an F-test for|
|---|---|
|_E_(_Y |T_ =<br>(3)<br>OLS<br>0.033<br>(0.025)<br>0.028<br>(0.024)<br>0.030<br>(0.022)<br>-0.006<br>(0.017)<br>No<br>0.6394|is normaliz<br>des only LL<br>rd deviation<br>el 2). Colu<br>d those who<br>e level are re<br>d religion, in<br>en living in<br>iables and w|
|(2)<br>Treatment<br>0.400<br>[0.312]<br>0.323<br>[0.279]<br>0.249<br>[0.267]<br>0.133<br>[0.244]<br>p-values =|Number of nets<br>tus, “ITNs” inclu<br>nt group, standa<br> regression (mod<br>last 5 months an<br>lustered at villag<br>old size, tribe an<br>and share of wom<br>ted groups of var|
|(1)<br>Control<br>Number of nets owned<br>0.367<br>[0.309]<br>Number of ITNs owned<br>0.295<br>[0.284]<br>Number of observed nets used the night before<br>0.218<br>[0.258]<br>Number of observed nets not used the night before<br>0.139<br>[0.248]<br>Controls<br>Joint tests on variables (with comparable sample size):<br>1-4|Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per household. <br>members. “Nets” refers to any bed nets, irrespective of their treatment sta<br>Columns (1) and (2) report sample means restricted to control and treatme<br>(4) report the difference between treatment and control groups using OLS <br>between households who report to have received the spraying campaign in the<br>participation with the treatment group indicator (model3). Standard errors c<br>include gender, age, education and marriage status of the respondent, househ<br>dwelling characteristics, sub-zone dummies and village characteristics (size<br>signifcance, we run a probit regression of the treatment indicator on the selec<br>the joint signifcance of the coeffcients on the selected variables.|

25
# **2.3 Country level estimates**

Table 3 presents breakdown of extended labor force by proximity to formal employment for each of 30 European countries as of 2008-2009 (data for Austria and Italy refer to 2007 and 2006, respectively), along with the LFS-based unemployment rate for the respective period of field work, and the estimate of the non-employed population which was recently informally employed. Figure 1, derived from Table 3, features current total informal employment and its two components, workers without contracts and informal self-employed, measured as proportions of extended labor force; on top of this, recent informal employment of currently non-employed population is shown in the same units. Adding the ̳recent‘ component significantly increases the estimated level of informality for a number of countries (see Figure 1 for details), but leaves the ranking basically intact. In the following discussion we refer to the current levels of informal employment, unless stated otherwise.

<!-- Start of picture text --> Share of Extended Labor Force Employed Informally<br>2008-2009<br>50<br>40<br>30<br>20<br>10<br>0<br>CYGR IL IT PTES PLUA SI BGCZSKRUROEEHULV LT IE UKATCHNLDEBEFR NODK FI SE<br>South East West North<br>Non-employed who recently were informal employees<br>Non-employed who recently were informal self-employed<br>Informal employees<br>Informal self-employed<br>Labor force extended to include discouraged workers<br>Austrian data refer to 2007; Italian data refer to 2006<br>Due to data limitations, shares of non-employed who were recently informally employed<br>are not perfectly comparable across countries. Average 'recent' period varies as follows:<br>10-15 months in the South and in the West, 11-18 months in the East, 10-11 months in the North<br>Source: Calculation with ESS data<br><!-- End of picture text -->

## **Figure 1 Selected European countries by share of extended labor force employed informally, 2008-2009**

_Notes:_ Informal self-employment includes all non-professional self-employed operating solely, as well as employers with 5 or fewer workers. Informal employees are those working without a contract (or those uncertain of their contract). Extended labor force includes persons which, during the reference week, were either employed or unemployed and willing to work. The latter category includes both those unemployed who were actively looking a job and those who were not actively looking for a job. See Tables 1 and 3 for details. _Source:_ Calculation with ESS data.

13