rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1. Average annual resource revenues share in GDP <!-- Start of picture text --> 15.5<br>16<br>14<br>12<br>10<br>8.6<br>8<br>6 4.8<br>4<br>2.1<br>2<br>0<br>Rest of the World Sub-Saharan Africa<br>non-RR RR<br>% GDP<br><!-- End of picture text --> Notes: Natural resource revenues share of GDP and in constant 2015 US$, 2004-2014 annual average. SubSaharan Africa RR includes 21 countries listed in IMF (2012) as resource rich and Sub-Saharan Africa non-RR includes 9 countries non listed in IMF (2012) with non-missing data. Source: authors’ calculations with data from UNU-WIDER Government Revenue Dataset 2021.

Figure 2: Average annual natural resource revenues, by country, between 2004-2014 <!-- Start of picture text --> 30 50<br>45<br>25<br>40<br>35<br>20<br>30<br>15 25<br>20<br>10<br>15<br>10<br>5<br>5<br>0 0<br>% of GDP (blue bars)<br>Angola Gabon Nigeria Chad Sudan Guinea Mali Niger Namibia Zambia Liberia Ghana Uganda Constant 2015 US$, billions (red bars)<br>Congo, Rep Equatorial Guinea Botswana South Sudan Sao Tome and Principe Mauritania Cameroon Congo, Dem Rep Cote d'Ivoire Zimbabwe Sierra Leone<br><!-- End of picture text --> _Notes: Natural resource revenues share of GDP (blue bars) and in constant 2015 US$ (red bars) for 2004-2014 annual average by resource-rich Sub-Saharan African country with natural resource revenues greater than zero. The following six countries from the resource-rich group are not included due to missing data: Central African Republic, Madagascar, Mozambique, Tanzania, and Togo. Source: authors’ calculations with data from UNUWIDER Government Revenue Dataset 2021._ 5
case – average annual income grows faster for capital owners than for other types of households.

- However, when all income goes to households, the average annual growth rate for rural households exceeds the average annual growth rate for capital owners. Rural households receive slightly over 50 percent of their income from agricultural labor. Subsistent agriculture is also largely a non-traded good and its prices will rise as household spend more on food from the additional income, raising the prices of agriculture and non-traded goods, therefore the return to agricultural labor. This implies that transfers to households, especially when targeted well, will likely benefit the poor and have more immediate impact on poverty reduction.

**Table 16: Macroeconomic effects under alternative spending plan**

(Real average annual growth rate over the simulation period)

||Base<br>Case|Bird in<br>Hand<br>(BIH)|BIH with<br>Public<br>Investment|Permanent<br>Income<br>Hypothesis<br>(PIH)|PIH with<br>Public<br>Investment|Borrow<br>(BOR)|BOR with<br>Public<br>Investment|Boom &<br>Bust(BB)|BB with<br>Public<br>Investment|
|---|---|---|---|---|---|---|---|---|---|
|Consumption|3.69|4.07|7.04|3.99|7.21|3.99|7.66|4.04|7.54|
|Private Investment|4.47|4.69|5.63|4.63|5.67|4.63|5.89|4.67|5.86|
|Public Investment|5.88|6.33|9.73|6.20|9.71|6.19|10.12|6.30|10.13|
|Total Investment|4.97|5.28|7.32|5.19|7.33|5.19|7.65|5.26|7.64|
|Government|1.98|2.03|5.33|2.03|5.41|2.03|5.88|2.03|5.81|
|Exports|4.50|3.75|8.35|3.78|8.63|3.81|9.27|3.81|9.09|
|Imports|4.14|4.45|7.21|4.35|7.34|4.35|7.80|4.42|7.71|
|GDP(at mktprices)|3.52|3.55|6.81|3.53|7.00|3.53|7.49|3.54|7.35|
|Household income:||||||||||
|Rural household|3.47|4.63|6.68|4.41|6.81|4.39|7.44|4.55|7.35|
|Urban household|3.41|4.05|6.53|3.87|6.61|3.87|7.08|4.00|7.02|
|Capital-owners|3.91|4.46|7.06|4.32|7.23|4.31|7.53|4.42|7.42|

_Source_ : Model simulations.

# **_Behavior of Consumption over Time_**

Figure 6 shows the effects on consumption over time. When there are productivity gains from investment and spending, consumption will grow steadily over time relative to the base case, appearing to benefit similarly across alternative spending plans. The dip in consumption during the middle periods for the “Borrow Now” or “Boom-bust cycle” seems small. At the end of the simulations, consumption is close to twice the reference level in the conservative BIH case and more than twice the reference case in the other spending plan.

