rafmacalaba/data-use-mentions-extended huggingface dataset:
Export product concentration declined over time for all country groups in Sub-Saharan Africa —although at different paces across the different groups. Countries that were abundant in minerals and metals experienced the largest decline in the Herfindahl index of product concentration: it dropped from 0.69 in 1990-94 to 0.46 in 2010-14. In the case of oil rich countries, the pace of decline was slightly slower than that of metal exporters, but the group maintained the largest degree of export product concentration by 2010-14 (0.67). Finally, non-resource rich countries exhibit the lowest degree of export product concentration in 2010-14 (0.21, down from 0.29 in 1990-94).

The evolution of export market concentration differs sharply across groups in Sub-Saharan Africa. The Herfindahl index of market concentration among non-resource rich countries remained almost invariant over time — as is also the case for the region as a whole. Export market concentration for oil rich countries declined sharply from 0.30 in 1990-94 to 0.15 in 2010-14 while that of countries abundant in minerals and metals increased from 0.08 in 199094 to 0.28 in 2010-14. Overall, oil rich countries tend to have a more diverse group of trading partners than minerals and metals-rich countries in 2010-14.

# 3.3 Natural Resources in Exports

There is no consensus in the literature about the effect of natural resources on growth —see for instance Havranek et al. (2016) for a meta-analysis of the long-term growth effects of natural resource richness. Before digging deeper and trying to understand the growth effects of natural resources, we explore the evolution of different indicators of trade in natural resources across regional country groups. The set of indicators of natural resource intensity in economic activity include natural resource exports as a share of GDP and natural resource exports as a share of total exports. For this information, we used the World Integrated Trade Solution (WITS) database on the SITC, Revision 1.0. Natural resource exports comprise the commodities in the SITC sections 0 – food and live
Chi et al. (2022) validate their models relative to censuses, both papers use the _R_<sup>2</sup> as a performance metric and focus on wealth estimates rather than poverty. Finally, van der Weide et al. (2022) examine how well machine-learning methods using remotely-sensed data can reproduce poverty maps estimated using census data. They do not, however, compare their estimates to a ground truth.

This paper then represents the first attempt to rigorously assess the performance of modern poverty mapping methods relative to true poverty rates. In what follows, Section 2 discusses the data we use for our experiments and Section 3 discusses the various methods we examine, including both machine-learning methods and some traditional poverty mapping methods that we use as performance benchmarks. Section 4 then considers the issue of model validation where we critically assess the _R_<sup>2</sup> as a validation metric and then propose some alternative metrics. Finally, Section 5 presents the results of our experiments and Section 6 provides some concluding remarks.

# **2 Data**

The Mexican Intercensal Survey was carried out by the Mexican National Institute of Statistics and Geography (INEGI). The sample consists of 5.9 million households and is representative at the national, state (32 states), and municipality level (2,457 municipalities). It is also representative for localities with populations of 50,000 or more inhabitants. The administered questionnaire gathered 4
**Figure 1: Relative bias of simulation results using Rapid Consumption estimation.**

<!-- Start of picture text --> 8%<br>6.4%<br>6%<br>4.2%<br>4%<br>1.6%<br>2%<br>0.6%<br>0.1% ‐0.6%<br>0%<br>‐2%<br><!-- End of picture text -->

**Figure 2: Relative standard error of simulation results using Rapid Consumption estimation.**

<!-- Start of picture text --> 100%<br>88.0%<br>80%<br>60%<br>40%<br>16.3%<br>20%<br>1.8% 0.2% 0.6% 1.0%<br>0%<br><!-- End of picture text -->

**Source: Authors’ own calculations based on NBHS 2009 data.**

# Durable Consumption Flow

The consumption aggregate includes the consumption flow of durables calculated based on the user‐cost approach, which distributes the consumption value of the durable over multiple years (Amendola and Vecchi, 2014). The user‐ cost principle defines the consumption flow of an item as the difference of selling the asset at the beginning and the end of the year as, this is the opportunity cost of the household for keeping the item. The opportunity cost is composed of the difference in the sales price and the forgone earnings on interest if the asset is sold at the beginning of the year. The current price of the durable is _pt_ . If the durable item would have been sold one year ago, the household would have received the market price for the item twelve months ago plus the interest on the revenue
those whose parents completed primary school in the case of downward mobility. They are estimated by country, birth decade of the “children” and gender.

