rafmacalaba/data-use-mentions-extended huggingface dataset:
care provider to the level of a doctor from the same country, a major investment in training resources. In these countries, even though the health care provider population still has ample excess capacity, there is an insufficient number of high-competence providers to reach patients in any configuration of human resources. Systemic quality improvements in such cases may require a complete overhaul of training infrastructures.

Our paper makes two fundamental contributions to the literature. First and foremost, we contribute to the literature on human resources for health, particularly in Sub-Saharan Africa. In contrast to a common view that there is a general human resource shortage, we show that published aggregate statistics and the SDI facility survey data are both consistent with substantial excess capacity for the median provider. Our result rationalizes and explains why recent studies<sup>10–12</sup> find that the causal impact of patient load on the quality of medical care is close to zero in multiple SSA countries: Most providers have plenty of capacity to handle additional patients. We further contribute to this literature by using variation across the facility surveys to show that excess capacity among providers is _also_ consistent with long queues and waits for the typical patient, because a small share of providers handles a large share of the total caseload.

Second, we show how data on individual provider competence can be combined with data on caseloads to quantify lost productivity, by estimating the potential gains from improved allocation. While such allocative gains have been shown to be quite important for quality improvement in high-income countries,<sup>13, 14</sup> our approach to estimating potential

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10 districts of interest, substantially increases predictive performance. Using post-lasso models instead of gradient boosting in the geospatial model, the second step of the partial registry method, only slightly affects the accuracy of the predictions. In contrast, using post-lasso models instead of extreme gradient boosting in the census model, used to impute per capita consumption into the simulated partial registry, greatly reduces the predictive power of the geospatial model. This large reduction in predictive power occurs both when using gradient boosting and post-LASSO for the geospatial model. This suggests that the census data may contain outliers, which introduce more noise into the partial registry predictions when using a linear model of log per capita consumption than when using gradient boosting.

The relatively poor performance of the PMT scores derived from the UBR data is a puzzle. The UBR PMT scores performed a bit better when the benchmark measure of welfare was constructed using data only from the 10 Malawian districts. Even so, the UBR PMT scores do not appear to be consistent with welfare predicted using the census data collected from these districts. Partly this may be due to the UBR data being taken from the initial phase of data collection, although it is also possible that the purpose of the partial registry may have led to measurement error. It would be useful to do these types of evaluations with further rounds of the UBR, even if compared against old census data, to see if later rounds of the UBR produce predictions that are more consistent with the census.

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forms such as leasing or factoring have been promoted as conducive to easing financing constraints of SMEs, as they are based on the underlying assets and cash flows rather than borrowers’ financial history (Berger and Udell, 2006). On the other hand, banks, particularly large banks, have also shown increased interest in SME financing, exploiting scale economies and technology (Beck, Demirguc-Kunt and Martinez Peria, 2011a). The question on the size of financial institutions – often intertwined with the ownership question – is directly related to entry barriers and minimum capital requirements imposed by policy makers in developing countries to foster a specific market structure (Beck et al., 2011b; Beck et al., 2011c and World Bank, 2011).

This paper uses a unique and confidential dataset to shed light on the relationship between the structure of the financial system and the size of its institutions, on the one hand, and access to financial services by enterprises, on the other hand. Specifically, using data from the World Bank and IMF’s Financial Sector Assessment Program (FSAP), we are able to compute both the relative importance of different segments of the financial system that cater to low-end customers, such as small and medium-size enterprises, as well as the average size of institutions within this segment. We then match these country-level indicators to firm-level indicators from the World Bank’s Enterprise Surveys on financing obstacles and actual use of deposit and loan services by enterprises in developing and emerging countries. In addition, we examine the relationship between financial structure and firms’ access to finance across countries at different levels of GDP per capita, across firms of different sizes, and across industries with different needs of external finance, to thus take explicitly into account the potential cross-county, crossfirm and cross-industry heterogeneity in the effect of financial structure on firms’ access to finance.

Our research speaks to several literatures. First, the financial structure literature has discussed the implications of bank- vs. market-based financial systems for firm, industry and GDP per capita
firms, or firms located outside the capital city). The program could then adjust its marketing efforts for future rounds to ensure it reaches out to these groups. Each subsequent funding round would again follow the same procedure of randomly selecting the target number of recipients from among the eligible firms.

We also considered two alternative forms of randomization. The first was a _phase-in design_ , in which some firms would receive the funding now, and others would receive it later. This did not seem practical for three reasons. First, not all firms apply at once. Second, if firms knew that they might receive the funding in the future, they might adjust their behavior today, and third, some of their funding requests might be time-sensitive.

