rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8 Determinants of fiscal policy volatility; by type of commodity**

|**Dependent variabl**|**e: Primary expenditure**<br>|**volatility**<br>|
|---|---|---|
||(1)|(2)|
|EMDE|2.900***|1.794**|
||(1.047)|(0.867)|
|Agriculture exporters|0.712|-0.592|
||(0.983)|(1.036)|
|Metal exporters|2.236*|-0.136|
||(1.337)|(1.353)|
|Energy exporters|6.185***|-1.125|
||(1.625)|(1.252)|
|GDP per capita|-1.243**|-1.114**|
||(0.523)|(0.453)|
|Resources rents||0.377***|
|||(0.089)|
|Constant|17.081***|16.017***|
||(5.593)|(4.818)|
|Observations|177|177|
|_R_-squared|0.292|0.450|

_Source_ : World Bank.

_Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. "Agriculture exporters," "metal exporters," and "energy exporters" are dummy variables that take the value 1 if a country is a net exporter of the respective commodity group, and 0 otherwise.

52
# 1. INTRODUCTION

It is widely accepted that investment in human capital and social services, when accompanied by growth, remain the primary means to reduce poverty and improve the quality of life. However, during the turbulent 1980s when the majority of developing countries faced severe macroeconomic crises, growth slowed markedly, to the point where many countries no longer registered positive per capita growth. In this environment, living standards often fell sharply, particularly among the poor. Early on, while most of international institutions were still focusing on how the developing world was going to reestablish macroeconomic balance and service its external debt, the OECD Development Centre launched a study of the effects of adjustment on income distrib!.ion and poverty.

When the OECD project was launched, little was known about how adjustment programs affected ik.come distribution. To begin with, no economic crisis like this one had occurred before. For the first time, policies to alleviate poverty had to take into account the need to stabilize the economy and to promote restructuring to restore (or establish) long-run growth through improved resource allocation. Secornd, the tools needed to analyze the joint effects of stabilization and microeconomic structural adjustment policies we- not yet developed.' Because the macroeconomic crises had pt,- .sive effects, analyzing them required an economywide approach that went beyond the descriptive approach used, by Cornia, Jolly, and Stewart (1987) among others, in their UNICEF study.<sup>2</sup> Most adjustment programs, whether they were supported by the IMF and the World Bank or not, emphasized the simultaneous applicatior of stabilization and structural adjustment measures. Stabilization policies emphasized demand management, while structural adjustment programs emphasized microeconomic reforms designed to have supply-augmenting effects. Despite this difference, however, stabilization and structural adjustment measures are not always easy to separate; exchange rate policies, for example, are a fundamental element of both IMF-supported stabilization packages and Bank-supported structural
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE population now lives within range of a GSM signal. Simulations undertaken for the AICD study indicate if all regulatory barriers to entry to the mobile market are removed, GSM coverage could be extended to 60 percent of Liberia’s population on a commercially viable basis. At the same time, providing coverage to the remaining 40 percent of the population would require some degree of public subsidy. This coverage gap for Liberia is the highest in Sub-Saharan Africa; most countries could reach 90–95 percent of their populations on a commercially viable basis, and a few (such as Kenya, Malawi, and Uganda) have already done so (figure 12). Bridging this gap would require the government to entice operators to service unprofitable areas by providing capital and operating subsidies totaling approximately $11 million per year for a decade.

Extending limited performance wireless broadband access to the entire population via public access facilities will also require substantial public support. Once Monrovia is connected to the submarine cable, more cost-effective broadband access will become available in the country. However, the issue still remains of how to expand this access to secondary cities or rural areas. A second set of simulations therefore explores the possibility of using limited performance wireless technology to provide broadband to a limited number of institutional connections (such as municipalities and companies) as well as public access facilities (such as telecenters). This type of technology does not provide the same speed as a national fiber optic backbone, but may be serviceable for some areas. About one-third of Liberia’s population could be reached with such services on a commercially viable basis, but the remaining twothirds could only be reached with public subsidy to cover capital and some operating costs. The associated subsidy is estimated to be approximately $8 million per year for a decade.

24
data against those using the RLMS data in Figure 1.9, Panel A and Panel B (Appendix 1). The trends as shown by the HBS data are very similar to those based on the RLMS data.<sup>19</sup> Yet, while our results apply to the majority of the population, it is possible that we may not be able to capture well the super-rich in the population, such as the top 1 percent of the income distribution. Analyzing the latter group requires more fine tuned assumptions as well as better data on the top part of the income distribution such as tax information. As such, we refer interested readers to the comprehensive studies by Novokmet _et al._ (2018) for the top 1 percent of the income distribution, and Treisman (2016) for the number of billionaires in the country.

