rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table S1.1.** OIC Countries and Donor Pool  Selection

|**Country**|**Joined**|**EFW data**|**Country**|**Joined**|**EFW data**|
|---|---|---|---|---|---|
|Afghanistan|1969||Malaysia|1969|*|
|Albania|1992||Maldives|1976||
|Algeria|1969||Mali|1969|*|
|Azerbaijan|1992||Mauritania|1969||
|Bahrain|1972||Morocco|1969|*|
|Bangladesh|1974|*|Mozambique|1994||
|Benin|1983||Niger|1969|*|
|Brunei|1984||Nigeria|1986|*|
|Burkina Faso|1974||Oman|1972||
|Cameroon|1974||Pakistan|1969|*|
|Chad|1969||Palestine|1969||
|Comoros|1976||Qatar|1972||
|Côte d’Ivoire|2001||Saudi Arabia|1969||
|Djibouti|1978||Senegal|1969||
|Egypt|1969|*|Sierra Leone|1972|*|
|Gabon|1974||Somalia|1969||
|Gambia|1974||Sudan|1969||
|Guinea|1969||Suriname|1996||
|Guinea-Bissau|1974||Syria|1972|*|
|Guyana|1998||Tajikistan|1992||
|Indonesia|1969|*|Togo|1997||
|Iran|1969|*|Tunisia|1969|*|
|Iraq|1975||Turkey|1969|*|
|Jordan|1969|*|Turkmenistan|1992||
|Kazakhstan|1995||Uganda|1974||
|Kuwait|1969||UAE|1972
region. Apart from the urban-rural divide these display also a north-south divide. The North-South gap is particularly striking, as even regional level urban poverty rates in the north do not come close to the rural poverty rates in south.

However, poverty trends over time display different patterns across regions. While urban and rural poverty rates seem to follow a longer-term decreasing trend in the southern regions, both are increasing in the northern regions, with the exception of urban poverty rates for the 0-9 year olds in the North Central region, who experienced lower poverty rates over time. Monetary poverty rates appear to have continuously increased over the three waves in the North East, and that despite the likely poorest households living in the most conflict affected areas not being included in the latest survey. South-South urban child poverty has, as Figure 6e shows, been decreasing visibly faster than rural poverty, hence resulting in a widening of the urban rural gap. Moreover, comparing Figures 5b and 5c for wave 3 indicates very similar urban child poverty rates throughout the South of between 9 and 12 percent, whereas children living in rural areas in the South West, especially the youngest, face somewhat higher poverty rates than rural children in other Southern regions. Rural poverty rates between 24 and 32 percent in the south can only be compared with urban poverty rates in the North Central and the North East of 33 and 37 percent. The respective figures based on the national poverty line are available in the Appendix (see Figures A.1a-c) 16
# Introduction

This paper describes the challenges of accurately measuring the upper tail of the income and wealth distributions in low- and middle-income countries. We first review the seminal contributions in the literature on measuring the top of the income distribution and then more recent work, focusing on the challenges in doing so and the solutions that have been proposed. We focus initially on incomes, but then devote a specific section to examining measurement of the top of the wealth distribution and the key differences compared with measuring income. We then identify gaps in the literature and the implications for those undertaking research in this area. Finally, we propose a research agenda to close the identified key gaps for practitioners, distinguishing between those that can be undertaken with current datasets and those that require new data.

Kuznets and Jenks’s (1953) landmark study brought together tax data, newly created national accounts and population estimates for the United States to estimate shares of income received by the top percentiles. Similar studies were subsequently undertaken in other countries, but the top of the distribution was then neglected, until being revived in the last 20 years thanks to the work of Piketty (2001, 2003). Following Piketty’s work, researchers have used tax tabulations from official reports, but the availability of tax microdata, also used by Piketty, has also generated further impetus to the research agenda of investigating the top of the income and wealth distribution. A 2022 special issue of the Journal of Economic Inequality, entitled “Finding the Upper Tail”, shows that this is an area of active research. We draw from several of the articles from that special issue below, as well as from Lustig (2020), when discussing the challenges that arise when measuring the top tail of the income and wealth distributions and the solutions.