The second alternative was a _randomized encouragement design_ . Under this approach, the program is open for all firms to apply. A baseline survey of firms would be conducted to learn about the potential demand among firms for the program, and to provide data for monitoring purposes on firms that get the grants and firms that do not. During the process of conducting the baseline survey, firms will be randomly chosen to receive additional “encouragement” to apply for the matching grants. This encouragement would be in the form of different marketing and informational approaches to increase awareness about the program. Since resources are limited and it is costly to visit firms individually, this approach would clearly not be feasible to follow for all firms. Therefore, the program fairly gives all firms the same _ex-ante_ chance of receiving this additional encouragement, and uses this to learn about the needs of firms for the program, the effectiveness of different marketing strategies, and the impact on firms of getting the matching grants. The danger with this approach is that the encouragement may fail to encourage many firms into applying, so larger samples are typically needed than with a randomized experiment of applicants. In our first attempt to evaluate a matching grant project, we tried unsuccessfully to use this type of encouragement design to measure the impact of the
# 1. Introduction

Investing in girls’ education has been called “the world’s best investment” (Sperling and Winthrop 2015). Roughly 15 percent of the world’s population in 2016 are girls younger than 18 years old. Educating girls frees them to raise their aspirations and increases their potential to achieve them. It allows them to access better health, higher earnings, and more control over their lives, which subsequently leads to positive impacts on their families, communities, and societies (World Bank 2018b). More and better education for girls and boys contributes to economic growth, labor productivity, and social mobility.

The effects of girls’ education extend across generations. Better educated women tend to have lower fertility rates (Castro Martin 1995). They also have lower child mortality rates: data from 175 countries between 1970 and 2009 show that 51 percent of the reduction in deaths in children younger than 5 years in the past 40 years could be attributed to the increased education attainment in women of reproductive age (Gakidou et al. 2010). In addition, mothers’ education is strongly associated with children’s educational attainment and achievement (Ermisch and Francesconi 2001; Schultz 2002).

Despite all the demonstrated benefits, girls face challenges in education that boys do not. For example, girls’ schooling is more sensitive to both direct costs and opportunity costs than that of boys (King and Winthrop 2015). When girls become teenagers, early marriage and adolescent pregnancy may prevent them pursuing further schooling (Field and Ambrus 2008). Social norms in certain cultural contexts may restrict them from accessing economic opportunities as well, potentially limiting the gains from investing in education. For example, 90 percent of women in Uttar Pradesh, India, reported that they needed their husband’s permission to work (World Bank 2018b). Even when women participate in the labor market, they are less likely to find a job. In the Middle East and North Africa, the female unemployment rate is twice that of
agencies and public debates.<sup>2</sup> While even in the absence of pacifying benefits there remain plenty of powerful arguments for anti-poverty programs, an empirical investigation of the argument that they also contribute to peace is key. Indeed, the nexus between transfers and conflict or peace has a range of policy implications, including for security policies: if foreign aid curbs conflict, peacekeeping troops could eventually be redeployed, while if aid fuels conflict, this may call for a reinforcement of security in targeted areas.

To address the question of whether cash transfers foster peace, we study the impact on conflict of a large-scale, randomized government-led cash transfer program in Niger. The national cash transfer program (“Filets Sociaux”) was set up by the government of Niger to deliver unconditional cash transfers for two years through monthly payments. Yearly benefits amounted to about 11 percent of household consumption. The program has had well-documented positive impacts on its core objectives related to household consumption and food security, as well as asset accumulation, income-generating activities, and psychosocial well-being (see, e.g., Stoeffler et al. 2020; Bossuroy et al. 2022; Premand and Barry 2022; Premand and Stoeffler 2022). We leverage the randomization of treatment assignment among nearly 4,000 eligible villages to estimate the causal impact of these cash transfers on conflict events. Geo-referenced conflict events are obtained from the GDELT data set (Leetaru and Schrodt 2013), which draws on media and NGO reports from a wide variety of international and domestic sources (as discussed in more detail below).

We do not find that cash transfers contribute to foster peace, at least not in the short-to-medium run. If anything, treated villages experienced an increase in conflict events during program implementation. This increase was mostly driven

> 1Anderton and Brauer (2021) estimate 100 million mass-atrocity-related deaths since 1900. In terms of economic costs, Mueller and Tobias (2016) estimate an average drop in GDP of 18 percent after a civil war, with only
on the supplemental survey protocol and survey instruments can be found at www.msu.edu/~gilesj/.

> 12A detailed discussion of a larger nine-province sample from the RCRE panel dataset, including discussions of survey protocol, sampling, attrition, and comparisons with other data sources from rural China, can be found in the data appendix of Benjamin, Brandt and Giles (2005). This paper makes use of village and household data from the four provinces where the authors conducted follow-up household and village surveys, which are Shanxi, Jiangsu, Anhui and Henan.

