rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7902

# **Abstract**

Using a partially constructed panel database of 48 Sub-Saharan African countries from 2000 to 2013, this paper analyzes the structure of housing finance in Africa, its determinants, and its impact on inclusive growth. The findings show that market capitalization and urbanization are key - positive determinants of housing finance, and the post-con flict environment is conductive to greater housing finance development. This result suggests that housing finance is driven by standard market forces of demand and supply. In addition, the analysis finds that housing finance development in Africa is not yet an effective tool for reducing economic inequality, at its current, very early stage. However, the paper shows that above a given threshold, housing finance could be efficient at reducing inequality. Finally, there is a slightly positive relationship between housing finance and greater economic development in Africa. All these findings suggest that policies to boost housing finance development in Africa would be fruitful in the medium to long terms.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group and the Office of the Chief Economist, Africa Region. 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 nguena@aaye.org, ftchanatchana@worldbank. org, and azeufack@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
35 really a QR tends to lead to overestimation of the welfare losses induced by the policy.

To illustrate this point, we perform two additional counterfactual exercises in which we use different means to reduce the quantity of imported fertilizer by 50%. In the first exercise, this is achieved via QRs, while in the second exercise it is done by increasing the iceberg trade cost of transporting fertilizer from the RoW to Sri Lanka.<sup>90</sup> Results are displayed in Columns (4) and (5) of Table VI. As expected, for all types of agents, welfare losses are less severe under QRs than under iceberg cost increases, with the average welfare effect on mobile workers even switching from negative to positive in the former case.<sup>91</sup>

# **VII.F Model Validation: Evidence from Night Lights**

Our model-driven counterfactual exercise replicates the stylized facts documented in Section IV, with large decreases in fertilizer imports, crop yields, and agricultural exports. To further validate our model, we now show that the decreases in real regional income (and, thus, in regional welfare) that are implied by night light intensity data also align with the predictions of our counterfactual.

A well-established literature (Henderson et al., 2012) has confirmed the validity of night lights as a reliable way to indirectly assess the spatial distribution of real income per capita when direct income metrics with the desired coverage or frequency are unavailable. The elasticity of per capita income to the average night light intensity of a geographical unit is often estimated to be around 0.3. We combine this elasticity with the observed reduction in average night light intensity in each Sri Lankan district between April 2021 (i.e. the last month before the ban was implemented) and January 2022 to estimate districts’ income losses in that period.<sup>92</sup> Estimated income losses are displayed in Figure VIIIa. The decreases in district-level real income predicted by the
variables.|
|Concentration|Measure of bank concentration. Sum of the market shares measured in total assets of the<br>three largest banks for the period 1989 to 1996, when available. Source: Cetorelli and<br>Gambera (2001).|
|Competition|Measure of bank competition. Average_H_-statistic for the period 1987—1996. Source:<br>Author’s calculations using data from Bankscope and following the methodology in<br>Claessens and Laeven (2004).|
|Accounting|Measure of accounting standards in the country in 1983. Source: Rajan and Zingales<br>(1998).|
|Property rights|Measure of property rights. Median value over 1995—99; score from 1 to 5. A higher score<br>denotes greater protection of property. Source: Heritage Foundation.|
|GDP per capita|The logarithm of GDP per capita in 1980. Source: World Development Indicators of the<br>World Bank.|
|Legal origin|Identifies the legal origin of the company law or commercial code of each country. There<br>are four possible origins: (1) English common law; (2) French commercial code; (3)<br>German commercial code; and (4) Scandinavian commercial code. Source: La Porta et al.<br>(1998).|

<u>Note:</u> This table describes the variables collected for our study. The first column gives the names of the variable as we use it; the second column describes the variable and provides the source from which it was collected.

16
Table A.22: Difference in technology sophistication in general business functions with and without outlying enumerators

||All Sample|Sample Without<br>Diferent<br>Enumerators|Diference|
|---|---|---|---|
|Vietnam||||
|Mean|1.934|1.947|-0.013|
|SE|(0.012)|(0.012)|(0.017)|
|Observations|1,499|1,341||
|Senegal||||
|Mean|1.406|1.404|0.002|
|SE|(0.011)|(0.011)|(0.016)|
|Observations|1,786|1,784||
|Bangladesh||||
|Mean|1.482|1.458|0.024|
|SE|(0.015)|(0.015)|(0.021)|
|Observations|903|798||
|Kenya||||
|Mean|1.938|1.936|.002|
|SE|(0.020)|(0.020)|(0.029)|
|Observations|1305|1296||

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data from the Firm-level Adoption of Technology (FAT) surveys in Vietnam, Senegal, Bangladesh and Kenya. Brazil, Ghana, India and Korea are excluded because they do not include significantly different interviewers. The average of technology sophistication in general business functions ( _MOSTj_ ) is compared between all sample and sample excluding significantly different enumerators. Standard errors in parenthesis.

compare variables in RAIS with variables we collected in FAT for the same firms.

