metadata: authors: 0: author: Geginat,Carolin
1: author: Ramalho,Rita
count: World
docna: 0: docna: Electricity connections and firm performance
            in 183 countries
docty: Policy Research Working Paper
subtopic: Social Policy,Legal Products,Judicial System Reform,Industrial Management,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Energy Policies & Economics,Legal Reform,Employment and Unemployment,Legislation,Regulatory Regimes,Energy Demand,Energy and Environment,Energy and Mining,Business Cycles and Stabilization Policies
abstracts: cdata!: This paper presents new data on
            electricity connections for businesses in 183 economies. The
            data cover information on procedures, time, and cost that a
            small or medium size business with a moderate electricity
            need has to invest to obtain a new electricity connection.
            The study finds significant variation in the time and cost
            to obtain such an electricity connection across countries.
            In low-income countries, for instance, it takes on average
            nearly twice as long as in high-income countries to connect
            a new customer to electricity, while the cost associated
            with a comparable connection is 70 times higher. The study
            finds that the poor performance of distribution utilities in
            low-income countries cannot only be explained by differences
            in income levels. The overall level of bureaucracy appears
            to be another important factor. The study also finds the
            data to be correlated with existing measures of the
            effectiveness of the electricity sector, suggesting that the
            hurdles related to obtaining an electricity connection
            mirror other problems in the sector, such as the quality of
            electricity supply and the incidence of bribe payments.
            Finally, the study finds that electricity connections affect
            firm performance. Simpler and less costly electricity
            connection processes are associated with better firm
            performance, in particular in industries with high
            electricity needs, such as manufacturing motor vehicles.
repnme: repnme: Electricity connections and firm
            performance in 183 countries
display_title: Electricity connections and firm
            performance in 183 countries
listing_relative_url: /research/2015/10/25206356/electricity-connections-firm-performance-183-countries
topicv3: Manufacturing Sectors m1326521 1281,Energy and Extractives m1326595 507,Construction Law and Permits m1326997 972,Power and Electricity Sector m1327094 515,Enterprise Surveys m1504072 2762
publishtoextweb_dt: 2015-10-26T00:00:00Z
no_of_pages: 56
author_keys: 0: author_key: 624620
1: author_key: 683601
model_extractions: WPS7460 Policy Research Working Paper 7460 Electricity Connections and Firm Performance in 183 Countries _Carolin Geginat Rita Ramalho_ Development Economics Global Indicators Group October 2015
Policy Research Working Paper 7460

# **Abstract**

This paper presents new data on electricity connections for businesses in 183 economies. The data cover information on - procedures, time, and cost that a small or medium size busi ness with a moderate electricity need has to invest to obtain a new electricity connection. The study finds significant variation in the time and cost to obtain such an electricity connection across countries. In low-income countries, for instance, it takes on average nearly twice as long as in highincome countries to connect a new customer to electricity, while the cost associated with a comparable connection is 70 times higher. The study finds that the poor performance of distribution utilities in low-income countries cannot only be explained by differences in income levels. The overall level of bureaucracy appears to be another important factor. The study also finds the data to be correlated with existing measures of the effectiveness of the electricity sector, suggesting that the hurdles related to obtaining an electricity connection mirror other problems in the sector, such as the quality of electricity supply and the incidence of bribe payments. Finally, the study finds that electricity connections affect firm performance. Simpler and less costly electricity connection processes are associated with better firm performance, in particular in industries with high electricity needs, such as manufacturing motor vehicles.

This paper is a product of the Global Indicators Group, Development Economics. 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 cgeginat@worldbank.org and rramalho@ifc.org.

