rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5930

# **Abstract**

This paper proposes two new indices of relative deprivation, derived from an extension of the concept of the generalized Gini for the measurement of distributional change. Population- and income-weighted relative deprivation indices are then defined and, using panel data from the Consortium of Household Panels for European Socio-Economic Research, this paper checks which of the various ways of defining individual deprivation best fits the answers given by individuals on the degree of their satisfaction with income. The analysis finds that the deprivation indices proposed are consistently and negatively correlated with income satisfaction as reported by respondents, that income weighted measures fit better than population weighted measures, and that this fit improves with countries that experienced deep institutional changes such as the transitional economies of Eastern Europe.

This paper is a product of the Economic Policy, Poverty and Gender Unit, Middle East and North Africa 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 pverme@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
# **3. Unemployment**

Defining unemployment can be a particular challenge, as the conventional subdivision of the economically active population between employed and unemployed does not capture the large number of those falling in neither of these categories. In many instances it is difficult to determnine the size of the labor force because of the prevalence of subsistence activities in the rural areas and the informal sector labor force comprising the self-employed, casual and unpaid workers in the urban areas. Basically, three broad categories of unemployed can be distinguished. First, there are the unemployed per se. those out of a job and actively looking for work. Second, there are the underemployed, those who are working voluntarily less than full-time, or those who are working long hours but earning less than the minimum living standards income. Third there are the "discouraged" unemployed, who are unemployed but not actively looking for work. In our analysis, we define the unemployeds as those who are between 15-64 years of age and are actively looking for work; and the discouraged workers as those not actively searching for work.

We see on the basis of GLSS data, that the overall unemployment rate for active persons has decreased slightly from 1.9 to 1.5 percent. The unemployment rates for the inactive persons, though of higher order has also declined from 14% to 8%. A close inspection of the data reveals that unemployment rates tend to decrease with age. Eventhough the total unemployment rates have declined the composition of the unemployed have changed with more younger people (age 15-24 yrs) in the category (Table 9). Overall, young people's stock of unemployed is about 25% higher than their share of labor force. Also most of the unemployed are females compared to males, eventhough the female labor force participation rates have increased dramatically over the years. The unemployment rates also vary by educational levels, and tend to decrease with education. In 1992, over 54% of the unemployed only had middle school level education and
Our assessment also sheds light on three additional questions that come up in poverty‐ related debates: first, how much poverty reduction can economic growth generate and how much will have to be done through redistribution? Second, does focusing on a money‐based poverty target jeopardize progress in other measures of wellbeing such as education, longevity, and health? Third, is a focus on extreme poverty—measured by the poverty lines of the poorest 15 countries—setting the bar too low? Our view is:

- The success of a strategy that relies on growth in private economic activity and rapid improvements in essential public services—two mutually reinforcing elements—complemented by targeted interventions to help the most needy, should discourage “growth versus redistribution” extremism.

- Evidence in support of this strategy should reassure those who worry that a “narrow focus” on monetary poverty risks neglect of non‐monetary measures of human welfare. In fact, quick and sustained progress in achieving the 3 percent extreme poverty goal is not possible without augmenting the assets of the poor, principally through public support for investments in education and health.

- By recognizing that individuals who escape extreme poverty are still vulnerable to falling back into destitution, and proposing policy actions to prevent this from happening, the strategy should at least partially address the criticism of setting the bar too low.

The evidence from China, Vietnam, India, and other countries that have reduced extreme poverty rates to relatively low levels supports the three‐point strategy. If ending extreme poverty is the goal, the growth versus redistribution debate, and doubts about the adequacy of the consumption‐based poverty as a development goal are not the issues that policy makers should worry about too much. Instead, policy makers should be trying to figure out how this strategy can be implemented in countries where the incidence of extreme poverty is still high. They will likely find that the 2.5‐point strategy is better suited for such countries. When implemented well, extreme poverty rates will fall quickly,
**Policy Research Working Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS1692|Regulating Market Risk in Banks:<br>A Comparison of Alternative<br>Regulatory Regimes|Constantinos Stephanou|December 1996|P. Infante<br>37642|
|WPS1693|Famines and Economics|Martin Ravallion|December 1996|A. Ramirez<br>85734|
|WPS1694|What Can New Survey Data Tell Us<br>about Recent Changes in Distribution<br>and Poverty?|Martin Ravallion<br>Shaohua Chen|December 1996|A. Ramirez<br>85734|
|WPS1695|Are There Dynamic Gains from a<br>Poor-Area Development Program?|Jyotsna Jalan<br>Martin Ravaliion|December 1996|A. Ramirez<br>85734|
|WPS1696|South-North Migration and Trade:<br>A Survey|Maurice Schiff|December 1996|M. Patena<br>39515|
|WPS1697|Strategies to Develop Mortgage<br>Markets in Transition Economies|Dwight M. Jaffee<br>Bertrand Renaud|December 1996|R. Garner<br>37670|
# **4.3 Possible Threats from the New Situation**