First, we analyze the correlation between sales per
# **1 Introduction**

Despite global calls for improvements in education, progress towards learning for all is slow. This deficit is particularly pronounced for poor children and children in low-income countries [Akmal and Pritchett, 2019]. But why do some students learn more in some schools than others? While there are many contributing factors at system, school, and household-level, one consideration receiving growing attention is school management—the processes and practices used by principals day-today as they run their schools [World Bank, 2018]. Academics and practitioners interested in this issue face two challenges: how to _measure_ school management accurately and cost-effectively at scale across schools and countries; and how to _explain_ any observed relationship between school management and learning outcomes in a way that elucidates the underlying mechanisms to guide policy. This paper addresses both of these challenges.

Our first contribution is to develop a new approach to measurement that can, in principle, be used with any existing public dataset containing items about school management. We illustrate using two public datasets as examples: the OECD’s Programme for International Student Assessment (PISA), and the Brazilian school census survey, Prova Brasil. The essence of our approach is to benchmark against the “state of the art”, but expensive, World Management Survey (WMS) in Bloom et al. [2015a]. We show how questions from these public surveys can be classified into WMS topics (53 PISA questions into 14 WMS topics and 33 Prova Brasil questions into 8 WMS topics), how the responses can be coded using the WMS scoring rubric, and finally how these grades can be built into a school management index. Our PISA-based index covers over 15,000 schools across 65 countries, and our Prova Brasil-based index covers nearly all public schools in Brazil. These indices are well-validated and can be used by researchers interested in studying the role of management in education systems across a far wider range of countries and schools than was previously possible.<sup>1</sup> All
.4|0.2|0.3|2.9|
|UK|47.8|20.6|7.9|117.1|51.4|65.5|310.4|
|Hungary|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Ireland|0.1|1.6|0.0|0.1|0.2|0.6|2.6|
|Luxembourg/||||||||
|1|2.5|22.4|4.4|3.2|2.0|10.5|45.0|
|Malta|0.0|0.3|0.0|0.0|0.1|0.2|0.7|
|Total<br>(€billion)|125.47|528.70|99.08|241.11|132.75|519.08|1646.19|
|Percent|7.6|32.1|6.0|14.6|8.1|31.5|100.0|

Source: ECB Consolidated Bank Statistics; IMF, Financial Soundness Indicators; Authors‘ calculations.

**37. Given that European bank leverage ratios remain high, deleveraging will likely continue, with an additional impact on EMDE credit.** European bank assets are still around 18 to 20 times equity, which is high internationally and may need to come down (to, say, 12 times equity). A further decline in leverage could happen through capital increases, but asset reduction may also be needed to reach a desired level. In a worse-case scenario (Scenario 1), banks would deleverage fully through loan reduction, in which case the impact on EMDE credit could amount to €978 billion, or
Policy Research Working Paper 10198

# **Abstract**

The COVID-19 pandemic has had catastrophic economic and human consequences worldwide. This paper tries to quantify the consequences of the pandemic on global inequality and poverty in 2020. Since face-to-face household survey data collection largely came to a halt during the pandemic, a combination of data sources is used to estimate the impacts on poverty and inequality. This includes actual household survey data, where available, high-frequency phone surveys, and country-level estimates from the literature on the impact of the pandemic on poverty and inequality. The results suggest that the world in 2020 witnessed the largest increase to global inequality and poverty since at least 1990. This paper estimates that COVID-19 increased the global Gini index by 0.7 point and global extreme poverty (using a poverty line of $2.15 per day) by 90 million people compared to counterfactual without the pandemic. These findings are primarily driven by country-level shocks to average incomes and an increase in inequality between countries. Changes to inequality within countries were mixed and relatively modest.

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://www.worldbank.org/prwp. The authors may be contacted at dmahler@worldbank.org, nyonzan@worldbank.org, and clakner@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/
loss impacts health. We also carry out a series of falsification and robustness checks to rule out spurious temporal or spatial correlation and that the observed health outcomes are caused by water- and not air- or vector-borne diseases.

