rafmacalaba/data-use-mentions-extended huggingface dataset:
5

**Figure 1 History of LPG prices in the Arab Gulf in 2022 U.S. dollars**

<!-- Start of picture text --> $1,600<br>$1,400<br>$1,200<br>$1,000<br>$800<br>$600<br>$400<br>$200<br>$0<br>Price of LPG in US$ per tonne<br>Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jan-18 Jan-19 Jan-20 Jan-21 Jan-22<br><!-- End of picture text --> _Source_ : RIM LPG Intelligence Daily. _Note_ : Prices are the average of Saudi Aramco contract prices for propane and butane.

The harm caused by the inadequate rate of progress is even greater than what the statistics about primary cooking energy suggest. This is because the metric defined for the purpose of measuring progress for SDG 7.1, “proportion of population with primary reliance on clean fuels and technology,” captures only two of several household activities—namely cooking and lighting but to date heating water, space heating, preparation of animal feed, and other activities have been excluded because such data are seldom collected systematically in national household surveys—and, even if all activities were to be captured, the metric still fails to capture important uses of polluting fuels:

- Information on primary reliance is usually obtained from national household surveys, almost all of which ask about the primary sources of energy for lighting and cooking. Many households use multiple fuels and technologies—referred to in the literature as fuel stacking or stove stacking— and it is unclear what criteria are used to identify primary sources. Is primary use defined by the number of hours each fuel or stove is used, the number of meals cooked, or the number of main dishes made? How do households account for the seasonality of the availability of different fuels,
4 Several points are evident from the national account data. First, during the period between the household surveys (taken as 1993-2001), cumulative growth in per capita GDP was only 6.1%, despite the sustained gains during the final years of the period. This is because the country faced substantial declines in per capita output at the beginning of the period. Second, output in the urban sector grew slightly more rapidly than in the rural sector over the full span of the period, and at the end of the period, urban growth substantially outpaced rural growth. Third, year-to-year growth rates in household consumption per capita differed from those for GDP per capita, but overall growth in household per capita consumption for the period was just slightly greater, at 8.3%.

**Table 2: Growth Data from National Accounts**

|**Year**|**GDP**<br>**Growth**<br>**Rate**|**Per**<br>**Capita**<br>**GDP**<br>**Growth**|**Per Capita**<br>**Rural**<br>**Growth**<br>**(monetary**<br>**+ non-**<br>**monetary**<br>**agriculture)**|**Per Capita**<br>**Urban**<br>**Growth (all**<br>**non-**<br>**agriculture)**|**Hhold.**<br>**Cons.**<br>**as**<br>**Fraction**<br>**of GDP**|**Implicit**<br>**GDP**<br>**Price**
to improve energy efficiency would be €356 billion over the next 35 years. For the power sector, this scenario needs 63% higher investment from the baseline when both scenarios includes energy efficiency investments. This is an expensive scenario for Romania’s energy sector.

This analysis recommends Romania to stay in the low demand green scenario (i.e., green scenario with implementation of energy efficiency measures in the demand side). Yet, the low demand green scenario requires 9 GW of intermittent resources (6 GW wind and 3 GW solar) and realization of this scenario would still be challenging. Note further that if energy efficiency in the demand side is not realized, the requirement of new intermittent capacity would be 11 GW, increasing the challenge further. Realizing energy efficiency measures is itself tough considering the hidden and transaction costs involved with them. Often, energy efficiency measures look very attractive in an economic analysis but their deployment in the real world is slow due to several barriers.

# **References**

Baloi, I.-C. (2010). Diagnosing the Potential of Renewable Energy in Romania. Timisoara Journal of Economics and Business, Vol. 3, No. 4, pp. 237-46.

British Petroleum (BP). 2015. BP Statistical Review of World Energy. BP, London.

Carstea, V. G., A.I. Danciulescu and I.G. Fintina (2010). Alternative Energies' Management in Romania--Member State of European Union. Romanian Economic and Business Review, Vol. 5, No. 1, pp. 114-20.

