rafmacalaba/data-use-mentions-extended huggingface dataset:
0.474|0.689|0.401|0.637|0.273|0.599|
|Sierra Leone|0.250|0.471|0.264|0.511|0.218|0.492|0.278|0.545|
|Swaziland|0.337|0.556|0.387|0.646|0.370|0.594|0.362|0.636|
|Tanzania|0.274|0.517|0.327|0.581|0.383|0.645|0.415|0.566|
|Togo|0.361|0.607|0.390|0.650|0.338|0.575|0.445|0.641|
|Uganda|0.325|0.586|0.400|0.625|0.473|0.681|0.455|0.668|
|Zambia|0.254|0.477|0.291|0.536|0.337|0.555|0.342|0.537|
|Zimbabwe|0.276|0.486|0.321|0.579|0.355|0.525|0.389|0.546|
|Mean|0.294|0.533|0.329|0.584|0.320|0.548|0.372|0.595|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of stunted and wasted children who fall into the bottom 20<sup>th</sup> and 40<sup>th</sup> percentiles of predicted values from the regressions with height-for-age and weight-for-height respectively as the dependent variable. For example, 26.7 percent of stunted children in Benin have predicted values
credit risk models (CreditRisk+<sup>25</sup> , Internal Ratings Based approach - IRB -, etc.) that incorporate PCR data. Transition matrices are useful devices to analyze the dynamics of portfolio credit risk - they show how borrowers risk grading has evolved in the past, what their average default rate has been and how far it has deviated from that average (its volatility). Portfolio models also exploit historical credit data, but are more efficient in that they better depict loan portfolio loss distributions. Either way, these techniques can provide insight as to banks’ ability to withstand expected and unexpected losses. They are of particular importance in those jurisdictions that have yet to adopt risk based capital and provisioning requirements for credit risk.

- **Stress tests** . Although banks (in particular, large and/or internationally active ones) perform stress tests as part of their credit risk management policies, central banks and bank supervisors are increasingly performing their own stress testing exercises.<sup>26</sup> These supervisory stress tests are designed to gauge the

> 23 These comprise models for ratings or rating downgrades, survival prediction and expected loss models, and others for obtaining early warning signals.

> 24 Marullo Reedtz and Trapanese (2000) find empirical evidence for the importance of the information in the PCR of the Bank of Italy in building off-site early warning techniques of bank frailty. 25 CreditRisk+ is a credit portfolio model developed in Credit Suisse First Boston (1997).

> 26 When reviewing banks stress tests, supervisors should at the minimum be wary of their results: “ _A few years ago, ahead of the present crisis, the Bank of England and the FSA commenced a series of seminars with financial firms, exploring their stress-testing practices. The first meeting of that group sticks in my mind. We had asked firms to tell us the sorts of stress which they routinely used for their stress-tests. A quick survey suggested these were very modest stresses. We asked why.
and technology linkages. The multidimensional polarity index places six developing countries among the top fifteen in the world over the period 2004-2008.<sup>17</sup> The index also documents the changing role of the old growth poles of Europe, the United States and Japan that dominated most of the last century. Over the last decade, these countries have ceded space to emerging market growth poles. This is driven by increasing ―polarity‖ of emerging market economies (World Bank, 2011). Figure 15 presents the declining concentration of global growth, as represented in Herfindahl-Hirschmann indices.<sup>18</sup> In examining the prospects for sustained growth in the new emerging growth poles, the World Bank report focuses on the roles of technological adoption and innovation on the supply side, as well as a shift toward greater domestic consumption on the demand side. The report suggests that adequate transitions on both fronts are feasible. Forecasts show that in the 2020s, the BRIC countries will be among the top 10 growth poles, joined by newcomer Indonesia.

> 17 See _Global Development Horizons 2011-Multipolarity: the New Global Economy_ , World Bank, 2011.

> 18 For more details on the methodology used for this measure, see Adams-Kane and Lim (2011).

23
in India complied with the policy and offered repayment deferrals to eligible borrowers.<sup>7</sup> However, all credit bureau reporting was paused in this first phase, and loans covered by the moratorium were not reclassified as restructured or non-performing.

Once the first phase of the moratorium ended, a second phase started where lenders were still encouraged to grant forbearance to borrowers in need. However, the incentives for doing so were reduced, as loans covered by a moratorium now had to be restructured, which entails processing costs and higher risk provisioning requirements but is still preferable to the standard requirement of having to declare loans as nonperforming if they remain in arrears for more than three months. In practice, lenders in this second phase weighed the administrative cost of offering payment deferrals against the credit risk among their customer population.

