rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

Many developing countries obtain a large share of their income from the exploitation of common-property renewable natural resources, including fisheries, forests, grazing grounds, and water resources. Their degradation has typically been caused by activities such as logging, agriculture, commercial fishing, and others.<sup>1</sup> It has also been associated with the extraction of exhaustible resources. For instance, mining and oil extraction has led to the depletion of water supplies, water contamination, deforestation, and more.<sup>2</sup> As is well known, a lack of property rights for natural resources results in their excessive exploitation compared to the social optimum and thus in a higher rate of depletion and a smaller long-run (steady-state) stock. López (1998) estimates the losses from non-cooperative behavior on common-property lands and lack of internalization of the external costs of biomass use in land allocation decisions in Côte d’Ivoire to be as high as 14 percent of total village income. He finds that the degree of internalization of the negative externalities is less than 30 percent and declines with community size. López (1997) obtains similar estimates for the income loss in Ghana. These and other studies make it clear that the problem of imperfect property rights is of crucial importance for many countries and may even result in total depletion of the natural resource and disappearance of poor communities.

A large number of studies have examined communities that had been stable for long periods but then started a process of impoverishment that worsened over time (see the forthcoming paper by López for a review of such studies). These communities typically experienced important changes over time – such as an increase in population or in market size which resulted in increased pressure on the natural resource and a decline in the degree of internalization of the negative externalities – but failed to develop adequate institutions to deal with them.

> 1 The classic case of natural resource depletion is that of fisheries which has affected a large number of countries over the years and was the focus of early natural resource depletion studies (e.g., Gordon 1954, Scott 1955). An example is that of Peruvian anchovies whose world price increased dramatically in the late 1970s and early 1980s. This raised the incentive to invest in fishing boats, an investment subsidized by the government when a tax would have been optimal. The higher prices and subsidies led to a dramatic increase in the fishing fleet and the stock of anchovies disappeared for several years, leading to a decline in the use of these boats and thus in fishing. This enabled the anchovy stock to replenish over time. 2 It is estimated that nearly one third of all active mines and exploration sites are located within areas of ecosystems with high conservation value (Miranda et. al., 2003).

2
In recent years, the number of studies that deal with the effects of politics on economics has flourished. These studies are particularly useful for developing economies in order to better understand why long overdue reforms are not implemented as they focus especially on the incentive framework. The emergence of an explicit political economy of development can be seen clearly in the evolving intellectual diagnosis of the “development problem” by donors and in how this diagnosis has translated into policy on development assistance to low-income countries (Adam and Dercon, 2010).

The studies dealing with PE analysis can be carried out at the (i) country level, dealing with issues of governance and decentralization; (ii) sector level, focusing on reforms in sectors such as electricity, transport, telecommunications, water, etc; and (iii) policy or project level.

For example, in a comparative analysis, Hickey (2006) attempted to identify the nature of politics most likely to reduce chronic poverty in developing countries. This analysis shed light on the politics underlying successful social protection policies, including pension schemes in India, Lesotho, Namibia and South Africa; assistance programs for vulnerable groups in Bangladesh and Mozambique; and mainstreaming social protection in national development plans in Uganda and Zambia. The author suggests that greater attention should be paid to interacting with the political leadership instead of civil society; to the link between political discourse and poverty analysis rather than simply focusing on poverty data; and to the importance of political contracts in sustaining pro-poor policies.

By analyzing the PE of sector reform in two sectors - agriculture, and water and sanitation, a World Bank study (2008) identified ways to improve the effectiveness of the policy reform process. By looking at stakeholder interests, incentives, institutions, risks and opportunities from a social analysis perspective, the study points out the need for development partners to establish a sustainable process for building broad coalitions and to promote transformative institutional change that includes supporting bottom-up accountability.

While PE studies appear to be useful in providing insight into the country context and facilitate a better and quicker understanding of the realities on the ground, translating PE analysis into action remains a challenge. Based on interviews of World Bank task managers, this paper attempts to assess the relevance of these studies in supporting policy reform and the implementation of projects and draws lessons on how they have influenced the implementation and design of the World Bank program in Zambia in various sectors.

