rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 12. Methodological quality of the studies on female sex workers. Andean Region; 2009**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>Limited<br>50%<br>Intermediate<br>40%<br>High<br>30%<br>20%<br>10%<br>0%<br>Peru n= 11 Ecuador n= 11 Bolivia n= 3<br><!-- End of picture text -->

**Source:** Authors’ calculation using information from _Database and References for an Evidence-based Strategy_ in Peru, Ecuador and Bolivia.

**Findings:** A comparison of Figure 11 and Figure 12 shows that the quality breakdown is similar in each country.  The proportion of both MSM and female sex worker studies with high methodological quality accounted for approximately 75% of the total in Peru, 50% in Ecuador and 25% in Bolivia.  When compared to the average of all studies, the studies of female sex workers in Bolivia were few in number, and weak from a methodological point of view.

# **Prisoners**

In **Peru’s** database, there were four studies done on prisoners. Three of them had high methodological quality; and one had limited quality. In **Ecuador,** there were six studies done on prisoners. Three of them had high methodological quality; two had intermediate quality; and one had limited quality. In **Bolivia,** there was only one study done on prisoners. It had high methodological quality (Figure 13).

In Figure 13, the data was represented in absolute values, because of the small number of studies analyzed.

28
Finally, buyer firms may foster productivity in their suppliers through increased training of the workforce, quality control, inventory management, technical assistance, or product development (see e.g., Amiti and Cameron, 2007; Javorcik, 2004; Blalock and Gertler, 2007). We estimate a variant of Eq. (2) where the diversity index given by Eq. (3) is calculated separately for suppliers and non-suppliers (column (1)) or for buyers and nonbuyers (column (2)). We identify suppliers and buyers based on the 1986 Chilean inputoutput matrix.<sup>19</sup> The estimates in Table 8 show significantly negative MAR externalities and no effects of competition nor average firm size. Regarding diversity, columns (1) and (2) show that the diversity of non-suppliers and the diversity of non-buyers are the most important types of sectoral diversity for TFP growth. These findings go against our priors from the agglomeration literature and the findings in Amiti and Cameron (2007). While our input-output linkages measures are imperfect - because they are based on a national input-output table rather than regional input-output tables - our findings suggest that the sectoral diversity unrelated to supplier and clients is what matters most for TFP growth. Such agglomeration externalities could happen through the exchange of ideas and workers through labor pooling, knowledge spillovers, or the availability of financing.

Evaluating the impact of agglomeration indices in 1992 on long-run TFP growth between 1992 and 2004 across Chilean sector- _comuna_ cells deals away with reverse causality problems, but the 1992-2004 period may be too long to identify certain types of

> 19 The implicit assumption is that input-output relations hold across locations. For any 3-digit sector _j_ we calculate the share that each 3-digit sector represents in its intermediate input usage, we rank those shares in ascending order and compute the cumulative sum of shares. „Suppliers‟ are the 3-digit sectors whose cumulative sum of shares in total intermediate inputs is closest (from above) to 90%. „Non-suppliers‟ are all other 3-digit sectors. Similarly, for any 3-digit sector j we calculate the share that each 3-digit sector represents in its sales, we rank those shares in ascending order and compute a cumulative sum of shares. „Buyers‟ are the 3-digit sectors whose cumulative sum of shares in output sales is closest (from above) to 90%. „Non-buyers‟ are all other 3-digit sectors. For any given sector _j_ , sector _j_ itself is always included as part of the set of supplier sectors as well as of the set of buyer sectors. However sector _j_ is excluded by definition from the calculation of the diversity index.

18
**Figure 1. Components of "Poverty-Reducing Spending"**

(As defined by HIPCs that Have Reached the Decision Point) <!-- Start of picture text --> Number of countries<br>0 5 10 15 20 25 30<br>Education<br>Health<br>Agriculture<br>Safety Nets<br>Infrastructure<br>Rural Development<br>Governance<br>Other<br><!-- End of picture text --> Source: IMF and World Bank, 2003, "Heavily Indebted Poor Countries (HIPC) Initiative: Status of Implementation" (Table 5).