**Figure 9:** Intergenerational Educational Mobility in LAC across cohorts

<!-- Start of picture text --> Jamaica Cuba<br>Bolivia Suriname<br>Trinidad and Tobago Jamaica<br>Cuba Mexico<br>Mexico Bolivia<br>Uruguay Panama<br>Chile Uruguay<br>Suriname Chile<br>Costa Rica Costa Rica<br>Peru Trinidad and Tobago<br>Ecuador Colombia<br>Argentina Peru<br>Panama Argentina<br>Colombia Ecuador<br>Dominican Republic Dominican Republic<br>Paraguay Venezuela<br>Saint Lucia Paraguay<br>Brazil El Salvador<br>Venezuela Saint Lucia<br>El Salvador Brazil<br>Honduras Honduras<br>Nicaragua Guatemala<br>Guatemala Nicaragua<br>Haiti Haiti<br>0 .2 .4 .6 .8 1 0 .1 .2 .3 .4<br>IGM UP IGM Down<br>1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>(a) Upward mobility (b) Downward mobility<br><!-- End of picture text --> Notes: The estimates are done by birth decade cohort of the children.

21
|686|686|518|686|518|518|518|

Note: Data on average labor costs per worker are from the _Banque de Donnees Economiques et Financieres,_ which is composed of about one thousand medium and large firms. There is one observation per industry per year, over period 1975-1994. Values in parentheses are "t" statistics. Significant coefficients at the _5_ and 1 percent level are indicated by one and two asterisks respectively.

22
# **What is already known?**

- Many country-specific studies have shown that low- and middle-income countries suffer severe shortages of facility inputs such as number of providers, drugs, equipment, and medical supplies.

- Individual country reports of Service Delivery Indicator surveys from Kenya, Niger, Senegal, Tanzania, and Uganda use summary statistics to show low rates of knowledge and high rates of absenteeism among health service providers.

# **What are the new findings?**

- This study offers a single metric that quantifies the relative contribution of staff knowledge, staff availability, and other facility inputs to the probability of care readiness which meets minimum quality standards.

- Our work shows severe gaps in care readiness in various countries in Sub-Saharan Africa, and health provider knowledge is a particularly severe constraint on the readiness of care across countries. Across the countries sampled and the evaluated medical conditions, which account for major loss of life across the continent, health care providers were able to correctly diagnose only 64% of cases, and the treatment plan was aligned with the correct diagnosis in 45% of cases.

# **What do the new findings imply?**

- While effective health care systems need to include a variety of components (knowledge, effort, equipment, and essential medicines), not all components merit the same initial priority.

- Our findings suggest that in the absence of provider knowledge, even improvements in other key areas such as effort, medication, and equipment cannot save patients’ lives.

- As a result, health systems will need to invest in better knowledge for their health providers along with other resources.

2
is chosen as the FE unit and applied to three-year thana-level panel data to control for the timevarying thana-level heterogeneity affecting all households living in a particular thana. In other words, while the first model controls for thana-level unobserved heterogeneity fixed over years, the second model controls for thana-level heterogeneity varying over time within a year. Although we believe that the second model is more appropriate, we present both model estimates to compare the results.

As the results of Table 6 suggest, none of the village-level attributes such as program placement of microcredit or banks are significant except rural electrification, as this matters to the growth of microenterprises. Thus, the presence of electricity in a village increases the adoption rate by 5.8 percent over the period of 10 years, implying that about half a percent of microenterprise growth per year has been due to rural electrification, an indicator of overall village development.

Because microenterprises are mostly family operated, household characteristics play a significant role in their adoption. For example, households with higher land assets are more likely to be engaged in farming, and hence, less likely to be engaged in rural nonfarm activity. In contrast, those with higher non-land assets are more likely to be involved in rural microenterprise activities. Also, as family labor is preferable (as well as cheaper) to hired labor, large family households are more likely to be engaged in nonfarm activities than small family households. With the increase of one family member, the probability of adopting microenterprise activities goes up by almost 2 percent.

# <u>3. Why does microenterprise growth matter?</u>

Microenterprise growth matters because it is expected to reduce poverty when it is combined with farm income growth. In fact, majority of households who adopt microenterprise activity have additional sources income from farm activities. Thus, the percentage of microenterprise owners 10
Policy Research Working Paper 7855

# **Abstract**

Climate-driven water variability is a natural phenomenon observed across river basins, but predicted to increase due to climate change. Environmental change of this kind may aggravate political tensions, especially in regions that are not equipped with an appropriate institutional apparatus. This paper argues that attempts to assess the ability of states to deal with variability in the future rests with considering how river basins with agreements have fared in the past. The paper investigates whether basins governed by treaties witness less tension (and by extension more cooperation) over shared water in comparison with those basins not governed by treaties, using the 1948– 2008 country dyads event data from the Basins at Risk project. The results provide evidence to suggest that the presence of a treaty promotes cooperation. Furthermore, the number of agreements between riparian countries has a significant positive effect on cooperation, which is robust across different specifications controlling for a broad set of climatic, geographic, political, and economic variables.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at bblankespoor@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
# **4. Conclusion**

