rafmacalaba/datause-encoder-data huggingface dataset:
- 58 -

## A.2 Adjustments to Efficiency Prices

4. Both fiscal and equity objectives are important in the health sector. Bank policies have defined no specific range of cost recovery levels to be used as a rule of thumb for health projects, but pointed out the suitability of a pricing approach which weighs the social value of additional public revenues against additional income to project beneficiaries. Health financing practices in the LDCs which permit insignificant participation by users have led to chronic funding problems, poor quality services, and failure in impleoenting health improvement programs. While existing practices have been based partly on equity considerations, paradoxically they have contrituted to inequities in the distribution of health resources. There **is** a clear need to reorient health financing policies to accommodate **some** reasonable balance between the **fiscal** and equity impacts of health projects.

# B. THE PRACTICE

# o.1 Proiect S-m nl-

**S.** This review is based on 11 projects (in Burkina, Nigeria, Lesotho, Mali, Botswana, Jordan, Morocco, Brazil, Colombia, Indonesia and China). The sample represents slightly over 61 percent of the total number of health projects approved by the Bank during FY84 and FY85.

# 5.2 Discussion of Cost Recovery **I-sues**

6. The SARs revealed no detailed information on pricing practices in health care, according to the different types of services discussed above. There were no references to efficiency pricing or the extent to which user fees were related to the economic cost of providing curative care. Nonetheless, the SARs provided an interesting description of the expenditures and financing of aggregate ykulsea«ctor health services in the sample countries. Total public sector expenditures for health ranged from US$7.2 million in Lesotho in 1983 to $7,560 oillion in China in 1981. The sample mean was nearly $761 million (about $81 million if China were excluded). Recurrent expenditures made up from 31 to 95 percent of total expenses, and averaged 39 percent.

7. Budgetary allocations were the primary source of health finance in six cases: Nigeria, Lesotho, Botswana, Jordan, Morocco and Indonesia, accounting for 81 to 100 percent of total expenditures. The relatively insignificant recovery of costs from users in these countries (ranging from almost 0 to 19 percent) was due to an explicit policy that health services should be provided **at** minimal expense to citizens. This was augmented by a failure to raise fees in line with inflation, inefficient collection, and poor quality of service which in turn affected people's willingness to pay. The reverse, however, was observed in Mali, Brazil and China where expenditures for health were largely financed via user charges, with respective levels of 54, 85 and 70 percent of total expenditures. Revenues from user fees were principally from the sales of drugs in Mali; insurance contributions in Brazil; and a combination of the two in China.
<!-- Start of picture text --> The Standard International Trade Classification (SITC Rev.1) The Harmonized System (HS 1988)<br>100%y I SFE 100%i ee,<br>py 8A wae7 r " - Ki‘<br>$= 22 o- rn a. ' 4;<br>so 80% 4<br>a<br>a<br>7<br>“<br>] 60% 60% f<br>a v=) ’<br>BS%cooBS6c ii<br>25 4<br>ao ao i<br>40% 40% é<br>‘<br>‘fy<br>n<br>Ui<br>20% "<br>20%,<br>0% 0%<br>1970 1980 1990 2000 2010 2020 1990 1995 2000 2005 2010 2015 2020<br>Year Year<br><!-- End of picture text --> — Dual recording - ‘Export value with dual recording ~ -Import value with dual recording
waste, electricity<sup>5</sup> produced from combustion of fossil-fuels and from renewable energy sources such as solar, hydro and wind.

Likewise, cooking energy types can be categorized as "renewables" (biomass, solar and biogas) and "non-renewables" (coal, kerosene, LPG, natural gas). Furthermore, there are also wide variations in the level of consumption and the patterns cooking energy use by households based on their levels of urbanization and income. These categorizations, in general, include "rural" and "urban" households, and "low" income and "high" income households. Besides, use of fuels for household cooking is also concentrated in certain countries, e.g., coal in China, charcoal in SSA, dung in India, kerosene in Djibouti and electricity in South Africa (Smith et al., 2012).

<!-- Start of picture text --> Development Production Source Urbanization Income<br>Traditional Primary Renewables Rural Low<br>Non-<br>Intermediate Secondary Urban High<br>renewables<br>Modern<br><!-- End of picture text -->

**Fig.3** . Schematic illustration of categorizing household cooking energy types. _Sources_ : UN (1982, 2011), IEA (2005, 2012).

# 3.2. Cookstoves and conversion efficiencies

Several types of cookstoves are used by households and these stoves are often associated with specific energy types. For example, traditional (3-stones), simple non-traditional (e.g., clay pot-style or simple ceramic liners), chimney, rocket, charcoal and gasifier stoves use solid fuels which are common in rural areas of developing countries. In contrast, more modern cooking stoves, such as LPG, natural gas and electric, are common in urban areas of both developing and 5 Electricity may be produced in a primary (solar, geothermal, hydro, wind) or secondary (fossil-fuel power plants) form. However, electricity used for cooking is considered as a secondary energy types.

