rafmacalaba/data-use-mentions-extended huggingface dataset:
in the generation, transmission, and distribution of electricity and are therefore excluded from<br>cost-of-supply estimations because capex is calculated separately based on existing assets.<br>Payments include principle payments—which reflect the repayment of previous capital<br>expenditures made by the utilities, typically recorded on cash flow statements—and interest<br>payments, typically recorded on income statements. Other financing costs may include realized<br>costs, including fair-value losses on cross-country and interest rate swaps, and other financial<br>liability payments. Losses on currency exchange reflect a devaluation of the local currency<br>relative to the loan currency. Depreciation costs on existing assets and property, and unrealized<br>fair-value losses on cross-currency and interest rate swaps are also excluded.<br>Unbundled sectors require a slightly different approach. With the exception of Nigeria, the<br>dominant state-owned distribution utility is used as the primary source of information. To<br>capture all loan payments in the value chain, particularly for generation investments which<br>typically constitute the major outlays, loan payments recorded in the financial statements of<br>state-owned utilities upstream of distribution are identified and included. To avoid double<br>counting, the cost of power purchases for the distribution company are reduced by the same<br>amount.|

_Source_ : World Bank staff.

# A1.3 Impairments, bill collection rates, and cash collected

Impairment of receivables represents non-collection of billed amounts, but the timing becomes an issue because the amounts could have been billed in the preceding financial years. For this reason, this study excludes impairment of receivables and provisions for impairments from calculations and instead uses utility-reported bill collection rates for the purpose of quantifying collection inefficiencies. When reported, bill collection rates are assumed to represent total cash collected in a given year as a proportion of total revenue billed in the same year.

Bill collection rates were not available in seven countries and assumptions had to be made. A 99-percent
**Potential Applications of the Method in Different Countries:** The lack of a national sample frame poses serious implementation-stage issues for many household surveys. An up-to-date digitized national sampling frame may not be available in some countries. The sampling frame might exist in other countries, but NSOs are unwilling to provide the World Bank and other international agencies access to the data. In other situations, the frame is based on the census enumeration areas, and because of their large spatial unit, conducting the second and third stages of sampling will cost a significant amount of money to achieve the necessary household size. Furthermore, if the sample selection is based on census enumeration areas, the household listing for the selected sampling units would also need additional time and resources because of their extensive spatial coverage and huge population totals. Our proposed method and strategy can be used to create a new national sampling frame if one of the previously described difficulties arises in future surveys conducted in other countries.

# **6 Conclusion**

This paper develops a new national sampling frame in the Republic of Armenia, serving as an example of developing nations with limited access to workable sampling frames for representative individual and household surveys. Specifically, pre-census enumeration areas (pre-EAs) have been created using an innovative approach for the automatic delineation of a national sampling frame, which offers strengths in various dimensions over other potential sources of sampling frames, such as those based on old census enumeration areas, census settlements, electoral precincts, and traditional gridded sampling.

The national sampling frame constructed in this paper divides the country into about 7,500 pre-EAs, most of which have population estimates strictly greater than zero. These population estimates are most recently updated and homogeneous, mostly between 100 and 1,000 people. The digitized enumeration areas with clear boundaries support the household selection, e.g., by enabling to avoid selecting households outside the selected PSUs.

Several methodological and practical contributions have been made to the survey sampling literature and entities that collect and leverage representative surveys,
_Note: * A total of 189 participants (98 in the treatment group and 91 in the control group were interviewed in all 3 rounds)._ Source: Saraf, 2019; World Bank Group, 2018 This intra‐category analysis seems to suggest that low‐touch psychosocial intervention for entrepreneurs in FCV contexts might be most effective in cases of mild to moderate depression that may risk becoming chronic and severe distress in future. Such a diagnostic and preventative approach could result in significant health care cost savings, as well as improvements in business productivity.

# 6.2 Well‐Being

At the baseline, personal well‐being was high (with average WHO‐5 score of 80). Despite this strong starting point, there were substantial improvements observed in well‐being of SME entrepreneurs in the treatment group, especially 3 months after the intervention. Table 7 shows the ITT estimates of treatment effect on the WHO‐5 score at 5 weeks and 3 months post intervention, as well as results of analysis using pooled data. At the 3‐month mark, WHO‐5 score among entrepreneurs in the treatment group is 4.73 higher on average compared to their counterparts in the control group (Column 2). The difference between the scores is statistically significant at the 10 percent level (p=0.074). Using pooled regressions, WHO‐5 scores of entrepreneurs in the treatment group in 2.7 higher on average, though the effect is not statistically significant.