Atkinson (2007) provided two motivations for better measurement of the top tail of the income distribution - to improve knowledge of the capacity of states to raise extra revenue through increased taxation levels and to measure the extent to which those
findings emerge. In 2007, the year of our analysis, average per capita expenditure in producing districts was around 10 percent higher than in non-producing districts of the same province. Producing districts also faced around 2.5 percentage points lower rates of extreme poverty, and had 2.5 percentage points fewer households with basic needs uncovered. Illiteracy was also lower. When compared to non-producing districts in _other_ provinces, impacts are larger: per capita expenditure in producing districts is 14 percent higher, and poverty rate is between three and four percentage points lower. We concentrate our analysis on 2007 because this is when the most recent census was conducted. The previous census had been done in 1993, that is, prior to the mining boom. When we use data corresponding to 1993, we find that mining districts did not exhibit any statistically significant difference with respect to otherwise similar districts, which reinforces the causal interpretation of the impacts of mining activity.

3
The analysis of individual dimensions of the JQM shows substantial variations across countries in all dimensions (figure 3). The highest average values for the four dimensions range from 0.91 to 0.98, and the lowest values span from 0.07 to 0.21. The dimensions with greater variations are benefits and stability. The countries with the largest averages for the benefits dimension are Mongolia (MNG) and Uruguay (URY), with values of 0.84 and 0.95, respectively, while the countries with the lowest averages are Peru (PER) and Rwanda (RWA), with values of 0.07 and 0.08, respectively. For the stability dimension, the countries with the largest averages are Ecuador (ECU) and Mongolia, with values of 0.98 and 0.96, respectively, and the countries with the lowest averages are India (IND) and Kenya (KEN), with values of 0.14 and 0.28, respectively.

Figure 3. Job Quality Decomposition among the Wage Employed, by Country <!-- Start of picture text --> 4 100%<br>80%<br>3<br>60%<br>2<br>40%<br>1<br>20%<br>0 0%<br>Income Benefits Stability Satisfaction Share of wage employees<br>JQM<br>Share of Wage Employees<br>BRA MNG URY TUR GEO LAO BOL BWA ECU CRI DOM VNM COL EGY PRY LBN PAN LKA IDN MOZ BGD MEX PAK NPL PER MMR HND NER MLI BEN TGO GNB PHL CIV BFA THA SEN RWA IND KEN<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1).

_Note:_ This figure shows JQMs by country for the wage employed only. The JQM is comprised of four dimensions, and the highest possible JQM attainable for
and a large proportion of beneficiaries are from the richest quintile. But when including private contributions, secondary education is progressive.

The state of public healthcare is quite different. Access to public facilities follows a bell curve, with the middle quintiles more likely to receive benefits than either the richest or the poorest. This result is not explained by differences in health status, as the reported incidence of illness (within the 15 days prior to the survey) is essentially uniform across socioeconomic groups. In the upper quintiles this result is to be expected, since the rich contribute more to finance their demand of public healthcare and are also more likely to seek care from private providers. Nevertheless, out-of-pocket contributions could be better targeted, as the analysis shows that more than 40 percent of beneficiaries from the top quintile do not pay anything whatsoever for care received in public facilities.

A great scope for distributional improvement is also observed in access to care for members of the lowerincome quintiles, and in particular access across multiple tiers of the healthcare system. Whereas large numbers of members of the top quintile are to access free public health services from all levels of the healthcare system, the analysis suggests that the poor is less likely to seek care at any level. It is not clear whether the poor also fail to move on to provincial or national facilities, as the information available only in the LCMS VI reports only the first provider consulted and not all visits. To the extent that the driving force behind the regressive nature of healthcare benefits among the lower income quintiles be that the poor typically access only first-tier facilities, which offer more basic, lower -cost treatments—while wealthier patients receive more expensive and sophisticated care at provincial and nation hospitals— healthcare expenditures on the poor will remain disproportionately low.

The benefits of agricultural-input subsidy programs follow a somewhat progressive pattern but clearly suffer from targeting problems. For each beneficiary in the top quintile there are almost two beneficiaries in the poorest quintile, but the distribution is far from progressive across all income groups.
.879)|(1.342)|(0.822)|(1.023)|(1.131)|
|Observations|2,751|1,930|1,037|4,496|4,496|4,496|
|R-squared|0.402|0.371|0.407|0.397|0.398|0.398|
|B. Unifed|||||||
|<br>President|0.245|-0.204|-0.085|0.034|0.322|0.306|
||(0.497)|(0.614)|(0.864)|(0.491)|(0.586)|(0.671)|
|Observations|2,126|1,402|678|4,409|4,409|4,409|
|R-squared|0.336|0.430|0.448|0.173|0.173|0.173|
|Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The table shows the regression discontinuity estimates of the presidential party status in legislative elections on multi-lane road (federal highway) outcomes during periods of divided (panel A) and unified (panel B) government. The estimation methods include: local linear regression in columns 1-3 with two MSE-optimal bandwidth choices ( _h_<sup>ˆ</sup> 1 and _h_<sup>ˆ</sup> 2) and one CER-optimal bandwidth choice ( _h_<sup>ˆ</sup> 3), calculated according to Calonico et al. (2017); spline polynomial approximation in columns 4-6 with second, third, and fourth polynomial, respectively. Estimations in panel A has no covariates while estimations in panel
Policy Research Working Paper 8715