9
varies among corridors: in Western and Central Africa, rent seeking activities has been more prevalent than in other sub-regions. Table 7 provides a simulation of the breakdown for the Lomé Corridor. Less is known in other sub-regions; however, it seems quite likely that transit overheads are in the range of 30% to 100% of transportation costs, while they should probably be in the range of 5% to 20%<sup>41</sup> .

> 41 The DOT in the USA estimates them at 4%.

36
worse and then improve with income—no Kuznets curve here. Of course, a number of local and visible environmental public goods do worsen at first and eventually improve with income—typically air and water quality. But this is not true of local pollutants with invisible or long term impacts (e.g. pesticide accumulation), global pollutants (e.g. greenhouse gases) or the destruction of bio-diversity. These get worse with higher income.

So economic growth causes environmental degradation—or has for much of the last 250 years—and will continue to do so without activist policies. However, many argue that the needed efforts would be too costly; that they would slow growth and destroy jobs; that poor countries can’t afford them.

However, environmental degradation itself is costly: more than 3% in Syria, Lebanon, Algeria, Morocco during the various years, 8% of GDP in Iran in 2002 (Croitoru and Sarraf, 2010). Further, much of environmental destruction is driven by market failures and inefficient policies. Correcting them can therefore result in a more efficient and productive economy, with increased output and prosperity. It may be possible to have green growth without too much of a cost, and possibly with some significant benefits.

Reconciling the three pillars of sustainable development does require policy efforts, particularly for the environmental one. Green growth policies are a critical part of such efforts and an essential part of implementing sustainable development.

# **Defining green growth**

Green growth is about making growth processes resource-efficient, cleaner and more resilient without necessarily slowing them.

Is it feasible? Direct contributions of environmental protection to economic growth are possible because the environment, which we can also think of as natural capital, is an input into the national production function, and environmental conservation can lead to increased inputs of natural capital and thus to an increase in income. The use of environmental assets is generally characterized by market failures – external costs and ill-defined property rights being common – and correcting these market failures can increase the effective supply of natural capital, an important input to the production function, hence increasing output; it can also increase human well-being directly by improving air and water quality, something that is not necessarily captured by conventional GDP statistics but is nevertheless the ultimate goal of economic policy.

Indirect contributions of environmental protection to economic growth are also possible because world economies are far from their “optimum.” Indeed, there are many market failures that are not directly due to environmental issues, but that have negative consequences on the environment and the economy. Correcting these market failures thus has co-benefits that go beyond the environment. An obvious example is urban congestion which causes air pollution but also reduces the productivity and economies of scope typically offered by cities.

3
Policy Research Working Paper 7154

# **Abstract**

The paper investigates determinants of civic participation in local budget processes in rural areas in the Kyrgyz Republic by using data from the Life in Kyrgyzstan survey, conducted in 2012. The analysis of the data suggests that although civic awareness and interest in local budget processes is relatively high, the participation rate in local budgeting processes is low. The paper also shows that interest, awareness, and participation are positively associated with the age, education, employment, risk-taking attitudes, trust, and social capital of respondents. The paper documents that unawareness and lack of participation are largely related to being female, of non-Kyrgyz ethnic origin, inactive in the labor market, recent internal migrants, and residents of communities with poor infrastructure.

This paper is a product of the Governance Global Practice 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 esenaliev@ sipri.org or gkisunko@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
A final section draws some conclusions. The headlines include: Most export growth for developing countries came through intensifying growth of existing products to existing markets in the 1995-2004 period under study. Poorly performing countries were in general able to keep pace with rapidly growing countries in intensifying growth, but experienced a higher death rate of products, so their overall growth at the intensive margin was discernibly slower. At the extensive margin – growth through introduction of new products and through selling existing products to new geographic markets -- was driven more by diversification into new geographic markets during the acceleration and maturation phases than by introduction of new products in the discovery phase. This suggests that governments should look for market failures that are constraining export growth much more broadly than just in the discovery phase.

# **Growth of Developing Countries Exports: The Contributions of the Extensive and Intensive Margins**

We begin with a decomposition of the growth of exports of 99 developing countries to 102 developed and developing country markets. The purpose is to identify the extent to which export growth has been driven by changes to existing bilateral flows (the intensive margin) or by new exports, either of products not formerly exported or existing products to new markets (the extensive margin).

