rafmacalaba/datause-encoder-data huggingface dataset:
and Scott (1999), Deb, Trivedi, and Varangis (1996), and LeyBourne, Lloyd, and Reed (1994)).<sup>**3**</sup> The present paper examines the price transmission from energy to non‐energy commodities. Specifically, it estimates transmission elasticities for 11 non‐energy price indices based on the following specification:

# **_log(NON_ENERGYt) = μ + β1log(ENERGYt) + β2log(MUVt) + β3TIME + εt,_**

where _NON_ENERGYt_ denotes the various non‐energy US dollar‐based commodity price indices at time _t_ , _ENERGYt_ denotes the energy price index, _MUVt_ denotes the deflator, _TIME_ is time trend, and _εt_ denotes the error term, the properties of which will be subject to empirical investigation; _μ_ , _β1_ , _β2_ , and _β3_ denote parameters to be estimated. Because the model is expressed in logarithms, the parameter estimates can be interpreted as elasticities.

Although the signs and magnitudes of the coefficients are not dictated by eco‐ nomic theory, _β1_ and _β2_ are expected to be positive because energy as well as other goods and services (as reflected by the measure of inflation) constitute key inputs to the production process of all commodities.<sup>**4**</sup> (See Baffes (2007), FAO (2002), and World Bank (2009) on the various transmission channels between energy and non‐energy markets; Kilian (2008) on the effects of energy shocks to the broader economy; and Schmidhuber (2006) on the impact of biofuels on agricultural markets.) On the other hand, _β3_ is ex‐ pected to be negative, at least for agriculture—consistent with the long term impact of technological progress on production costs as well as the low income elasticity of most food commodities, especially cereals.

As an alternative to the above specification, one could deflate both indices by di‐ viding _NON_ENERGYt_ and _ENERGYt_ with _MUVt_ , effectively restricting the sum of the _1_ + _2_ energy index and inflation coefficients to unity (i.e., _β β_ = 1). Estimating nominal in‐ dices, however, relaxes the homogeneity restriction so that a direct estimate of the im‐

**_— 4 —_**
_Effects of U.S. Agricultural and Biotechnology Policies_ , ed. Joshua S. Graff Zivin and Jeffrey M. Perloff, 15–50. University of Chicago Press.

- Gouel, Christophe. “Rules Versus Discretion in Food Storage Policies.” _American Journal of Agricultural Economics_ . doi:10.1093/ajae/aat016.

———. 2012. “Agricultural Price Instability: A Survey of Competing Explanations and Remedies.” _Journal of Economic Surveys_ 26 (1): 129–156.

- Gouel, Christophe, and Sébastien Jean. 2012. “Optimal Food Price Stabilization in a Small Open Developing Country.” _The World Bank, Policy Research Working Paper_ 5943.

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- HLPE. 2011. “Price Volatility and Food Security.” _The High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Report_ 1.

- Hoddinott, John, John A Maluccio, Jere R Behrman, Rafael Flores, and Reynaldo Martorell. 2008. “Effect of a Nutrition Intervention During Early Childhood on Economic Productivity in Guatemalan Adults.” _The Lancet_ 371 (9610) (February): 411–416.

- Innes, Robert. 1990. “Uncertainty, Incomplete Markets and Government Farm Programs.” _Southern Economic Journal_ 57 (1): 47–65.

- Irwin, Scott H, and Dwight R Sanders. 2012. “Testing the Masters Hypothesis in Commodity Futures Markets.” _Energy Economics_ 34 (1): 256–269.

- Jacks, David S, Kevin H O’Rourke, and Jeffrey G Williamson. 2011. “Commodity Price Volatility and World Market Integration Since 1700.” _Review of Economics and Statistics_ 93 (3): 800–813.

- Jacoby, Hanan G, and Emmanuel Skoufias. 1997. “Risk, Financial Markets, and Human Capital in a Developing Country.” _The Review of Economic Studies_ 64 (3) (July): 311– 335.

- De Janvry, Alain, and Elisabeth Sadoulet. 2006. “Progress in the Modeling of Rural Households’ Behavior Under Market Failures.” In _Poverty, Inequality and Development: Essays in Honor of Erik Thorbecke_ , ed. Alain de Janvry and Ravi Kanbur, 155–182. New York: Springer.

41
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were born ten years ago. Extreme poverty rates have fallen from 61 percent to 13 percent between 1990 and 2012.

The number of people living under $1.90 a day fell from almost 2 billion in 1990 to about 700 million in 2015—a fall in the global headcount ratio from more than 40 percent to less than 10 percent.<sup>13</sup> Besides their numbers, the geographic distribution has changed. Back then, more than 80 percent of the world’s extreme poor were in East and South Asia. Today, 82 percent live in South Asia and Sub‐Saharan Africa. This is familiar stuff (Figure 4).