A recent review of the Enhanced HIPC Initiative conducted by the World Bank’s Operation Evaluation Department (OED) confirms that the Initiative has indeed earmarked debt relief “savings” for poverty reduction, which has in turn been translated into substantial allocations of debt relief resources for social expenditures (World Bank 2003b). In particular, the increased resources provided by debt relief have been disproportionately targeted for allocation to the social sectors, for a sample of HIPC countries for which data were available (Figure 2). Of the total amount of resources, some 49 percent were allocated to the education and health sectors (World Bank, 2003b).

Academic studies, perhaps following this trend, have also used social spending or spending on the education and health sectors as a proxy for pro-poor spending (See Appendix I). Using this definition, studies have tried to estimate the statistical relationship between aid flows and pro-poor budgets (e.g., Gomanee, Girma, and Morrissey 2003), or to determine whether decentralization leads to higher pro-poor spending (e.g., Schneider 2003), or to simply assess a country’s fiscal stance (e.g., Mooji and Dev 2004).

**Figure 2. The Projected Allocation of HIPC Resources**

<!-- Start of picture text --> Structural reforms<br>2% Other<br>8%<br>Governance<br>4%<br>Infrastructure<br>8%<br>Rural Development<br>13%<br>Social sectors<br>65%<br><!-- End of picture text --> World Bank, 2003, The Heavily Indebted Poor Countries (HIPC) What accounts for the unusual consensus regarding social sector spending as key to poverty reduction? The OED study suggests that these conditions are a result of the demand by the advocacy NGOs, who have equated poverty reduction with social expenditures. Other proponents argue that the donor bias toward direct spending on the 2
<mark>WPS3536</mark>

# **REGULATORY EFFECTIVENESS: THE IMPACT OF REGULATION AND REGULATORY GOVERNANCE ARRANGEMENTS ON ELECTRICITY INDUSTRY OUTCOMES**

# **By**

**Jon Stern (London Business School)**

# **and**

**John Cubbin (City University)**

## **ABSTRACT**

The paper reviews a number of studies of the effectiveness of utility regulatory agency and governance arrangements for the electricity industry, particularly for developing countries. It discusses governance criteria and their measurement, both legal frameworks and surveys of regulatory practice. It also discusses the results from econometric studies of effectiveness for regulatory agencies in the electricity and telecommunications industries and compares these with the results from econometric studies of independent central banks and their governance. The paper concludes with a discussion of policy implications and also of priorities for information collection to improve understanding of these issues.

World Bank Policy Research Working Paper 3536, March 2005 _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._ 1
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approximately 4,000 middle schools for girls in stipend and non-stipend districts. These are the schools that could be eligible for the program at both baseline and follow-up.<sup>12</sup> In order to extract contextual information and construct covariates, this analysis uses several household surveys. The household surveys include the Pakistan Social and Living Standard Measurement survey (PSLM) collected in 2004-05, the Punjab Multiple Cluster Survey (MICS) collected in 2003, and the Pakistan Integrated Household Survey (PIHS) collected in 2001. They use multi-topic questionnaires focusing on a comprehensive set of social and economic indicators as well as information on infrastructure supply and quality, thus allowing for construction of some covariates at the district level. These surveys are representative at the district level and disaggregated by urban and rural areas.<sup>13</sup> Other sources include educational censuses and administrative data that provide information on the supply of educational services and other programs. This evaluation uses the National Education Census collected in 2005 on both public and private schools to construct additional covariates. It also relies on the administrative data of other elements of the educational reform implemented in Punjab at the same time as the FSSP, such as distribution of free textbooks, recruitment of new teachers, construction of school facilities, and improvement of school councils.

The main data source for the estimation of impacts at the individual level is two waves of the MICS collected in 2003 (pre-program) and 2007-08 (post-program). This household survey covers a wide range of social and economic indicators, all repeated in both rounds for comparability. The sampling design follows the sampling frame of the previous population census (1998) and provides estimates for the province, for urban and rural areas, and for each of the districts in Punjab and the Cantonment of Lahore. Sampling for 2003 was stratified by rural, urban, and large city areas. In the follow-up survey in 2007-08 the sample size was vastly

> 12 The sample of eligible schools includes only girls’ schools that offered grades 6-8 at both baseline and follow-up (less than 1 percent of middle schools are mixed). Schools that were nonfunctional upgraded to middle level or above or downgraded to primary or lower level during the program implementation period were excluded, as these would not have been eligible for the stipend at both baseline and follow-up. Of the remaining schools, only those schools that had non-zero enrollment in grades 6-8 at both baseline and follow-up were retained. Finally, although in theory all public schools in Punjab are single-gender, the data shows that a small number of mixed schools exist. These schools were also excluded from the final sample.

