rafmacalaba/data-use-mentions-extended huggingface dataset:
contains monthly time series on nighttime light intensity observed at the subnational level from January 2019 to December 2020. To account for seasonal fluctuations in NO2 and nighttime lights series, we construct year-over-year deviations in 2020 relative to the same month in 2019, expressed in percentage changes.

A potential problem with these satellite-derived variables is the possibility of capturing noise, and its consequences are exacerbated given that we work with large-area zonal statistics. For example, Mills et al. (2013) point out that the Annual VNL Version 1 product has not been filtered out from aurora lights, fires, boats, biomass burning, and glass fares. Also, a failure to account for cloud pressure may artificially elevate the air pollution level observed in the data. To remove the influence of extreme outliers—whose values are more likely to be driven by noise—we look at the median values of nighttime lights/NO2 concentration in a given group of subnational regions when analyzing the data. For example, when aggregating data by region/income group, we look at the median subnational region in a given month. Likewise, we pick the median subnational region in a given country when constructing the country-level data.

In response to the crisis, Google has published trends in the movement of people across different categories of places, like workplaces, transit stations, and residences, using data derived from cell 4
citizen participation, specifically in the context of working to address community problems. The primary independent variable is a composite gender bias score, derived as the first component of a principal component analysis (PCA) based on four indicators that capture the prevalence of gender-biased social norms in the country of analysis: (i) women should focus on domestic roles while men should work; (ii) men are better political leaders than women; (iii) if a woman earns more than a man, she is likely to face difficulties; and (iv) in times of scarce jobs, men should have a greater right to employment than women.

Each of these four gender bias indicators is coded as 1 if the respondent agreed or strongly agreed, and 0 otherwise. The composite gender bias score is then normalized on a scale from 0 (low machismo) to 1 (high machismo). By using the first component of principal component analysis (PCA), we derive a single variable that captures the combined variability of the four gender bias indicators, simplifying the analysis of how gender bias influences individual decisions to engage in solving community problems.

The decision to use the first principal component from the PCA is based on two widely used, independent methods. First, the Kaiser-Guttman criterion, which recommends retaining components with an eigenvalue greater than one, supports using the first component (Kaiser, 1960; Guttman, 1954). Second, the elbow method, a visual approach, indicates that the first 11
water shortages and sales is 0.002; that is, a 1% increase in water shortages per day results in an approximately 0.002% decline in sales. A one standard deviation increase in incidents of water shortages per day (equivalent to 0.19 extra shortage) results in a loss of US$268,699 in revenues for the average firm in our sample, which is about 1.8% of the average revenues. For firms in low and lower-middle income economies, a one standard deviation increase in outages is associated with US$271,628 in lost revenues.

> 8 Water intensity can differ for the same industry across economies. Economies can use different amounts of water to manufacture the same product. Data on water intensity for specific manufacturing industries in developing economies are sparse, thus the categorization of water-intensive industries is based on data from European economies. Sources: <u>http://ec.europa.eu/eurostat/statisticsexplained/index.php/Water_use_in_industry#Water_use_in_industry, http://www.wrap.org.uk/sites/files/wrap/PAD101-201%20-%20Manufacturing%20sector%20water%20report%20-</u> - <u>%20FINAL%20APPROVED%20for%20publication%20 %2012,03,12.pdf</u>

14
**Figure 3: Top 10 Chinese provinces where OFDI to Africa is originated: government verse private led projects**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>State-owned Non State-owned<br>Number of<br>Projects<br><!-- End of picture text --> _Source: Shen and Wang_ Available data from the Zhejiang Provincial Commerce Department confirm that its private sector plays a vanguard role in investing in Africa. According to the provincial records, which capture the provincial OFDI flows more comprehensively than that of MOC, out of approximately 2,000 Zhejiang investment projects overseas as of March 2012, there were 570 African projects, nearly 30% of the total. The registered amount of capital invested in Africa was $640 million according to the Department, suggesting a relatively small average project size slightly exceeding $1 million.

#  **_Chinese OFDI is widely spread across Sub-Sahara Africa. However, some countries are more attractive than others to Chinese companies._**

MOC data show that Chinese OFDI projects, including both state-owned and private enterprises, are widely spread in 44 countries in the Sub-Sahara Africa. The top five recipient countries are Nigeria, South Africa, Zambia, Ethiopia and Ghana, which all together make up 40% of the total projects in the region. If including the second top five countries, i.e., Tanzania, Congo (Kinshasa), Angola, Sudan and Kenya, the top ten countries represent almost two-thirds of the total. The rest is unevenly distributed across the continent, as illustrated by the map below.

