rafmacalaba/data-use-mentions-extended huggingface dataset:
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

social impact indicators under Table IX, suc h as welfare effects among income groups,<sup>2</sup> may be extremely time consuming to collect on a consistent basis.

As in some other areas of social and economic studies, data collection work can be initiated by relevant international organisations and can then be carried forward through co-operation with other international and national bodies. The International Energy Agency (IEA), the US Department of Energy’s Energy Information Administration (EIA), the Latin American Energy Organisation (OLADE) and the African Energy Policy Research Network (AFREPREN) provide a good starting point for international energy data. Due to the dynamic nature of electricity reform it is important that data is collected on a consistent, ongoing basis.

Not all data will be available for all countries in all years but this does not necessarily mean that the process cannot begin. Existence of a shared framework and co-ordinated effort is a first step and an incentive to join such an effort or to integrate them into the existing data collection programs. Costs and required resources are justified by the social and economic importance of the electricity sector. Electricity reforms can have a significant effect on many development goals (such as contained in the Millennium Development Goals<sup>3</sup> ) that are directly or indirectly dependent on developing sustainable energy systems in developing countries.

We tentatively suggest the following strategy for World Bank data collection in order to build a comprehensive resource base on electricity sector reforms:

- i. Agree a list of desirable indicators and countries,

- ii. Begin a search and summarise the main published (international) data resources,

- iii. Conduct a systematic search of internal World Bank resources e.g. by collecting latest country mission reports on energy sector,

- iv. Make inventory of missing data and countries,

> 2 Although welfare impacts would seem to be extremely important we do not highlight these as
education (see summary statistics in Table A15). Particularly, we estimate the following probit model for each quarter in the 2014q1‐2017q4 sample (see Table A16 for the full regression output): Pr�officially poor� �1��f��residence�, age�, occupational status�, household type�, education�� where _f_ is the cumulative distribution function of the standard normal distribution. Estimation of the equation for each period separately allows certain factors to affect the probability of being (officially) poor differently each period. For example, if the crisis affected the population in rural areas more, the rural residence status would have a greater impact on the risk of poverty in crisis periods. After the probability function is estimated, we define vulnerability as having a probability of being poor in excess of 10 percent.

_Figure 11: Official poverty and vulnerability, by quarter, 2014‐2017._ <!-- Start of picture text --> 35%<br>29.4% 29.2%<br>30%<br>25%<br>20% 16.1%<br>15% Official poverty<br>10% Vulnerability<br>5%<br>0%<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note: Cash income is measured in real 2014 roubles per capita and converted into an index with the 2014 level set to 100.

Source: Authors’ calculations based on quarterly HBS.

The average vulnerability level, as defined above, during the period 2014q1‐2017q4 is 21.7 percent (Figure 11). Unlike the official poverty measure, vulnerability grows substantially during the crisis. Vulnerability starts growing at the onset of the crisis from 16.1 percent in the beginning of 2014, and it almost doubles to the peak 29.4 percent in the fourth quarter of
characteristics of the neighborhood in which they locate.

# **1 Using Landscan Data and Defining Urbanized Areas**

## **1.1 Landscan data**

To analyze measures of economic density, we need fine spatial resolution data to calculate neighborhood effects and variation in clustering within a city. And we need the most accurate data available. For countries like the USA, both finely gridded population and employment data are available from censuses. However, in most developing countries that is not the case. Population data are only available at a coarse scale such as regional or local government political unit, and economic censuses in Sub-Saharan Africa are generally nonexistent. Even when they do exist (e.g. Uganda), they tend not to be publicly available.

Our primary data source is LandScan (2012) from Oak Ridge National Laboratory in the USA, which is now being used in some research (e.g. Desmet et al. (2018)). Oak Ridge takes population data from censuses and other sources worldwide on as fine a spatial scale for each country as they can obtain. They then create a measure of an ambient population for each 1km grid square on the planet, in a process we describe below. The ambient population is meant to represent where people are on average over the 24 hour day. To assess the ambient population, they appear to use nocturnal and diurnal population estimates for at least some areas of the globe, although these are not publicly available. Later in a ground-truthing exercise for Nairobi and Kampala, we will demonstrate our own interpretation of how nocturnal and diurnal populations might be estimated and combined.