# **Abstract**

The relationship between the length of paid maternity leave and the proportion of female workers in the private sector is explored using firm-level survey data for 66 mostly developing countries. The paper finds a large, positive, and statistically significant relationship between the two. According to the most conservative estimate, an increase of one week of paid maternity leave is associated with a 2.6 percentage points increase in the share of workers in a typical firm that are female. As expected, the stated relationship is much larger when the government pays for maternity leave versus the employer. The results are robust to several controls for firm and country characteristics and other possible heterogeneities in the maternity leave and female workers relationship.

This paper is a product of the Global Indicators Group, Development Economics. 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/research. The authors may be contacted at mamin@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
# **5. Average Treatment Effects**

In the estimation, we merged NAT grade 4 in SY 2002/03 and NAT grade 6 in SY 2004/05 using elementary schools in SY 2002/03.<sup>17</sup> Although the selection of TEEP is based on province-level poverty indicators summarized in the Social Reform Agenda, we conjecture that income distributions overlap between TEEP and non-TEEP school districts (see Figures 3.1 and 3.2). In our matching estimation, we control for the interactions of municipality income category and regional dummies, as well as school-level initial conditions including pupil–teacher ratio, grade 4 total enrollment, number of multi-grade classes, and proportion of locally funded teachers. In the Philippine context, local income level not only summarizes broad socioeconomic factors but also proxies the availability of private schools, which affects the competition between public and private schools and therefore the ability distribution of students in public schools (see, for example, Yamauchi 2005). It also controls local labor market conditions.

The first-stage logit regression result is reported in Table 5.1. The dependent variable is 1 if the school is located in a TEEP area and zero otherwise. The results show that income categories, distinguished by regions, significantly explain TEEP placement. Except for income category 5, which is the poorest group, the effect is monotonic. In region 7, central Visayas, which is omitted as the benchmark case, the effect of income category 5 is negative. In other regions, western and eastern Visayas, the income effect is monotonic throughout all income classes.

> 17 Our analysis pertains only to elementary schools in SY 2002/03, which offered grades 1 to 6. To maintain a valid cohort, we dropped primary schools, where only grades 1 to 4 are taught.

16
Estimating the causal link between such communication and attitudes towards the state from observational data poses challenges as governments strategically adjust their communication in response to public opinion.

To address our research questions, we leverage randomly assigned informational treatments shared with Pakistani citizens through a phone survey. We conducted our experiment during the early stages of the pandemic (in May and June 2020) with 5,771 respondents in Lahore and Faisalabad, the two most-populous cities in Pakistan’s Punjab province. Pakistan provides an excellent setting to explore our research questions since many Pakistanis distrust the state and increasingly rely on non-state actors for basic services (Cheema et al., 2017, Acemoglu et al., 2020).

We designed the informational treatments to increase citizens’ support for the govern-

> 1For instance, see Daron Acemoglu’s presentation on “Unkowns, Challenges and Opportunities in the Time of Covid-19” (April 9, 2020) on the Royal Economic Society Covid-19 Hub.

2
as the understanding of new laws (OECD 2019). A recent World Bank study explores critical considerations for effectively engaging the private sector, along with channels and mechanisms that have been successfully leveraged; it draws from cross-country experiences and sheds light on the importance of effectively engaging the private sector (World Bank 2020).

# **2.3. Other data sources**

The Worldwide Governance Indicators (WGI) provide aggregate and individual governance indicators for 200 countries and territories over the period 1996-2021, for six dimensions of governance: voice and accountability, political stability and absence of violence / terrorism, government effectiveness, regulatory quality, rule of law, control of corruption.

The OECD Indicators of Regulatory Policy and Governance (iREG), based on the 2021 OECD Regulatory Indicators Survey results, present up-to-date evidence of 39 OECD member countries’ regulatory policy and governance practices.<sup>2</sup> The iREG indicators broadly measure three key principles on stakeholder engagement, regulatory impact analysis, and ex-post evaluations. In particular, the composite indicator on stakeholder engagement for developing regulations measures the adoption of good regulatory practices to engage with affected parties when developing new regulations, including different methods and openness of consultations, as well as transparency and feedback mechanisms. Despite advances in these and other aggregate measures of ‘governance’, however, there is no global database providing objective, quantitative, and comparable measures on existing administrative and institutional arrangements that enable business regulatory reforms.