# _Attrition Bias_

Our estimation results are rather consistent for the different periods of varying lengths, which helps reduce concerns about potential attrition bias. But as discussed earlier, we offer further robustness checks using two popular inverse probability weighting methods that adjust for attrition: one by Fitzgerald _et al._ (1998) and the other by Wooldridge (2002). The intuition behind these methods is that, since households that drop out of the panel sample may have different characteristics from those that remain (e.g., higher education achievement or income level), we can reweight the latter by explicitly taking into account their characteristics. We apply these reweighting methods and show estimation results for the medium-term mobility and the long-term mobility in Table 1.4 (Appendix 1). Estimates using Fitzgerald _et al._ ’s method are qualitatively similar to the results shown in Table 1 and Table 3, while those using Wooldridge’s method display

> 19 We use the unweighted HBS data because population weights are not available. Official estimates by the Russian National Statistical Agency “Rosstat” also suggest that the Gini coefficient remains stable between 1994-2015 around 0.41 (Rosstat, 2016). See also Yemtsov (2008) for discussion on other issues related to reweighting and non-response with the HBS data.

26
1 Introduction Individual welfare is a multidimensional concept. Economists tend to favour measures of well-being that are closely oriented on economic theory, such as consumption or income. Yet, going beyond these standard measures is useful for various reasons. Following Sen (1999), increasing economic resources is essential as a means to enhancing individual welfare, but it is just an instrument and not the desired outcome. The overarching objective is the expansion of real freedoms, or capabilities, enjoyed by the members of the society.

Even a wider array of objective measures is unlikely to capture the notion of well-being adequately. Interpersonal comparisons of welfare cannot be made by looking solely at the “revealed” preferences of people as evident from their demand behaviour (Pollack and Wales (1979) and Browning (1992)). This has severe consequences. As is well know from Arrow’s Impossibility Theorem (Arrow (1950)), a social welfare function cannot be constructed in the absence of interpersonal comparisons of individual welfare. In addition, participatory poverty assessments around the world have shown that a good life or well-being has both material and psychological dimensions. It includes aspects of “empowerment”, or the effective inclusion of people in making decisions on economic, social, cultural and political issues. ? Lack of voice, discrimination, social barriers, mistreatment by public officials, alienation from service providers, and language barriers all affect the ability of individuals to achieve their potential and create a sense of powerlessness. While economic well-being and empowerment are related, they represent separate and imperfectly correlated concepts.

separate consideration of economic well-being and power is especially important when nations embrace systemic institutional reforms. The fall of the Berlin Wall in 1989 gave rise to political and economic liberalization in Eastern Europe, the Former Soviet Union, and in most countries of Africa and many nations in East Asia. Such reforms are likely to have different impacts on economic and power status. Not everybody benefits in the same way, and some even lose out, as traditional values or authority structures are challenged, and the stakes of economic competition are raised. In particular, while the move toward greater political participation may result in higher trust and representation of large parts of the population, disruptions to the inherited economic structure and a slow emergence of new economic opportunities can worsen 3 For example, World Bank characterizes poverty along four dimensions, including opportunity, human development, vulnerability, and empowerment. Empowerment is defined as ”...the expansion of assets and capabilities of poor people to participate in, negotiate with, influence, control, and hold accountable institutions that affect their lives” (World Bank (2002b)).
4 C. Performance. By and large, past performance of the state- and donor-supported agricultural credit operations has been below expectatlons. Most of the programs reached only a minority o? the faming population, while benefits were frequently concentrated among wealthIer farmers. Many of the irstitutlons established or supported for dolivering credit programs have not developed Into self-sustalning credit faciltles. Furthermore, In many Instances, the subsidy dependence of these institutlons has become significant and has been rilsing. This, In turn, made credit programs an extremely costly affair for their sponsorlng governments. For example, the agricultural credit systems of the World Bank's three most knportant agricultural credit borrowers In the eighties, Brazil, Mexico, and India, have all suffered from severe equlty erosion. In Brazil and Mexico, highly negative Interest rates In an Inflationary environment generated the erosion, whilo In India the equity erosion resulted from dlsnal loan collectlon.<sup>1</sup> In the agracultura! credit systems of Brazil, Mexico, and India, administrative interventions retarded the development of efficient financial markets and had negative knplicatlons for other sectors In the economy by depriving them of loanable funds and Increasing thelr borrowing costs.