When there are no productivity gains, the timing and changes in consumption become distinct across the spending plans. Consumption in the PIH and “Borrow-Now” cases start at a high level relative to the BIH case, but all three will converge to about 5 to 10 percent 30
London Club debt nor the debt to international financial institutions can be rolled over. Net Treasury bill financing has been negative since mid-1998, issuance small, and auction placements consistently below offer volumes. In addition, as we will discuss further, a significant amount of Bulgaria's governmnent debt has a floating interest rate and thus makes the repayment profile volatile. Refinancing risks will increase as debt service rises in the medium term.

Evaluation of the _currency risk_ is simplified under the CBA. The CBA credibility appears strong. Thus we treat the Euro-denominated debt not much different from domestic debt. Bulgaria's cross-currency risk boils down to the US dollar/Euro risk, as the share of other foreign currencies is modest, both for the assets and liabilities. Bulgaria's natural hedges to currency risk in its government debt portfolio include foreign exchange reserves, mainly denominated in the Euro, and current account cash flows, essentially net exports of goods and services, mainly denominated in US dollars, with a deficit emerging in the Euro-denominated trade.

With respect to foreign exchange reserves, we observe a large mismatch between the bias toward the Euro in their structure and the bias toward the US dollar in the structure of Bulgaria's sovereign debt. In stylized terms, we present a simple combined balance sheet of the government and the central bank in table 2.

|**Table 2**<br>**Simplifie**<br>|**d balan**<br>|**ce shee**<br>|**t asof**<br>|**12/31**|**/ 98**<br>|||||
|---|---|---|---|---|---|---|---|---|---|
|**(bill BGL)**|**BNB**|**(issue**|**ep)**||**MOF**||**Net**|**exposu**|**re**|
|l|**~~~BG**|**qUSC**|**EUH**|**BGq**|**USO**|<br>**EU **|**_BGq**|**US5[**|**EUF**|
|**Currency reserves**||**259**|**416**||||**0**|**258**|**416,**|
|**Gov. Account**|**-65f**|**-258**|**-103**|**651**|<br>**258**|<br>**103**|**0**|**0**|l|
|**Gov debt**|_|||_<br>__|<br>_<br>**-10709**|_<br>**-1761**|**0**|**-10709**|**-176**|
||**-651**|**0**|**4059**|**651**|**-10451**|**-1658**|**0**|**-10451**|**2400.6**|
|Central b|ank's ex|posure:|euro|||Sovereig|n debt|: US$||

Source: World Bank staff estimates With respect to the current account flows, Bulgaria's large share of foreign and domestic debt denominated in US dollar broadly satisfies general hedging objectives. This holds, even though, the lev is pegged to the Euro, making Bulgaria's exposure to currency risk determined primarily by the US dollar/Euro exchange rate. Indeed, strengthening of the dollar since October 1998 (Chart _5)_ had so far cost Bulgaria's budget an extra $80-100 million in debt service cost. In the current account, however, this loss was mirrored by increases in the dollar-denominated export revenues. Chart 6 compares currency composition of net merchandise exports and debt service payments in 1998 and shows that the US dollar exposure on the sovereign debt was broadly matched by the dollar inflows on net exports. The peg to the Euro serves as a natural hedge to Bulgaria's large deficit in Euro-denominated trade.

_15_
Policy Research Working Paper 7496

# **Abstract**

The growing availability of free or inexpensive satellite imagery has inspired many researchers to investigate the use of earth observation data for monitoring economic activity around the world. One of the most popular earth observation data sets is the so-called nighttime lights from the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime lights and several economic variables. These correlations are based on data measured in levels, with a cross-section of observations within a single time period across countries or other geographic units. The findings suggest that nighttime lights could be used as a proxy for some economic variables, especially in areas or times where data are weak or unavailable. Yet, logic suggests that nighttime lights cannot serve as a good proxy for monitoring the within-in country _growth rates_ all of these variables. Examples examined this paper include constant price gross domestic product, nonagricultural gross domestic product, manufacturing value added, and capital stocks, as well as electricity consumption, total population, and urban population. The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power. The one exception for which Defense Meteorological Satellite Program nighttime lights could serve as a proxy is electricity consumption, measured in 10-year intervals. It is hoped that improved data from the recently launched Suomi National Polar-Orbiting Partnership satellite will help expand or improve these outcomes. Testing this should be an important next step.