8
To examine how urban forms evolve across stages of development, we examine built-up area growth across income groups.

As a final robustness check, we run the same regression as in Table 4 using population, income and builtup from the Functional Urban Area (FUA) database developed by the OECD and the European Commission. The spatial entities represent the commuting areas of the urban centres in 2015, which are geographically larger than our urban extents as they often contain multiple urban centers. Annex 2 presents a similar table as in Table 4. The new coefficients are similar to the ones estimated using our urban centers.

Table 5 reports the results of the between and within estimator following equation (1) for cities per country income group as defined by the World Bank. The coefficients of each column can be interpreted as the income and population elasticities of built-up surface for cities in each income group.

Growing population is associated with a larger increase in built-up surface in poorer countries. As population doubles, cities’ built-up increases by 34% in low-income countries, 31% in lower-middle income countries, 25% in upper-middle-income countries and by 28% in high-income countries.<sup>7</sup> Cities in poorer countries grow more horizontally than cities in richer countries. In contrast, rising incomes are associated with slower built-up surface expansion in poorer countries than in richer ones. As urban population doubles, the city’s built-up area increases by 5 percent in low-income countries, 9 percent in lower-middle-income countries, 11 percent in upper-middle-income countries, and 10 percent in highincome countries. The significant jump in the income elasticities between low-income and middle-income countries is indicative that the evolution of urban form follows broader stages and processes of economic development.

Next, we examine the extent to which rising incomes and stages of economic development influence the shares of extensive margin and infill development. We build a panel of two periods of built-up growth: 1990-2000 and 2000-2015. We estimate the share of infill as : 1 2 (2) where denotes the share of infill
# 1. **Introduction**

This paper lays down the analytical framework that was used in designing a series of surveys of the views of public officials concerning their institutional environment and in analyzing the information that was generated in 15 countries. It describes how the survey results help to challenge preconceptions, map the strengths and weaknesses of a public sector, and identify potential pay-offs from reforrn interventions.

The need for an analytical framework emerged from the lessons of past experience in the World Bank. The Operations Evaluation Department reported that during 1980-97, only one-third of the Bank's closed civil service reforrn interventions had successful outcomes.' Other reviews of the Bank's public sector reform efforts have identified shortcomings of the Bank's approach in this area, pointing out the risks of a narrow and 'technocratic' view of what is needed for public sector reform, and of a reliance on 'best practice' models that have not been feasible in the particular country setting.<sup>2</sup> The Bank's most recent strategy for reforning public institutions has identified that for the approach to be effective: "... _we need to work with our partners to understand and address the broad range of incentives and pressures - both inside and outside of government - that affect public sector performance."'_<sup>_3_</sup> The strategy paper also points out that for the analytic work to be useful: _"We need to start with a thorough understanding of what exists on the ground and emphasize goodfit rather than any one-size-fits-all notion of best practice. And we need to work with our clients and other partners to develop and apply analytic tools effectively. "_ The framework laid out in this paper addresses both these needs. Public officials are not inherently rapacious rent-seekers; they respond to the incentive structure they face. There is ample evidence, both theoretical and empirical, to suggest that the performance of public officials is greatly determined by the institutional environment that they find themselves in
natural disasters and other humanitarian crises, and to fund environmental and other projects with significant spillovers across national borders that do not always conform well to IDA’s country‐based approach. To the extent humanitarian and cross‐ border problems are not correlated with per capita income and policy quality, allocations of trust funds responding to these problems are likely to be less poverty‐ and policy‐selective than IDA allocations. For several reasons, therefore, trust funds can potentially undermine IDA’s “performance‐based allocation” (PBA) system intended (1) to direct more aid to countries where its development impact is likely to be greater, and (2) to create incentives for policy improvements in recipient countries.

Our data on disbursements by World Bank trust funds for the 2002‐2012 period allow us to study whether and how trust fund aid is allocated differently from bilateral and multilateral aid, particularly with respect to policy‐ and poverty‐selectivity. We analyze trust fund disbursements using linear regression models that account for time‐invariant recipient characteristics and common

> 8 The CPIA measures numerous aspects of policies and institutions, grouped into four “clusters” or broad policy areas: (macro)economic management, structural policies, equity and social inclusion, and public sector management and institutions. A detailed description can be found at http://siteresources.worldbank.org/PROJECTS/Resources/40940‐ 1244163232994/6180403‐1372096800800/CPIAcriteria2012.pdf.

> 9 Donor countries can and, surprisingly, do unilaterally increase their IDA contributions beyond what is seen as their fair share (Manning 2014). These additional contributions do not increase the formal voting power of the donor, although higher voluntary contributions might function as an informal leverage effect for their preferences. It could be that these additional contributions are popular domestically (Milner 2006). For example, the United Kingdom committed more than the USA to IDA15 and IDA17 replenishments USA (Manning 2014).