7. As an OED study of agricultural credit projects In 24 countries points out, these projects have often failed to become vehicles to upgrade farm technology.<sup>2</sup> The programs reached a minority of the farming population and benefits were frequently concentrated among wealthier farmers. For example, a study In Ccsta Rlca has shown that Income distrlbutlon could be J. **Yaron** and P. **_iegel,_** Sank *Lnding for Agricull Credlt FYM2M88 Agrulfture and Ruwa Developmt Departrert, Decembe 1968. 2 Documnt JAC 8845 of th Jont Audit Caommitt"e. **Aproach Far:** A Review of Bank Lendhg for Agricultural Credit (1961-1987) dated October S. 196P - 3. pam 6. The edy oovered Oh period beor 1976.
secondary education discussed above. Returns also differed with age: For the younger working population below 35 years of age we found that all returns were several percentage points lower than for the working population older than 35.<sup>15</sup> <u>Incidence of Hunger. The survey also revealed a characteristic of poverty which had not</u> been much on the radar screen of public debates before: poverty went hand in hand with food insecurity – and the degree of such insecurity was at alarming levels in Cali in 1999. As reported in Table 3, about one-third of the population in the poorest income quintile had family members having been hungry at least once over the past year while not commanding the financial resources to purchase food. And a full 60 percent of poor parents stated that they had to reduce nutrition for their children because of insufficient resources (over a one year period). Even though these figures might have been somewhat upward biased, the results pointed to a serious problem of food insecurity among the poorest of the poor in Cali.<sup>16</sup> This is especially worrisome as the reach of nutrition programs was extremely small. Only 4.4 percent of the poorest population group reported having access to food aid.

<u>Health Care and Insurance. The EPSOC survey showed that the poor had a higher</u> demand for health care than better-off income groups. The poorest income quintile had a selfassessed rate of illness of around 30 percent (over a period of six months); the wealthiest population quintile of about 20 percent. Poorer families were much more likely to seek help in public health centers or hospitals while private doctors and clinics emerged as the preferred providers for better-off families. For many of the poor such visits cut into the family budget deeply since only half of them held any form of health insurance. While the system of beneficiary identification SISBEN was well-targeted (i.e., it reached a much higher proportion of the population in income quintile 1 than in income quintile 5), its coverage was relatively low. Only 22 percent of the poorest population reported to have access to the SISBEN network – and with it to subsidized health services. Access to private and public health insurance like the _Instituto de Seguridad Social_ (ISS) and the _Empresa Privada de Salud_ (EPS) was low for the poor and much higher for the better off.

> 15 One caveat of such estimates exists, however.  They are calculated only for the occupied population and hence do not take the unemployed into account.  Hence, average returns over the whole labor force (employed and unemployed) are likely to have been lower.

> 16 The results reported in the Table potentially have an upward bias since some respondents in the survey might have thought that answering ‘yes’ to food insecurity questions would have given the household a chance to access subsidy programs.

13
**<u>Table A.4 Baseline Balance (Weighted) with Attrition</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**No**|**rmalized Me**|**an Differe**|**nces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of gram panchayats (GPs)|77|58|58|58|**Control-**|**Control-**|**Control-**|**Arm 1-**|**Arm 1-**|**Arm 2-**|
|Number of households|1,605|1,208|1,219|1,243|**Arm 1**|**Arm 2**|**Arm 3**|**Arm 2**|**Arm 3**|**Arm 3**|
|Tractor|.1242|.1272|.1641|.1138|-.0066|-.0805|.0226|-.0739|.0292|.1031|
|Mobile phone|.9711|.9652|.9708|.9649|.0238|.0012|.0249|-.0226|.0011|.0237|
|Toilet|.7965|.7386|.7431|.743|.0972|.0899|.09|-.0072|-.0072|.0001|
|**Education of the head of household**|||||||||||
|Secondary|.2366|.1896|.1948|.2138|.0812|.0718|.0386|-.0094|-.0427|-.0333|
|College/graduate/postgraduate|.1063|.1093|.1096|.1038|-.007|-.0075|.0057|-.0006|.0127|.0132|
|Illiterate|.3139|.3552|.3566|.3354|-.062|-.064|-.0325|-.002|.0294|.0315|
|**Primary occupation of the head of household**|||||||||||
|Cultivation|.1837|.1754|.2246|.1835|.0152|-.0718|.0003|-.087|-.0149|.0721|
|Agricultural labor|.08021|.1051|.1158|.08909|-.0608|-.0849|-.0226|-.0242|.0383|.0624|
|Nonagricultural labor|.2978|.3441|.3281|.3345|-.0703|-.0463|-.0559|.0239|.0144|-.0095|
|Salaried work|.1444|.1354|.119|.1152|.0183|.0531|.0614|.0348|.0432|.0083|
|Business|.0933|.0831|.06091|.09174|.0255|.086|.0038|.0607|-.0216|-.0822|
|**B. Village characteristics**|||||||||||
|SC/ST majority village|.4498|.4015|.4159|.5101|.0692|.0484|-.0853|-.0207|-.155|-.134|
|Village head w/college degree|.09097|.1291|.08778|.04988|-.0864|.0079|.1139|.0942|.1982|.1061|
|Village head w/high school degree|.1452|.12|.1641|.1521|.0524|-.037|-.0137|-.0893|-.0661|.0233|

