rafmacalaba/data-use-mentions-extended huggingface dataset:
a proportionally larger budget adjustment for a given reduction in the primary deficit as a percentage of GDP—and greater risk from contingent liabilities.

# **V. Summary of Recommendations**

EM governments typically borrow both in local and foreign currencies, are more vulnerable to sovereign debt crises and have less fiscal and monetary policy space than their AE counterparts. It would therefore make sense to have a separate DSF and DSA template for EMs. It would also be worthwhile to explore a common template for EMs and LICs given the shift to market borrowings in LICs with market sentiment now playing a big role in their debt sustainability, as set out in Gill and Pinto (2023).

The main modification we recommend in the 2023 DSA template from the EM perspective is to include the calculation of a composite real interest rate broken down into its local and foreign currency components to enable a better understanding of debt dynamics based on primary deficits and (r-g), and guide borrowing costs.

In addition, the DSA should include historical time series on primary deficits, the composite real interest rate and its local and foreign currency components, growth rates, and a discussion of contingent liabilities. If residuals have been historically high, some explanation should be provided. Lastly, the simulations going forward should be based on the stochastic properties of these variables and the comparator countries for cross-checks should be restricted to EMs.

> 9 These EMs were <mark>Brazil, India, Indonesia, Türkiye, and South Africa.</mark>

14
4 In the first study, Beck, Demirguc-Kunt and Martinez Peria (2008) report that the average share of SME lending is smaller in developing countries (16 percent of total lending) by comparison with the average share in developed countries (22 percent of total lending). Banks in developing and developed countries are primarily attracted by the potential profitability of the SME sector and serve SMEs primarily through dedicated SME units. Government programs are considered favorable and prudential regulations are not perceived as burdensome. Scoring models are used by most banks but they are just one of the inputs in loan decision. Banks in developing countries report that macroeconomic instability is the main obstacle to SME lending, rather than flaws in the legal and contractual framework. However, the second study (Beck, Demirguc-Kunt and Martinez Peria, 2009), based on the statistical analysis of the dataset concludes that the differences in SME lending between developing and developed countries are actually explained by differences in the quality of the legal and contractual environment (weaker in developing countries). Overall, their analysis suggests that the enabling environment is more important than firm size or bank ownership in shaping bank financing to SMEs.

The study by de la Torre, Martinez Peria and Schmukler (2009) relies primarily on on-site interviews with 37 banks in Argentina, Chile, Colombia and Serbia. The survey did not focus on measuring SME lending, but included 92 questions covering the strategic approach to SME lending, business models, and risk management. The authors complement the information from the interviews with a survey by the International Finance Corporation (IFC) across 8 developed and developing countries and annual surveys undertaken by FRS (Inmark Group) in 7 countries. All in all, data from 48 banks and one leasing company in 12 countries was used in the analysis.

This study investigates to what extent the conventional wisdom on SME lending holds in practice – the conventional view is that large banks are not attracted to SME lending and that the SME business is dominated by small banks and based on relationship lending.
) – excluding _i_ -specific origin.<sup>25</sup> The stock of immigrants from RoW is only marginally affected by the presence of bilateral visa provision. When origin and destination _ij_ share an RTA including visa provision, the stock of immigrants in _j_ from the rest of the world reduces by 0.1%. The result is robust to the inclusion of the Schenghen dummy control.

– Table 4 about here –

> 22We base on the World Bank classification of country by income levels, and consider “North” as high-income countries, and “South” all remaining low and middle-income countries.

> 23Data on the number of visas granted by US embassies is from the Report of the Visa Office ( `https://travel. state.gov/content/travel/en/legal/visa-law0/visa-statistics.html` ). We have data on the number of working-permit related visas granted yearly in the period 2000-2020.

> 24When we include origin fixed effects in estimation, the non-linear PPML estimator discards all those origins whose number of visa does not change over time. This explains the reduced number of observations in column (2) of Table A3.

> 25By excluding _i_ -specific origin from the total stock of immigrants in _j_ , the dependent variable used in Table 4 is indeed _ij_ specific and the number of observations almost the same as in the baseline specification (not exactly the same because some observations are dropped by perfect collinearity with fixed effects).