For Landscan, as for WorldPop<sup>1</sup> , the Global Human Settlements Layer<sup>2</sup> , and similar data sets, a key element in this process involves taking population numbers at some upper level of spatial scale and allocating people to fine grid squares based on where they are likely to live and possibly work. The
**Table** **<u>C1: Estimated Elasticities of Substitution for Varieties in Tanzanian Imperfectly Competitive Goods Sectors</u>**

|**Sector in our Model**|**Matching HS‐3 Code from Broda et al estimates weighte**|**d elasticity of substitution**|
|---|---|---|
|Processedfood|110,150,151,160,170,180190,200,210|10|
|Beverages & tobacco products|220,240|2|
|Textile & leather products|510‐630, 650, 420|4|
|Manufacture of basic & industrial chemicals|280‐391|3|
|Manufacture of fertilizers & pesticides|310|2|
|Petroleum refineries|271|4|
|Glass & cement|680‐702|5|
|Iron steel & metal products|720‐831|7|
|Mining & quarrying|270, 271|4|
|Grain milling<br>Source: Authors calculations based on estim|110<br>ates from Broda, Greenfield and Weinstein (2006).|3|

# **Appendix D: Engineering Services in Tanzania - Restrictiveness Index**<sup>**31**</sup>

Table 1 below is a table taken from Nguyen-Hong (2000) for Vietnam in which the components of the engineering restrictiveness index as well as the scoring options are presented. The actual scores in table 1 are for Vietnam. The scores for Tanzania and the reasons for the scoring follow below the table. The Tanzania scores are based on the results of the World Bank Regulatory Survey in East Africa<sup>32</sup> and the World Bank Survey on Applied Policies in Services<sup>33</sup> .

# **Barriers to establishment**

# **Form of establishment Score 0.5**

All forms of legal entities (sole proprietorship,
ranked are roads and public works. WBI data show that the percentage of paved roads in the CIS-7 is rather high as compared to the average in other transitional economies with over 92% of roads paved although we do not have statistics on the general state and maintenance of roads.

# **Financial barriers**

_Sources of finance_ . According to enterprises interviewed in the BEEPS, among the sources of finance, banks contribute to a very small share of enterprises in the CIS-7 (only about 4.3% of enterprises reported to be financed by banks). This is a phenomenon that extends to all transitional economies but it is much more evident in CIS countries in general and in the CIS-7 in particular. As a main source of finance, firms use by far internal funds and retained earnings. In Armenia and Azerbaijan, a very large share is also constituted by finance from family and friends. In some countries, the state is also still a supplier of finance, particularly in Azerbaijan, Uzbekistan and Kyrgyzstan. State finance is on average a larger contribution in the CIS-7 than in the other transitional economies.

_Interest rates_ . Among the biggest obstacles to businesses in relation to banking, high interest rates is what most firms rank at the top in the BEEPS. In general, the problems faced by enterprises in relation to banking are the same across transitional economies but the CIS-7 stand out for having a larger proportion of enterprises declaring that there is a lack of access to long-term loans or lack of money to lend altogether as if these countries suffered from liquidity shortages. This shortage seems particularly acute in Azerbaijan and this may be associated with corruption of bank officials which is ranked as one of the major problem in this country. Real interest rates are higher in the CIS-7 than anywhere else in transition. In 2002, real interest rates were estimated at about

> 15 World Bank Indicators, see www.worldbank.org

19
716|5,034|4,682|6,072|2,932|225|487|4,793|4,923|3,305|3,230|3,181|
|N Treatment 2|8,980|4,621|4,359|5,475|2,767|363|375|4,467|4,513|3,068|2,930|2,982|
|_R2_|0.855|0.852|0.843|0.853|0.822|0.797|0.900|0.851|0.864|0.468|0.424|0.227|
|||||* p|< 0.05, ** p|< 0.01, *** p <|0.001||||||

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Administrative data, school year 2015/2016, own calculations.

45
Ghana, Kenya, Nigeria, South Africa and Tanzania. Among these countries, Nigeria stands out, with around three million workers engaged in formal manufacturing between 2010-2012 (Nigerian Manufacturing Sector Report 2014). However, very little systematic information exists about the manufacturing sector in Nigeria. To the best of our knowledge, no longitudinal census of manufacturing firms in Nigeria exists. There is a World Bank Enterprise Survey for the year 2014, but it only covers 19 of Nigeria’s 36 states and the total employment generated using weights is implausibly small.

This essay considers the prospects for growth of manufacturing in Africa. We begin with a description of broad trends, drawing heavily on the results in Diao et al. (2021). Three striking patterns emerge. First, there has been a rapid increase in the number of African manufacturing enterprises with fewer than 10 employees. We call these firms “small” and “informal” although some of these firms are likely to be formally registered; for the most part, we do not have enough information to make this distinction. In contrast, in Asian comparator countries, the share of employment in small manufacturing 2
# **C.2.4 Tigray**

Table C.4: Tigray – MTI Implementation

|||||**Fracti**<br>**MT Speakers**|**on of:**<br>**Region Speaking**|
|---|---|---|---|---|---|
|**Language**|**Year**|**Region**|**Grades**|**Living in Region**|**Language as MT**|
|Tigrigna|1991|Tigray|1-8|0.93|0.95|

_Source_ : Author’s analysis based on information from Boothe and Walker (1997) and Zenebe Gebre (2014). _Note_ : MTI = mother tongue instruction; MT = mother tongue.