Table 7. Effect of Treatment on Well‐Being

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**ANC**<br>|**OVA**<br>|**P**<br>|**ooled**<
**Figure 4** : Proportion of facilities with access to minimally adequate communication tools by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Tanzania<br>Sierra Leone<br>Kenya<br>Tanzania<br>Sierra Leone<br>Kenya<br>0% 20% 40% 60% 80% 100%<br>Non-hospital Hospital<br>Internet<br>Phone<br><!-- End of picture text -->

# Emergency transport capacity

In the case of an emergency, rapid transportation of a patient to a better-equipped referral center may be needed. Emergency transport is ideally provided by an ambulance, which is equipped with life-saving supplies and equipment and can accommodate limited medical care en route. However, when an ambulance is not available, other facility-sponsored emergency transport (such as an official vehicle or repurposed facility utility van, for example) can be a stopgap. Where no emergency transport is available (or where patients are responsible for their own emergency transport), this increases the likelihood that a severely sick patient may not receive timely care at the level of facility required.

Are ambulances available at facilities in Kenya, Sierra Leone, and Tanzania to transport patients? Overall, the SDI data indicate that 95% of facilities in Sierra Leone have an ambulance on call at the facility; followed by 71% in Kenya, and 55% in Tanzania. It is worth noting, however, that 14% of facilities with an ambulance in Sierra Leone did not have fuel for the ambulance available on the day of the survey; the same is true for only 7% of facilities in Kenya, but 41% in Tanzania. While systematic lack of fuel is not captured in the survey, these data do suggest that a facility’s ambulance may be unusable from time to time due to an empty tank. In Tanzania, 32% of facilities report that no
how to measure the impact of trade policies on countries’ productions by taking all international supply linkages into account. The ‘sectoral-bilateral’ perspective of value-added accounting proposed in this paper offers a promising conceptual framework to address this issue, since it provides a precise measurement of the GDP of a country that is exposed to a trade policy implemented by any economy for a given sector and a given trade partner. We exploit this measure to derive an indicator of the effective incidence of tariffs on value-added. This may contribute to development of more refined measures of the spillovers of trade policies in global value chains using ICIO data.

43
during 2002–2007 and a modest increase (but still a significant decline) to 3.35 percent during 2008–2014.

However, a reversal pattern is evident on the exports of fuel products and ores and metals. Fuel product export shares from Africa surged from 4 percent only during 1990–1995 to 56.84 percent during 2002–2007 and then dipped to 39.43 percent during 2008–2014. The export rates of ores and metals indicate that the amount of metals and minerals that Africa has exported to China has increased. Though smaller in magnitude during 2002–2007 than in the preceding periods, Africa’s positive trade balance in ores and metals is in line with the production and imports patterns described above (cf. Figures 2a and 2b, and Figures 3a, 3b, and 3c in Appendix I).

In sum, while the overall trade balance between Africa and China had been negative, there was still a positive balance in average trade flows for commodities such as fuel products and ores and metals. Second, this positive balance reflected the patterns of metals and minerals production and imports from Africa and to China, respectively, which have been more distinct since China’s WTO accession in 2001. In the next section, we describe the data we used for the empirical analysis.

# 3. DATA AND VARIABLES

# _A. Data_

We combined several data sources in order to carry out the empirical analysis. The first source is the <mark>National Oceanic and Atmospheric Administration, National Geophysical Data Center</mark> (NOAA-NGDC),<sup>7</sup> which provides satellite data on night-time light intensity, recorded daily between 8.30 p.m. and 10.00 p.m. local time, across countries around the world. We cleaned<sup>8</sup> and used this data to approximate measures of local economic development in Africa, consistent with Mveyange, (2015), Alesina et al., (2015)
model, last updated in 2013 using the EMME/2 software. The model was designed by INRO Consultants at the University of Montreal and adopted by the City of Cape Town in 1991. The model implements an equilibrium route assignment based on the distribution of trip origins and destinations in relation to the transport network. The model is calibrated by means of the General Household Transport Survey, on-board surveys and cordon counts.

> 33This corresponds to the category “Single Residential 2 - Incremental Housing”.

17
Policy Research Working Paper 7873

# **Abstract**

Using a new, nationally representative sample of Chinese households, this paper studies how social capital affects access to credit and its implications for consumption levels. The paper focuses on two specific forms of social capital: private social networks and membership in the Communist Party. Although party affiliation is linked to higher consumption in rural areas, those benefits are direct and thus do not work through credit markets. The main finding is a strong link between private social networks, use of informal credit, and household consumption. Instrumental variable regressions indicate that the link is causal. However, the study finds no evidence that social capital has facilitated formal credit market development in China, as it has in countries with higher levels of private sector development.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 rcull@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
lower tiers of government vis-à-vis the center, it has also increased the possibility of political capture within these lower tiers. In 1979, Indonesia established “village governments” with locally-chosen village heads accountable to village councils that would determine budget priorities. A study of 48 rural villages showed that, since village heads chose the members of the council, accountability to the villagers was weak; only 3 percent of the village proposals were included in the district budgets. Those villagers who participated in government organizations were more likely to speak out at village council meetings, crowding out the voice of others in the village (World Bank 2001).