Notwithstanding sizable and significant effects on a sample that is representative of the population of interest, the success of the mentoring intervention at scale is not guaranteed. The difference between the implementation protocol in our research setting—where we had an active role in guaranteeing the smooth progress of the field experiment—and the government operations can translate into contrasting aftermaths of the program. For example, the criteria for closing schools were very different in the two situations. Although the official enrollment threshold for closing schools is six students, schools in the second experiment were allowed to remain open if they had at least three enrolled students in either of the two school years when the experiment took place. In addition, children in schools with more than 29 enrolled students were required to transfer to schools in the regular public school system. As a result, only two schools closed in the sample of 230 schools in the second experiment (see Section 2.2).<sup>14</sup> Hence, a threat to the success of the program in the new policy situation may come from the possible school closures as a result of the mentoring intervention under the government implementation. If instead the presence of a mentor increases the probability that schools remain open, this mechanism could represent an opportunity to learn something about the mechanisms behind the scalability of the program in our context. Figure 1 shows the relationship between school closures, as measured by the year-to-year presence in the school census two years after the second experiment, and school size, as measured by the number of enrolled students in the school year before the second experiment. The green (lighter) line shows how the probability of school closures varies by school size for schools that did not receive the program during the government intervention. The probability of school closure is bimodal, and its two modes are positioned around the two critical enrollment thresholds.

> 14While school closures are obviously a “non-negotiable” aspect (List,
R-squared|0.123|0.478|0.078|0.356|0.034|0.203|0.074|0.171|
|Observations|400|400|400|400|400|400|400|400|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of probit models. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws and their matched controls. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

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described above, a minimum of three moments (for both the NSS and CPHS log consumption data) are required to estimate all three of these parameters. All three moments of the CPHS log consumption distribution can readily be estimated using the observed CPHS consumption data. Estimation of the moments from the NSS consumption distributed is complicated by the fact that there is no NSS survey for the same moment in time for which we have CPHS. We have established however that the third moment of NSS 36
survey are given in the data appendix). In the collection of this dataset the World Bank worked with a private survey fi rm. Given the detail and the sensitive nature of some

> 15For example, if fi rms change their technology then the demand for labor schedule changes and employment can increase or decrease, depending on whether the new technology is more or less labor intensive. Furthermore, assume that the wage of informal workers plus the potential penalty cost from hiring them is approximately equal to the net wage of formal workers plus social security costs, so that total labor costs with each type of worker are the same. Then, instead of a change in employment, fi rms may just choose to substitute formal for informal workers.

> 16This is usually labeled as “ fi scal kink” (or cunha fi scal). Given that the total labor costs of formal workers greatly exceed their net wage, there is a strong incentive for fi rms to hire informally and for workers to be hired informally, if the savings from such an exercise (unpaid social security bene fi ts) can be shared between both parties.

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# **5.1 Demand**

Of the 11,410 households who used their own well for cooking and drinking at baseline and who were offered an arsenic test, 2,829 (25%) bought a test under one of the selling schemes. To estimate the average treatment effect of selling schemes B and C relative to A, the following equation is estimated using a linear probability model: where _buysvh_ is equal to one if household _h_ in village _v_ and stratum _s_ bought a test at baseline and zero otherwise, _Bv_ and _Cv_ are village-specific indicator variables for the respective treatments, _Xsvh_ is a set of predetermined household and tube well characteristics, and _εsvh_ is an error term. To account for the stratified design, regressions also include strata fixed effects ( _δs_ ). Recall that treatment was stratified by the prevalence of unsafe wells based on BAMWSP data and by union. The coefficients of interest are _β_<sup>_B_</sup> and _β_<sup>_C_</sup> , which capture the causal impact on demand of selling schemes B and C, relative to A, respectively. All standard errors and statistical inference are robust to the presence of intra-village correlation of residuals.