The initial calculations of the maximum impact of the MTI reform for on time starters are similar to that of Oromia. In Tigray, the reform is implemented one year earlier due to the use of the Ge’ez script, but both provinces introduced a single language for eight years of primary school. The timing can also be seen in Appendix Table C.6.

The birth year specific MTI intensity measure for Tigray again precedes the timing of Oromia by one year, and is described by the following equations:
decide the level of investment in abatement in order to minimize the total cost of pollution control, which is a summation of levy and own expenditure in pollution control. Data on more than a thousand industrial firms identified as China national top water polluters in 1993 have been acquired from the China State Environmental Protection Administration (SEPA). These top polluters, mostly large and state-owned, have been closely monitored by SEPA and local environmental agencies. Accurate information on production outputs, energy use, investments, pollution abatement expenditures, pollution discharges, paid pollution levies, etc. were available for analyses. However, the information on subsidies for different firms was not available, preventing a rigorous modeling and simulation on subsidies. To incorporate subsidies into the model, we collected a regional-level data set on income, education, industrial development, etc.

The econometric results indicate the Chinese pollution charge-subsidy system have been effective in promoting investment in firm-level pollution abatement and have significantly contributed to the national pollution control effort. The two components both have affected the pollution abatement investment positively. The subsidy component has enhanced the total abatement investment level through the direct effect, while the charge component has significantly affected the total abatement investment either through the indirect effect by providing incentives for more own investment, or through the direct effect by providing environmental agencies with more funding for subsidies, or both.

Section II discusses the policy context in which the Chinese charge-subsidy system is introduced. Section III presents the modeling framework of a firm's investment effort in pollution abatement under a charge-subsidy system. Econometric results of the _5_
other words, we have the following relations: A competitive land market equilibrium determines the rental rate once the labor endowment is pinned down by local labor market clearing and migration conditions in (10) nd (11) above. Given the CRTS technology in agriculture, the land rental rate (denoted as _R_ ) is given by the marginal productivity of land in the aggregate production function:

## **Proposition 1**

_Assume that (i) in the initial equilibrium without land restrictions, at least one woman participates in the labor force, and (ii) a household loses the land if it migrates after the land restrictions. The imposition of land restrictions in a village would lead to a decline in the village wage rate, but a higher probability of women’s labor force participation._ In rest of the paper, we test the predictions from proposition 1 using data from and a policy experiment in Sri Lanka, and contrast them with the predictions from the collateral and insecure property rights mechanisms.

# 3. Data and Variables Definitions

The main data source for the estimation of the female labor force participation and wages is the Household Income and Expenditure Survey, 2002 (HIES, 2002) of Sir Lanka. We use the rural sub-sample of HIES 2002. The HIES 2002 collected information from a nationally representative sample of 16,924 households drawn from 1,913 primary sampling units. The survey covered 17 of Sri Lanka’s 25 districts, and 249 of its 322 Divisional Secretariat Divisions (DSDs).<sup>9</sup> From the 16,924 households in the survey, about 17,140 females are in the working age group (25 to

> 9Data collection in the North and Eastern provinces was not possible due to on-going civil conflicts at the time of survey field work.

11
**h. External Aid.** To the extent that countries do not have to incur the burden of taxation, they may not have the incentive to use resources in the most cost-effective way.   Another channel through which aid-financing may affect efficiency is through the volatility and unpredictability of its flows.  Given that this financing source is more volatile than other types of fiscal revenue (Bulir and Hamann, 2000), it is difficult to undertake mediumterm planning with in activities funded with aid resources. If this is the case, we would expect a negative association between aid-dependence and efficiency in those activities funded with aid, mostly health services. To our knowledge there are no previous attempts to establish a relationship between efficiency and the degree to which activities are financed by external aid.  There is, however, recent evidence of a negative association between donor financing and some health outcomes (Bokhari, Gottret, and Gai, 2005)

**i. Institutional Variables.** Countries with better institutions, more transparency, and less corruption are expected to have higher efficiency scores. Similarly, countries that have suffered wars or state failures are expected to register lower efficiency scores.  To capture these effects we included different indicators: the ICRG International Country Risk Indicators, the Worldwide Governance Research Indicators, in particular the Control of Corruption component (Kaufmann, et.al, 2002).  We also included a dummy variable if there had been some type of state failure, such as internal wars, from the State Failure Task force database.

