rafmacalaba/data-use-mentions-extended huggingface dataset:
12 to reduce oil intensity than net exporters. Upper middle-income countries had the largest proportion of countries, one-third, in which declining oil intensity had offset one-fifth or more of the oil price effect through consumption.

Among net oil exporters, nearly half had declining oil production between 1999 and 2008. Despite rising oil prices, oil exports as a share of GDP declined in one-fourth of the exporters—that is, the oil price effect through output had been offset by a fall in oil production combined with the effect of the inverse of GDP.

Although the full set of data to carry out decomposition analysis is not yet available, preliminary data are available from the U.S. Energy Information Administration (EIA) to compute vulnerability for 2011, when the average price of oil reached the highest level in history in real terms; the average price in 2012 was slightly lower. The data available as of March 2013 were used to calculate vulnerability in 2011 as well as the change in vulnerability between 2009 and 2011, during which period the world price of oil in U.S. dollars increased by more than 60 percent in real terms. The EIA database is periodically revised and further revisions are expected; hence these results should be viewed as preliminary. Table 2 provides summary statistics by income, region, and oil trade status. The results by country can be found in appendix B. More than two-thirds of the countries in the sample reduced oil intensity between 2009 and 2011. The median vulnerability for net importers in 2011 was 5 percent of GDP, and that for net exporters was -16 percent. The median increase in vulnerability for net importers between 2009 and 2011 was 1.5 percent of GDP, and the median decrease in vulnerability for net exporters was 3.1 percent of GDP.

Table 2: Vulnerability in 2011 and Change in Vulnerability 2009 to 2011 by Income, Region, and Oil Trade Status (% of GDP)

|**Classification**|**Oil trade status**|**
for mining in the south than in the north. Input downstreamness is measured as the average distance from primary inputs suppliers (Antras et al. 2012). Overall, we find stronger intersectoral linkages with mining in the south than in the north.

For the second prediction, i.e., whether regional differences in intersectoral linkages led to regional differences in sectoral composition of regional output between 2003 and 2013, to identify the counterfactual, we distinguish between heavy manufacturing industries (chemicals, machinery, metals, and nonmetallic minerals) and light manufacturing industries (such as food, textiles, paper, and wood). Based on world input-output data for 43 countries, we find that heavy manufacturing and mining show strong intersectoral linkages, as more than 85 percent of mining intermediate inputs in manufacturing are used in heavy manufacturing. We test if employment growth in heavy manufacturing industries occurs in mining districts (districts with at least one mining firm). The elasticity of employment for heavy manufacturing with respect to employment in mining (an elasticity of 0.31) is almost 50 percent larger than the corresponding elasticity for light manufacturing (0.21).

In our subsequent empirical model, the intensity of intersectoral networks is identified jointly by the location of a mining firm and the year in which it was established. We interpret the effect of the mining shock on intersectoral linkages as causal if the contemporaneous and lagged effects are positive and strong, and lead effects (placebo) are weak and negative. Constructing a district-year panel with 119 districts and 24 years (1990–2013) based on data on the location and the year of establishment of a firm collected in the 2014 IBES, we find stronger intersectoral linkages between mining and heavy manufacturing in the south than in the north. The relationship between mining and heavy manufacturing firms in the south appears to be causal, based on the placebo tests.

Finally, we test if average firm-level employment (in different sectors) is higher in mining districts in the south and if average employment in a
Policy Research Working Paper 9857

# **Abstract**

This paper examines living conditions—mainly access to infrastructure and basic services—in Kinshasa, by focusing on how they vary within the city and how they are related to household characteristics. First, drawing on a household survey conducted in the capital province in 2018, the paper shows that many Kinshasa residents live with substandard housing and inadequate levels of access to infrastructure and basic services. Second, the level and quality of access to basic services are highly correlated with residents’ consumption and education levels, as well as their neighborhood characteristics. Third, despite the presence of negative externalities from the high population density, poor households benefit from living in dense neighborhoods by gaining a minimum level of access. The paper argues that it is imperative to increase the supply of affordable housing to lessen the inequality of access to services in Kinshasa.