This paper is a product of the Macroeconomics and Fiscal Management 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 DAddison@worldbank.org and BStewart@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
|**Table4.8:Educationle**|**velsinMoldova**|||
|---|---|---|---|
|**EducationLevel**|**Eligibility**<br>**(ages)**|**Duration**<br>**(Grades)**|**Deliveryfacilities**|
|**Preschool**|1-6||-Preschoolinstitutions|
|**Primarv**|7-10|**1-4**|-|
|**Secondary**||||
|Lower<br>Lower|11-15<br>11-15|5-9<br>59|-Gymnasiums<br>-General secondaryschools<br>~~~~~~~(GSS)<br>-Lyceums|
|U|16-17|10-11<br>(old sstem)|-GSS|
|pper|16-18|y<br>10-12<br>(new system)|-Lyceums|
|Vocatio-<br>Professional<br>nal|After<br>Gymnasium|||
|Polyvalent|After<br>Gymnasium|Between3and6<br>years||
|Colleges|Gymnasium:<br>1lg<br>Lyceums:12g.|Durationvaries,<br>dependingon<br>gradeadmitted.|Graduates fromGSS(grade<br>11)and Lyceums(grade12)|

Source: Tibi et al. (2002). Note: The Government of Moldova plans to upgrade the GSS to Lyceumns. Lyceums are located in Munucipalities and towns. Access to Lyceums **in** rural areas is very limited. Lyceum graduates get the Baccalaureate degree, required to enter University.

The effects of economic crisis on children have shown differential effects depending on the age of children, gender and place of residence. An important analytical issue is whether to use panel or cross section data to examine enrolment since the panel sample would lead to confound the effect of the crisis with those of aging. The evidence from developing countries suggest that while the effects on younger children is negative because of delayed enrolment, among teenage children the effects are mixed: some point to increased enrolment due to low opportunity costs in the labor market, while other suggest a decreased enrolment to replace adult labor that goes to the market.

Enrolment rates in Indonesia, a country whose enrolment rates were close to universal levels in the mid nineties, experienced some decline before and after the financial crisis in late 1997. Enrolment slightly decreased by 2 points among children 7-12 and it was more pronounced among girls, mainly associated with delayed entrance to school since drop out rates were relatively similar (Beegle et al., 1999). Enrolment for this age group also decreased more among children in the poorest quartile (between 5 and 6 points) and in rural areas (almost 4 points). For children aged 13-19 the evidence is less clear. Some survey data shows that enrolment decreased more especially among males from the poorest quartile and in urban areas, reflecting the need to participate in productive activities (Beegle et al., 1999), while another survey indicates that children of 14 or more were more likely to be enrolled probably because of diminished eamings opportunities (Frankemberg et el, 2001). Multivariate analysis indicates|that enrolment declines were

20
2,499 firms, was contacted to determine whether these firms were still in existence or if they had failed and/or became inactive, the sampling weights of the ES 2008 were adjusted, based in the non‐response firms. These adjusted weights were then used in order to produce the estimations that are, therefore, representative for the nonagricultural private economy within each country.

We classify the firms into groups of: i) young and older firms; and ii) innovative or non‐innovative companies. To distinguish the age status of the firm, we define a dummy ������ for firms up to five years old in before (2007) the crisis. To distinguish the innovative status of the firm, we used as a criterion the introduction of new product or process in the period 2005‐2007, according to ES 2008.<sup>4</sup> As we do not have this information for 2009, we then define a dummy ����� that equals one if the firm has introduced a product/process in 2005‐2007 period, and repeat this value for the post‐crisis period.<sup>5</sup> Alternatively, we use as a criterion to classify innovative companies the development of R&D activities in 2005‐2007 period, also according to ES 2008.<sup>6</sup> Table 1 presents the estimated proportions of innovative and non‐innovative companies across countries, following the two criteria, while Table 2 shows the estimated proportions of firms by age. Non‐innovative firms as well as older firms make the largest groups in all countries.

> 4The question used in ES 2008 was “In the last three years, has this establishment introduced new products or services?”

> 5Therefore, even though we recognize that innovation process is not static over time, we work with an innovation category that is time invariant, as we assume that innovative firms in the precrisis scenario (2004‐ 2007) are potentially innovative after the crisis (2009).

> 6The question used in ES 2008 was “In the last three years, has this establishment invested in research and development (in‐house or outsourced)?”

4
_35_ _Price Support at Any Price?_

# 6. Main conclusions from the simulation analysis

The main conclusion we draw from the simulation analysis is that _price support is not the only, and moreover not the most efficient way in which Poland can support its agricultural sector._ This conclusion does not mean that food prices should not be stabilized. However, the stabilized domestic price level should not exceed the world price level.

The main challenges the sector has to face, namely finding a new equilibrium position in the market economy and integrating with the EU require a deep restructuring of the sector to close (or reduce) productivity gaps vis-a-vis the EU agriculture and the other sectors of the Polish economy. _Reduction of productivity gaps is a precondition for an appropriate income and living standard for farmers, as well as for a successful competition on the single market without borders with Western European farmers._ _The most efficient way to solve the structural problems of the Polish agriculture is acceleration of reduction of the agricultural employment._ Such a policy may require significant investment in promotion of the new jobs for farmers, and for training. As a result, not only the low labor productivity problem may be solved, but also a substantial improvement may be obtained in a level of the rural areas development and living standards. Best support for the Polish agriculture is to increase its productivity. _The market-led process of reducing the agnicultural employment must not be slowed down. Instead, supporting policies may be introduced if there are possibilities of accelerating and easing the process._ Can Poland afford transfers to the agricultural sector that allow for an accelerated restructuring?