Figure 2 shows graphically the simple comparison of take up rates across arms without the inclusion of controls or strata fixed effects. A first clear result is that neither the group sales nor the addition of the metal placard made any appreciable difference for demand. A second finding is that demand was overall quite low, with about one quarter of households purchasing the test in each of the three experimental arms. As in many earlier studies looking at demand for health-related preventive products, even a small fee led to low demand, despite the potentially vital information provided by the tests.<sup>15</sup> Table 2 displays the corresponding regression results. Column 1 shows see that, as expected, the small differences in demand between arms A, B and C are not statistically significant. Column 2 shows that
(i) Non-VG; (ii) VG without toilet at SBM baseline; and (iii) VG with toilet at SBM baseline.

Table 9 displays these findings. Interestingly, impacts on sanitation loan uptake are significantly positive across the board and similar in magnitude, for each type of household, irrespective of their VG status or of their subsidy eligibility status determined by toilet ownership at SBM baseline. Results on toilet uptake, however, reveal that within the subsidy ineligible group, impacts are strongest among non-VG households. These households experienced an impact on toilet uptake of 28pp (significant at the 1% level). Importantly, the results show that there was no impact on toilet uptake for VG households with toilet recorded at SBM baseline, despite the observed impacts on sanitation loan uptake for this sub-group. This suggests that there are unobserved constraints specific to these VG households which do not allow them to translate the sanitation loan into new toilets. Possible reasons include lack of additional financing sources needed to complement micro-credit to cover the actual toilet cost over and above the loan amount; and/or differential perceptions about the benefits of improved sanitation.

Overall, results in Table 9 suggest that a significant portion of non-VG MF client households would benefit from financial support but are - unlike their VG counterparts - unable to access government subsidies. Appendix B compares VG and non-VG households in terms of a wide range of socio-economic characteristics collected during the endline survey, other than those on basis of which official VG status is defined (e.g. caste, female headed, etc.). At endline, non-VG MF client households without a toilet at survey baseline appear slightly better off in terms of some characteristics, particularly in terms of educational attainment of household head and some key productive assets such as agricultural equipment. Overall, however, they are not strikingly different than their VG counterparts, which is not very surprising given the poor settings in which these households reside. These results could explain why non-VG households turn to sanitation loans in the absence of getting
Patagonian region. The concentration coefficient is estimated at +0.08, indicating a broadly egalitarian but mildly regressive distribution.

> 7 This exercise is based on expenditure reported by 70 Patagonian households in the OPSM survey, which account for 45% of the households surveyed in that region. An important limitation is the fact that the survey was undertaken during the summer period. Given that higher income households probably spend more on winter heating than lower income households, the distributional incidence for the winter period could be significantly more regressive.

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sampling strategy and cover in a comparable way a range of businessenvironment topics including sales, number of employees, age of firm, manager experience, ownership characteristics, employees’ education, the degree to which firms see crime as an obstacle, and the location of the establishments, among others.<sup>7</sup> Appendix B displays the list of all firm-level variables used in the analysis.

To test our hypotheses, we link the enterprise survey data to metropolitan area–level data by obtaining GPS coordinates or detailed location names for all establishments in the Enterprise

> 7 We looked at the country classification at the time of the survey.

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our focus on ambiguity with stylized facts from the Serbian customs data. Section 4 develops the theoretical framework incorporating ambiguity, describes the equilibrium, and derives comparative static predictions of the model. Section 5 calibrates a numerical version of the model and conducts counterfactual analysis. Section 6 concludes. The general framework describing the neo-additive capacity and the Choquet integral is relegated to the appendix as are additional details about the model calibration and conventional 6
paper complements the literature by providing a better understanding of the role of policies in reducing migration barriers.

The remainder of the paper is organized as follows: section 2 describes the data sources, section 3 describes the empirical specification, section 4 describes the results, section 5 presents the potential welfare impacts of the BLAs, and section 6 concludes.

# **2 Data**

## **2.1 Data on migration**

This paper uses the most comprehensive data set on bilateral migration stocks spanning from 1960 to 2020, in ten-year intervals. Migration stocks for years 1960, 1970, 1980, 1990, and 2000 are taken from World Bank (2022) (see Ozden<sup> ̈</sup> et al., 2011, for methodological details). The data for 2010 and 2020 come from UNDESA (2020) which employs similar methodology. These data sets use information collated from censuses and surveys at destination and origin countries to construct a matrix of bilateral migration stock data from each of the origin countries to each potential destination country. This combination of datasets is used widely by many research studies and reports (including in Clemens, 2020, Shrestha, 2023, World Bank, 2023).