We conducted our main experiment during this second phase of India’s consumer credit moratorium, when debt forbearance was still widely granted but had become _de facto_ optional from the perspective of the lender. While banks could still avoid classifying loans as nonperforming if they granted forbearance during this time window, this now entailed a non-zero cost of marking these loans as restructured. As a result, banks exercised greater discretion in making customers eligible for repayment deferrals and generally offered forbearance only to customers with a realistic prospect of eventual repayment. This provides an ideal setting for our experiment in which we vary whether customers receive a moratorium offer as well as whether this offer is presented as an initiative of the lender or the regulator.

# **2.2 Beliefs about Debt Forbearance in the Population**

The impact of debt relief policies has been a hotly contested topic of public debate, with proponents emphasizing their stabilizing effects and critics worrying about their potential to generate moral hazard.<sup>8</sup> To provide additional motivating evidence for our study, we conducted a nationwide online survey in India with a sample of respondents representative of the population of consumer loan borrowers in terms of age, income,
9 being prepared, only preliminary data on the 1995 budget execution were available, and these were also not sufficiently detailed.

Thus, instead of precise data on regional budget spending on subsidies, the paper utilizes the available broad measures of subsidy incidence which characterize important components of the total subsidy expenditure but still do not provide comprehensive coverage of the total regional spending on these purposes. Three primary budget items were used as broad measures: national economy expenditures (the major, most informative indicator); total investments; and budget loans. National economy expenditures, as defined by the Russian budget classification in 1992-94, were expenditures on agriculture, housing, transportation, and other sectors (including food price subsidies<sup>10</sup> ). While far from perfect, all of these indicators are still informative enough to reflect both major trends in regional fiscal policies and the scale of market distortions imposed by the provision of subsidies. The selection of these indicators should be considered as a reasonable compromise between a demand for more accurate measurement of fiscal processes and actual constraints associated with the undeveloped state of the Russian fiscal statistics.

The major potential distortions deriving from the analysis of these "imperfect" indicators are the following: (i) While most of the budget spending reported under the heading 'hational economy" represented in fact various subsidies and transfers to corresponding sectors of the regional economy, some portion of the actual expenditures under this heading could have a different functional purpose. The most important example is probably spending on housing, which covers both housing subsidies (the dominant item) and expenditures on urban development (up to 15% of the total housing expenditures). Similarly, expenditures on agriculture include some relatively small amounts spent on administration and provision of public services to this sector (e.g. veterinary services). While by our estimates 80-85% of the total subsidized loans funded by regional governments were granted to local enterprises, the remaining part was allocated among non-commercial entities (such as universities) and therefore cannot be treated
# **Abstract**

Geographical imbalances in the health workforce have been a consistent feature of nearly all health systems, and especially in developing countries. In this paper we investigate the willingness to work in a rural area among final year nursing and medical students in Ethiopia. Analyzing data obtained from contingent valuation questions, we find that household consumption and the student’s motivation to help the poor, which is our proxy for intrinsic motivation, are the main determinants of willingness to work in a rural area. We investigate who are willing to help the poor and find that women are significantly more likely to help than men. Other variables, including a rich set of psycho-social characteristics, are not significant. Finally, we carry out some simulations on how much it would cost to make the entire cohort of starting nurses and doctors choose to take up a rural post.

JEL classification: D1, J22, J64 Keywords: health care delivery, health workers, labour supply, public service ii
# **Survey finding rates**

The survey finding rate, however, varied by treatment status. We were able to find 94 percent of T1 applicants, 69 percent of T2 applicants, and 68 percent of the control group. Given the scant information we had to locate the applicants, the finding rate of the control group (68 percent) is quite high. For reference, Clemens and Tiongson’s (2019) study, which interviews applicants to the Korean EPS migration program in the Philippines based on administrative data, was able to locate and interview only 44 percent of the applicants. The extensive field engagement of the enumerators and, perhaps, the rural setting of Bangladesh, where villagers tend to know about each other, led to the high finding rate in our study.

The almost universal finding rate of the T1 group stems from a few key reasons. First, as the T1 group were required to interact with local officials to prepare for the government intermediation, the local officials were likely to know of them or have updated contact information. Second, winning the lottery made them well-known in the communities and the villagers were also more likely to know them. Third, households with a migrant, particularly international migrants, are more likely to be known in the villages. Given the high desire to migrate internationally in this context, villagers could be more likely to know of international migrants and their families in their village.