This paper is a product of the Poverty and Equity 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 ybatana@worldbank.org or snakamura2@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
_Table 10: Summary Statistics - green activities index and its components before and after imputation_

||**N.**<br>**Obs.**|**Mean**|**Std. dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**_Before imputation_**||||||
|Monitor Energy Consumption|8,445|0.62|0.49|0.00|1.00|
|Strategic Objectives Mention Env. or Climate Change|7,289|0.22|0.41|0.00|1.00|
|Manager Responsible for Env. or Climate Issues|7,298|0.15|0.36|0.00|1.00|
|Monitor Water Usage|4,683|0.48|0.50|0.00|1.00|
|Targets on Energy Consumption|8,363|0.32|0.47|0.00|1.00|
|Targets for CO2 Emissions|8,330|0.07|0.26|0.00|1.00|
|**_After Imputation_**||||||
|Monitor Energy Consumption|8,445|0.62|0.49|0.00|1.00|
|Strategic Objectives Mention Env. or Climate Change|16,000|0.22|0.41|-1.07|1.71|
|Manager Responsible for Env. or Climate Issues|16,000|0.15|0.36|-1.50|1.39|
|Monitor Water Usage|16,000|0.46|0.50|-1.21|2
variables. EMIS collects information from official registries and government sources (e.g., Dion Global Solutions Limited in India).<sup>26</sup> The financial and ownership information in the global BOS database is also cross-checked against other publicly available sources that collect information in real time for listed and large firms around the globe such as Factiva, World Scope, and Global 2000 (Forbes).

The Global BOS database also benefited substantially from existing knowledge within the World Bank and other research and development institutions. To facilitate the analysis and to improve coverage of key financial, ownership, and employment variables, we constructed country-level data sets (registries). Through each registry, we were able to identify additional firms and ownership structures that were not present in ORBIS. The registries draw on data on SOES collected by the World Bank in coordination with government counterparts in the context of 150 operational projects and 20 analytical support projects from 2015 to 2019. Some examples include the information obtained for the preparation of the Integrated State-Owned Enterprise framework (iSOEF) reports for countries such as Angola, Niger, and Chad as well as country-level ASAs like the Pakistan Advisory Support to Public Expenditure Management. In addition, the Global BOS database incorporates publicly available information from other multilateral institutions or international organizations. Reports and databases constructed by other institutions such as the OECD (2017), the IMF (2021), the EBRD (2020), and the Inter-American Development Bank (2019) were also used.

Finally, the construction of the BOS database relied on field work carried out by World Bank country teams in consultation with country and sectoral experts. Country teams provided important reports and databases collected under ongoing client dialogue (i.e., PER in Mozambique), reviewed the information, and provided important insights to ensure the accuracy of the information. Teams provided expert knowledge on:

- Corporate legal structures/forms

- Business registry and statistical institute databases

- Government participation in key enabling sectors

The information from World Bank projects, external
which crop cutting and self-reported yields are both available. This approach allows the authors to replicate the relationship between yields and input obtained with crop-cut yields using the predicted yield showcasing the validity of this imputation framework. However, their exercise is conducted with one crop (sorghum) and only considers one agricultural season limiting the external validity of the framework they develop.

Against this background, our analysis attempts to address these limitations. We take advantage of the availability of two consecutive rounds of the nationally representative agriculture survey of Mali collected as part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)<sup>1</sup> project to validate their approach when used in a surveyto-survey imputation framework. Moreover we extend the imputation exercise to more crops: in addition to sorghum, we work with millet, maize, rice, groundnut and cowpea allowing us to generalize the conditions on which the approach is valid. For reasons of computational ease and tractability, we focus on the estimation of the mean value of yields. We go beyond within-survey imputations exercises to look at survey-to-survey imputations. We also explore which set of covariates are more potent for the imputations exercises. Finally we also explore the effect of training sample sizes and strategies. By varying the size of the training sample, we investigate whether reducing the sample size can still yield reliable imputation results, with the aim of balancing cost efficiency and the accuracy of imputation methods.

Our main findings are threefold: (i) farmers’ reported yields are good predictors of crop-cut yields but the results point to a greater predictive power of integrated geo-spatial variables; (ii) on average the imputation exercises work better for crops that have low intercropping rate, and are more commercialized, which can be related to the accuracy in standard units of the farmerreported yields; (iii) in most of the cases, the imputation approach provides accurate results in the within-survey imputation framework and collecting crop-cut measurements on a sub-sample of plots can offer a cost-effective approach while still achieving reliable ML predictions of
29 and Warner note that trade reform is usually just one part of a government’s overall reform plan for integrating an economy with the world systems. They argue that trade liberalization is usually accompanied by a much broader range of reforms, such as price liberalization, budget restructuring, extensive privatization, deregulation and attempting to achieve macroeconomic stability. This has been especially clear in the postcommunist countries. In almost all cases, trade reform has been merely part of the overall institutional harmonization with the advanced market economies. As such, to some degree, the Sachs and Warner measure of openness serves as a proxy for an entire array of features that are characteristic of a healthy economic regime, and hence is an appropriate measure for use in our regressions in explaining economic growth.