The paper is structured as follows. Section 2 identifies the importance of carrying out PE studies. Section 3 briefly presents the PE analyzes conducted in Zambia. Section 4 discusses the implications of 3
# The Impact of an Accountability Intervention with Diagnostic Feedback: Evidence from Mexico<sup>_∗_</sup>

Rafael de Hoyos<sup>_†_</sup> Vicente A. Garcia-Moreno<sup>_‡_</sup> Harry Anthony Patrinos<sup>_§_</sup>

## **1 Introduction**

The increasing availability of standardized tests in developing countries opens the door to a wide range of policy interventions to improve student learning outcomes. Under certain conditions, the implementation of standardized tests in itself can be conductive to an increase in learning outcomes. When test scores are made public, they can eliminate heterogeneous perceptions among agents in the system (authorities, school directors, teachers, parents and students) regarding the quality of education services. Given sufficient levels of initial asymmetries on perceptions regarding the quality of education, the additional information brought about by test scores can be enough to generate a new equilibrium yielding a more transparent and accountable system providing higher quality education services.

The second quality-enhancing change that can be triggered by the availability of test scores is the development of school-specific improvement plans. If the standardized tests are universal, then every school in the system could have access to a detailed diagnosis of the main challenges in the subject areas and grades assessed by the tests. If these detailed diagnoses and school improvement plans are followed by what can be loosely defined as _pedagogical interventions_ , or actions to address the problems identified, then the final outcome can be a system delivering higher quality services.

> _<u>∗</u>_ The authors gratefully acknowledge funding from the World Bank’s _Research Support Budget_ and

> the comments of participants at the World Bank’s economics of education seminar.

> _†_ The World Bank. E-mail: rdehoyos@worlbank.org.

> _‡_ Teachers College, Columbia University. E-mail: vag2120@tc.columbia.edu

> _§_ The World Bank. Email: hpatrinos@worldbank.org
the number of calls made or received) and measures of mobility (e.g. where, how far and how often a customer travels). Data are aggregated by taking the mean over all customers, if appropriate, or else a variety of threshold values—for example, the number of customers living in a network polygon who regularly travel beyond 80 kilometers of their home location.

- **Polygon activity-oriented.** The second set is based on activity occurring within the polygon, regardless of the home location of the customers involved. This is akin to taking a survey of people passing through an area. In this case, features based on activity include the number of customers entering the network polygon but living outside of it, how often they visit, and the volume of inbound and outbound calls that are processed. These features are calculated directly at the network-polygon level and do not require further aggregation.

# **_Fitting the Model_**

Estimating poverty rates from CDR features is an example of a “supervised” machinelearning problem, or one in which input data are used to predict known outputs—in this case CDR features and poverty rates. Once the model is built, it can be applied to new input data for which the corresponding output is not known. In this case, the unknown output would be either different geographies or different points in time.

Supervised machine-learning problems are either classification-based, in which case the output variable is one of a discrete set of classes (e.g. male/female, poor/not poor, etc.), or regression based, in which the output variable is a continuous real number expressed as a decimal, ratio or percentage. Poverty rates are most naturally modeled as a continuous output variable. However, it is also possible to group the data into a small set of classes reflecting low, moderate, or high rates of poverty. Both approaches were examined in this example, which tested a variety of scenarios by mixing different training and test datasets and employing both regression and classification methods. Figure 5 illustrates the different combinations of data and methodology.<sup>11</sup>

> 11 For more information, see: Hong and Frias-Martinez (2015).

**11**
estimation allows us to test for the equality of the coefficients in (1). In order to avoid aggregation biases, equation (1) was also estimated by firm size.