This, unfortunately, creates a differential finding rate of 26 percentage points between treatment groups. As explained above, the differential rate reflects the nature of the lottery program and the resulting migration rather than any differences in some underlying economic characteristics. In any case, treating differential finding the same way as differential attrition and estimating bounds (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.
# **Appendix Table 4: Is the Business Visually Bigger After Three Years?**

||All firms|Survivors|All Firms|Survivors|
|---|---|---|---|---|
|Assigned to Training|0.042**|0.037|||
||(0.021)|(0.023)|||
|Spillover Group|0.019|0.019|||
||(0.022)|(0.023)|||
|Assigned to Mentoring|||0.059**<br>(0.028)|0.058*<br>(0.031)|
|Spillover Group for Mentoring|||0.022<br>(0.030)|0.017<br>(0.032)|
|Assigned to Training Only|||0.034|0.022|
||||(0.028)|(0.029)|
|Spillover Group for Training|||0.028<br>(0.026)|0.023<br>(0.027)|
|Sample Size|2864|2571|2527|2265|
|P‐value: Mentoring=TrainingOnly|||0.440|0.286|

Notes: Robust standard errors in parentheses, clustered at the market level. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively. Outcome is whether photograph of business inventories in Round 4 survey is shows business to be larger than baseline photograph shows.

Columns 2 and 4 condition on the firm surviving, columns 1 and 3 code closed firms as not being bigger.

49
they were more likely to have higher education than male PFPS workers (see Table 5-15), and as we will see below, workers with higher education lost jobs at a lower rate. Therefore, our model would predict women retaining more of their (typically non-production) jobs than men. Of course, if one controls for educational

|_Table 5-15 Jordan: Priv_|_ate Sector W_<br>PFPS<br>(Percenta<br>Employe|_age earners_<br>ges of all PFP<br>es)|_by Educati_<br>S<br>Formal n<br>wage ear<br>(Share o|_on and Gender p_<br>on-permanent<br>ners<br>f PFPS workers)|_re-COVID_<br>Informal w<br>(Share of i<br>works)|age earners<br>nformal wage|
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|
|Less than Basic|15.6|1.8|7.1|0.4|42.9|1.0|
|Basic Education|21.7|1.5|22.9|2.9|25.3|1.9|
|Secondary Education|12.7|1.7|10.5|3.8|15.6|1.1|
|Higher Education|30.2|14.7|29.7|22.7|10.7|1.5|
|Total<br>Source: LMPS 2016.|80.2|19.8|70.2|29.8|94.5|5.5|

attainment this may not be the case. Moreover, the female advantage does not carry over to non-PFPS workers.
For instance, hazardous waste facilities are disproportionately sited in minority communities (Boer et al., 1997; Graham et al., 1999). Analyzing environmental justice in the fossil fuel industry, Carpenter and Wagner (2019) find that air pollution is higher in counties with higher unemployment levels. This pattern is inconsistent with the fair treatment notion of the U.S. Environmental Protection Agency (EPA), which states that “no group of people should bear a disproportionate share of the negative environmental consequences resulting from industrial, governmental and commercial operations or policies” (EPA, 2020). While such environmental injustice in the context of air pollution is well documented in high-income countries, evidence remains scarce for lower income countries and at the global scale.

This paper addresses this gap by documenting the regressive nature of the air pollution and health burden associated with the continued use of coal power plants. It finds that, globally, 2.32 billion people are exposed to air pollution originating from coal power plants. We find that air pollution from coal plants increases with income levels, though at a diminishing rate at high income levels. In other words, coal power plants are located in richer countries, and richer

> 1Data comes from the 2014 National Emissions Inventory (NEI) compiled by the U.S. EPA.
This paper has also benefitted from the discussion at two workshops on measuring the contribution of health to human capital held at the World Bank on March 1, 2018 and May 14, 2018, and a Bank‐wide review meeting held June 11, 2018.  The data used in this paper have benefitted from an extensive consultation process organized by the office of the World Bank Chief Economist for Human Development, which resulted in many expansions and refinements to the school enrollment and stunting data used in the HCI.  The HCI will be published in the 2019 World Development Report and accompanying special report on human capital. _The views expressed here are the author’s, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent._
the case groupings were often more costly. Providers believed that the intensity of resource use was underestimated for more complicated medical cases.