# **Institutional Quality**

Knack and Keefer (1995) examines the effects of property rights on economic growth and find that property rights were able to explain about 8 to 10 percentage points of the observed variation in cross-country growth rates for the period 1974 to 1989. As proxies for the levels of property rights in countries, they used indicators provided by two private international investment risk services who evaluate country risk to potential foreign investors. More specifically, the data were obtained from the Business Environmental Risk Intelligence (BERI) and the International Country Risk Guide (ICRG). ICRG data were generally taken for the year 1982 and focused on expropriation risk, rule of law, repudiation of contracts by the government, corruption in government and the quality of the bureaucracy. On the other hand, BERI data were generally for the year 1972 and include indicators for bureaucratic delays, nationalization potential, contract enforceability and infrastructure quality. For this paper, we use the simple average of the ICRG indicators as a measure of institutional quality.

# <u>Expropriation Risk and the Rule of Law</u>

Both of these measures are proxies for the security of property and contract rights. The former measures the risk of expropriation while the latter measures whether there are established
depend on government responses and mitigating strategies (Koren and Peto, 2020). For instance, government restrictions and shutdowns disrupt supply chains and the labor market. Layoffs, furloughs, and wage cuts, in turn, affect consumer demand for various services through income and wealth effects. Consumers could also cut back spending on certain goods and services and increase precautionary saving due to elevated economic uncertainty.

To gauge the role of supply and demand-driven shocks, we interact data on government responses, such as social distancing and lockdown restrictions, with information on the spread of the pandemic. Government responses are correlated with the spread of the pandemic, with highly affected countries expected to implement stricter measures. However, variations in the timing and spread of the pandemic and government measures across countries allow us to identify the impact of the pandemic before and after government measures are put in place. This will provide important evidence on how much of the effects are driven by supply-side restrictions arising from social distancing and lockdown measures and demand-driven changes in economic activities.

We employ fixed effects models and exploit the temporal and spatial variations in the spread of COVID-19, as measured by the number of confirmed cases and deaths, across 182 countries that are currently affected by the disease.<sup>2</sup> As of May 20, over 5 million people have been infected by COVID-19, and over 320,000 people have deceased from the disease. While the spread of the pandemic is relatively swift, there are spatial and temporal variations in the exposure and intensity of the pandemic, which allows us to identify the impacts of the pandemic.

We find that demand for some services jumped (dipped) immediately as countries experienced COVID-19 and associated business closures and lockdowns. As countries detect their first case of COVID-19 in March 2020, we observe significant spikes and dips in demand for some services. Demand for services that require face-to-face interaction, such as transport, hotel and restaurant, tourism, and retail, has substantially contracted, with hotel and restaurant services worst hit
resource exports (as percentage of GDP), and manufacturing imports (as percentage of GDP). These variables will broadly capture the decline in commodity prices and the disruption of global value chains as a result of the pandemic. Finally, we gather data on exports to advanced countries, exports to developing countries outside the region, and intra-regional exports. All these variables are expressed as a percentage of GDP. Therefore, we explore whether exposure to inter- or intraregional trade exacerbates or mitigates the impact of the pandemic. All the trade data was collected from the World Bank’s World Development Indicators (WDI).

_International Financing Flows_ . Gross capital inflows as a percentage of GDP is used as the measure of exposure to or dependence on international financing flows. We assume that countries with greater dependence on foreign financing flows will exhibit a greater decline in growth. We also look at the impact of the composition of gross inflows to investigate whether certain types of financing flows either exacerbate or mitigate the growth impact of the pandemic. Consequently, we run additional regressions that include gross foreign direct investment (FDI) inflows, portfolio investment inflows, and other investment inflows. The different types of gross inflows are expressed as a percentage of GDP, and the data has been collected from the IMF’s Balance of Payments Statistics (BOPS6).