11
use these true poverty measures to gain insight not only into the credibility of model validation based on direct estimates, but also the performance of machinelearning methods when validated against the true poverty measures. Given that population censuses rarely collect detailed data on income or expenditures, we construct our census from a large-scale household survey conducted in Mexico: the Mexican Intercensal Survey of 2015.

Prior to presenting our simulation results, we discuss three ways in which the _R_<sup>2</sup> can be misleading in terms of model assessment. Specifically, we show analytically that the _R_<sup>2</sup> based on direct estimates is biased downward, that it is insensitive to differences in location and scale between the poverty estimates and the reference measures, and that it is context-dependent in the sense that it is influenced by the variance of the true poverty rates. We argue that the mean squared error is a better measure for understanding the strength of the association between the poverty estimates and reference measures. In addition, we argue that the ultimate concern is to understand how different methodological choices affect poverty by influencing the efficiency of targeting. We therefore propose a targeting experiment through which we examine the relative ability of alternative

> 3See Jean et al. (2016), Yeh et al. (2020), Lee and Braithwaite (2020), and Aiken et al. (2022) for examples of other recent applications.

> 4For examples of the above procedure, see Jean et al. (2016), Pokhriyal and Jacques (2017), Steele et al. (2017), Lee and Braithwaite (2020), Yeh et al. (2020), or Chi et al. (2022). Note that a few studies use a similar procedure, but fit the machine-learning model to household-level data and then aggregate the model’s predictions to the geographic unit of interest (Blumenstock et al., 2015; Hersh et al., 2021; Aiken et al., 2022). Others rely
Policy Research Working Paper 10134

# **Abstract**

Measuring job quality across countries has been challenging and has relied typically on a single indicator, such as formality or wages. To contribute to this critical policy issue, this paper presents a first global estimate of job quality departing from microdata. It assembles a harmonized global data set of labor force and household surveys to produce a measure of job quality across four dimensions: sufficient income, access to employment benefits, job stability, and adequate working conditions. The results for 40 developing countries show significant variation in job quality across countries, economic sectors, and sociodemographic characteristics, including age, location, and educational attainment. Countries in the Latin America and the Caribbean region have relatively higher levels of job quality, while countries in Sub-Saharan Africa display the lowest levels of job quality. Most workers in the sectors of finance and business services, public administration, and utilities have, on average, better jobs. Higher education matters in securing greater job quality, while the average job quality of wage employment is relatively similar between men and women but with some variation in income and working conditions.

This paper is a product of the Poverty and Equity Global Practice and the International Finance Corporation. 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 shovhannisyan@ifc.org, vmontalvatalledo@worldbank.org, crodriguezc@worldbank.org, and kstamm@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
Policy Research Working Paper 6265

# **Abstract**

Using a cross-section of more than 25,000 domestic manufacturing firms in 78 low and middle-income countries from the World Bank’s Enterprise Surveys, this paper assesses how mediating factors influence intraindustry productivity spillovers to domestic firms from foreign direct investment. It identifies three types of mediating factors: (i) foreign direct investment spillover potential, (ii) domestic firm absorptive capacity, and (iii) the host country’s institutional framework. It finds that all three affect the extent and direction of foreign direct investment spillovers on domestic firm productivity. However, the impact of mediating factors depends significantly on the level of domestic firms’ productivity and the structure of foreign ownership.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at tfarole@worldbank.org and dwinkler2@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
of teachers and as much as 35% in the case of health workers.<sup>17</sup> Averaging across seven African countries, more recent research found that 23% of teachers were absent from school, and 44% were absent from the classroom.<sup>18</sup> Absenteeism – coupled with teachers spending time in class but not teaching – resulted in, on average, just 23⁄4 hours of teaching time per day instead of the scheduled 51⁄2 hours.

Later research looked at another aspect of the quality of education and health services – provider knowledge. The study of seven African countries also tested primary school teachers’ knowledge of their subject by asking them to grade mock student tests in language and mathematics. Teachers did well enough on spelling and simple grammar, with two‐thirds grading at least 80% of the questions correctly. But they did much worse in other areas, averaging 44% on vocabulary and comprehension, and just 25% on composition. In mathematics, the results also were worse the more complex the task. On average, 91%

> 15 Filmer D, Hasan A, Pritchett L. A Millennium Learning Goal: Measuring Real Progress in Education: Center for Global Development, 2006.