**Figure 4: Distribution of the world’s extreme poor ($1.90 poverty line)**

<!-- Start of picture text --> 1981 1990<br>Total Number of Poor: 1.9 billion Total Number of Poor: 1.9 billion<br>EAP EAP<br>10% 15%<br>ECA ECA<br>LAC LAC<br>27% 51%<br>58% MNA 30% MNA<br>SAS SAS<br>4%<br>3%<br>1%<br>SSA SSA<br>1% 1% 0%<br><!-- End of picture text --> <!-- Start of picture text --> 2005 2015<br>Total Number of Poor: 1.4 billion Total Number of Poor: 700 million<br>EAP 1% EAP<br>12%<br>28% 27% ECA 4% 1% ECA<br>LAC LAC<br>49%<br>4% 2% MNA MNA<br>33%<br>1% SAS SAS<br>39%<br>SSA SSA<br><!-- End of picture text --> Source: Global Monitoring Report 2015/16 and PovcalNet in August 2016. Note: All numbers for MNA region are projections.

Less appreciated perhaps is that, much as it was in 1973 when President McNamara announced the World Bank’s goal of reducing absolute poverty, 70 percent of the world’s extreme poor still live in rural areas. This is despite the fact that half the world is now urban, up from a third back in the 1970s. In 2020, half the people in the

> 13 The 2015 figures for global extreme poverty are statistical projections. The most recent estimate of the global extreme poverty rate is 12.7 percent for 2012.

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Policy Research Working Paper 7335

# **Abstract**

Over the past decade, decentralization of fiscal and policymaking authority has become a cornerstone of development organizations’ recommendations for good governance. Yet the institutional design of multilayered government can create tensions as new elites attempt to fill governing spaces long occupied by traditional patrons. This paper uses the case of post-conflict Sierra Leone to explore the power-sharing dynamics between traditional hereditary chiefs and newly elected community councilors, and how these dynamics affect the provision of local public goods. The paper uses data on several measures of local service provision and finds that councilor/chief relationships defined by competition are associated with higher levels of public goods provision as well as greater improvements in these goods between council areas over time. Relationships defined by frequent contact in the absence of disputes as well as higher frequencies of familial ties between the two sets of actors are associated with worse local development outcomes. This evidence suggests that greater competition between elite groups is beneficial for local development, whereas collusion or cooption between old and new elites harms the provision of local public goods.

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 abc24@uw.edu.

_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
some low fragility ones are almost never in conflict, but because of the secular trend, the countries with similar conflict risk tend to enter and exit the conflict state in the same years. Once a country enters conflict, it tends to remain so until global conditions improve enough to bring it back below the conflict threshold.

Because we imposed an inverted “U” shaped trend for g(t) and f(t), conflict and wheat both follow a pattern of starting and finishing the period at low levels and peak in the middle of the period, as shown in Figure A9. This corresponds with observable patterns in wheat production and conflict in the 36-year period in the NQ data. Because we have chosen forms such that σg > σu and σf > σz, the variance in both conflict and wheat is dominated by the trend rather than by year-on-year idiosyncratic variation around the trend, as is true in the actual data.

In this model, there is no true causal effect of aid on conflict in the DGP. If there is a correlation between aid and conflict, it comes from aid agencies preferring to send food aid to more conflict prone places. We now replicate NQ’s identification strategy by drawing 100 random samples of 36 years and 126 countries using the model above and then estimate the relationship using 2SLS that mirrors NQ, with the following first and second stages: where Ci are country fixed effects, Tt are year fixed effects, and Xit are country and year level controls. Figure A10 shows the density of estimated β coefficients from the second stage for two specifications. The “parsimonious controls” specification includes only time and country fixed effects and the “lagged dependent variable” specification includes conflict in period t-1 as a control. This corresponds to the estimated coefficient of aid on conflict in the parsimonious specification of NQ’s IV strategy. The distribution of estimated β’s never includes the true zero value; rather all the estimates of the relationship between aid and conflict are positive. Clearly, including the year and time (region-year in NQ) fixed effects or the control for lagged conflict does not prevent the IV coefficient from picking the up the bias that arises from the endogenous determination of aid and conflict 60
|Male|-0.35|0.35|-0.24|0.48|-0.26|-0.01|
|---|---|---|---|---|---|---|
||[0.14]**|[0.91]|[0.27]|[1.03]|[0.28]|[0.02]|
|Observations|603|597|603|593|603|597|
|R-squared|0.16|0.21|0.19|0.28|0.04|0.02|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1. The sample is T2, T3 and T4

# 6. **<u>Summary and conclusion</u>**

Individuals in developing countries, and especially in Sub-Saharan Africa, have limited access to financial products that help mitigate the numerous risks they face. There is a fast growing literature showing the high demand for and significant impact of health, weather and crop insurance (Cole et al. (2013), Karlan et al. (2014), Dercon et al. (2014), Thornton et al. (2012), Delavallade (2014)) as well as of savings products (Dupas and Robinson (2013)). However, and while this is a pressing policy question, literature is still scant as to which of these financial products might be the most efficient at favoring risky investment, fostering agricultural production and improving welfare. This paper addresses this question in the context of a field experiment conducted simultaneously in rural areas of Senegal and Burkina Faso between June and August 2013. 800 participants were randomly offered one among four financial products – weather index insurance, low-commitment agricultural investment savings, high-commitment agricultural investment savings and high-commitment emergency savings.