Air pollution concentrations in urban areas are determined by a balance of factors between pollutant generation (and subsequent accumulation) and pollution dispersion. Generally, indicative findings point towards the gradual deterioration of ambient air quality within the study areas. But more specifically, PM2.5 pollution data sets show significant spatiotemporal variations in pollution profiles across all the sampled locations. Spatiotemporal patterns generally indicate strong disproportionate correlations between pollution levels (for PM2.5, NO2 and O3) in central business districts and outside central business districts. Notably, concentrations for Kampala and Wakiso were systematically higher than upcountry locations (i.e. Eastern and Western clusters) with some PM2.5 diurnal temporal exceptions. The influences of traffic levels and urban topology (presented in seminal literature) were also confirmed in the case study areas. Despite varying pollution levels, PM2.5 and NO2 results indicate a wide measure of substantial exceedances of WHO, EU and USEPA temporal (24-hour) and annual limits.

We further conclude that inconsistent and haphazard weekly profiles and the probable absence of the ‘weekend effect’ highlights the need for multiple pollutant assessments and also reiterates the inadequacy of sparse monitoring data sets. This further corroborates the need for multiple sensor deployment with a plausible mixture of both static and complementary mobile monitors to estimate pollution levels and inform the development of high-resolution pollution maps while identifying micro-level/localized drivers. In addition, the strong influence of rainfall patterns on seasonal particulate levels justifies the need for long-term monitoring to reflect actual seasonal variations.

Informed by the observed level of completeness for static monitoring data sets and comparison of data sets for the two mobile scenarios, we further conclude that engaging volunteers for opportunistic monitoring has huge potential, not only for high-resolution air quality monitoring but also as an avenue for increasing awareness of air pollution. In other words, the practicability and the potential of a low-cost monitoring network to succeed in the setting of Uganda and perhaps Sub-Saharan African are highly demonstrated. Determining the actual costs of setting up and
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### CHAPTER IV

# BINARY COMPARISONS OF REAL **OUTPUT** AND PURCHASING POWER MEXICO/BRAZIL IN 1975

## Introduction

This chapter supplements chapter III, which showed two binary comparisons between Brazil/USA and Mexico/USA. with a direct comparison between Mexico and Brazil.

The major conceptual difference between this and the previous chapter is that we are able here to make comparisons for our sample industries for our preferred "national accounts" concept of value added instead of the "US census concept", which we had to use for the detailed industry comparisons of chapter III because the necessary information was not available for the USA.

Another advantage of a direct Mexico/Brazil comparison is that we did not have to make an adjustment for different benchmark years, as was the case in the comparison with USA.

## Levels of Real Gross Output in 17 Industries

This chapter covers the same sample of industries as in chapter III. We attempt to match products of Mexico (country M) with products of Brazil (country B), in one case in terms of the unit values of country B (cruzeiros) and in the other case in terms of the unit values of country M (pesos). We made two separate comparisons, namely the ratio of country M's quantity of output to that of country B, in unit values of country M and in unit values of country B: and <!-- Start of picture text --> 1 (QM PM)<br>M Y_ __Y ---- (4.la)<br>E (QB *PM)<br>y y<br>z (QM * pB)<br>B B (4.lb)<br>I (QB * PB)<br>y y<br><!-- End of picture text --> Table 4.1 shows total gross value of output for these 17 industries as derived from the industrial
# The Impact of the COVID-19 Pandemic on Women-Led Businesses<sup>*</sup>

Jesica Torres,<sup>†</sup> Franklin Maduko, Isis Gaddis, Leonardo Iacovone, and Kathleen Beegle<sup>‡</sup> JEL classification: D22, J16, L20, L25, O10 Keywords: COVID-19, Firms, Gender, Uncertainty, Entrepreneurship

*We have greatly benefited from discussions with Caren Grown, Denis Medvedev, and participants at various seminars at the World Bank, including the webinar _Learning from and Using Surveys to Assess the Gendered Socio-Economic Impacts of COVID-19_ organized in collaboration with UN Women. We thank the Women Entrepreneurs Finance Initiative (We-Fi), a groundbreaking partnership that aims to unlock financing for women-led businesses in developing countries, as well as the USAID, the IFC, and the World Bank for financial support. We are grateful to the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. Finally, we also thank our World Bank colleagues and the numerous organizations listed in detail in Apedo-Amah et al. (2020) who supported the collection of the survey data. All errors are our own. The views expressed in this article are solely those of the authors and do not necessarily reflect the views of the World Bank, its Executive Directors, or the countries they represent.