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# **Appendix I. Sampling design of Taobao Village Survey**

In 2017, 2,118 Taobao Villages were reported to exist in China. The Taobao Village Survey uses a stratified sampling design to obtain a nationally representative sample of 100 villages. The 2,118 villages are first ranked according to their annual sales, then divided into five strata, and within each stratum 20 villages were selected randomly. Specifically, the strata are defined as follows:

- Stratum 1 includes the 20 Taobao Villages with the highest annual sales. All 20 villages in this stratum are selected with certainty.

- Stratum 2 includes 80 villages that rank from the 21st to the 100th Taobao Villages in the annual sales ranking. Within these 80 villages, 20 villages are selected randomly.

- Stratum 3 includes the next 200 villages in the annual sales ranking. Twenty villages are selected randomly, each with probability of 0.1.

- Stratum 4 includes the next 500 villages in the annual sales ranking. Twenty villages are selected randomly, each with probability of 0.04.

- Stratum 5 includes the remaining 1,318 villages. Twenty villages are selected, each with probability of 20/1318.

After the first round of listing, due to refusal of interviews and inability to get in contact, 14 villages (8 in stratum 2, 3 in stratum 3, 2 in stratum 4, 1 in stratum 1) are further selected into the sample as replacements. At the end, 80 villages complete the interviews, 12 villages from stratum 1, 18 from stratum 2, 18 from stratum 3, 14 from stratum 4, and 18 from stratum 5.

The area of each selected village was then divided into a grid with equal sizes and estimated number of households within each grid. For large villages, we randomly select grids to do households listing. For small villages, all households are listed. Ten e-households and 10 none-households are randomly selected from the listing.

Sampling weights are calculated as the inverse of the product of non-response rate
# **4. Identification strategy**

We identify the effects of the reform with a difference-in-difference strategy that compares formal firm creation before and after the reform across regions based on the level of distrust in regional commercial courts. Our main regression model takes the form: Log�# new firms�,���α� �β� �δreform� ∗distrust� �ε�,�, (1) where the outcome variable is the log of the number of new firms in region _r_ in year _t_ , and α� and β� are region and year fixed effects, respectively. The variable _reform_ t is equal to 0 before 2005 and equal to 1 after (and including) 2005. The variable _distrust_ r is the measure of distrust in the court responsible for region _r_ . As described in section 3, it represents the fraction of survey respondents in region _r_ who say they have at least some distrust in courts. The coefficient δ measures the effect of the reform on the number of newly registered firms. We expect it to be positive and statistically significant, indicating that more firms registered in years after the reform compared to years before the reform and this difference was larger in regions with higher levels of distrust in courts. The number of observations in equation (1) is 108 (12 court-regions times 9 years of data). Standard errors are bootstrapped<sup>5</sup> and clustered at the region level.

The identification assumption is that, in the absence of the reform, the level of distrust in courts would not have been correlated with the change in number of new firms over time. To test whether this assumption is plausible, we estimate the following variant of equation (1) using prereform data only, i.e. data for the years 2003 and 2004: Log�# new firms�,���α� �β� �γyr2004� �δyr2004� ∗distrust� �ε�,� (2) where yr2004�
Using Poverty Lines to Measure Refugee Self-Reliance<sup>1</sup> Johannes Hoogeveen and Robert Hopper Development; O15 Human Resources; Human Development; Income Distribution; Migration.

1 Microdata used in this paper to estimate refugee income were provided by: Agustin Arakaki, Aziz Atamanov, Juan Monroy Barragan, Mohamed Coulibaly, Maria Davalos, Alvin Etang, Arden Finn, Sebastian Leander, Vincenzo di Maro, Chinedu Obi, Aboudrahyme Savadogo, Ganesh Seshan, Nistha Sinha, Wondimagegn Tesfaye and Christina Wieser. Suggestions for improvement received from Johan Mistiaen are gratefully acknowledged.
of Estonia is even more developed than the average of EU15 in IT infrastructure. The strong growth in electronics and telecommunication sectors has likely contributed to the recent economic performance of Estonia – the real GDP growth rate was 4.8% in 2003, far above the regional average level.<sup>9</sup> Figure 4a shows that, besides Estonia, three countries (Latvia, Romania, and Turkey) perform above their benchmark levels in port efficiency. The other new and candidate EU member countries are lagging behind; in particular, Slovak Republic and Hungary exhibit a

> 8 Hungary and Estonia share the similar development level of customs regimes.

> 9 For the sake of comparison, the real GDP growth rate of Hungary was 2.8% in 2003 (data source: CIA Fact Book).

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We next look at returns at the country level. Table 6 reports yearly real average returns for all countries for which we have more than 10 years of bond price data. Countries are ranked by average real returns. We also show the standard deviation of returns, average excess returns (above UK/US bonds), as well as the number of sovereign default events and the share of years in default. The country comparison is complicated by the fact that some countries, including most advanced countries, no longer issue government bonds in London or New York. Moreover, some countries only entered international capital markets recently and, for individual countries, there are years or even decades without pricing data, resulting in an unbalanced sample. Appendix B1 provides details on data coverage.