# **5. Results**

This section discusses the results of the estimation using both wages and value added productivity<sup>5</sup> . The employee characteristics that are examined include the following: (i) schooling; (ii) training; (iii) potential experience; (iv) gender; (v) union membership; and (vi) type of contract. The characteristics of the firms that are examined are: (i) R&D; (ii) export orientation and foreign ownership; (iii) quality control; (iv) firm size; (v) location; and (vi) sector of activity. For every relevant variable, the estimation (with sampling weights) was done for all firms, which are the pooled coefficients. Then the estimation was done for every group of firms, classified by size, so that we were able to differentiate the effect by firm size.<sup>6</sup>

# **_Individual Characteristics of Employees_**

# . **Schooling**

Education has been singled out as the most important factor in determining wages and earnings inequality in Mexico. This realization has increased the importance of education over time. Not surprisingly, the results of the wage-productivity equation indicate that schooling increases wages as can be seen in Table 5. The coefficients are significantly different from zero in both 1993 and 1999 for all firm sizes.

These findings are not surprising. A worker receives higher wages with more education, conditional on other relevant individual characteristics as well as on characteristics of the firm. In the 1999 findings, an additional year of schooling was shown to yield a 10 percent increase in wages versus the 9 percent found in the 1993 findings. Typical rate of return studies in Mexico show that schooling increases wages _5_ Joint estimation using wages and average product per worker were also estimated. The results are similar to those discussed in this paper Results are available upon request

> **6** The results of the pooled joint estimations are on Appendix C. The results by firmn size are available upon request.

10
ine¢ cient in transmitting information. Because corn markets are considered to be well-functioning markets, in the sense that prices and transactions are transparent, centralized, and very liquid, it is surprising and interesting to ...nd that they might not always behave as one would expect in a classical supply-demand model with inventories and utility and pro...t-maximizing agents. This raises several relevant questions regarding macroeconomics, ...nancial markets and industrial organization. First, if a very well developed market fails to deliver fully informative prices, what can then be said regarding other markets, where illiquidity, information asymmetry and search costs are more relevant? Moreover, since these results are found for quarterly data, the implications for business-cycle accounting could be important. In several developing countries commodity markets such as corn have a signi...cant impact on overall GDP and exports. If the prices that are being used for measurement are not market-clearing ones, important distortions could be taking place. Also, since annual prices do not di¤er from fundamentals as much as quarterly ones do, another potential question arises: Should we produce national accounts using prices measured at quarterly, annual, or some other frequency? How much time do markets need to become fully informative? Many business-cycle theorists believe that shocks on the real side of the economy, such as shocks to TFP and commodity prices, trigger and propagate economic ‡uctuations. Commodity price shocks clearly play important roles in developing nations today, particularly resource exporters and have also played a large role in economic ‡uctuations in the past. Economists and policy makers believed, for example, that the commodity-price declines in the late 1920s and early 1930s contributed to the length and severity of the Great Depression. In 1933, the Roosevelt administration’s e¤orts to raise commodity prices, particularly prices of farm products like corn, formed the centerpiece of its e¤orts to resurrect the economy. These results have implications for the sources of shocks and the accuracy of the interpretation of the shocks derived from RBC models. Given that productivity is a key driver of the cycle, a price system that does not track fundamentals closely could result in ine¢ cient resource allocations, or in other words, aggregate productivity losses. Therefore the implications of these ...ndings are relevant regarding short-term business cycles. In the short run (at business-cycle frequencies), the majority of commodity price shocks do not re‡ect supply and demand, but could instead be a¤ected by speculative factors. These distortions could have a signi...cant impact on commodity producing economies.