Fourth, a host of other problems, not associated with service delivery, have nevertheless helped to undermine service delivery in decentralizing economies. For instance, the “soft-budget constraint” facing sub-national governments has led to overborrowing (Rodden, Eskeland, and Litvack 2003) and, in the case of Argentina, a major macroeconomic crisis at the end of 2001. The social impact of the Argentinian crisis has clearly been a deterioration in service quality: poverty rates jumped 40 percent, 12 percent of the people with formal health insurance discontinued their policies, medical supplies were in short supply throughout the public hospital network and in 2002 a third of the provinces experienced school closings of 20-80 days out of a 180-day school year (World Bank 2003).

2
# **4.1 Population exposed to power plants’ emissions**

Using the modeled air pollution map, we estimate global exposure headcounts to coal power plant pollution around the world. We estimate that globally 2.32 billion people are exposed to SO2 emissions from coal-fired power plants, 1.29 billion of which are in China and India alone. Figure 5 shows the top 10 countries with the highest population and highest proportion of population affected by coal plants’ pollution. China, India, and the U.S. have 895.7, 392.4, and 157.0 million people exposed respectively, due to their high plant concentrations and population densities. The left panel of Figure 7 visualizes the proportion of population that are exposed to any level of coal plants’ pollution relative to national population in each country. Most countries show similar patterns as Figure 1 given within-country pollution dispersion. Czechia and Germany have 97 percent and 83 percent of their population exposed, including to high transboundary pollution flow from foreign plants. The proportion of affected population ranges from 0 to 97 percent, with an average of 12 percent; 13 countries have over half of their population exposed to air pollution from coal plants.

Focusing on cross-border pollutant flows, Figure 8 shows the share of national populations that are exposed to foreign plants’ pollution (left) and that face higher foreign pollution than domestic pollution (right). Overall, 247.5 million people world-wide are exposed to transboundary air pollution from foreign coal plants. The top affected countries are shown in Figure 6. Transboundary pollution affects 24.1 percent of the population in Czechia, where Poland and Germany are the primary sources of transboundary flows.

Globally, 160 million people face SO2 pollution from coal plants that exceed the World Health Organization (WHO) guideline value of 40 _μ_ g/m<sup>3</sup> – and this does not account for pollution from other sources. These WHO threshold values are evidence-informed quantitative recommendations based on a systematic review
Bank, Washington, DC. Available at: https://openknowledge.worldbank.org/handle/10986/36783</mark> <mark>Lara Ibarra, Gabriel; Campante Cardoso Vale, Ricardo. 2022. Brazil 2020 Data Update: Methodological Adjustments to the World Bank’s Poverty and Inequality Estimates (English). Global Poverty Monitoring Technical Note; no. 21 Washington, D.C. : World Bank</mark> 42
gies, especially in the form of reduced consumption and drawdown of savings, with only slow improvements through the second year of the pandemic.

In addition to the short-term impacts analyzed in this paper, one of the key pathways of longer-term welfare impacts will be through the learning inequalities that have been accentuated by school closures. It is estimated that globally, about 1.6 billion students in over 190 countries were affected over the pandemic (Munoz-Najar et al. 2022). While a detailed investigation of learning losses throughout the pandemic is beyond the scope of this paper, the HFPS data also show that in LICs, children stopped learning in more than half of households where children had been enrolled prior to the pandemic, in contrast to much lower figures for UMICs where governments and households had more resources available to support distance learning or facilitate in-person learning with containment measures (Table A6.26). Over 20 percent of students in LICs and LMICs had not returned to school in 2021. Moreover, children were less likely to access education if household adults had lower levels of education (Figure A6.6). Evidence from COVID-19 indicates these same households are more vulnerable to economic crises, consistent with negative feedback loops that may worsen future inequalities. The loss in human capital could be detrimental for future poverty among the poorest. Azevedo et al. (2022) estimate that the share of children with more years of – – education than their parents a measure of absolute inter-generational mobility could decline by 8 percentage points or more in UMICs, with the largest declines in the Latin America region.