# **Data considerations**

A careful analysis of export growth and diversification requires trade data that are consistent across countries and products at a meaningful level of commodity disaggregation. It is generally accepted that import data are more reliable than export data. This is especially the case for developing countries. Hence to investigate export growth of developing countries we use mirror statistics from importing countries and seek to use data for the largest number of importers that report over a period suitable for an investigation of export growth. At the same time we require detailed commodity data from a classification that is not subject to major changes that could influence the results. In other words zeros can appear and disappear, as trade volumes allocated to a particular code can change owing to changes in classification. In practice there is a trade-off between the amount of product 5
individuals with characteristics that tend to leave them in poverty or the geographical differences in the returns to these characteristics<sup>1</sup> . In other words, would individuals who live in different regions of the country, but are otherwise identical, have comparable standards of living, indicating that the returns to identical characteristics are similar? Or are their returns and standard of living quite different across regions?

Several studies have investigated this question using income as a measure of household welfare typically obtained from the annual PNAD surveys and employing different methods. Duarte et al. (2004) utilized a semi-parametric model (following DiNardo, Fortin & Lemieux, 1996), to investigate the educational disparities between the Northeast and Southeast regions as a partial determinant of income differences. They concluded that “more than 50% of the income difference is explained by the difference in schooling.” However, factors other than education were not considered in their study. Bourguignon et al. (2002) find that “most of Brazil’s excess income inequality is due to underlying inequalities in the distribution of two key endowments: access to education and to sources of nonlabor income, mainly pensions.” This conclusion is based on a comparison of the relative roles of three components – the distribution of population characteristics, the returns to these characteristics, and the occupational structure of the population – in accounting for the income distributions between Brazil and the United States.

Guimarães et al. (2006) account for differences in labor income between the metropolitan areas of the Southeast and Northeast regions using the quantile regression decomposition method in Machado and Mata (2004). The paper finds that the difference in the returns to education accounts for a larger share of the income gap than the differences in the distribution of education, suggesting that policy interventions focused on education alone are not likely to be sufficient in decreasing regional inequality. It is possible that the higher returns to education that Guimarães et al. (2006) finds in the Southeast may be due to agglomeration effects in booming metropolitan areas. As described in the New Economic Geography literature, agglomeration economies are characterized by increasing economies of scale. With well developed infrastructure, a high degree of market specialization, greater competition, information exchange, and more efficient matching in the labor market, the environment is conducive to lowering costs and producing higher returns (Venables 2005, Krugman 1998). Thus, one could expect metropolitan areas of leading regions to have both high returns – from increasing economies of scale – and a higher concentration of individuals with valuable human capital assets (both observable education and unobservable ability and motivation) as talented workers are attracted to the higher rates of return and wider range of employment opportunities.

> 1 This question was posed by Ravallion and Wodon (1999) for Bangladesh.

2
# How Much Oil is the Islamic State Group Producing? Evidence from Remote Sensing<sup>*</sup>

Quy-Toan Do<sup>1</sup> , Jacob N. Shapiro<sup>2</sup> , Christopher D. Elvidge<sup>3</sup> , Mohamed Abdel-Jelil<sup>4</sup> , Daniel P. Ahn<sup>5</sup> , Kimberly Baugh<sup>6</sup> , Jamie Hansen-Lewis<sup>7</sup> , and Mikhail Zhizhin<sup>6</sup>

> 1 _Research Department, The World Bank_

> 2 _Politics Department and Woodrow Wilson School, Princeton University_

> 3 _Earth Observation Group, NOAA National Centers for Environmental Information_

> 4 _Middle East and North Africa Chief Economist’s Office, The World Bank_

> 5 _Johns Hopkins University SAIS and Office of the Chief Economist, U.S. Department of State_

> 6 _Cooperative Institute for Research in the Environmental Sciences, University of Colorado_

> 7 _Economics Department, Brown University_

_JEL Classification:_ D74, Q35 _Keywords:_ oil, remote sensing, flaring, Islamic State group

*This paper is a joint product between the Empirical Studies of Conflict Project (ESOC) and the Development Research Group of the World Bank. The authors are grateful to Jishnu Das, Shanta Devarajan, Fabrice Mosneron Dupin, Tarek Ghani, Guy Grossman, Martyn Howells, and Roy van der Weide for helpful comments. The views and opinions expressed are those of the author(s) and do not necessarily represent the views or official positions or policy of the U.S. Department of State, U.S. National Oceanic and Atmospheric Administration, the U.S. Department of Commerce, the U.S. Government, or the World Bank, its Board of Executive Directors, or the governments they represent. The institutions above does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the institutions above concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Correspondence: Quy-Toan Do (qdo@worldbank.org), Jacob Shapiro (jns@princeton.edu), Chris Elvidge (chris.elvidge@noaa.gov), and Daniel Ahn (pahn4@jhu.edu).
# **References**

- Kaufmann, Daniel, Aart Kraay and Pablo Zoido-Lobatón (1999a). “Aggregating Governance Indicators.” World Bank Policy Research Working Paper No. 2195, Washington, D.C.