We then replicate the global analysis for Peru, which is one of the 10 most forest-rich countries in the world and has experienced rapid land use change over the last two decades, with 3.4 million hectares of forest lost between 2001 and 2020. One key advantage of this case study is that we are able to complement the DHS data with additional health surveys implemented by Peru’s National Institute of Statistics (a.k.a., INEI) using the DHS methodology. This provides us with a longer repeated cross section and the ability to control more comprehensively for time and location specific trends.

Our analysis indicates that deforestation increases DD incidence among children under five in downstream communities, but also offers new evidence of early-life exposure to deforestation on childhood stunting, a wellknown indicator of later-life productivity. Our results suggest that a 1 percent decline in upstream forest cover increases the probability of DD by 5.8 percent. These results are consistent with previous evidence (Herrera et al., 2017) and highlight the vital role that upstream forests play in regulating water flow and quality downstream. We also present new evidence on the long-lasting and significant health impacts of deforestation, with a 1 percent decline in upstream forest cover around the time of birth increasing the probability of stunting by 3.9 percent. This effect is particularly large in early childhood, measured by the first thousand days of life. These global results also hold for Peru for DD but less so for stunting, which could be due to significant governmentand donor-supported health and sanitation campaigns during our sample period aimed to reduce stunting.

Our analysis has both analytical and practical implications. From an analytical point of view, this study contributes to a growing literature on the complementarities between human and natural capital. It provides further evidence of the importance of
household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school.

Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. The second result is likely to be due to large rates of grade repetition and of delay entry that were exacerbated by the need to remove boys from school due to the negative economic effects of the conflict on households more exposed to the violence.

The paper is structured as follows. In section 2, we present a literature review on the impact of violent conflict on development outcomes in general and education in particular. Section 3 provides a descriptive background of the conflict in Timor Leste and the country‘s education sector. In section 4, we describe the datasets, discuss our identification strategy and present some descriptive results. Section 5 discusses our empirical results, as well as a range of robustness checks. Section 6 concludes the paper.

# **2. Literature review**

An emerging body of literature has provided valuable empirical evidence on the effects of violent conflict on income and consumption levels, and more generally on the welfare of
cities, and that the attitudes in the two context types are shaped by different sets of factors, with, for example, concerns about economic contribution being strikingly greater in urban areas and security concerns greater in camp-like contexts.

# **5.2 Refugee Perceptions of Hosts**

In Table 6, we use the full cross-country sample to study the relationship between refugeehost interactions and refugees’ trust in hosts. The dependent variable is a dummy variable indicating whether the refugee respondents trust their host population. In Columns 1-3, we report the results of OLS regressions with country-, neighbourhood-, and household fixed effects respectively. Columns 4-6 report the IV estimates, instrumenting the interaction index with the average interaction index among other household members and neighbours. As before, we consider three sets of control variables: a long list defined in Table A.4 (Column 4) in the Appendix, a list selected using the double LASSO procedure (Column 5), and no control variables except the averages of the perception index among other household members and host households living in the same community (Column 6). We highlight three findings.

First, the relationship between interactions and perceptions is also positive and significant also among refugees, but less robust. The OLS regressions indicate that a one standarddeviation increase in the interaction index is associated with a 13 to 17 percentage point increase in the likelihood of reporting that host nationals are trustworthy. When limiting the analysis to the Uganda sample, OLS regressions still point to a positive and significant association between refugee-host interactions and trust in Kampala, but not in Nakivale (Table A.7 in the Appendix). However, the cross-country IV regressions in Table 6 columns 4-6 indicate that this positive association in the OLS is not robust, as the coefficient of the interaction index is close to zero and not statistically significant. However, we do have some concerns about these IV specifications.<sup>22</sup> The IV results for Uganda are also complicated to interpret (Table A
when the father is college educated.<sup>9</sup> The relative magnitudes of the estimated parameters of the 8A large literature on intergenerational income mobility in the context of developed countries emphasizes the attenuation bias in the intergenerational income elasticity estimates due to measurement error in yearly income data. See, for example, the seminal analysis by Solon (1992). Measurement error is likely to be less of a concern in education data (Deaton (1987)).

> 9If one relies on the linear CEF estimates, s _/_ he would miss the gender convergence at the right tail of the father’s schooling distribution.

3
openness from Chinn and Ito (2006); Political regime characteristics from the Polity IV Project. We document these variables in more detail and provide summary statistics in Table A2 in the Online Appendix.