In conclusion, unrecovered and unmitigated welfare losses from the COVID-19 crisis could worsen inequality in the longer term, an economic version of “long COVID”. Jobs and income losses went largely unmitigated, as reflected in the strong correlation between income losses and food insecurity. The adoption of negative coping strategies during the pandemic, including the use of savings and sale of assets, could hurt households’ productive capacity and ability to recover from the crisis.
*a**-<br>based; 0.5 - quarter to half of transfersare uncondition l<br>and formula-based; 0 - all transfers are either conditional or<br>discretionary; 0.25 or 0.75 -LG are treated asymmetrically<br>Derivative ofLG unconditional transfers andLG vertical|
|tonomy<br>.<br>LG borrowing free-<br>dom|o-1<br>Discrete:<br>0,<br>0.35,<br>0.5,<br>0.75,1|gap. See formula (2)<br>1 - borrowing is not reculatedby CG; 0.5 - borrowing only<br>fromOG or underOG approval or regulation; 0 -borrowing<br>isnotallowed;0.25 or0.75-LGaretreatedasymmetrically|

Source: Authors’ calculations based upon data sources reported in Annex Table Al.

lg_expaut = 1 — lg_vergap * (0.75 — 0.5 * lg_transf), (2)
Bank, the port platform performance including cargo dwell time has been long monitored (tracking of delays within the logistics chain in Douala have been thoroughly monitored by several sources since the end of the 1990’s, first as part of adjustment and debt reduction initiatives and then at the initiative of Cameroonian stakeholders).

The methodology used has been to combine readily available data from the business and port community in Douala, with statistical analysis from ASYCUDA data provided by Cameroon Customs and analyzed at WCO headquarters. To our knowledge, this had never been carried out as comprehensively in developing countries. Cameroon is a specific case since ASYCUDA has been implemented for all customs procedures, from the manifest lodging to the exit note, which enables a very complete follow-up of import processes. These analyses have been complemented by interviews with consignees, port operators, clearing and forwarding (C&F) agents and shippers. It is 2
averaging the data before and after treatment; and 4) using an empirical variance-covariance matrix. Bertrand, Duflo and Mullainthan (2004) find that the empirical variance-covariance matrix outperforms the others, but it does not work properly with small samples. Cameron, Gelbach, and Miller (2008) state that the block bootstrap works properly with small numbers of groups.<sup>12</sup> In our case, bootstrapped, clustered standard errors are similar to clustered standard errors. In most cases, the latter method reports greater standard errors, and in some cases, conventional standard errors are larger than the latter method. As a conservative criterion, we decided to report the method with the largest standard errors in each case. Depending on the nature of the outcome variable, continuous, count, or fractional, we use different estimators that will be discussed in the results section.

# **<<A>>** _III. DATA SOURCES AND VARIABLE CONSTRUCTION_

We begin by describing the data sources and variable construction for the analysis of the impact of nationalization on access to water sanitation networks. We then turn to the data sources and variable construction for the analysis of the impact of nationalization on water quality.

## **<<B>>** _Access to sanitation networks_

The data regarding the percentage of households with water-sealed toilets connected to sewer lines are obtained from the annual Uruguayan national household survey, Encuesta Continua de Hogares (ECH), conducted by the National Statistical Office of Uruguay, Instituto Nacional de Estadística. The ECH is the main source of socioeconomic information on Uruguayan households and their members at the national level. The surveys were conducted throughout the year with the objective of generating a description of the socioeconomic situation of the entire population.

The ECH also includes questions about household living conditions. In particular, the survey asks whether water-sealed toilets are connected to sewer lines. Therefore, we generate a dummy variable that takes the value one if the household is connected to sewer lines and zero otherwise.
productivity—while in both cases the coefficients are positive, they are not statistically significant. However, column (5) shows that the component of management practices that deals with the communication of targets to personnel is significantly associated with higher labor productivity. This suggests that it is not the number, nor the difficulty of targets that motivates workers to achieve better outcomes, but the communication of the targets throughout an establishment’s workforce and not just to the upper echelons of management. Finally, column (6) shows that there is no significant link between the basis of managers’ performance bonuses and labor productivity. The absence of a link between bonuses and performance is surprising, as previous analyses based on Chinese data by Kato and Long (2006), Lin, Shen, and Su (2011), and Feng, and Johansson (2017) all found a positive and significant relationship between executive pay and firm performance.

Table 6: Determinants of labor productivity

|**Y = Log (salesper worker)**|||||||
|---|---|---|---|---|---|---|
||(1)<br>Average<br>score|(2)<br>Response to<br>problems|(3)<br>No. of metrics<br>monitored|(4)<br>Difficulty of<br>targets|(5)<br>Knowledge<br>of targets|(6)<br>Basis of<br>bonuses|
|**Management score**|0.537***|0.266*|0.0744|0.143|0.287***|‐0.0940|
||(0.205)|(0.147)|(0.116)|(0.107)|(0.0939)|(