This paper is divided into four sections. The ...rst section introduces and explains the main 5
# **VI.2 Stage 1 – Exporter-, Importer- and Year-Fixed Effects**

||**TELECOMMS**<br>**EQUIPMENT**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1990)**|**MEDICAL**<br>**DEVICES**<br>**(HR**<br>**1993)**|
|---|---|---|---|
|**Dependent variable**|**prtelcoexp**|**prmedexp**|**prmedexp**|
|**lngdprep**|0.9707997***<br>(0.0214004)|0.85233***<br>(0.015611)|0.8510174***<br>(0.0155708)|
|**lngdppcrep**|0.3637227***<br>(0.0328771)|0.4189913***<br>(0.0201369)|0.418886***<br>(0.0200892)|
|**lngdppar**|0.4336976***<br>(0.0190137)|0.3779301***<br>(0.0142132)|0.3759521***<br>(0.0141995)|
|**lngdppcpar**|0.1673107***<br>(0.0195422)|0.003295<br>(0.0153621)|0.0082063<br>(0.015261)|
|**lndistwces**|-0.4456965***<br>(0.0251412)|-0.4206721***<br>(0.0174487)|-0.4212635***<br>(0.0174051)|
|**comlang_off**|0.5900403***<br>(0.0479839)|0.4923552***<br>(0.0364522)|0.4892066***<br>(0.0364333)|
|**contig**|-0.3267596***<br>(0.1189587)|-0.6160077***<br>(0.0801713)|-0.6177519***<br>(0.0802193)|
|**colony**|0.6496437***<br>(0.1271608)|0.3966678***<br>(0.0721545)|0.3965382***<br>(0.0720975)|
|**lleither**|0.0882706<br>(0.0666988)|0.080331<br>(0.0505465)|0.0784831<br>(0.0504791)|
|**isleither**|-0.2863373***<br>(0.0539094)|-0.41289611***<br>(0.0410639)|-0.4158109***<br>(0.0410465)|
|**WTO**|0.3293407***<br>(0.0685001)|0.3066158***<br>(0.0401811)|0.3098297***<br>(0.0401675)|
|**FTA**|0.2173083**<br>(0.0938367)|0.4858283***<br>(0.0740086)|0.4767499***<br>(0.0737233)|
|**MRA(****_sector_) **|0.5364777***<br>(0.102508)|0.5362539***<br>(0.1408981)|0.4731507***<br>(0.1301937)|
|**HR(****_sector_) **|-0.6853898*<br>(0.3878362)|-0.1942201<br>(0.1761689)|-0.1373173<br>(0.1936103)|
|**HR(****_sector_)oecd_sgp**|0.213935*<br>(0.1255157)|0.2862628***<br>(0.0640037)|0.3588753***<br>(0.0730391)|
|**HR(****_sector_)_dev**|-0.0360732<br>(0.0655061)|0.0069602<br>(0.0447864)|-0.0478884<br>(0.045776)|
|**No. of observations**|29,315|40,608|40,608|

Robust standard errors (clustering by country pair) are in brackets

*** significant at the 1% level

** significant at the 5% level

* significant at the 10% level

38
|Nigeria|KdSWB|0.61|0.60|0.64|0.71|0<sup>.6b</sup>|0.7b|0.9b|o.gb|
|---|---|---|---|---|---|---|---|---|---|
|Nigeria|KtSVTB|0.47|0.44|0.45|0.48|0.4b|0.4b|0.4b|**0 **<sup>**.**</sup>**5**<sup>**b**</sup>|
|Nigeria|BOSWB|0.48|0.47|0.48|0.41|**0.5b**|0<sup>.</sup>5<sup>b</sup>|0.8b|0.8b|
|Nigeria|EdSWB|0.39|0.39|0.33|0.35|0.8b|0.9b||0.gb<br>o.9b|
|Senegal|SDE|0.41|0.40|0.41|0.41|0.70b|06b|_o.5b_|0.4b|
|South Africa|TMGENI|0.97a|0.98g|**1.04a**|1.088|3.1|3.0|3.1|3.3|
|SouthAfrica|RAND-W|0.96a|**097T**|1.038|1.09a|2.4|2.3|2.3|2.5|
|Togo|RNET|0.31|0.30|0.30|0.33|1.1|1.1|_1.5_|_1.5_|
|Tunisia|SONEDE|0.88a|0.868|0.86a|1.Ola|1.7|1.8|1.8|1.9|
|Uganda|NWSC|0.76|0.77|0.78|0.79|1.2|1.2|1.0|1.3|
|Zambia|LMSC|**1.01a**|_1.05_a|_1.058_|1.068|1.5|1.4|1.3|1.2|