5
# **5. Discussion**

It is important to note a few limitations to our use of the Chilean model firm data. An assumption embedded in our methodology is that Chile’s geographical conditions are shared by all utilities. In our model, we assign a unit asset base (unit capital cost) value based on data from 15 Chilean firms, while in reality capital costs are strongly influenced by the geographical conditions of the area it serves. For example, different countries have access to different water sources that facilitate (or complicate) extraction, leading to lower (or higher) expenditures on assets. In addition, access to technology plays a role: newer and better machinery can greatly reduce a utility’s operational expenditures. Also, Chile is among the world’s most open economies, with low to nonexistent import taxes, while commercial barriers in other countries drive up the cost of imported assets, resulting in higher capital expenditures. Quality standards also vary between countries: low-income countries usually have lower standards than highincome countries, necessitating more investment to improve service quality.

The most directly comparable estimate of subsidies was conducted by Kochhar et al. (2015). Instead of the model firm approach taken here, the authors use a reference full cost-recovery price of $1 per cubic meter, which is taken from work done by the Global Water Intelligence in 2004 (GWI 2004) and is assumed to be the same for drinking water and wastewater. They then adjust this price for each country to account for three factors: (i) general price inflation that occurred between 2004 and 2012; (ii) lower labor costs in low- and middle-income countries; and (iii) varying levels of water scarcity. Revenue is approximated using data on utility drinking water and wastewater tariffs for a sample of over 80 countries in 2012 from the GWI. The authors estimate that water and sanitation subsidies provided through public utilities were about $456 billion, or 0.6 percent of global gross domestic product (GDP)
the distribution evident in the 1997 Census of Manufacturers for the United States. Only 51% of U.S. manufacturing establishments have fewer than ten employees. More broadly, about 74% of establishments have fewer than ten employees in the U.S. County Business Pattern data (regardless of sector). From an establishment size distribution perspective, India has an extreme concentration of very small establishments, as many others have noted.

These unorganized establishments account for 79% to 83% of all manufacturing employment in India. This concentration of employment among small establishments contrasts even more sharply with the United States, where the vast majority of output comes from larger establishments that would be part of the organized sector using India’s definitions. Despite 6
note leverages mobile phone surveys carried out in July 2021, December 2021 and 2022, and March 2023.<sup>7</sup> While these surveys are useful to capture behaviors, and cost and time efficient, they also have limitations related to low response rates, a limited reach of respondents and a higher bias due to self-declaration. Despite these

> 6 <u>https://public.emdat.be/</u>

> 7 The survey was part of the High Frequency Mobile Phone Surveys (HFS) implemented by the World Bank in several countries around the world to understand household welfare in the wake of the Covid-19 pandemic. This note uses data from the first and second waves of 2021, the third wave of 2022 and the fourth wave of 2023. The first wave of the survey included questions to estimate the wealth quintile of households (see methodological footnote on the creation of wealth quintiles below). The second, third and fourth waves of the survey included a Disaster Risk Module. A total of 2,631 households were interviewed in the second wave while 1,487 households were interviewed in both wave 1 (July 2 to August 5, 2021) and wave 2 (November 29 to December 22, 2021). For the third wave, a new sample of 1,530 respondents was interviewed (November 6 to December 11, 2022). Also, for the fourth wave, there were 1,532 new respondents. A more detailed description of the survey and the questions included can be found in the appendix.

2
We classified roads into four categories based on expected speed, as seen in Table 3. The classified road network was converted to a raster layer with a 30‐meter tile size to use as an input for the Cost Time layer.

**_Table 3: Road Network Classification_**

|**Road Type **|**Abbreviation**|**Class**|
|---|---|---|
|Strategic Road Network|SRN|**1**|
|District Road Core Network|DRCN|**2**|
|Strategic Urban Road|SUR||
|Urban Road|UR|**3**|
|District Road Core Network<br>_(unpaved)_|DRCN<br>_(unpaved)_||
|Village Road|VR||
|Others/non-recognized|NR|**4**|
|Paths|Paths||

Visual comparison of early model results, road network data and WorldPop population data revealed significant pockets of population uncovered by existing roads geospatial data. Further review using Bing, Google and Digital Globe satellite imagery indicated that in most cases minor roads or paths did in fact reach these population clusters. We contracted Kathmandu Living Labs, a Nepali non‐profit organization specialized in open geospatial data, to “trace” major missing roads and major paths from freely available satellite imagery into OpenStreetMap. Tracing work focused on pre‐determined priority areas covering roughly 7000 km<sup>2</sup> . Tracers were instructed to connect their traced roads or paths to the nearest road or path where possible, to ensure the connectivity of the transport network data set.