_First,_ one may ask the opposite question: can Poland afford a slow path of restructuring of the agricultural sector? The sector would become a growing burden for the economic growth. Earlier or later a kind of an income support may appear unavoidable from the social point of view.

_Second,_ Poland has already reached a level of transfers to the sector over $ 2bn yearly (OECD, 1995). The problem is therefore not if the resources are to be transferred, but rather how they should be transferred to obtain the highest efficiency.

_Third,_ the restructuring process does not necessarily have to be so costly for the other economic agents. Figure 15 shows the net burden for the rest of the economy (taxpayers, other sectors, consumers) obtained in the simulation experirnents". Let us notice, that only price support systems create a heavy burden. In the case of policies of reduction of the number of farmers, 17 'Price support" experiments show effects of various paths of reaching CAP price levels. "Alternatives" experiment **s** how effects of policies reducing agricultural employment (without price supporting). 'Gradual integration" experiments represent a mix of the slowest path of reaching CAP prices and policies aimed at employment reduction,
Krugman, Paul & Elizondo, Raul Livas, 1996. "Trade policy and the Third World metropolis," Journal of Development Economics, Elsevier, vol. 49(1), pages 137-150, April.

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Quintero, L. E., and M. Roberts. 2018. “Explaining Spatial Variations in Productivity: Evidence from Latin America and the Caribbean.” Policy Research Working Paper 8560, World Bank, Washington, DC. Redding, Stephen and David E. Weinstein (2020) "Measuring Aggregate Price Indexes with Taste Shocks: Theory and Evidence for CES Preferences", Quarterly Journal of Economics, 135(1), 503-560, 2020. Rosenthal, S. S., and W. C. Strange. 2008. “The Attenuation of Human Capital Spillovers.” Journal of Urban Economics 64: 373–89.

16
# 1. Introduction and context

Excessive exposure to air pollutants can have severe negative health impacts and is a leading cause of death (WHO 2021), and emissions from fossil fuels burned by vehicles and for domestic use are known to substantially contribute to urban air pollution (Andrade et al. 2010; Parrish et al. 2009; Zhang and Smith 2007). As a result, many cities adopt low-emission zones, clean-burning fuels, and other measures to combat adverse effects on health and well-being. But although such policies have been found to be effective, they can incur substantial upfront costs for consumers and require enforcement (Zhai and Wolff 2021; Jones et al. 2012). In his seminal work, _The Economics of Welfare,_ Arthur C. Pigou (1920) introduced an alternative mechanism applicable to anthropogenic emissions, proposing the use of taxes—and thus price incentives—to curb activities that cause negative externalities to a socially optimal level.

The relationship between fuel prices and consumption has been studied extensively in economic research and one would expect price changes to have follow-up effects on air quality. Although the exact magnitude of impact depends on price elasticity, lower fuel prices result in higher consumption, which, intuitively, should increase levels of anthropogenic fine particulate matter (PM2.5) pollution. This paper expands on available research on the price-pollution channel—which mostly focuses on city- and state-level analysis—by looking at the global relationship between fuel prices and consumption. Thus, our main contribution to the literature is an investigation into whether higher fossil fuel prices are tied to lower urban pollution levels globally.

To properly examine this notion, we estimate the relationship between PM2.5 concentrations, fossil fuel prices, fuel consumption, and country-specific variables using the Common Correlated Effects (CCE) estimator by Pesaran (2006) and a novel panel dataset that spans 133 countries over 20 years. Our findings suggest that, at the global level, transport fuel prices are significantly negatively correlated with air quality. Coefficient estimates for gasoline and diesel show a 22.2- and 31.1microgram per cubic meter (μg/m<sup>3</sup> ) reduction in PM2.5 concentration for each US$1 increase in price, respectively. Coal prices, on the other hand, seem to have no significant effect. We discuss several reasons why the relationship between fuel price and air pollution may not be as pronounced as between price and consumption, including technological modernization, dedicated pollution control measures such as air filters, regulation, and timescales for effects to materialize.

The remainder of this study is structured as follows. Section 2 provides an overview of existing literature on air pollution and fossil fuel prices. Section 3 summarizes the data sources and analytical methods used (including selection of the CCE estimator). Section 4 presents the results, and section 5 discusses policy implications.

2
<!-- Start of picture text --> e<br>s ° © ee<br>e e , “ee<br>e<br>e e S&S<br>ef<br>e hd<br>~ & °<br>Ge ©<br>e@<br>e<br>e<br>@<br>e<br><!-- End of picture text --> Ln(GDP;,) = a + B In(lights,,) + Ci+ Qe + eit Ln(GDP;+) In(lights;,) Prt i