> 13 It is important to note that, although lower-level data provide more precise estimates, the questionnaires are significantly shorter; thus, some key economic welfare indicators are excluded.

9
# Steered Away from the Fields: Short-Term Impacts of Oxen on Agricultural Production and Intra-Household Labor Supply

Andrew Brudevold-Newman, Aletheia Donald and L ́ea Rouanet<sup>*</sup> _JEL Codes_ : O12, O13, J22, Q16, D13, J16 _Keywords_ : mechanization, productivity, gender, labor, social norms

> *Brudevold-Newman: World Bank, abrudevoldnewman@worldbank.org; Donald: World Bank, adonald@worldbank.org; Rouanet: World Bank, lrouanet@worldbank.org. This paper greatly benefited from comments by Kathleen Beegle and seminar participants at CSAE, PacDev, the NOVAFRICA Conference on Economic Development, and the International Conference on Development Economics. Ezechiel Djallo, Dede Houeto and Lacina Traore provided excellent research assistance. This paper is a product of the Gender Innovation Lab, within the Office of the Africa Region Chief Economist. We gratefully acknowledge financial support from the World Bank’s Umbrella Facility for Gender Equality (UFGE). The UFGE is a multi-donor trust fund administered by the World Bank to advance gender equality and women’s empowerment through experimentation and knowledge creation to help governments and the private sector focus policy and programs on scalable solutions with sustainable outcomes. The UFGE is supported with generous contributions from Australia, Canada, Denmark, Finland, Germany, Iceland, Ireland, Latvia, the Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States, the Bill and Melinda Gates Foundation, and the Wellspring Philanthropic Fund. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank Group, its Board of Executive Directors, or the governments they represent. AEA RCT Registry ID: AEARCTR-0009714. All errors and omissions are our own.
**3**

legal origin of each country as an instrumental variable to extract the exogenous component of banking sector development. Legal scholars note that many countries can be divided into countries with English, French, German, or Scandinavian legal origins and those countries typically obtained their legal systems through occupation or colonization. Thus, we take legal origin as exogenous. Moreover, LaPorta, Lopez-de-Silanes, Shleifer, and Vishny **(1997, 1998;** henceforth LLSV) show that legal origin substantively affected (a) laws concerning bank credits, **(b)** systems for enforcing bank contracts, and (c) standards for corporate information disclosure. Each of these features of the contracting environment helps explain cross-country differences in banking sector development [Levine, Loayza, and Beck **1998;** Levine **1998, 1999].** Thus, after extending the LLSV data on legal origin from 49 to **63** countries, we use the legal origin variables as instruments for banking sector development to assess the effect of banking development on economic growth, capital growth, productivity growth, and private saving rates.

The second econometric technique that we use to control for simultaneity bias also eliminates any omitted variable bias induced **by** country-specific effects. We use a panel dataset, with data averaged over each of the seven 5-year periods between **1960** and **1995.** We use a GeneralizedMethod-of-Moments (GMM) estimator proposed **by** Arellano and Bover **_(1995)_** and Blundell and Bond **(1997)** to extract consistent and efficient estimates of the impact of banking sector development on growth and the sources of growth. Specifically, dynamic panel procedures typically take first differences of the observations in _levels_ to eliminate country-specific effects [Arellano and Bond **1991;** Holtz-Eakin, Newy, and Rosen **1990].** Then, lagged values of the regressors from the _levels_ regression are used as instruments to eliminate inconsistency arising from simultaneity bias. This _diference_ dynamic-panel estimator, however, frequently suffers from weak instruments, which produces biases in