> 16 Gabrysch S, Nesbitt RC, Schoeps A, Hurt L, Soremekun S, Edmond K, Manu A, Lohela TJ, Danso S, Tomlin K, Kirkwood B, and Campbell OMR. Does Facility Birth Reduce Maternal and Perinatal Mortality in Brong Ahafo, Ghana? A Secondary Analysis Using Data on 119,244 Pregnancies from Two Cluster‐Randomised Controlled Trials. The Lancet Global Health 2019; 7(8): e1074‐e87.

> 17 Chaudhury N, Hammer J, Kremer M, Muralidharan K, Rogers FH. Missing in Action: Teacher and Health Worker Absence in Developing Countries. _Journal of Economic Perspectives_ 2006; 20(1): 91‐116.

> 18 Bold T, Filmer D, Martin G, Molina
and equity are balanced. Two-thirds of countries in the region had some form of STR for small businesses in March 2024, and in the remaining countries, small businesses that reach a certain size are expected to comply immediately with more complex corporate or personal income tax regimes. Among the countries with STRs, three stylized facts emerge about how they have been designed. Firstly, only 40 percent of countries apply a minimum turnover threshold, which means in the remaining countries, as soon as any business, no matter how small, makes any revenue, they are required to pay tax under the STR. Secondly, around 60 percent of countries rely solely on a percent of turnover for determining the amount of tax owed, whereas in the remaining countries, either a set fee or a combination of both is used. Thirdly, the relative size of tax obligations levied on small businesses varies considerably, with the profit rate that businesses would need to make for their level of taxation to be equal under the STR and CIT regimes ranging from 2 to 20 percent.

To shed light on the real-world implementation of an STR, we conduct a detailed examination of tax administrative data from the TOT regime in Kenya and identify three major shortcomings. Firstly, the minimum turnover threshold that aims to exempt the smallest businesses from paying tax is not binding in practice. On average, around 70 percent of businesses that pay TOT should be exempt as their declared turnover is below the minimum threshold (collectively, these businesses pay around one-third of the total amount of TOT collected). Survey data suggests that this is very likely due to a lack of knowledge by taxpayers and potentially due to a belief that filing a non-zero amount in tax will decrease the likelihood of being audited by the Kenya Revenue Authority (KRA). Secondly, efforts to mobilize revenue by increasing the amount of tax required from small businesses are partly offset by businesses lowering their declared turnover. Results from an event study show that, on average, in response to a three fold increase in the
households consider their generator as a sunk cost: once the up-front cost of the generator is paid, there is little incentive for household to pay for service improvement, possibly reducing the WTP. Second, the Doing Business survey is considering outages for the average firm in the capital city, possibly leading to a large underestimation of the number and magnitude of disruptions. Moreover, not all countries are included in the Doing Business survey and the missing countries are mostly in the bottom 50% of the GDP per capita distribution, and therefore the most likely to experience numerous outages (Figure 1). This would increase the likelihood of underestimating the cost of outages. Finally, using a WTP to estimate the well-being impact of power outage creates a strong bias against the poorest. Since these disruptions affect mostly people in poor countries where WTP are mechanically low – due to the wealth effect –, the impact expressed in dollars may appear small, hiding significant implication for immediate well-being, but also for the ability of households to prosper and escape poverty.

# 2. The impact of water disruptions

## 2.1. Water disruptions have a strong impact on health

The few studies focusing on health impacts of water provision disruptions and dysfunctions consistently show negative effects on health due to water disruption episodes (Figure 5)<sup>7</sup> . Water outages can affect health through a number of different channels. Disruptions may force households to use unsafe alternative water sources. It may also affect household hygiene practices. Water disruptions (including low-pressure episodes) may increase risk of water supply contamination, what affects consumption once water is accessible again. Contamination may occur if water is unsafely stored during disruptions or filtration and purification systems are malfunctioning. Most of these papers focus on systemic water intermittencies while fewer focus on one-off water disruptions.

