rafmacalaba/data-use-mentions-extended huggingface dataset:
might not be the most efficient tool for this specific purpose. More broadly, our analysis reveals the importance of focusing on policies and interventions that improve the productivity of schooling, rather than solely providing additional schooling.

In Figure 4 we show the full set of 150 studies from 46 countries, highlighting the subset of impact evaluations that include cost-effectiveness data.<sup>12</sup> The first important takeaway from this figure is that, by and large, the subset of interventions with cost-effectiveness data are not systematically biased towards high or low impacts, although within each category the studies with cost-effectiveness data may skew one way or the other. This figure further enables us to assess LAYS gains in absolute terms, rather than per $100, and decompose whether an intervention is cost-effective due to being effective, cheap, or both. For example, health products are moderately effective in improving outcomes, with up to 0.2 LAYS gains per child, but are cheap. Thus, in Figure 3 we see the modest absolute gains translate into up to 10 LAYS gains per $100 per child, marking these health interventions as highly cost-effective. Other interventions are highly effective but expensive, and therefore less cost-effective. Giving merit-based scholarships can yield up to 1 LAYS, but since this policy is relatively expensive, it is less cost-effective than the health programs, delivering 3 LAYS per $100 at the upper end of the distribution of studies. Finally, Figure 4 also includes a new category: nutrition interventions (such as school feeding), which did not enter the cost-effectiveness analysis in Figure 3 due to a lack of cost data. We observe that school feeding is relatively effective in improving LAYS, although with high variance. Taken together with the findings in Figure 3, this indicates that health and nutrition interventions can translate into meaningful education outcomes.

# **4.2 Specific cost-effectiveness studies**

## **4.2.1 Effectiveness and cost-effectiveness**

Next, we examine specific interventions to explore the
Lee, 2009) resulted in wide confidence intervals for most of the outcomes (except migration and income). Nevertheless, we investigate this issue more deeply in Appendix A.2, and show that under reasonable assumptions the estimates presented in this paper are robust.

# **Comparison of the sample with the population**

Our sample strategy yields a representative sample of the lottery applicants. However, the lottery applicants themselves are a selected sample. For instance, they have a member who is interested in international migration for low-skilled work. They were also expected to finance the migration themselves, albeit at a lower than market cost of migration. This excludes interested applicants who face borrowing constraints to finance migration.

Indeed, as **Appendix Table 1** shows, the study sample is different from a nationally representative household from the Household Income and Expenditure Survey (HIES) of 2016/2017 (BBS, 2017).

12
the findings in a database<sup>11</sup> . This database provides detailed information, including the stated name of manufacturer and lot number, dates of collection and testing, types of tests performed, and names of the testing facilities. The Pharmaceutical Security Institute also collects information on incident trends, although its database is less detailed.

Stock-out rates (absence) of essential drugs in health facilities are a much-cited indicator of the availability of essential drugs in health facilities. When measured over enough time, this indicator can help assessing whether absence of drugs reflects a supply or a demand issue. Not all factors influencing stock-out rates are related to governance, but given the often serious problems of abuse that plague the distribution and use of pharmaceuticals, the policies discussed above are expected to influence stock-out rates.

The World Health Organization (WHO), in collaboration with the global NGO Health Action International (HAI), compiles data on the prices and availability of essential medicines in many countries. HAI collects data on price and availability on a list of essential medicines (defined by WHO) through surveys carried out in six different regions in each country. In larger countries, it collects data at the state level. The sample consists of medicine outlets in the public, private, and other (non-profit) sectors in the country. The survey data are available to the public through a database maintained by HAI.<sup>12</sup> The database contains information on drug pricing and availability for 49 countries with 15 from Sub-Saharan Africa, 7 from Latin America and

> 11 See http://www.usp.org/worldwide/medQualityDatabase/

> 12 <u>http://www.haiweb.org/MedPriceDatabase/</u>

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**Table 2. A functional tiering of World Bank knowledge products**

|**Tier**|**Knowledge products included**|**P-code**|**Z-code**|
|---|---|---|---|
|Flagships|World Development Report; Doing Business; Global Economic<br>Prospects; regional and sectoral flagships.|132|52|
|Debates|Regional, sectoral and multi-country reports; Doing Business<br>regional reports.|2219|628|
|Extended<br>core|Country Economic Memorandums; Poverty Assessments; Systematic<br>Country Diagnostics; Public Expenditure Reviews; Reimbursable<br>Advisory Services; Financial Sector Assessment Programs; Risk and<br>Resilience Assessments; Country Private Sector Diagnostics; Human<br>Capital Reports; Infrastructure Sector Assessment Programs; Debt<br>Management Performance Assessments; Debt Sustainability<br>Analyses; Agricultural Sector Reviews; Pandemic Preparedness<br>Reports; Environmental Assessments.|1500|6|
|Other<br>country|Policy Notes; Economic Updates; other studies; technical assistance.|6103|1258|
|Research|Journal articles; working papers; impact evaluations; Doing Business<br>country profiles.|1725|3927|
|Capacity|Toolkits; databases; conferences; courses; training.|488|82|
|Internal|International Development Agency papers; strategy documents;<br>briefs; newsletters; evaluations.|665|490|