European Commission (EC) 2015a. Carbon Market Report 2015. Annex 1: Report on the functioning of the European carbon market, EC, Brussels. <u>http://ec.europa.eu/clima/policies/strategies/progress/docs/com_2015_576_annex_1_cover_en.pd f.</u> European Commission (EC) 2015b. Submission by Latvia and the European Commission on behalf of the
. To estimate hourly earnings, we divide the monthly earnings by four times the reported weekly working hours for each worker. When daily working hours are reported, we calculate the weekly working hours by multiplying the number of days worked in a week. The Gambia survey did not report working hours, so we exclude it from any analysis involving hourly earnings. (Detailed data processing notes can be found in Appendix Table A1.) When reporting earnings and performing the multivariate analysis, we limit the sample to wage employees (i.e., who self-identified as a paid employee) with at least some secondary

> 17 In separate data on a wider range of benefits but asked only of teachers, we find that teachers – particularly civil servant teachers – across 10 African countries do report receiving a wide range of benefits (Table A5). For example, 54 percent of teachers in Benin (and 90 percent of civil service teachers) report receiving a housing allowance; those numbers are 67 percent (88 percent) in Côte d’Ivoire. The set of benefits vary across countries, but at least 30 percent of civil service teachers report at least three benefits in each of the 10 countries, and in one (Senegal) they report seven benefits.

11
such as child minding and the Child Development Workers) and some being improvements on already existing services such as growth monitoring and micronutrient supplementation; (2) the delivery of many of the program components is not centralized, so actual treatment under various components is difficult to ascertain and model; and (3) information campaigns about ECD in the community as well as home-based visits are an important aspect of the program mechanisms. In all, program impact is not likely to depend on service utilization in ways that may be true for programs that offer a single centralized service.<sup>13</sup> ________________ 12 Children less than age six months are generally not included because among full-term infants the risk of iron deficiency in this age range is relatively low due to adequate iron provisions from the perinatal period (World Health Organization 2001). Hemoglobin levels were determined by diluting blood samples with a cyanmethemoglobin reagent in a spectrophotometer and using the proportional relationship of the absorbance of the reagent with the concentration of hemoglobin to determine the latter quantity. This method is one of two generally recommended as best for assessing hemoglobin levels in surveys (World Health Organization, 2001).

13 Moreover, for some of the new components of the program such as child minding, utilization rates were far too low to allow us to use them in the analysis. Several other program components such as receiving home visits from barangay health workers, immunization, or being weighed at the barangay health center are difficult to examine because so few children did not receive these services. Thus the impact of the utilization of each ECD treatment is likely to provide a biased estimate of the overall ECD program effect. Nevertheless, we did experiment with defining treatment as whether the child had any contact with the barangay health station in the year prior to the survey for children living in program regions 6 and 7 only. We 13
country data set and employ pooled probit analysis to estimate the contribution of the key factors to inflation starts.<sup>2</sup> Some earlier research has examined the determinants of inflation in EMEs.<sup>3</sup> To the best of our knowledge, however, there has not been any empirical study investigating the contributing factors to inflation starts in the case of EMEs, which would enhance the ability of policy makers in these countries to pursue appropriate policy actions in a preemptive manner.

The empirical findings suggest that an increase in the output gap—above-trend real economic activity—agricultural shocks, and expansionary fiscal policy raise the probability of inflation starts in EMEs. On the other hand, the results indicate that a more democratic environment and an increase in capital flows relative to GDP reduce the probability of inflation starts in these countries.

The remainder of the paper is structured as follows. The next section provides a brief overview of the literature on the determinants of inflation in EMEs. Section 3 discusses the definition of inflation episodes and inflation starts. Section 4 presents the empirical framework and estimation results. Section 5 highlights the salient features of inflation starts, in view of the empirical findings. Finally, Section 6 concludes the paper by summarizing the policy implications.

# **2. Review of the Literature**

The nature of the mechanisms underlying the dynamics of inflation in EMEs has stimulated much discussion. A quick glance at the literature points to two different

> 2 Our approach is very similar to a recent study by Boschen and Weise (2003), which focuses on the OECD countries.  This study includes the following countries: Argentina, Brazil, Colombia, India, Indonesia, Israel, Korea, Malaysia, Mexico, Peru, the Philippines, South Africa, Thailand, Turkey, and Venezuela.  The selection of the countries is dictated by data availability for the period under consideration.