The results in Table 6 are consistent with our findings so far. Sovereign bonds of countries with a history of serial default tend to show higher returns, but also higher volatility.<sup>43</sup> The returns look surprisingly high even for countries that have defaulted multiple times and over long periods, such as Argentina, Brazil, Ecuador, Greece, Mexico, Ukraine, or Venezuela. These countries feature long-run excess returns between 4% and 12%. It is also remarkable that not a single country in Table 6 shows a negative arithmetic return, on average, and only two countries have negative excess returns (Bolivia and China).

Figure 8: Sovereign risk-return profiles: Returns and standard deviations, 1815-2016 <!-- Start of picture text --> 15 CIV ECU<br>CHE PAK<br>EST<br>NGA<br>IRQ UKR<br>FIN MEX<br>10 CRSR IB PO DEUL<br>VEN<br>FRA CZK<br>PHL<br>LBN ZMAWEPAN R NIC HC TUB IURY BRPR CHLJPNTURATARGITA CO GRC G SLVLTM DOMPRY<br>5 MYSNLDUS bondsHRVVNMEGYIDNSWEBELDNK ESPPERHUNAUTHND RUSBGR<br>ZAFIRLTHANOR ROU<br>AUS<br>NZL<br>CANUK
; iii) estimating poverty in completely inaccessible areas with correlates derived from satellite imagery and other geo‐spatial data; and iv) employing a special sampling strategy for the nomadic population.

Further improvements in terms of human resource capacity should be considered to minimize disruptions to the quality of the data, besides field team training and stringent security protocols. Also, future applications should consider refining the model to predict poverty from satellite imagery by incorporating predictors with higher spatial frequencies, as well as data on building footprints, which are likely to improve the estimates. Other alternatives are thresholding some of the distance variables or applying a sigmoid transformation to capture variations in small areas. Furthermore, the accuracy of satellite‐based imputations should be assessed based on a reference data set, ideally in a more stable environment.

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its Management and Importance of Salinity Research. International Research Journal of Biological Sciences, 1: 1‐3.

<mark>Strobl E, Strobl RO (2011) The distributional impact of large dams: Evidence from cropland productivity in Africa.</mark> _<mark>Journal of Development Economics</mark>_ <mark>96(2): 432‐450.</mark> <mark>Tanji, K. K., and N. C. Kielen. 2002.</mark> _Agricultural Drainage Water Management in Arid and Semi‐arid Areas._ Rome: Food and Agriculture Organization of the United Nations.

<mark>Thompson, W.R. 2004. “Complexity, Diminishing Marginal Returns, and Serial Mesopotamian Fragmentation.”</mark> _<mark>Journal of World‐Systems Research</mark>_ <mark>10 (3): 613–52.</mark> <mark>Tum, M., & Günther, K. P. (2011). Validating modelled NPP using statistical yield data.</mark> _<mark>b</mark> iomass and bioenergy_ , _35_ (11), 4665‐4674.

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Policy Research Working Paper 9392

# **Abstract**

This paper analyzes the main trends and patterns of nominal lending interest rates and lending-deposit interest rate spreads in emerging markets and developing economies. Using data from 140 emerging markets and developing economies, analysis shows that nominal lending rates and spreads declined between 2003 and 2017, with regional heterogeneity. In addition, it finds that less economically and financially developed countries tend to exhibit higher lending rates and spreads. These higher rates tend to be driven by higher spreads, not deposit interest rates. Also, illustrative regressions suggest that relevant correlates of nominal lending rates include inflation, public debt, and policy interest rate (macro-fiscal conditions); overhead costs, nonperforming loans, and non-interest income (banking characteristics); and credit bureau coverage and time to resolve insolvency (business environment). Finally, illustrative decompositions of the level and 10-year change between 2007 and 2017 of nominal lending rates find relative differences across regions. On the decline of nominal interest rates in that decade, rising public debt and nonperforming loans have pushed rates up, which was counterbalanced by a reduction in inflation, the policy interest rate, and overhead costs and a better business environment. Since the global financial crisis, a common global factor has increased in importance and has contributed to the downward trend in nominal lending rates.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at efeijen@worldbank.org and izuccardi@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
**Figure 2.4. Yemen: export and import intensities in 2020 (%)**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EXP-OUTshr* IMP-DEMshr**<br>percent<br><!-- End of picture text -->

*Ratio between exports and production; **Ratio between imports and domestic demand. Source: Authors’ calculations based on the 2020 Yemen SAM.

**Figure 2.5. Yemen: sectoral factor cost composition in 2020 (%)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Male labor Female labor Capital Natural resources<br>porcentaje<br><!-- End of picture text --> Source: Authors’ calculations based on the 2020 Yemen SAM.

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