# 5. Regression analysis

The construction of a consolidated database of knowledge products containing tens of thousands of observations, each with information on its performance and the process through which it was prepared, makes it possible to assess what makes for “good” analytical work at the World Bank using statistical tools. The assessment is in the spirit of a production function, with a measure of performance as the explained variable, and a series of “inputs” to the
*$**|**9.7**<br>**$**|**9.6**<br>**$**|
|Excluding shocks|8.5<br>$|9.9<br>$|9.5<br>$|
|Excluding shocks and variables measuring changes|8.8<br>$|11.1<br>$|11.0<br>$|
|Excluding variables measuring changes|8.6<br>$|10.9<br>$|10.9<br>$|
|Excluding locational effects, shocks and changes|9.6<br>$|11.3<br>$|11.3<br>$|
|Excluding locational effects, marital status, shocks and changes|9.0<br>$|11.4<br>$|11.3<br>$|
|Only education, sex, age and occupational status of heads|9.8<br>$|11.5<br>$|11.8<br>$|
|Only education, sex, and age of heads|9.6<br>$|12.0<br>$|11.7<br>$|

_Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases.

In order to observe how the socioeconomic characteristics of the middle class compare relative to other groups, we estimate a two-group mean-comparison _t_ -test to observe if there is a significant difference between the characteristics of the middle class and those of the lower class. For that purpose, we first divide the lower class in two groups: i) _lower_ lower-class, or poor class, defined as those households with per capita income below the official poverty lines as measured in each country, and ii) _upper_ lower-class, or vulnerable
sup>12</sup> this share further increased over time, to reach 65.4 percent in 2008.<sup>13</sup> (Figure 9) shows

> 12 Lall, S. (2000). “The technological structure and performance of developing country manufactured exports, 198598”, Oxford Development Studies, 28(3), pp. 337–369.

> 13 World Bank calculations from Comtrade data

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of CTs in larger metropolitan cities.

Based on this limited literature on the intersection of CTs and urban services, research questions that explore the relationship between governance and access to basic amenities are multifold. Put simply, with the rise in population and spatial expansion, there is a change of scale in terms of service provision. In particular, for water and sanitation services, it might be necessary to develop networks and to shift from on-site sanitation or community or individual water systems. For roads and solid waste management, expansion of services and better organization might be required. One of our questions is as to whether there is a difference between census towns, villages and statutory towns both in terms of the level of services and the manner in which people make demands (or not) on urban services. This will require understanding how services are provided in different types of settlements as well as the level of differentiation in different parts of the town studied, or even beyond its boundaries.

# **_Services_**

The profiles for each settlement based on census data (Table 2) as well as the qualitative interviews carried out in each settlements reveal significant variance in provision and expectation of basic infrastructure, such as water supply. From Table 2, it is clear that among the three settlements in Madhubani, Satghara (CT) is most comparable to the sites in the other states. Jhanjharpur (NP) despite being a statutory town lags behind in most service indicators as compared to the CTs in other states. Indeed, it is similar and often worse than Simri, a village in the same district. In Bihar, therefore, there does not seem to be a major benefit from ST status. Erein’s position as being on

> 8http://www.livemint.com/Politics/364m2CaVtj2HoR88spSpNM/Zero-amenities-yet-census-towns-hit-the-property-jackpot.html

> 9  http://blogs.economictimes.indiatimes.com/policypuzzles/an-urban-chaos-issue/

22
– such as the East Asian financial crisis or the 2008‐2012 Great Recession – and time‐invariant country characteristics may also influence policy choice, we add country ( _θk_ ) and year dummies ( _τt_ ) to equation (7). Indexing countries by _k_ , we get:<sup>8</sup>

# **3. Data**

In this study we analyze the association of World Bank operations with the quality of economic policy. Following Smets and Knack (2016), one of our dependent variables measures the quality of economic management, as derived from the World Bank’s CPIA ratings.<sup>9</sup> The CPIA assessments are subjective ratings of 16 policy indicators, grouped into four “clusters”, updated annually by World Bank staff.<sup>10</sup> Possible scores on each indicator range from one to six, including half‐point increments (e.g. 3.5). For this analysis, our main dependent variable is the simple average of CPIA clusters A and B, which reflect policies aimed at achieving economic growth and poverty reduction by means of encouraging the market mechanism, and directing government activity towards rule‐making, the regulation of markets, and compensation for market failures such as natural monopoly. Cluster A covers macroeconomic and debt policy, and cluster B structural policies, including those about trade, the financial sector, and the regulation of private enterprise.<sup>11</sup> The mean score of this CPIA‐based policy quality indicator in our sample is 3.6, with a standard deviation of 0.48.<sup>12</sup> On the one hand, the CPIA is arguably an appropriate policy measure because its content reflects the views of World Bank management and staff regarding what policies are most conducive to poverty reduction and the effective use of aid resources. On the other hand, the CPIA indicators reflect the subjective judgments of World Bank staff. However, they
and FAB can form the basis of discussions and roadshows, while also facilitating the subsequent structuring and pricing process.