_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
# **Electricity Connections and Firm Performance in 183 Countries**

Carolin Geginat and Rita Ramalho<sup>1</sup>

## JEL classification: D22, F63, O43, L51, L94

Keywords: Business Environment, Electricity, Firm performance, Indicators, Institutions, Regulation 1 2121 Pennsylvania Avenue NW, Washington, DC 20433, cgeginat@worldbank.org, rramalho@worldbank.org. This paper would not have been possible without the extensive research of the team who collected the data for the new indicators on the process of obtaining an electricity connection. The team included Maya Choueiri, Caroline Frontigny, Nadia Ram, Jayashree Srinivasan, Susanne Szymanski and Marina Turlakova, who also provided excellent research assistance. The full data set for each country can be found at www.doingbusiness.org. We would like to thank -- without implication – Penelope Brook, Sylvia Solf, Jorge Luis Rodriguez Meza, David Francis and Murat Seker for helpful discussions.
# **I. Introduction**

Infrastructure services such as roads, water, electricity and telecommunications matter for private businesses. Where the quality and accessibility of infrastructure services are poor, companies’ productivity and growth suffer. World Bank Enterprise Surveys, conducted in 108 economies from 2006 to 2010, report that, globally, managers consider electricity the most serious obstacle to their business<sup>2</sup> . Moreover, poor electricity supply has been shown to have adverse effects on firms’ productivity and the investments they make in their productive capacity, thereby hindering the local economy (Calderon and Serven (2003), Dollar et al. (2005), Reinikka and Svensson (1999), Eiffert (2007)). For instance, Iimi (2008), using firm level data, estimates that in Eastern Europe and Central Asia eliminating existing electricity outages could lead to an increase of the gross domestic product by 0.5 to 6 percent.

Given the link between the quality of the electricity supply and firm performance, many enterprises in developing countries counter outages by relying on self-supply through a generator. This solution has been shown to be significantly more expensive than if the electricity is supplied by any utility (Foster and Steinbuks, 2009). This holds especially true for small firms for which the cost of self-supply can be prohibitively high (Lee et al. 1996)<sup>3</sup> .

<mark>This paper provides new data that help understand the challenges faced by utilities as they help firms connect to power. More specifically, we present data on the process of obtaining an electricity connection for distribution utilities in 183 economies, detailing the required associated procedures along with their time and cost. The new indicators follow a methodology similar to the one developed in the seminal paper by Djankov et al (2002) on the “Regulation of Entry” that was then expanded into several indicators within the World Bank’s Doing Business report to measure the efficiency of different public service providers. As La Porta et al (1999) and Djankov et al (2002) before us, we find that low-income countries are less efficient at providing public services, including in the particular case of electricity connections.</mark> This paper adds to the literature on enterprise performance and business regulation – in addition to complementing existing research on electricity regulation by providing new data and analysis on the service quality of distribution utilities. In recent years, numerous studies have tried to establish the effectiveness and determinants of success of different electricity sector reforms (see Jamasb et al. (2005) for an overview of the literature). One reform aspect that has received

> 2 According to World Bank Enterprise Survey data for 108 economies, 22% of managers consider electricity the most serious constraint to their business before competition from the informal sector (13%) and access to finance (12%) (see Table 15a).

> 3 One of the consequences of these prohibitively high costs of self-supply is that new firms in countries with weak infrastructure services are more likely to locate near the city center or in an old industrial area with easy access to good utilities and other essential services (Lee (1985,1989)).

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particular attention by researchers and policy makers is whether better sector performance for infrastructure services can be linked to the quality of regulatory institutions (Kirkpatrick et al. (2002), Cubbin and Stern (2006), Andres et al. (2008)). For example, Cubbin and Stern (2006) show how the existence of a regulatory law and high quality of regulatory governance are associated with higher per capita electricity generation in a sample of 28 developing countries. And Andres et al. (2008) construct an Electricity Regulatory Governance Index to illustrate how the existence and quality of regulatory agencies has an effect on the performance of utilities in Latin America. We add to this literature by proposing an indicator set that measures some aspects of electricity regulation as they apply to distribution utilities. Some studies (Estache and Rossi (2004), Gassner et al. (2009), Kumbhakara and Hjalmarsson (1998)) have found private electricity utilities to be more efficient than public utilities. Our data show that privately owned utilities do not necessarily deliver connections faster, but they do so at a lower cost than their publicly owned counterparts.