Insurance was found to have the most consistent impact on input use and purchase. As a result, yields were higher for those who bought more insurance. There is some evidence that as a result individuals who were offered insurance were better able to manage risk.

Significant gender differences in take-up were found. Women invest significantly less in the insurance product. Given the impact of purchasing insurance on agricultural investment, yields and wellbeing, our results suggest that this lower take-up of agricultural insurance disadvantages women. The reason hypothesized for this lower take-up among women is the fact that women face higher levels of risk that are uninsured by a rainfall product and that directly impact the yield they realize (as well as other outcomes)—for example risks of childbirth as a result of very high fertility rates or risks of lost income and production as a result of caring for sick children. In an environment in which theses costs are uninsured, and these costs fall primarily on women, a rainfall insurance product carries less value for women than for men. Further work is needed to understand whether this is indeed the main factor behind the gender difference in demand and if it is, to understand how financial products can be better designed to meet the different risk needs of women.

Our findings are consistent with previous studies showing individuals’ preference for savings products offering liquidity in the presence of labeling (Dupas and Robinson, 2013).

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<mark>An interesting example of the application of this approach is South Africa’s use of it in its equitable share transfers to the provinces (South Africa 2006). The equitable share formula applicable for 2006–08 focuses almost entirely on need factors, with only a 1 percent weight given to negative needs (per capita GDP). The formula uses the following shares:</mark>

- <mark>A basic share (14 percent weight) is derived from each province’s share of the national population.</mark>

- <mark>An education share (51 percent) is based on the size of the school-age population (5–17) and the average number of learners (grades R–12) enrolled in public ordinary schools over the past three years.</mark>

- <mark>A health share (26 percent) is based on the proportion of the population with and without access to medical aid.</mark>

- <mark>An institutional component (5 percent) is divided equally among the provinces.</mark>

- <mark>A poverty component (3 percent) is based on incidence of poverty.</mark>

- <mark>An economic output component (1 percent) is based on data on GDP by region.</mark>

_(b) The representative expenditure system using direct imputation methods_ seeks to create a parallel system to the representative tax system on the expenditure side. This is done by dividing subnational expenditures into various functions, determining total expenditures by each jurisdiction for each function, identifying relative need/cost factors, assigning relative weights using direct imputation methods or regression analysis, and allocating total expenditures of all jurisdictions on each function across jurisdictions on the basis of their relative costs and needs for each function (see table 3 for a compilation of need factors used in industrial countries).
during the interview (such as always asking open-ended questions and asking for examples), but these are not available in self-reported questionnaires. We address this issue by focusing on the topics that have a direct equivalent in the WMS to allow for a clear benchmark for our new index. If principals are reporting “good” information in these surveys that allow us to capture similar signals as the WMS scores, we should see similar distributions of scores across the common countries and a similar overall relationship between management and student test scores across countries. For PISA, we compare the distribution of scores and the performance correlations for the common countries as there are no school identifiers available. For Prova Brasil, we use school identifiers to match schools directly and hence provide a one-to-one comparison of the index standardized values.

**Validation of new indices.** As a first validation exercise, we compare the distribution of our PISA-based management index with the distribution of school management as measured by the WMS data in Figure 1 for all countries that the WMS has collected data.<sup>12</sup> The PISA and WMS distributions are reassuringly similar. The Kolmogorov-Smirnov test for equality of distributions rejects in only one of the 9 cases, Italy, where the PISA-index is somewhat more dispersed.<sup>13</sup>

As a second validation exercise, and to ensure we are picking up important variation with our management index, we conduct a basic check of the correlation between our measure and student performance. For each country we separate schools into quartiles of the management measure, and in Figure 2 we show, for each quartile, the average PISA test scores for math, reading and science (in deviations from the global mean). The graph includes all students and schools across the 65 countries available in the 2012 PISA dataset. This simple relationship suggests that students in schools in the bottom quartile of management within their country score are, on average, about 6 points lower than the PISA global mean, while students in schools in the top quartile of management within their country score, on average, about 5.5 points higher than the PISA global mean. To put this into context, 41 PISA points in math are the equivalent of a year of learning. The range of

> 12Independent researchers conducted the WMS in Colombia and Mexico during 2015, with guidance and supervision from the original WMS team. These data were not available to Bloom et al. [2015a]. India is not included in Figure 1 because it did not participate in PISA in 2012.

> 13We show comparisons between the WMS and PISA sub-indices for operations management in Figure B.1 and people management in in Figure B.2 in Appendix B and confirm that the distributions are consistent for both sub-indices.

9
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