46
WPS4101 Political Accountability and Regulatory Performance in Infrastructure * Industries: An Empirical Analysis Farid Gasmi Université de Toulouse I (GREMAQ, IDEI) gasmi@cict.fr Paul Noumba The World Bank pnoumbaum@worldbank.org Laura Recuero Virto Université de Toulouse I (GREMAQ) recuerovirto@hotmail.com

# Abstract

The aim of this paper is to empirically explore the relationship between the quality of political institutions and the performance of regulation, an issue that has recently occupied much of the policy debate on the effectiveness of infrastructure industry reforms. Taking the view that political accountability is a key factor that links political structures and regulatory processes, we investigate, for the case of telecommunications, its impact on the performance of regulation in two time-series-cross-sectional (TSCS) data sets on 29 developing countries and 23 developed countries covering the period 1985-1999. In addition to confirming some well documented results on the positive role of regulatory governance in infrastructure industries, this paper brings some empirical evidence on the impact of the quality of political institutions and their modes of functioning on regulatory performance. This first analysis of the data sets shows that the (positive) effect of political accountability on the performance of regulation is stronger in developing countries. An important policy implication of this finding is that future reforms in these countries should give due attention to the development of politically accountable systems.

JEL-codes: L51, H11, L96, L97, C23 Key words: Infrastructure industries, regulatory performance, political accountability.

# World Bank Policy Research Working Paper 4101, December 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _*_ We thank Jean-Paul Azam, Antonio Estache, Frannie Léautier, and Wilfried Sand-Zantman for useful comments and discussions on the topic studied in this paper. Part of this research was undertaken while Farid Gasmi and Laura Recuero Virto were visiting the World Bank Institute during the summer of 2006. These authors thank members of the Institute, in particular, Gabriela Chenet-Smith for their warm hospitality.
prices of various commodities. Serra et al. [61] used nonlinear time-series models to assess the price relationship within the U.S. ethanol industry. They used daily data on ethanol corn and crude oil prices and identified equilibrium relationships between these prices. They found that when corn prices are high relative to fuel prices, ethanol prices are mostly affected by the price of corn. When the price of corn is low relative to the price of fuel, then ethanol prices are likely to follow the price of fuel. Similarly, Balcombe and Rapsomanikis [9] established a consistent long-term equilibrium between ethanol prices and the price of sugarcane and oil using data from Brazil.

In summary, the literature suggests that one has to contend with several factors in order to explain the causes for the food crisis. On the demand side, another major factor is rapid economic growth in emerging economies, which increased demand for meat, a highly grain-intensive product. On the supply side, bad weather in key grain-producing regions (especially wheat-growing regions such as Australia), stagnation of productivity growth (due to underinvestment in agricultural research and technology and infrastructure such as irrigation), and increase in production costs (due to high energy prices) have resulted in slow or negative growth in production. Prices spiraled even further as a result of policies such as export bans on grains and import tariffs on non-grain biofuels (especially the U.S. import tariffs on cane ethanol from Brazil) and on account of speculative activity in reaction to such policies. Lastly, the depreciation of the U.S. dollar relative to major world currencies has also been a contributing factor to commodity price increases. Historically, when the dollar is weak, commodity prices tend to be higher and, when the dollar is strong, commodity prices tend to be lower. However, with different countries adopting different policies toward biofuels and trade, assessing the country-level impacts of these factors require case-by-case analysis.

With several such factors at play, identifying the contribution of any one factor such as biofuel is a challenging task. The estimates of the impact of biofuels that can be found in the literature are wide ranging, ranging between 3% and 75%. One reason why the optimistic estimates may be an underestimate is because of a lack of representation of the market for inventory. We are not aware of any standard equilibrium models including those mentioned earlier that incorporate an explicit representation of the market for inventory.

# **3. Historical trends**

Historical trends in production, consumption, inventory, and price at the global level for four major crops, namely, maize, wheat, rice, and soybeans, are shown in figures 3(a), 3(b), 3(c), 11