The new data presented here complement existing electricity data in important ways. Our data set significantly surpasses the country coverage for even the most broadly available indicators such as electrification rates, value lost due to electrical outages and electrical power transmission and distribution losses (country coverage for these indicators varies from 84 countries to 129 observations compared to 183 for our data set – see Table 6). Also, our data are updated annually, which is a higher frequency than for most other data sets. Unlike other data, the methodology also allows for important insights into the drivers of strong or weak utility performance. By measuring the different procedures involved in getting an electricity connection and the associated delays and costs, the indicators can help identify specific bottlenecks and cost drivers in individual connection processes. The data also offer insights into selected regulatory issues around connection processes by recording procedures. Lastly, our data set is the first of its kind to provide policy makers across countries with detailed comparable information on connection costs.

We examine the data set in view of three questions: (i) First, we examine if the new data can reasonably be used as a proxy for the overall performance of the electricity sector in different countries. To test this hypothesis we look at whether a more efficient connection process is associated with better electricity sector outcomes. In particular, we look at the relationship with a country’s electrification rate, losses in the electricity system, the value lost due to outages as reported by firms through the World Bank’s Enterprise Surveys, and the perceived quality of the electricity supply and the frequency of bribe payments to utilities as reported by the experts surveyed by the World Economic Forum. We find that one additional procedure in the process for obtaining an electricity connection is not only associated with a 13 percentage point decrease in the perceived quality of the electricity supply, but also an 18 percentage point increase in the frequency of bribe payments to utilities. Furthermore, we find that a 1 percentage point increase in connection costs (as a % of GNI per capita) is associated with an increase of 3.2 percentage points in the transmission and distribution losses as percentage of 3
output. We also find that an improvement of one position on the Getting Electricity index is associated with half a percentage point of sales lost by firms due to outages, and that a country’s income per capita has greater predictive power for its electrification rate than the efficiency of the electricity connection process.

(ii) Second, we examine if the overall bureaucratic environment in individual economies influences the efficiency of connection processes. To that end, we compare the new indicators with other measures of government efficiency such as the procedures, time and cost associated with starting a business, registering a property title or dealing with construction permits. With that we add to the business regulation literature by providing new results on how the overall regulatory environment in a country is related to provision of electricity services. We find that connection processes tend to be more lengthy and cumbersome in countries where other administrative processes are also burdensome. On whether majority publicly owned<sup>4</sup> or majority privately owned utilities are more likely to be affected by the general bureaucratic environment are not conclusive.

(iii) Third, we use firm level data from the Enterprise Survey data set to test whether the measures developed here are associated with firm performance. We find that total sales and sales per worker are higher where the provision of electricity connections is more efficient controlling for several firm and industry characteristics.

The next section describes the data sample. Section III presents descriptive statistics on the new data collected. Section IV presents the basic results on the relationship of the new data with other measures of public service efficiency and sector outcome indicators. Section IV discusses the firm level data and its relationship with the electricity variables. Finally, section V concludes.

# **II. Data**

# _Constructing the new indicators_

This paper is based on a new data set which summarizes the process of obtaining electricity connections for small-and medium sized companies across 183 economies<sup>5</sup> in 2010/2011. We are interested in all the procedures that an entrepreneur needs to carry out to legally connect to electricity. We also calculate the official costs and the time necessary for the completion of each procedure. The methodology developed in this paper follows closely the methodology for the Doing Business Starting a Business Indicators which, in turn, are based on the paper by Djankov et al (2002). The data set used here is the original data set developed for the getting electricity

> 4 For the rest of the paper we refer to utilities that are more than 50% government owned as public utilities and to utilities that are more than 50% privately owned as private utilities.

> 5 The unit of observation is an economy which in most cases is equivalent to the territory of a country. However, there are a few exceptions, such as, Puerto Rico and Hong Kong SAR, China.

4
indicator set. The data have evolved as countries have improved their electricity connection processes over time.

We collect our data from distribution utilities and independent professionals such as electricians, electrical engineers and construction companies in the main business city of each economy. In total close to 600 respondents contributed to the data for 183 economies. Our economies span a wide range of income levels and geographic regions. The sample includes 47 high income, 50 upper-middle income, 54 lower-middle income, and 32 low-income economies. Following the World Bank regional classifications, the data cover 46 economies in Sub-Saharan Africa, 18 in the Middle East and North Africa region, 24 in East Asia and the Pacific, 8 in South Asia, 32 in Latin America and the Caribbean, 24 in Europe and Central Asia and 31 in OECD high-income economies.