A migrant, in these data sets, is typically a person who is born in a country that is different from the country in which they are currently living. To that extent, it includes, in principle, people who have migrated for various reasons and channels, including those who migrated through irregular channels (Ozden<sup> ̈</sup> et al., 2011). However, it is possible that these data sources may misreport irregular migrants for various reasons. To the extent such misreporting exists, measurement errors can mute the estimated relationship.

In addition, this dataset only captures the _stock_ of migrants and does not capture the _flow_ of people between these countries. Migrants who move temporarily, particularly between the census years, will not be fully captured by this dataset. If
# **2. Public debt in Sub-Saharan Africa**

Public debt levels in Sub-Saharan Africa were on a downward trajectory until 2012. Throughout the end of the 1990s and the 2000s, 30 African low-income countries (LICs) benefitted from more than US$100 billion in debt relief provided under the Heavily Indebted Poor Country (HIPC) and Multilateral Debt Relief Initiatives (MDRI). Public debt levels in Sub-Saharan Africa therefore declined until 2012 to a median (average) of 34 (38) percent of GDP. The interquartile range declined sharply from 64 percent of GDP in 2004 (the previous year to the MDRI being launched) to about 21 percent of GDP in 2012.

After 2012, the dynamics and composition of public debt changed significantly ( **Figure 1** ). Between 2012 and the end of 2018, public debt in the region increased from an average of 38 to 59 percent of GDP. About three-quarters of the countries in Sub-Saharan Africa saw their public debt relative to GDP rise by more than 10 percentage points, while more than 40 percent of the countries experienced an increase of more than 20 percentage points. And the composition of public debt changed significantly. Countries shifted away from traditional concessional sources of financing, toward more market-based debt and lending from new bilateral creditors. The share of multilateral and concessional debt declined while the share of non-Paris Club debt increased (see **Section 3** for more details).

**Figure 1. General Government Gross Debt** _(% GDP)_

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>25th Percentile Median Average 75th Percentile<br><!-- End of picture text --> _Source: IMF World Economic Outlook Database, October 2018._ 3
important but remains the exception rather than the rule. Following Beine and Jeusette (2021), an example of direct measure of mobility can be found in survey data where people are directly asked about their migration history, whereas an indirect measurement means that migration is inferred rather than observed, as in the case of differences in migration stocks reconstructed from censuses. Importantly, econometric analyses using measures of mobility that are computed or derived from proxies tend to find

> 10 A caveat is in order here to remind readers of the potential differences and inconsistencies between ‘stated’ and ‘revealed’ migration preferences, which compound over the already complex and multi-faceted nature of the migration phenomenon, that can take many different forms both across space and over time. The issue is also related to the heterogeneity of the climate risk perceptions of the individuals interviewed.

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Section 4 concludes with a proposed roadmap for reforms with strong catalytic impact for debt market development in MENA.

# **2. Overview of Government Debt Markets in the Non-GCC MENA Region**

## _2.1. Selected MENA markets and macroeconomic conditions_

**The paper focuses on five countries in the non-GCC MENA region that have government bond markets with minimum size and greater potential for market development: Egypt, Jordan, Lebanon, Morocco and Tunisia (MENA-5).**<sup>**6**</sup> These countries have sizable debt-to-GDP ratios and domestic tradable debt (Figures 1 and 2) and have, to different degrees, implemented measures to develop their debt markets. They are all middle-income, oil-importing countries with diversified exports. These macroeconomic characteristics help explain their greater need to issue debt and rely on domestic debt markets<sup>7</sup> .

**Figure 1**

**Figure 2**

<!-- Start of picture text --> TotalCentral Government Debt: Domestic vs. External TotalCentral Government Debt: Domestic vs. External<br>200 2004-2009 (% of GDP) 160 2004-2009 (USD Billion)<br>Domestic External Domestic External<br>180 140<br>160<br>120<br>140<br>120 100<br>100 80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>Lebanon Egypt Jordan Morocco Tunisia Syria Algeria Lebanon Egypt Jordan Morocco Tunisia Syria Algeria<br>Source: World Bank LDB Working Database Source: World Bank LDB Working Database<br>% of GDP USD Billion<br><!-- End of picture text -->

**Other countries in the non-GCC MENA region have had, in general, fiscal and current account surpluses and have less developed debt markets (Figures 3 and 4).**<sup