> **3** Also, see the time-series studies **by** Rousseau and Wachtel **(1988)** and Neusser and Kugler **(1998).**
and .2 4 respectivelv In the second column of Table 3 and Table 4, the impact of defaults in the 19Qs are Presented. The sample is restricted<sup>onlv to those countries</sup> that were sovereign borrowers in the period.<sup>The dummy variable D30 is</sup> estimated to have a positive and statisticallv<sup>significant effect on the</sup> spread (the coefficient of this dummy variable is<sup>.11, and .10 in</sup><sup>**the two**</sup> alternative specifications with respective t-values of 4.9, and<sup>2.1).</sup> In Table 5, a summary of estimated parameter and<sup>'t'</sup> **values for the** remaining dummy variables that were described in tables **1** and<sup>2 are</sup> presented. The model presented for this estimation<sup>is the</sup><sup>**one of Table**3.</sup> The results indicate first, that the effect of defaults<sup>prior to 1930s were</sup> not statisticaily significant. Second, the repayment<sup>problems in the recent</sup> history (post-war -pre 1968 period) are found to<sup>be important. Finally,</sup> even when we control for the more recent repayment<sup>problems of the post war</sup> era, dummy variables defined to incorporate information<sup>on the repayment</sup> problems of the 1930s are still found to be<sup>significant, though the</sup> parameter estimate is reduced. (This is evident<sup>from a specification which</sup> contains both OR60 and D3 0 19 dummy variables.) In order to investigate the robustness of our results we<sup>first</sup> conducted influence diagnostics suggested<sup>by Krasker Kuh and Welch (1983),</sup> so as to ensure that our results are not driven by<sup>a few influential</sup> observations. We find that the results<sup>are not a consequence of</sup> disproportionately influential results.<sup>Second, we have employed</sup> specifications in which the IMF standby agreement<sup>variable is not</sup> incorporated, 'F' tests support the specifications<sup>that incorporate standby</sup> agreement variable. As would be expected, specifications<sup>that exclude the</sup> standbv agreement variable yield a higher parameter for<sup>credit history dummy</sup>
# **1. Introduction**

Nepal depends on imports for its fossil fuel supplies. This implies that almost all fuel-based CO2 emissions and most of the local air pollutants are caused by imported fuels. Coal, the most carbonintensive fuel, currently accounts for 36% of the total national CO2 emissions from fossil fuel combustion.<sup>3</sup> Brick making is an energy-intensive process due to the high-temperature heat requirement and coal is the most common fuel used for heat in Nepal. The brick industry alone consumes more than one-third of the total coal supply in Nepal (ICIMOD, 2019a), and this industry accounts for 27% of the total national CO2 emissions from coal consumption (Sadavarte et al., 2019). Several cities in Nepal are suffering from local air pollution as the pollution levels are many times higher than standards set by the World Health Organization (WHO). The capital city Kathmandu was the world’s most polluted city with its Air Quality Index (AQI) 265 at 11:06 am on April 10, 2024. The fine particulate matter pollution (PM2.5) level was 34 times the WHO’s annual air quality guideline value at that time.<sup>4</sup> Brick kilns are one of the sources of PM2.5 emissions in the city. The 2023 average PM2.5 concentration in Nepal was 8.5 times the WHO annual air quality guideline value.<sup>5</sup> Nepal is experiencing rapid urbanization and post-earthquake reconstruction of buildings with rising demand for bricks. For example, between the last two census years (2011 and 2021), the number of housing units built with brick walls increased by 21% (CBS, 2014; NSO, 2023). If the current practice of brick manufacturing continues, CO2 emissions from the brick industry will increase, thereby challenging Nepal’s ability to meet its nationally determined contribution (NDC) by 2030<sup>6</sup> and net-zero emission target by 2045. Moreover, local air pollution from the brick industry, particularly PM2.5, causes major health problems. It is estimated that ambient PM2.5 caused 11,619 deaths in 2015, and around one-fifth of the total PM2.5 in the country is emitted from brick kilns (World Bank, 2019).