Priority areas fit two criteria:

**1.** Population density greater than 1 person / 100m<sup>2</sup> in the WorldPop data set

**2.** Travel times to any medical facility greater than 24 hours in the preliminary monsoon model

We employed
/sup> Some monetary transfers do not involve direct cash disbursements to beneficiaries but, instead, lower prices of basic goods and services thus increasing the purchasing power of households. Regulated low prices of staples (e.g., bread, rice, public transport) or energy inputs (e.g., gasoline, electricity and heating) are common examples in the world. In the case of Mongolia, tariffs for electricity and central heating are regulated below cost recovery production costs, and interest rates on certain mortgages are regulated below market interest rates.

The cost of these “indirect” transfers is not always explicit in the fiscal budget because it can be borne by para-fiscal entities (e.g., central banks) or even the private sector, (which may be only partly compensated for the difference between market and regulated prices). In the case of Mongolia, the indirect subsidies on mortgage interests is borne by the Central Bank of Mongolia,<sup>7</sup> while the electricity and heating subsidies are borne by the central government which, at least partially, compensates public and private companies engaged in production and distribution of electricity and central heating. There are no explicit figures on the size of the indirect subsidy in terms of reduced interest rates, but a rough estimate would suggest it represents about 1.5 percent of GDP.<sup>8</sup> Data from official sources indicate that subsidies to private and public companies have

> particularly if expenditures in pension benefit enjoy a substantial subsidy from the government. This analysis adopts a monetary transfer interpretation for all pensions in Mongolia.

> 6 For more details on the Child Money Program see (International Labour Office, 2016), on pensions section 3.3 of (World Bank, 2018) and on welfare programs see (Onishi and Chuluun 2015) and (Carraro, Byambaa, & Marzi, 2015).

> 7 The program was transferred to the government in April 2016 but continued to be financed by the central
An important takeaway is that heterogeneity in the actual output levels (right), is primarily large around the income levels where output is also highest (around $4,000 for deforestation, $6,000 for pollution, and $8,000 for the carbon weight of a single dollar production value). This indicates that the theorized Environmental Kuznets tipping points are also the points at which an averaged result, such as obtained from a linear regression, provides the poorest indication of relationships at the individual country level. While a few general rules could be extracted from the marginal effects, such as the inequality, income and population density effects, the larger part of the environmental data seems to relate heterogeneously to economic variables.

# **4.3 Further results on average curvature**

<!-- Start of picture text --> Estimated standardized environmental degradation levels across income<br>100<br>Forest loss/ha/GDP p.c.<br>80 Pollution mcg/m3/GDP p.c.<br>Carbon kg/$/GDP p.c.<br>60<br>40<br>20<br>0<br>1100 1800 3000 4900 8100 13400 22000<br>Logarithmic GDP per Capita<br>Estimated standardized environmental degradation levels across income<br>100<br>Forest loss/ha<br>80 Pollution mcg/m3<br>Carbon kg/$<br>Carbon kg p.c.<br>60<br>40<br>20<br>0<br>1100 1800 3000 4900 8100 13400 22000<br>Logarithmic GDP per Capita<br>Normalized log intensity levels<br>Normalized degradation levels<br><!-- End of picture text --> Figure 4: Normalized predicted environmental output levels across income. Predictors are held at expectations conditional on GDP. The _R_<sup>2</sup> ’s from
32 Regression 3 controls for terms of trade shocks. The dollar-denominated indices of export and import prices are missing for many countries in International Financial Statistics. This not only reduces the sample size by about 25 percent, but does so by removing countries with lower than average values of checks and higher than average fiscal costs of crisis.33 Unsurprisingly, given the selection bias introduced by the variable, the coefficient values on checks and the interaction term decline in this regression. Among those countries that report terms of trade data, the regression indicates, as predicted, that adverse terms of trade shocks are positively, though not significantly, related to fiscal transfers.

|Table 2: Incomple<br>Vhite-adjusted sta|teinformation andthemagnitude ofbanking crises<br>ndard errors in parentheses|
|---|---|
|Dependent<br>variable: Fiscal<br>costsofcrises|(1)<br>(2)<br>Q)<br>Radio andTV<br>Newspaper<br>Availabilityof<br>station<br>independence<br>trade statistics<br>independence<br>{exportor<br>import prices|
|Information|1.8<br>-0.08<br>7.6|
|(seecolumn<br>heading)|(2.2)<br>(2.8)<br>(4.7)|
|No.ofyearsfrom|<br>|1.6<br>15<br>1.3<br><br><br>|
|<br>firstyearof crisisto <br>year ofnext<br>election|<br><br><br>| (1.6)<br>(1.6)<br>(1.7)|
|R|0.11<br>0.09<br>0.17|
|N|36<br>36<br>