> 3 See, for instance Loungani and Swagel (2001) and Mohanty and Klau (2001) as well
4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>54<br><!-- End of picture text --> _Note:_ The figure plots difference-in-difference regression results with region and round controls and standard errors clustered at the household level (see Equation C.2). We display coefficients for the interaction of lagged income diversity indices (Indices 3-6) and a head-of-household gender indicator. Male-headed households serve as the comparison group. Horizontal lines represent 95 percent confidence intervals.
Policy Research Working Paper 8629

# **Abstract**

The Russian Federation offers the unique example of a leading centrally planned economy swiftly transforming itself into a market-oriented economy. This paper offers a comprehensive study of inequality and mobility patterns for Russia, using multiple rounds of the Russian Longitudinal Monitoring Surveys over the past two decades spanning this transition. The findings show rising income levels and decreasing inequality, with the latter being mostly caused by pro-poor growth rather than redistribution. The poorest tercile experienced a growth rate that was more than 10 times that of the richest tercile, leading to less long-term inequality than short-term inequality. The analysis also finds that switching from a part-time job to a full-time job, from a lower-skill job to a higher-skill job, or staying in the formal sector is statistically significantly associated with reduced downward mobility and increased income growth. However, a similar transition from the private sector to the public sector is negatively associated with income growth.

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/research. The authors may be contacted at hdang@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
risk profile evolves between inspections, with the possibility to promptly identify changes in their financial condition.

Besides its usefulness for conducting on-site supervision, PCR data can support continuous off-site bank monitoring. PCRs databases can be programmed to produce regular, timely supervisory reports (for internal use) containing key risk indicators that summarize banks’ exposure to credit risk. At the minimum, they should characterize the overall quality of a bank’s portfolio and of its various segments, depicting with particular detail the risk profile of the largest borrowers. These reports can also measure bank exposure to concentration risk by type of borrower, region, business sector, credit type, etc. It is important that they indicate the extent of connected lending and verify banks’ compliance with prudential regulation for borrower risk classification. This can be accomplished by comparing the rating for the same borrower in different banks or computing transition matrices to benchmark the deterioration of a bank’s portfolio relative to other banks.

In recent years, bank supervisors have been modernizing their toolboxes for assessing banks’ financial performance and risk profile, adapting diverse credit risk management methods that had been introduced or developed by the banking industry. This has

> 22 When revising a bank rating system, the supervisor may wish to perform some tests of discriminatory power and calibration with PCR data to assess the reliability of that model. For further details see section below on Basel III.

8
|. .<br>S7|
|S7|Data Quality Grades Correspondence . . . . . . . . . . . . . . . . . . . . . .|. . S14|
|S8|Efect on Composite Estimate for 2020 when Normalized to C-Grade Weight|. . S18|
|**List **<br>S1|**of Tables**<br>Updated Data and Assumptions in Henderson et al. (2012) Baseline.<br>. . . .|. .<br>S9|
|S2|Efect of Extending DMSP-OLS Baseline with More Recent VIIRS Data. . .|. . S10|
|S3|Proxy: No Country Time Trend.. . . . . . . . . . . . . . . . . . . . . . . . .|. . S11|
|S4|Proxy: By-Country Data Quality Grade, No Country Time Trend. . . . . . .|. . S12|
|S5|Data Manipulation: Role of Country Time Trend. . . . . . . . . . . . . . . .|. . S13|
|S6|Growth in 2020 (%): Understatement.<br>. . . . . . . . . . . . . . . . . . . . .|. . S16|
|S7|Growth in 2020 (%): Overstatement. . . . . . . . . . . . . . . . . . . . . . .|. . S17|

i
; (7) the European Union; and (8) the Rest of the World (ROW). The baseline scenarios assume an export substitution elasticity of 1.5, domestic substitution elasticity of 1, and import demand elasticity of 0.5.

> 27 The term “economies” is used in lieu of “countries” because Nepal’s customs data treat Tibet, Hong Kong, and Taiwan as sources of imports that are distinct from the People’s Republic of China even though the three economies are (politically) part of China.
# **Small Area Estimation of Non-Monetary Poverty with Geospatial Data**

Takaaki Masaki David Newhouse Ani Rudra Silwal Adane Bedada Ryan Engstrom _This paper is a product of the Poverty and Equity global practice and a background report for the 2021 World Development Report. We are grateful to Nadia Belghith, Carlos Castelan, Robert Cull, Andrew Dabalen, Kristen Himelein, Dean Joliffe, Pierella Paci, Carolina Sanchez-Paramo, and Thomas Walker for their support. We thank Paul Corral, Kristen Himelein, Peter Lanjouw, and William Seitz for insightful comments and suggestions, and Partha Lahiri, Carl Morris and Roy van der Weide for helpful discussions. Finally, we thank Albina Chuwa, Dilhanie Deepawansa, Keith Garrett, Amara Satharasinghe, and Elizabeth Talbert for their help in obtaining data. All remaining errors are ours._
health care and health litigation. Section III develops a simple model of the political economy of health care- the aim of the section being to generate a set of hypotheses, whose robustness can be tested. Subsequently, Section IV, using a unique dataset of stakeholder perceptions from Uruguay, empirically evaluates the veracity of these theoretically derived hypotheses. The final section concludes with a critical evaluation of the findings and implications of the previous sections.