> †Corresponding author. E-mail address: jtorrescoronado@worldbank.org.

> ‡Torres: The World Bank; Maduko: University of Exeter Business School and The World Bank; Beegle and Gaddis: The World Bank and Institute of Labor Economics (IZA); Iacovone: The World Bank and Hertie School.
, and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. Standard errors in parentheses.

# **7. Conclusions**

Our findings show that even when poverty rates are very high, there are important differences between poverty risk according to gender and displacement status. Distinguishing between different types of households beyond the gender of the household head improves our understanding of poverty in situations of forced displacement. The association between a household’s demographic composition, its income profile and poverty risk shows that women’s lack of economic empowerment and their caring responsibilities elevate poverty risk. If we had simply stopped at female headship, we may have concluded that – aside from widows - women are better off in Somalia.

Our analysis extends the gender and poverty literature by establishing a link between the demographic and income profiles of households and the risk of experiencing poverty in situations of displacement. Using data that is typically available in household surveys, we showed how this can be done. Our starting point is that women as individuals or households dependent on women’s income may be at greater risk of poverty due to two interrelated gender norms. These two norms are first, the norms assigning women a disproportionate responsibility for care work and the provision of household services, and second the norms that restrict women’s ability to access paid 26
# **1. Introduction**

Over the past two decades, the Chinese state and its state-owned banks have become the largest official lenders to the developing world, but the size and characteristics of China’s lending boom have remained exceptionally opaque. Lack of transparency owes, in large part, to the fact that the main Chinese lenders require strict confidentiality from their debtors and do not release a granular breakdown of their lending.

To address the significant gap in the data and analysis of modern international lending patterns, Horn et al. (2021) compiled a comprehensive data set of Chinese overseas loans. They document the large scale of China’s lending boom from 2000 to 2017 and find that around 50% of China’s lending was not captured by the most widely used international debt statistics. “Hidden debts” are a significant challenge to international surveillance and the pricing of risk.

As of 2021, China’s lending boom to emerging markets and developing economies (EMDEs) appears to have largely ended. Following the path of many prior lending booms, the current one has turned sour, with growing debt sustainability problems and default risks resurfacing in many debtor countries. Of the 73 low-income countries eligible for the Debt Service Suspension Initiative (DSSI) introduced at the outset of the Covid-19 crisis, more than 50% are estimated by the IMF and World Bank to be currently in debt distress or at high risk of a sovereign debt crisis. Against this backdrop, the knowledge gap on Chinese lending has expanded beyond debt (stocks, flows and terms) to include “credit events” (default and debt restructuring operations). This paper takes a step toward filling that gap.

Details on the crisis response by Chinese lenders are hard to come by, for several reasons. Chinese official loans and related credit events are not on the radar screen of credit rating agencies such as Moody’s or Standard & Poor’s. Data on Chinese defaults and restructuring is also not collected systematically by international organizations such as
, indicating a reasonable amount of independent variations for each group of BE indicators.

Competition is associated with lower employment growth and higher productivity. Compared to firms that report no competitors, those facing one or more competitors have lower employment growth. In contrast, relative to firms that report no competitors, those facing one competitor (two or more competitors) have a labor productivity level that is 25 (18) log points higher. When using TFP as the productivity measure, firms facing one competitor also have the highest productivity edge, though those facing two or more competitors no longer have an edge.

_Government Protection_ . Corruption is negatively associated with firm growth. The Enterprise Survey’s Corruption Obstacle indicator is associated with lower firm growth, but

> 14 The World Bank HLO indicator is not part of the Doing Business project. We include it for ease in exposition, and because DB indicators and the HLO indicator are all country-level indicators produced by the World Bank.

> 15 An exception is for firm employment growth. The Enterprise Survey specification surpasses the Global Competitiveness specification only slightly.

14
of more countries (that tend to be developing economies) uncovers a lower average RTA effect. These benchmark results will be key in understanding the effects of the heterogeneous trade agreements.

With these benchmark results at hand, I now focus on the RTA effects specific to South Asia countries. I continue to use data for the manufacturing sector to be able to compare the previous results and the literature in general. I am interested in disentangling the effects of the intra-regional RTAs and the RTAs that a South Asian country signs with a third country outside the region. Table (2) introduces these additional effects of RTAs in South Asia. Column 1 is the benchmark average result from table (1). Column 2 adds an interaction term of RTA in which at least one of the countries is from South Asia. It shows that the additional coefficient is 0.11. This leaves the trade creation effects on RTA 7