In 2009, PPTP groups were abandoned and formal diagnosis-related groups (DRGs) were introduced, known locally as _dijagnostičko terapijske skupine_ (DTS). DRGs, which have much more refined case-groupings, are the most effective system of classification in health care and are used in most European countries, the United States, and Australia. DRG classifies cases of acute care into groups requiring similar consumption of hospital resources and that have similar clinical characteristics. Unlike PPTP, DTS has 671 groups of therapeutic-diagnostic procedures, which means that it is much more accurate in the pricing process. An example of the groups for cataract procedures is shown in Figure 2. Croatia’s DTS system is based on the Australian Refined Diagnosis Related Groups (AR-DRG) version 5.1, but modified for the Croatian context.<sup>1</sup> DTS was first piloted in four hospitals beginning in February 2006. As of April 2007, it was introduced in all Croatian hospitals, initially running in tandem with the existing billing system. During this period, HZZO actively worked with hospitals to ensure the appropriateness and quality of coding. One of the greatest challenges with the introduction of the Australian system in Croatia was the difference in DRG/DTS costing between the two countries. The original AR-DRG system used Australian data on resource use, clinical practice, and hospital billing. A second challenge was related to a form of ―gaming‖ known as ―code creep‖ in DRG systems, i.e. coding patients as having more serious or complicated conditions than they actually have. In Australia six different mechanisms are now employed to reduce this risk of upcoding<sup>2</sup> .

Using data over 10 years, this study examines the efficiency and quality impacts across three provider payment systems in Croatia: (1) the original fee-for-service (FFS); (2) broad case-based groupings under the PPTP system
**Table 1: Description of Individuals**

||Mean<br>SD|<br>M|edian|
|---|---|---|---|
|**Country GDP per capita ($, approx.)**|6,000|||
|**Borrowers**||||
|Gender is female (%)|39|-|-|
|Age (years)|35.8|12.8|34.0|
|**Has a mobile phone (%)**|100|-|-|
|**Credit bureau record (%)**|85|-|-|
|Entities reporting:||||
|At least one (%)|59|||
|At least two (%)|31|||
|At least three (%)|16|||
|**Average weekly mobile phone use**||||
|Calls out, number|32.0|25.6|26.0|
|Calls out, minutes|41.6|39.9|32.0|
|SMS sent|31.3|26.3|24.4|
|Days of mobile phone data preceding plan switch|107|14|112|
|**Credit**||||
|Default Rate (%)|11|-|-|
|N|7,068|||

Source: authors’ analysis from telecom data.

26
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# **Mama Knows (and Does) Best: Maternal Schooling Opportunities and Child Development in Indonesia**<sup>_∗_</sup>

AMER HASAN Education Global Practice _World Bank_ NOZOMI NAKAJIMA Education Policy and Program Evaluation _Harvard University_ MARCOS A. RANGEL Sanford School of Public Policy _Duke University_

**JEL Codes** : I28; O15; I21; J13; I26; D10

**Keywords** : maternal education, school construction, human capital, child outcomes, parenting

> _∗_ Authors are listed alphabetically. Corresponding author: Rangel (marcos.rangel@duke.edu). We would like to thank the Strategic Impact Evaluation Fund (TF0A0234) for their generous support. Dedy Junaedi, Upik Sabainingrum, Anas Sutisna, Lulus Kusbudiharjo and Mulyana were instrumental in managing the fieldwork. Esther Duflo graciously shared the data on the INPRES school construction programs. 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.
resources. However, the following conclusions can be drawn from preliminary observations from the case studies and the survey of dialogue participants:

- a. The roundtable dialogue can help improve mutual understanding among different stakeholders. As discussed before, communication between the citizens and the government and between the citizens and the polluters is not adequate in China, especially in the rural areas. People do not really have good understanding with each other, even though most of them thought they did.

- b. The roundtable dialogue provides a platform for all stakeholders to speak for themselves. Almost all resident representatives and some of the company managers appreciated the opportunity to speak for themselves. There is little chance for ordinary citizens to speak publicly on issues which affect their quality of daily lives, their health or their economic activities. For those companies which have good environmental performance, it is also a chance for them to show their neighbors what they have done to control their pollution. Even for some companies which cannot control pollution to a desired level, it is still an opportunity for them to explain the reasons why they did not get there and to get better understood. Environmental authorities in China do not have much chance to communicate their work to ordinary people, especially to those people who do not read newspapers or have no TV access.

- c. The roundtable dialogue provides a channel for different stakeholders to negotiate terms of action for the future. During the dialogues, many community representatives and company managers requested that government officials as well as company managers take action on some specific issues. To some of the requests, promises are given. But to some others, reasons are given for not being able to take actions. Of course, there are some requests which received no responses. There is evidence that most promises are kept at least partially and actions are taken. There is also evidence that different stakeholders work together to solve some issues collaboratively after the dialogues.

- d. The roundtable dialogue generates pressure on the bad performers and gives appreciation to good