# **3 Empirical strategy**

To assess whether disbursements of aid are accompanied by money flows to havens, we estimate the following baseline model:

> 10The analysis is limited to projects that were approved after 1984.

8
to consistently delineate urban areas across countries using two different approaches: the Degree of Urbanization (DOU) and Dartboard (DB) approaches. The DOU approach classifies cells in a gridded population layer into different urban and rural categories by applying uniform population and population density thresholds to all countries (Dijkstra et al., 2021). Meanwhile, the DB approach also classifies gridded population cells but distinguishes different urban and rural categories based on each country’s own population density distribution (de Bellefon et al., 2021). In other words, the DOU is an absolute measure of urbanization, while the DB is a relative measure. These new measures suggest that official urban definitions tend to underestimate urban population shares in Sub-Saharan Africa (Figure 1). Moreover, the DOU and DB approaches distinguish multiple types of urban areas, providing a more nuanced understanding of urbanization beyond a simple urban-rural dichotomy (Cattaneo et al., 2022). For this study, a novel dataset was created by integrating these new urban classifications into the existing household budget surveys (HBS), covering 221,000 households and approximately 1 million individuals from 20 low- and middle-income countries across the world.

Based on this new international database of urban poverty, we primarily focus on 16 countries in Sub-Saharan Africa (SSA), where 60 percent of extreme poverty is concentrated (World Bank, 2022). We first examine a set of questions regarding poverty incidence across different geographic areas. One fundamental question is _whether and to what extent poverty incidence is lower in urban compared to rural areas_ . While productivity tends to be higher in denser areas due to agglomeration effects (Duranton and Puga, 2004; Duranton, 2015; Glaeser and Gottlieb, 2009; Gollin et al., 2002; Grover et al., 2021; Michaels et al., 2012) and the sorting of higher-ability individuals into these areas (Combes et al., 2008), monetary poverty is not necessarily lower in urban areas once the higher cost of living is considered<sup
# **Appendix 8: Comparison of GEM and non-GEM Business Service Providers**

In order to compare the characteristics of the business service providers offering services in the GEM program to other providers in the market, we conducted a survey of 333 service providers (62 GEM service providers and 271 non-GEM service providers). This covered all four types of service providers in the program: accounting firms, marketing firms, HR providers, and general business consultants. The sample of non-GEM service providers was obtained through industry association lists, online searches, word of mouth, and business directories. Kantar TNS RMS surveyed these providers between November 2017 and June 2018.

Overall, we see the service providers selected for the GEM project tend to be larger than the nonGEM providers. There is an average of 46 workers for GEM providers compared to 28 for nonproviders; 57 percent have multiple branches compared to 35 percent of non-GEM providers; and 66 percent are registered as companies versus 35 percent of non-GEM providers. They have slightly more skilled staff, with 78 percent of staff having university degrees compared to 71 percent of non-GEM firms, and an average of 12 workers who belong to professional associations, compared to 9 in non-GEM firms. However, they serve similar customer bases, with a similar mix of firms by employee size category. Their usual contracts are also similar in price and duration across the two groups, with an average duration of 65 to 67 days per year, and mean cost of one day per week of services of 94,000-104,000 Naira. This median cost is much lower, at 40,000 per month for GEM firms and 37,500 per month for non-GEM firms, with the amount firms in the GEM program reported paying of 50,000 per month lying between the median and mean of typical payments.

We asked providers what they recommend as the minimum size in terms of employment and revenue for a small business client seeking their services. The GEM firms report a mean (median) of 4.7 (
<!-- Start of picture text --> % of Base CO2-Equiv Tons<br>I 5.00e+08<br>8 4.00e+08<br>6 3.00e+08<br>4 2.00e+08<br>2 1.00e+08<br>0 0<br>1990 1995 2000 2005 2010 2015 2020<br>=== Total HFC Emissions, non-A5 Countries<br>=——= Total HFC Emissions, A5 Countries<br>CFC Phaseout, A5 — CFC Phaseout, nA5<br>— HCFC Phaseout, A5 — HCFC Phaseout, nA5<br><!-- End of picture text --> <!-- Start of picture text --> % of Baseline % of Baseline<br>1 2<br>rc) 18<br>8 1.6<br>7 14<br>6 12<br>51<br>48<br>36<br>24<br>Jl 2<br>0 0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions ———— Phaseout Schedule —— Average Emissions——— Phaseout Schedule<br><!-- End of picture text -->