- ——— (1999b). “Governance Matters.” World Bank Policy Research Working Paper No. 2196, Washington, D.C.

- ——— (2002). “Governance Matters II – Updated Indicators for 2000/01.” World Bank Policy Research Working Paper No. 2772, Washington, D.C.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2004).  “Governance Matters III: Governance Indicators for 1996, 1998, 2000, and 2002”.  World Bank Economic Review.  18:253-287.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2005).  “Governance Matters IV: Governance Indicators for 1996-2004.  World Bank Policy Research Working Paper No. 3630.  Washington, D.C.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2006a).  “Measuring Governance Using Perceptions Data", in Susan Rose-Ackerman, ed.  Handbook of Economic Corruption.  Edward Elgar.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2006b).  “Governance Matters V: Aggregate and Individual Governance Indicators for 1996-2005”.  World Bank Policy Research Working Paper No. 4012.  Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007a). “The Worldwide Governance Indicators Project: Answering the Critics". World Bank Policy Research Working Paper No. 4149. Washington, D.C.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007b). “Growth and Governance: A Reply/Rejoinder". Journal of Politics. 69(2):555-562.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2007c).  “Governance Matters VI:  Aggregate and Individual Governance Indicators for 1996-2005”.  World Bank Policy Research Working Paper No. 4280.  Washington, D.C.

- Kaufmann, Daniel and Aart Kraay (2008).  "Governance Indicators:  Where Are We and Where Should We Be Going?"  World Bank Research Observer.  Spring 2008.

- Meisel, Nicolas and Jacques Ould Aoudia (2007).  "A New Institutional Database: Institutional Profiles 2006".  Documents de Travail de la DGTPE 2007/09.

- Seligson, Mitchell (2008).  “The AmericasBarometer Approach to Measuring the Quality of Governance”. Lecture presented at the “Actionable Governance Indicators Conference,” World Bank, Washington, D. C. April 24, 2008

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19 When asset prices rise, banks tend to lend more to the sectors affected by the bubble. Mexico's case is revealing: as real estate prices fell in the early 1980s, lending to the construction and housing sectors fell as a percentage of total bank lending. When real estate prices rose, the proportion of bank lending to these sectors doubled, reaching a peak in 1995, when real estate prices stopped growing and reversed the trend (Table 11). In Colombia, a similar relationship between real estate prices and credit to housing and construction is observed.

When the crash occurs, non-performing loans rise as borrowers become insolvent, and the ratio of non-perforning loans is greater in the housing sector (Table 12). In Colombia, non-perforning loans jumped from 6% at the end of 1997 to 10% in 1999 and to 13% at the end of 2000.

Argentina did not have a banking crisis of the magnitude experienced by Mexico or Colombia.<sup>4 2</sup> In this country, credit to the construction sector did not increase during the rise of real estate prices. On the contrary, it fell (Table 11) and a similar trend occurred in housing mortgage credit. In the first quarter of 1991, credit to the construction sector was 6.7% of total credit; by the second quarter of 1994 it had fallen to 4.0%. Once the Tequila crisis hit in late 1994, the non-performing loans of this sector increased from 20% of total loans to the sector to 24%, which is much smaller than the Mexican case and slightly larger than in the Colombian case.

Another difference in banking practices between (a) Argentina on the one hand and (b) Colombia and Mexico on the other is revealed by the non-performing loans coverage ratio.<sup>43</sup> While Argentina's banking sector coverage was 55% in December 1994, it quickly rose to 68% in 1996. In Mexico, the coverage ratio was 49% in 1994, and it had risen to 55% by 1997. When the crisis begun in 1998, Colombia had an extremely low 38% coverage ratio, which then increased to 46% by the end of 2000.

Finally, the Chilean experience, as derived from the Conley-Maloney data on housing prices, shows that the bubble was accompanied by rising domestic credit. In this case, the credit explosion took place in 1976-1979, prior to the rise in property prices.44 However, the credit contraction did follow the fall in real estate prices, and the banking crisis in late 1981 followed the collapse in real estate prices. This is another example of the balance sheet effect operating. This bubble and the credit expansion occurred immediately before the banking crisis of the early 1980s.

> 42 World Bank staff estimates that the fiscal cost of the banking sector crisis in Mexico amounted to about 19% of GDP, and IMF staff estimates Colombia's banking sector rescue costs to be around 8% of GDP, while there were no fiscal costs in Argentina.

> **43** The ratio of provisions for loan losses to non-performing loans.

**_44_** We were unable to ftnd a uniforrn data series of credit for the non-financial private sector, and, therefore, we present only non-manipulated series. However, all the available series show the credit explosion in 1975-1979.