# **II. Literature Review**

Virtually all developed and many developing countries provide some form of publicly financed health services to their citizens. Given the scarcity of resources, governments have had to devise mechanisms to determine the nature of health service allocation. The ensuing political economy of health care allocation is thus, characterized by potentially complex considerations of both: (1) _efficiency_<sup>_5_</sup> - are publicly financed health services efficiently/poorly allocated? and (2) _equity_<sup>_6_</sup> - what are the implications of providing different levels of heath care services to (sub-sets of) the population? Unsurprisingly, all elements of this process are potentially subject to classical principal-agent problems as the interests of the agents (health bureaucracies and providers) may deviate from those of their principal (health care consumers). The magnitude and nature of any such principal-agent dynamics varies depending on how the institutional context conditions the nature of interaction between the two. Specifically, a wide range of institutionally induced incentive structures have been developed/used over time, to try and mitigate conflicts of interest between public health care providers (agents) and (potentially multiple) consumers (principals) (Besley, 2006).<sup>7</sup> One such institutional mechanism that can alter the dynamics of interaction, between health care providers and consumers, is health litigation. That is the use of court rulings to change decisions regarding the allocation (efficiency) and access (equity) of the public health care system.<sup>8</
firms in Bangladesh, India and Sri Lanka, median employment in small formal firms is 10 (compared to 35 employees for all formal firms in these three countries), and in rural firms is 1.5. In the comparison of small formal vs. informal manufacturing firms in India, median employment in small formal firms is again 10 (compared to 20 employees for all formal manufacturing firms in the India surveys), and in informal firms is 4. The remaining size disparity between small formal firms, on the one hand, and rural or informal firms, on the other, is why we condition on size in specification 2 as described above.

# _Levels comparisons between rural and formal sector firms_

The results for the comparison of rural with formal sector firms are clear and echo the results reported above in Section 7 for the large city / small city comparison using the Enterprise Survey data (Table 16). Rural firms almost always report that the elements of the business environment represent less serious obstacles to their firm’s production and growth than is the case for managers of formal sector firms. This is highlighted by the predominance of red shaded cells in both specifications. (Note that the number of observations for “Competition” and “Crime, theft and disorder” is very small and these results should be treated with caution.) The results in the lower part of the table are interesting because they accord with what one would expect of rural firms: they have much lower engagement with officials (bribes, inspections, management time) and report lower costs of corruption and the range of institutional constraints. Rural firms report similar levels of power outages and they are more likely to have a generator than formal sector firms. They rate the problems associated with access to electricity as less problematic than do formal sector firms, which suggests that they make less use of electricity in their production process.

# _Comparison of rankings of constraints by rural and formal sector firms_

Table 17 presents the results in a slightly different way in order to
2020 distribution, we also construct a counterfactual 2020 distribution. The income and consumption distributions would have certainly changed in 2020 even in the absence of the COVID-19 pandemic. Hence, the change from 2019 to 2020 captures both the changes due to the pandemic and those changes that would have happened even without the pandemic. For instance, a number of countries in Sub-Saharan Africa were expected to have negative per capita GDP growth before the pandemic. Hence, not all negative income growth in 2020 can be attributed to the pandemic. To estimate the net impact of the pandemic, we construct a counterfactual distribution for 2020 using growth forecasts from before the pandemic.<sup>11</sup> This counterfactual distribution potentially captures all the anticipated changes before the pandemic but not including the pandemic. The difference between the observed 2020 distribution and the counterfactual distribution provides an estimate of the net impact of the pandemic. The counterfactual distribution is further discussed in Section 3.2.

> 9 We use per capita GDP growth rates from the World Development Indicators (WDI).

> 10 We construct our global data set with grouped data of 1,000 bins of national welfare for each country in 2019 derived using the microdata in PIP. This is then projected to 2020 using the growth rates available from various data sources described in this section.

> 11 We use the forecasts that were published in January 2020 in the World Bank’s Global Economic Prospects

> (GEP). It is important to note that the counterfactual projection is distribution neutral.

7