> 3 What are the main sources of CO2 emissions in Nepal? https://www.iea.org/countries/nepal/emissions#what-are-the-mainsources-of-co2-emissions-in-nepal

> 4 “Kathmandu world’s most polluted city, again”. Kathmandu Post, April 10, 2024. <u>https://kathmandupost.com/climate-environment/2024/04/10/kathmandu-world-s-most-polluted-city-again.</u>

> 5 <u>https://www.iqair.com/nepal.</u>

> 6 Nepal has not specified an emission reduction target under the NDC, instead it has specified activities to be undertaken. These include: (i) expanding clean power (mainly hydro) capacity from current 3,000 MW to 15,000 MW; (ii) 90% of all passenger vehicles sold to be electric; (iii) 25% of households to switch to electric cooking; and (iv) maintaining current 45% of the total area of the country covered by forest (MOFE, 2021).

2
> - 33 -

# **Table 1: TESTING** FOR UNIT ROOTS: DICKEY-FULLER (DF), AUGMENTED DICKEY-FULLER (ADF) AND COINTEGRATING REGRESSION DURBIN-WATSON TESTS (CRDW)

|VARIABLE|OF<br>H10:I(1)|DF<br>HO0:I(2)|AOF<br>HO0:I1()|ADF<br>HO:I(2)|DF W/ C<br>110:I(1)|DF W/ C<br>H10:I(2)|ADF WI C<br>H10:I(1)|
|---|---|---|---|---|---|---|---|
|PRIVr|2.85|-3.48|1.88|-2.81|-0.75|-4.30|-0.46|
|GDP|3.39|-3.54|2.32|-3.13|0.13|-4.81|0.82|
|RI|-3.83|-6.99|-3.51|-6.49|-3.75|-6.85|-3.51|
|R/W|0.31|-5.87|0.31|-5.30|-1.61|-6.18|-1.47|
|MARKUP|-0.69|-2.53|-0.80|-1.99|0.53|-2.95|0.77|
|ICOSTS|-0.79|-4.67|-0.78|-3.77|-0.96|-4.58|-0.85|
|**PVCRD**|**2.74**|-4.20|1.51|-3.54|.1.22|-5.21|0.88|
|BYCRDY|-1.01|-5.34|-1.02|-4.79|-0.02|-5.48|0.16|
|GVIINF|0.19|-3.36|0.53|-2.92|-1.04|-3.26|-2.41|
|CRITICAL<br>VALUES|t-2.61||||t.3.20|||
||ADF W/ C D<br>H0:1(2)|F W/ CST D<br>H0:I(1)|F W/ C&T<br>H0:I(2)|ADF w/ C&TAD<br>H0:I(1)|F<br>W/ C&T<br>H0:I(2)|CRDW<br>HO:I(1)|CROW<br>HO:I(2)|
|PRIVI|-3.30|-1.75|-4.21|-1.74|-3.21|0.04|1.76|
|GDP|-4.44|-1.55|-4.79|-1.86|-4.89|0.05|2.00|
|R|-6.30|-4.00|-6.69|-3.46|-6.15|1.46|2.68|
|R/W|-S.29|-3.58|-5.91|-3.08|-5.39|0.49|2.59|
|MARKUP|-2.23|-1.32|-2.64|-1.34|-1.98|0.06|1.10|
|ICOSTS|-3.65|-0.60|-5.03|-0.44|-5.15|0.32|1.70|
|PVCRD|-4.47|-1.00|-6.24|-1.59|-5.46|0.06|2.16|
|PVCRDY|-5.01|-1.54|-6.11|-1.76|-5.42|0.22|2.25|
|GVIINF|-2.93|-0.80|-3.22|-0.71|-3.95|0.17|1.57|
|DBFGD|-S.84|-2.82|-8.04|-4.11|-5.67|0.33|2.87|
|GOVEX|-2.37|-1.58|-2.73|-2.54|-1.95|1.33|2.47|
|_GOVI_|-2.02|-0.88|-2.83|-1.20|-1.94|1.09|2.29|
|NrGVEX|-3.92|-1.44|-4.61|-0.74|-4.35|1.98|2.71|
|CRITICAL<br>VALUES||t-2.85||||CRDW-1.07||
|NOTE:_Wl_<br>C IS WIT|HADRIFT|TERM; W/|C.T IS WIT|II OOTIIAD|RIFT TERM|AND A TIM|E TREND.|