# **5.3 Do Institutional factors such a ownership and governance matter?**

Recent studies have shown that institutional factors at the discretion of the management as well as environmental factors beyond the control of managers or regulators affect water efficiency (e.g, see Ferrier and Valdmanis 1996, Valdamnis 1992, Ozcan and Luke 1993, Rosko et al. 1995). Some of the factors that influence the efficiency of water utilities cited in the literature are: corruption (various indices), governance (various indices) etc. This can be tested from the results obtained here.

> The efficiency scores of water utilities are examined using a _censored tobit model_ to

> identify factors influencing inefficiency. The environmental variables are excluded as their numbers are not sufficiently large to undertake a multivariate analysis.

> In the tobit model, for computational convenience it is preferred to assume a censoring point at zero (e.g, see Greene, 1993).

Formally, the tobit model is defined as follows: Y= ii+ (i Y = _Y; f Y'_ > 0 (12) Y= =Oif _y;_ <O where ij _-_ N(O, a2), and yj is the observed inefficiency score; _15_
**Table S3.2: Inequality decomposition, per adult equivalent consumption**

||Theil<br>within|Theil<br>between|Share within|
|---|---|---|---|
|_Scale A_||||
|Total consumption|0.06|0.39|12.30|
||(0.01)|(0.03)|(1.36)|
|Non-food consumption|0.15<br>(0.01)|0.79<br>(0.05)|15.89<br>(1.69)|
|Food consumption|0.01|0.24|5.66|
||(0.00)|(0.01)|(0.67)|
|_Scale B_||||
|Total consumption|0.05|0.38|11.99|
||(0.01)|(0.03)|(1.34)|
|Non-food consumption|0.14|0.77|15.67|
||(0.01)|(0.05)|(1.67)|
|Food consumption|0.01|0.24|5.39|
||(0.00)|(0.01)|(0.63)|
|_Scale C_||||
|Total consumption|0.06|0.40|12.14|
||(0.01)|(0.03)|(1.32)|
|Non-food consumption|0.15|0.79|15.89|
||(0.01)|(0.05)|(1.69)|
|Food consumption|0.01|0.26|5.50|
||(0.00)|(0.01)|(0.65)|
|_Scale A – without education_<br>_expenditure_||||
|Total consumption|0.06<br>(0.01)|0.39<br>(0.03)|12.97<br>(1.37)|
|Non-food consumption|0.16|0.80|16.97|
||(0.02)|(0.05)|(1.64)|

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors’ calculations.

Note: Sensitivity of the inequality decomposition to the choice of equivalence scale. Each panel presents a decomposition using individual consumption measured by the per adult equivalent cell consumption, with the specified equivalence scale. N=1763 households and 4293 cells. Bootstrap standard errors (250 replications) between parentheses. _Scale A:_ adult: 1; child 0 to 14: 0.5; _Scale B:_ adult: 1: child 5 to 14: 0.5; child 0 to 4: 0.2; _Scale C_ : male adult: 1; female adult: 0.83; boy 5 to 14: 0.77; girl 5 to 14: 0.71; child 0 to 4: 0.52 for food consumption; scale A weights for non-food consumption.

**Table S3.3: Sensitivity of FGT indices to equivalence scales**

||_Nut_<br>|_rition povert_<br>|_y line_<br>|_B_<br>|_asic needs poverty_<br>|_line_<br>|
|---|---|---|---|---|---|---|
|Scale|Headcount|Poverty<br>gap|Squared<br>poverty gap|Headcount|Poverty gap|Squared<br>poverty gap|
|_Household_<br>_consumptio_|_per adult equiva_<br>_n_|_lent_|||||
|Scale A|0.167|0.042|0.016|0.428|0.143|0.064|
|Scale B|0.145|0.035|0.012|0.390|0.125|0.054|
|Scale C|0.183|0.046|0.017|0.435|0.148|0.068|
|_Cell_<br>|_per adult equiva_<br>_consumption_<br>|_lent_<br>|||||
|Scale A|0.182|0.051|0.020|0.463|0.160|0.075|
|Scale B|0.159|0.043|0.016|0.430|0.140|0.063|
|Scale C|0.196|0.055|0.022|0.469|0.167|0.079|