# **3. Building the Methodology**

The methodology for _Business Reform Committees_ was developed in several steps. First, a conceptual framework was developed, drawing on as much feedback as possible from experts across the World Bank and from other organizations that have worked on similar initiatives (such as the OECD). Second, the team designed the questionnaire and disseminated it to more than 200 government officials in the two rounds of data collection of the project. Namely, in the first round of the project, the team reached out to 127 countries and territories
EMDEs**

<!-- Start of picture text --> Index Commodity importers<br>68 Commodity exporters<br>64<br>60<br>56<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **B. Investment growth in EMDEs with strong and weak improvement in political stability, 2010‐15**

<!-- Start of picture text --> Percent<br>6<br>4<br>2<br>0<br>Weak improvement Strong improvement<br><!-- End of picture text --> Sources: Political Risk Services International Country Risk Guide (ICRG).

A. Lines show unweighted annual average, as measured by the ICRG index, for each group. A decrease in the index denotes deteriorating political stability. Includes 95 EMDEs.

B. “Strong improvement” and “Weak improvement” indicate improvement in political stability from 2010‐15 in the top and bottom one third of the distribution, respectively. Difference in medians between “strong improvement” and “weak improvement” subsamples is significant at the ten percent level. Group medians for 61 EMDEs during 2010‐15.

32
secondary level are actually enrolled at the lower secondary level in Indonesia in 2015. While this is the majority of the age cohort, the relationships between aspirations, expectations, and outcomes could be different if out-of-school adolescents were still in school and considered, especially since they likely have either lower aspirations or face greater constraints to continuing their education.

# **5. Results**

## **5.1. What do students aspire to and expect when imagining their future education?**

## _High aspirations, lower expectations_

Indonesian adolescent students in our sample dream of completing high educational levels. When asked what level of education they would like to complete if they had no constraints and could study as long as they liked, three-quarters of students aspired for postsecondary education, dominantly university education (Figure 1).<sup>5</sup> This proportion is similar to the average for a handful of low- and middle-income countries for which we have similar data: the 74 percent aspiring for university in Indonesia are similar to Vietnam in which 75 percent of 12-year-olds —including those who are not students anymore— respond to a similar question that they aspire to complete university, higher than Ethiopia (69 percent) and India (68 percent) but lower than Peru (79 percent) (Favara et al. 2018).<sup>6</sup> Among students in Indonesia, almost half aspire for a PhD —the highest level representing eight years of postsecondary education and more than twenty years of education in total— that is three times more than any other level, be it postsecondary magister or technical and vocational high school.<sup>7</sup>

> 5 Only 3 percent of the students wish for a vocational postsecondary education.

> 6 Data on aspirations for these countries are from the Young Lives Surveys, which measure educational aspirations with a similar survey question in: “Imagine you had no constraints and could study for as long as you liked, or go back to
significant impacts are seen for the vocational training treatment, which lowers the intention to migrate by 5 to 7 percentage points, and of considering the backway by 9 percentage points.

Columns 7 and 8 show that these youth have lower intentions to migrate to Senegal in the next five years, with only 12 percent of the control group saying they “surely” will, and 22 percent of the control group sure or likely to migrate. The Senegal treatment increases these intentions by 3 to 7.5 percentage points. This impact on surely or likely migrating also holds after multiple testing corrections.

Overall, these results show our interventions had lasting impacts on migration knowledge and intentions, with the change in intentions stronger for the policy interventions providing alternatives to irregular migration.

# **5.3 Treatment impacts on backway, Senegal, and internal migration**

Table 4 provides the estimated treatment effects on our different primary migration outcomes. Columns 1 to 3 examine impacts of the different treatments on migration to Senegal. We see that 11 percent of the control group has gone to work in Senegal at some point in the 18-month followup period. However, most of this migration is short-term and shorter distance, with only 1.7 percent having gone to Dakar, and only 1.6 percent residing in Senegal at the time of the follow-up survey. None of our treatments had sizeable or significant impacts on migration to Dakar, but the Senegal and vocational training interventions increased the likelihood of residing somewhere in Senegal at the time of the follow-up survey by, respectively, 2.2 and 2.6 percentage points, which more than doubles the rate in the control group. These effects continue to hold after adjusting for multiple hypothesis testing.

Our survey data provides descriptive information on this form of migration. The migrants are about evenly split between urban and rural areas in Senegal. Very few are involved in agricultural activities, unlike in their rural areas of origin, with the main occupations being working in construction and self-employment activities such as small-scale trading