The responsiveness of growth to the pandemic through its exposure to other international financing flows such as remittances and tourism revenues is also examined. We gather data on workers’ remittances as a percentage of GDP from the World Bank (2020c).<sup>17</sup> The pandemic led to a series of containment measures that included international travel restrictions and there is evidence that economic activity is collapsing in countries that are highly dependent from tourism revenues. The data on international tourism receipts (as a percentage of GDP) is collected from the WDI.<sup>18</sup> Finally, the extent of international capital flows across developing countries is alternatively measured by the Chinn-Ito index
Poverty headcount ratio at urban<br>10<br>poverty line (% of urban<br>5 population)<br>0<br>1998 2005<br><!-- End of picture text --> _Source_ _<mark>:</mark>_ <mark>The United Nations MDGs Report 2011 (http://mdgs.un.org/unsd/mdg/Default.aspx?q=poverty); WDI 2010</mark> Policy makers should consider reducing the dependency on oil exports and diversifying economic activities by creating more jobs.<sup>3</sup> Especially for rural poverty reduction, income

> 1 A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

> 2 Gender inequality index is measured by a composite measure reflecting inequality in achievements between women and men in three dimensions: reproductive health, empowerment, and the labor market. An index of 1 represents perfect inequality, whereas an index of 0 represents no inequality.

> 3 The country’s financial institutions and its integration within world markets also improved over the past decade. This can be reflected by raising a share of total trade to GDP ratio from 61 percent to 66 percent during this period (IMF 2010). However, recent global financial crisis and the collapse of international oil prices during 2008-09 hit hard on Yemen’s vulnerable economy, which depends heavily on the oil industry. Although oil production has fallen over time, income from oil exports remains the single most important source of government revenue. A sharp decline in average oil prices from $98 per barrel in 2008 to $60 per barrel in 2009 and contraction of oil production led to a significant drop in government oil exports. As a result, Yemen had a record high fiscal deficit in 2009,

2
# **GAS**

4.19 The only social policy practiced in the gas sector is the Patagonian subsidy, which is designed to compensate households in the country’s coldest region for the relatively high heating costs that they face. This is achieved via a discount of around 75% on an initial ‘subsistence’ block of natural gas or LPG consumption, that is regionally differentiated to reflect climatic conditions, and varies from 62.5 to 1,250 cubic meters monthly. This subsidy is financed by a national surcharge of US$0.004 per cubic meter of gas sold from the wellhead, which generated US$100 million of revenues in the year 2002.

4.20 In common with the national Tariff Compensation Fund for electricity, the Patagonian subsidy is based on a principle of horizontal equity across regions, and takes no account of ability to pay. In fact, the poverty rate in Patagonia is among the lowest in the country. In November 2002, it stood at 37% as against 46% at the national level. Moreover, due to this relatively low poverty rate and to its small population, Patagonia contains barely 3% of Argentina’s poor households.

4.21 The distributional incidence of the Patagonian subsidy can be estimated on the basis of survey data on natural gas and LPG expenditure in that region (Figure 10)<sup>7</sup> . The figure illustrates that a relatively small proportion of the beneficiaries and of the benefits accrue to the bottom income quintile, but are instead concentrated in the middle quintiles of the income distribution. The Patagonian subsidy has an associated error of inclusion of around 70%. The error of exclusion is 4% with respect to the total poor resident in the Patagonian region. The concentration coefficient is estimated at +0.08, indicating a broadly egalitarian but mildly regressive distribution.

> 7 This exercise is based on expenditure reported by 70 Patagonian households in the OPSM survey, which account for 45% of the households surveyed
been tried but not yet rigorously evaluated. In the context of strict budget constraints, having clear data on the best investments among those interventions that have been evaluated can be useful, and it can help governments and other education stakeholders to avoid investing in programs that have proven ineffective. However, it should not stop policy makers from continuing to innovate and test new programs that relax constraints on girls’ access and learning.

# 6. Conclusion

Previous reviews of what works to improve girls’ education tend to focus on girl‐targeted interventions. That approach omits key evidence of the impact of general education interventions on girls. This review innovatively brings together a large evidence base of general interventions that report effects for girls. Based on 177 studies from 54 countries, this review finds that girls’ access to school is more responsive to changes in costs, distance, and health conditions; while girls’ learning is more likely to be improved by structured pedagogy and interventions that help teachers to teach at the right level.

While this review focuses on girls’ education, the global learning crisis impoverishes both girls and boys (World Bank 2018b). Our findings demonstrate that gender neutral interventions hold great promise for girls’ learning as well as for boys. Considering the limited resources that education systems in most low‐ and middle‐income countries possess, the most practical approach to help girls learn may be to make schools better for all children. Such an approach may also be more politically palatable to voters than programs that restrict their benefits to girls.

Finally, attending school and acquiring learning are not the finish line for girls’ education. The ultimate objective is that girls can empower themselves through education and achieve their life aspirations. To this point, very few evaluations have included either long‐term follow‐ups or these broader measures of well‐being. But gaining literacy and numeracy are the foundation for positive longer term outcomes.

# References

- Adoho, Franck, Shubha Chakravarty, Jr. Korkoyah, Dala T., Mattias KA