Source: « Pauvreté et Structure Familiale » (PSF) survey, authors’ calculations. Note: Sensitivity of the poverty indices to the choice of equivalence scale. First panel presents poverty measures using per adult equivalent household consumption, with 3 different equivalence scales. Second panel does the same with per adult equivalent cell consumption. N=1763 households and 4293 cells. Bootstrap standard errors (250 replications) between parentheses. _Scale A:_ adult: 1; child 0 to 14: 0.5; _Scale B:_ adult: 1: child 5 to 14: 0.5; child 0 to 4: 0.2; _Scale C_ : male adult: 1; female adult: 0.83; boy 5 to 14: 0.77; girl 5 to 14: 0.71; child 0 to 4: 0.52 for food consumption; scale A weights for non-food consumption.

18
# Oil Price Volatility, Economic Growth and the Hedging Role of Renewable Energy

## Jun E. Rentschler<sup>1,2</sup>

Keywords: Oil Price Volatility, Economic Growth, Renewable Energy, Risk Management JEL Classification: C32, C51, Q42, Q43 Sector Board: Energy and Mining (EM)

> 1 The World Bank, Sustainable Development Network, Office of the Chief Economist, Washington D.C., USA

> 2 University College London, Dept. of Economics, Energy Institute, 30 Gordon Street, London, WC1H 0AX, UK The author would like to thank John Besant-Jones, Marianne Fay, Stéphane Hallegatte, Malcolm Pemberton, Ingo Rentschler and Janna Tenzing for useful comments on an earlier version of this paper. Remaining errors are the author’s.
**Table 2:** _List of Countries in the Sample and Information on DHS Data_

|HIPCs|DHS|Non-HIPCs|DHS|
|---|---|---|---|
|Benin|2011-2012|Albania|2008-2009|
|Bolivia|2008|Angola|2011*|
|Burkina Faso|2010|Armenia|2010|
|Burundi|2010|Azerbaijan|2006|
|Cameroon|2011|Bangladesh|2011|
|Chad|2004|Cambodia|2010|
|Comoros|2012|Colombia|2010|
|Democratic Republic of Congo|2013-2014|Dominican Republic|2013|
|Republic of Congo|2011-2012|Gabon|2012|
|Cˆote d’Ivoire|2011-2012|Kenya|2008-2009|
|Ethiopia|2011|Kyrgyz Republic|2012|
|The Gambia|2013|Laos|2011-2012<sup>†</sup>|
|Ghana|2014|Lesotho|2009|
|Guinea|2012|Maldives|2009|
|Guyana|2009|Moldova|2005|
|Haiti|2012|Namibia|2013|
|Honduras|2011-2012|Nepal|2011|
|Liberia|2013|Nigeria|2013|
|Madagascar|2008-2009|Pakistan|2012-2013|
|Malawi|2010|Peru|2012<sup>#</sup>|
|Mali|2012-2013|Swaziland|2006-2007|
|Mozambique|2011|Tajikistan|2012|
|Niger|2012|Timor-Leste|2009-2010|
|Rwanda|2010|Yemen|2013|
|S ̃ao Tom ́e and Pr ́ıncipe|2008-2009|Zimbabwe|2010-2011|
|<br>Senegal|2014<sup>#</sup>|||
|Sierra Leone|2013|||
|Tanzania|2010|||
|Togo|2013-2014|||
|Uganda|2011|||
|Zambia|2013-2014|||

*Malaria Indicator Survey (MIS) from the DHS Program #Continuous DHS †Joint Unicef Multiple Indicator Cluster Survey (MICS) and DHS. Published by UNICEF (2015).

26