# **6.1 Practical considerations**

**State-contingent debt instruments** The structure of SLBs is reminiscent of state-contingent debt instruments (SCDIs), which share the fundamental idea to link debt service with a state variable.20 Traditionally, SCDIs were attractive to investors due to their counter-cyclical, diversification and hedging properties. They were also appealing to issuers since debt repayments are linked to the fiscal capacity and because SCDIs could broaden the country’s investor base (IMF, 2017). However, SCDIs enjoyed limited uptake due to liquidity and pricing concerns, and measurement issues such as delayed reporting.

In the current climate of sovereign debt distress and associated debt restructuring negotiations, linking debt repayments to a state variable could be worth exploring, especially for sovereigns exposed to climate change risks and environmental degradation (Volz, 2023). The benchmarked KPIs developed under the REACH framework could inform debt (re-)structuring decisions. Similar to catastrophe bonds or other disaster risk financing instruments, SLBs could thereby provide downside protection in the case of natural disasters, since the benchmarked KPIs account for external factors. At the same time, SLBs could expand governments fiscal space and aid in their mission to reach their Sustainable Development Goals (SDGs) or Nationally Determined Contributions (NDCs).

**Geospatial deforestation data** It is worth emphasizing that the viability of both evidence-based frameworks hinges on the data environment. In the application, annual data was sufficient to demonstrate the framework. However, for model validation and reliability, more granular data would be needed. For deforestation, remote-sensed satellite data is a highly promising way to provide objective, regular and reliable observations. Subannual and subnational geospatial data on land use and land cover transitions can help us understand the drivers, locations and trends in deforestation and degradation, which could in turn produce more sophisticated and effective KPIs. An SLB that targets deforestation due to cropland expansion may
testing of the survey instrument that asking about the period during the day for the activity, rather than the number of hours, was a more accurate way of understanding how a child‟s time is allocated. These time periods use the school schedule as a reference period and ask whether a child did a specific activity before school opened, during school hours, during school lunch break, after school closed, all day, or occasionally throughout the day. We use these periods to calculate the number of hours a child engaged in child labor activities.<sup>10</sup> The specific child labor activities we focus on in this paper include: household chores such as cooking, fetching water, sweeping, and doing dishes; tending for siblings or sick members; and engaging in farm labor.

Over 70 percent of children engage in child labor activities and spend on average 5.5 hours per day on them. Sixty percent of children perform household chores, and on average, over 3 hours per day are spent on these chores. There is significant variation in the amount of time spent on chores, suggesting that while the majority of children do them, there are some that are spending an entire day doing so, while other spend a limited time. The average number of hours spent tending for siblings or sick members in the household is 0.6 hours. Twenty-six percent of children engage in this activity, and for those children doing the activity, they spend over 2 hours per day doing so. On average one hour is spent on farm labor and 30 percent of children engage in this activity. For those working on a farm, they do so for an average 3 hours per day.

# _2.2 Rainfall and ArcGIS Interpolation_

To estimate our instrumental variables strategy described in Section 3.2, we need rainfall data for each of the 75 survey villages for each year from 1992 to 2003, which corresponds with the in utero and early childhood years for the primary school-aged children in our survey
(2015a): total mortality is weakly pro-cyclical, and so selected cause-specific death rates, except for poisoning and other diseases (specifically Alzheimer’s). Additionally, we found homicides to be strongly counter-cyclical. Our results therefore reconfirm that of Ruhm (2015a), although the contexts are different: while Ruhm’s studies are based on state-level time series, our study is based on a cross-section of counties during the Great Recession.

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# 3. EMPIRICAL ANALYSIS

Our empirical analysis makes use of data gathered as part of a 1992 World Bank field survey of 26 plants in the pulp and paper industry. The plants were distributed throughout Bangladesh, India, Indonesia and Thailand. The survey gathered the following information about each plant:

- The process technology in place. Four processes were in operation: stone ground; soda; kraft sulphate and sulphite; and waste-paper-based pollution. In our analysis, we characterize the stone ground process as "clean" because it does not use polluting chemicals.

- Ownership in three categories: state-owned enterprises (SOE), domestic privately-owned enterprises (POE) and

- multinational enterprises (MNC).