The data on educational and health indicators are not available on a continuous annual basis for many countries. Thus, averages of the variables were computed over sub-periods both in the first stage calculation of efficiency score and in the second stage of regression analysis. Specifically, educational indicators are averaged over two periods (1975-80 and 1996-2002) and health indicators over two periods (1996-99 and 2000-02). This discrepancy in the sub-period construction is due exclusively to the lack of data for earlier years. The averages are treated
# **_VII.A. The estimated impact of management practices on profits and productivity_**

<u>Profits: Overall we estimate a total increase in profits of around $228,000, with our</u> calculations outlined in Table A5. We could not obtain accounting data on these firms’ profits and losses. Public accounts data are available only with a lag of 2-3 years at the firm level (rather than plant, which is what we would want), and in our interviews with firm owners they told us they under-report profits to avoid tax and also move profits to years when they want a loan (to have proof of income). When asked for their internal accounts the firms were evasive and would not provide them, beyond occasional comments that profits were in the range of $0.5m to $1m per year.<sup>25</sup> So we estimated the changes from the quality, inventory and efficiency improvements. Our methodology is simple: for example, if an improvement in practices is estimated to reduce inventory stock by X tons of yarn, we map this into profits using conservative estimates of the cost of carrying X tons of yarn. Or if it reduces the numbers of hours required to mend defects we estimated this reduction in hours on the firm’s total wage bill. These estimates are medium-run because, for example, it takes a few months for the firms to reduce their mending manpower.

These estimates for increases in profits are potentially biased. There is a downward bias because we take firms’ initial capital, labor and product range as given. But in the long run the firms can re-optimize, for example, with more machines per weaver if quality improves (as dealing with breakdowns is time consuming). Furthermore, many of the management practices are complementary, so they are much more effective when introduced jointly (e.g. Milgrom and Roberts, 1990). However, the intervention time-horizon was too short to change many of
country-specific city and settlement definitions, which means there is no consistency across countries. Second, although occasionally definitions are somewhat density based, most defintions are based on qualitative and subjective criteria including governance elements. Many countries define cities based on status in the political-spatial hierarchy, local political boundaries defined historically, or through an application or evaluation process to redefine rural areas as cities, which tends to under-represent newer fast-growing agglomerations due to delays both in application and evaluation as well as the granting of status.

We employ a consistent density based definition across our African countries, using LandScan (2012) population per grid square. We set population per grid square, or density thresholds to define cities and settlements. To do so, we apply a smoothing algorithm so that each own grid square is assigned the average density of neighbors within 7km. Smoothing is essential to avoid large doughnut holes in cities, due to terrain factors, airfields, parks, big open public spaces and the like. We define a core city as a set of contiguous grid squares all of which have a density greater than or equal to 1,500 per sq. km. and the population of these contiguous squares must sum to 50,000 or more. The area included in these contiguous squares over 1,500 per sq. km. defines the area and population of what we call the city core. We then add in a fringe to each city core, which includes all contiguous grid squares with population density over 500 per sq. km. The core combined with a fringe is called a city.

For smaller urbanized places that are stand-alone, we require a collection of contiguous grid squares all with (a smoothed) population density over 500 per sq. km., which collectively sum to 5,000 or more. We call these low density [LD] settlements. Full details of these urban definitions are given in Appendix B.

The process and impact of threshold decisions are illustrated in Figure 3 for Nairobi. Core city areas are in dark blue
million life years gained over 50 years and would have a significant impact among the poor. In contrast, Pichón-Riviere et al. (2014) estimate that tobacco use in Chile would reduce life expectancy by nearly 4.0 years among women and 4.3 years among men. For Russia, Maslennikova et al. (2013) estimate that, if taxes were raised to 70 percent of the retail price, along with other policies, such as banning smoking in public places, 3.7 million tobacco-related deaths would be averted, although this may be a low prediction because secondhand smoking effects were not taken into account. According to the WHO, secondhand smoke is responsible for over 890,000 premature deaths a year (WHO 2017a).

Tobacco taxation has been recognized as one of the most effective tobacco control strategies. In highincome countries, a 10 percent increase in the price of cigarettes is associated with a decrease in the demand for cigarettes of approximately 4 percent (World Bank 1999). In low- and middle-income countries, an equivalent increase is associated with an average 6 percent reduction in cigarette consumption (Chaloupka et al. 2000). Higher taxes have the additional benefits of reducing exposure to secondhand smoke and increasing government revenues. Maslennikova et al. (2013) estimate that a 10 percent rise in price would reduce cigarette consumption among people under 35 by 3 percent and among people over 35 by 1 percent. This may be because price elasticities for tobacco products are higher among young people. Lance et al. (2004) use data from three waves of the Russia Longitudinal Monitoring Survey–Higher School of Economics (RLMS-HSE) (1996, 1998, and 2000) to estimate the demand for cigarettes among men and find that the price elasticity of smoking participation was between –0.106 and –0.050. However, young people (ages 13–19) were found to be much more price sensitive; the total

> 4 <mark>‐ Randomly selected participants living in mid size towns in