In medium to low income countries, frequent disruptions to potable water supply are widespread (Kumpel & Nelson, 2016). From 2004 to 2013, the International Benchmarking Network (IBNET), documented

> 6
# (b) <u>Implementation</u>

Packages were prepared in July 2015, at GRADE premises (in Lima). Each ‘GYM package’ was personalized for each school based on the number of classrooms in grades seven and eight, as reported in the 2014 Peru School Census.<sup>16</sup> Once packages were ready, a private courier company was hired to deliver them to all schools in the treated group. Each package contained an official letter from MINEDU (signed by the Director of the Secondary Level) addressing the head teacher of the school. This letter was placed on the outside of the package so that it was visible to the person who received it.

To locate schools, the courier was given the name of the institution, full address and (when available) contact phone numbers, as reported in the publicly available information system of MINEDU called ESCALE. The initial aim was to deliver all packages during August 2015. However, there were problems with some of the deliveries because the information given to the courier company was not detailed enough and/or was not accurate. For this reason, for a group of schools the courier company was given a physical map to locate the district (elaborated with Google Maps, using the geo-referenced location from the school) and the most up to date information about phone numbers (which was not necessarily information that was publicly available at that time). This information was obtained from the data division of the Ministry ( _Sistema de Información de Apoyo a la Gestión de la Institución Educativa_ , SIAGIE). Based on this strategy, the delivery of packages was completed between August and September 2015. As part of the protocol, each school that received the package signed a delivery receipt.

# (c) <u>Monitoring delivery and compliance</u>

A small tracking team, composed of two individuals with experience in telemarketing, was trained to monitor–through phone calls–all schools that had received the ‘GYM package
The paper is structured as follows. We proceed in Section 2 by providing a short discussion of the literature we build on, and a description of the data we use. In Section 3 we provide summary statistics on student learning outcomes and teacher content knowledge. In Section 4 we present a statistical model of cumulative knowledge acquisition, show how to estimate the key structural parameters, and discuss how we identify these parameters empirically. In Section 5 we present reduced form evidence followed by the main results, show specification and placebo tests, and present the findings from various simulations that use the estimated structural parameters. Finally, we conclude in Section 6 with a short discussion of the implications of our findings.

# 2. Related literature, data, and context

## _2.1 Related literature_

This paper is related to a growing literature on the impact of teacher quality on student learning outcomes. Several studies, primarily from the U.S. but more recently also from other countries such as Ecuador and Pakistan have demonstrated the importance of teachers using a value-added approach, with effect sizes ranging from 0.1 to 0.2 SD (Rockoff 2004; Rivkin et al. 2005; Aaronson et al. 2007; Chetty et al. 2014b; Araujo et al. 2016; and Bau and Das 2017).<sup>4</sup> While data that track individual student achievement over time, and link students to their teachers, are becoming increasingly more common in high-income countries, this is usually not the case in low- and middle-income countries.<sup>5</sup> Therefore, finding alternatives to estimating value-added models that rely on those data sets is necessary in order to provide insights into the education production function.

Our results confirm the importance of teacher quality and demonstrate the external validity of the value-added findings to a low-income environment where the average quality of teachers is low but also the variation in measured teacher quality is likely much greater.

Our work complements the literature in two additional ways. First
et al. 2020, Zhou, Yu et al. 2020). CFRs, however, have the disadvantage that cases are not defined consistently across countries. An alternative approach is to measure the agespecific mortality rate, using deaths attributed to COVID-19 by age group divided by the underlying population in the age group (Goldstein and Lee 2020). The analysis in this paper builds on the Goldstein and Lee analysis of 5 countries and employs data for 26 countries, including 9 middle-income countries and one low-income country.<sup>2</sup>

> 2 Following the World Bank country classifications, the high-income countries are Australia, Canada, Chile, Denmark, France, Germany, Hungary, Italy, Japan, Netherlands, Norway, Portugal, Republic of Korea, Spain, Sweden, Switzerland, the middle-income countries are Argentina, Brazil, Colombia, Malaysia, Mexico, Pakistan, Peru, Philippines, South Africa, and the low-income country is Afghanistan.

2