- Characterization of geographical location by degree of urbanization: Big city (population greater than 1 million), small city (population less than 1 million), or rural area.

- The size of the plant measured by total employment.

- Whether foreign financing and/or donor aid supported plant construction.

- The existence of local pressure to abate observed pollution.

- The cost competitiveness of the plant relative to other plants in the domestic market.

- The pollution abatement efforts undertaken by the plant. The focus of this measure for the most part is end-of-pipe abatement.

To this data base, we have added the following information about the economic and regulatory environment in which each plant operated: 10
226|936|30,816|
|(% of Population)|39%|88%|73%|46%|77%|55%|53%|

_Source:_ UNDESA International Immigrant Stock 2020 for immigrant stock; World Bank World Development Indicators for population in 2020.

Figure 3 presents data on contractual temporary migrants from two separate and detailed surveys. The first is from Saudi Arabia (Expatriate workers survey, 2015), the largest destination country in the Persian Gulf. The second is from Bangladesh (Bangladesh Return Migration Survey 2018/2019), one of the largest migrant-sending countries to the region. The panels on the left column are based on the data from Saudi Arabia and the panels on the right column are based on the Bangladesh data. The first panel shows that the largest origin country in Saudi Arabia is the Arab Republic of Egypt, followed by India, Pakistan and Bangladesh. In the case of the Bangladeshi migrants, the largest destination (Panel B) is Saudi Arabia, followed by the United Arab Emirates, and Oman. Migrants under these temporary contractual arrangements typically migrate at a young age, with a median age of 27 (Panels c and d), partly due to the physically demanding nature of the jobs offered in these destinations. Temporary migrant workers from lower-income countries, such as Bangladesh, tend to be very low-skilled (Panel f). There is a number of high-skilled migrants in Saudi Arabia (Panel E), but these tend to come from high-income OECD countries or other Arab countries like Egypt.

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in the past year, coded as zero for firms that do not export, and winsorized at the 99th percentile.

5. **Export innovation (new product-country combination)** : This is a binary variable coded as one if the firm exported to a product-country pair that they had not exported to at all in the past three years, and zero otherwise. Coded as zero for firms that do not export.

6. **Inverse Hyperbolic Sine of Total Export Value in the past year** . This takes the inverse hyperbolic sine transformation of total exports (measured in US dollars), and includes exports coded as zero for firms that do not export.

7. **Inverse Hyperbolic Since of Export Labor Productivity** : This is the inverse hyperbolic sine of the ratio of total export value in the past year (measured in US dollars) to the average number of workers used in the past year (obtained from the PILA database, which has monthly data on formal workers). This is coded as zero for firms that do not export, and winsorized at the 99th percentile.

8. **Standardized export outcomes index** : This index is calculated as the average of the normalized z-scores of primary export outcomes 1 through 7, where each z- score is defined by subtracting the mean and dividing by the standard deviation of the respective outcome.

Our measures of general management and export-specific practices come from survey data collected by Innovations for Poverty Action. Our outcomes are defined as follows:

1. **Export Practices** : The proportion of 15 export-specific practices being used by the firm, coded as zero for firms that close. The 15 practices measured are whether the firm has done any of the following in the past 12 months: received quality certification on export products; planned production with time for external markets; planned resources or training needed for export processes; participated in missions of public agency offerings to learn export quality standards; traveled to external markets to understand consumers there;
April (compared to the year before).<sup>24</sup> For the whole sample, the linear coefficient is statistically significant at the 5 percent level (Table 4, column 1). However, as visible in Figure 5, since large increases in nighttime light intensity in some states have not been accompanied by increases in electricity consumption, a quadratic model provides a better fit (Table 4, column 2). This suggests that the relationship between changes in electricity consumption and nighttime light intensity is stronger if both are declining, or if nighttime light intensity is at least not increasing strongly. In fact, abstracting from those states in which nighttime light intensity increased more than 10 percent results in a strong linear relationship that is significant at the one percent level. For a one percent larger decline in nighttime light intensity, electricity consumption declined by 1.4 percent (Table 4, column 3). Note that this monthly cross-sectional relationship of the two proxies is very similar to the

> 24For this analysis we drop 3 outliers. For Arunachal Pradesh, we have issues with cleaning the night light data, so that year-on-year quarterly growth rates as well as DDH and DD for which declines in electricity have been very large.

17
by Cihak et al. (2013), bank accounts per 1,000 adults, are missing for 36 out of 60 countries. Including financial access in the analysis would therefore severely limit our cross-section of countries resulting in the non-negligible loss in the degrees of freedom. Nevertheless, we have examined alternative (less than ideal) financial access indicators from the GFDD database (bank branches per 100,000 adults and ATMs per 100,000 adults), which are available for almost all countries in our sample. However, we fail to find these indicators to be decisive for the long-term growth.

4