To ensure that the data are comparable across economies, respondents in the 183 economies covered were presented with a standard case study. Based on the case study, distribution utilities in the main business city of each economy were asked to describe the procedures for obtaining an electricity connection, along with the time and cost of completing them (see appendix I for a description of the case study used). The electricity distribution utility surveyed was the one serving the area (or areas) in which commercial warehouses are most commonly located. If there was a choice between several distribution utilities, the one serving the largest number of customers was selected. From the responses of each utility, a list of procedures was drawn up and verified through e-mail and telephone interviews with independent professionals such as electricians, electrical engineers, electrical contractors and construction companies. In some cases, regulatory agencies were also contacted.

The data are constructed using a standardized case study of a small to medium enterprise seeking an electricity connection for its newly built warehouse that will be used to store refrigerated goods. We assume that the warehouse is located in the country’s most populous city, within the official city limits and near other similar warehouses. Furthermore, it is assumed that the warehouse is not located in a special economic or investment zone, so that the electricity connection is not subject to subsidization or a faster service under a special investment promotion regime. If several options are available in terms of location, the location where electricity is most easily available is selected.

The electricity connection that is needed for the new warehouse is a 3-phase, 4-wire Y, 140 kVA connection. This connection type is representative of that of an electricity-intensive small or medium size firm. Indeed, it is about seven times the needs of a one-family household in a relatively rich country - but it is significantly smaller than that of a large manufacturing or industrial firm. The connection is done either via an overhead or an underground connection; depending on what is more common in the country, and the area in question. We assume that the internal wiring of the warehouse (which is done by a private electrician and not the utility) is completed and we record only the procedures, time and cost associated with realizing the external 5
part of the connection works. The connection works end at the electricity meter. Lastly, the monthly electricity consumption for the warehouse is estimated at 0.07GWh.

# _The new “Getting Electricity” variables_

The “Getting Electricity” Indicator is composed of three sub-indicators: the number of procedures, the time and the cost of obtaining an electricity connection. Table 2 lists the distribution utilities that were surveyed in each country. The table also indicates their ownership structure and details the procedures, time and cost indicators for each one of them.

A procedure is defined as any interaction between the company employees - or the company’s main electrician (that is, the one who did the internal wiring) – and with external parties, such as the electricity distribution utility, electricity supply utilities, government agencies, other electricians and electrical firms. Interactions between company employees and steps related to the internal electrical wiring, such as the design and execution of the internal electrical installation plans, are not counted as procedures. Procedures that must be completed with the same utility, but with different departments are counted as separate procedures. The study assumes that the information needed to understand the process is readily available (thereby not leading to unnecessary delays) and that no bribes are paid to speed up the process. If the company can, but is not required to, request the services of professionals (such as a private firm rather than the utility for the external works), these procedures are only recorded if they are commonly done. For all procedures, we only count the most likely cases<sup>6</sup> and those often followed in practice for connecting a warehouse to electricity.

Time is recorded in calendar days. The measure captures the median duration that the electricity utility and experts indicate is necessary in practice, rather than required by law, to complete a procedure with minimum follow-up and no bribe payments. It is also assumed that the minimum time required for each procedure is 1 day. Although procedures may take place simultaneously, it is assumed that they cannot start on the same day (that is, simultaneous procedures start on consecutive days). In a few extreme cases (Guinea-Bissau, Liberia and Madagascar), where connection requests are denied - or de facto not carried out because only generators are used in the country additional - wait times of up to one year are added. It is assumed that the company does not waste time and commits to completing each remaining procedure without delay. The time that the company spends on gathering information is ignored. It is assumed that the company is aware of all electricity connection requirements and their sequence from the beginning.

Cost is recorded as a percentage of the economy’s income per capita. We use the 2010 gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the

> 6 For example, if respondents report that necessary material is provided in 50 percent of all cases by the utility and not purchased by the customer, no additional procedure to purchase the material is recorded.

6
midyear population according to the World Bank’s World Development Indicators 2011. Costs are recorded exclusive of value added tax and only include the fees associated with completing the procedures to connect a warehouse to electricity. This includes the costs in related to obtaining clearances from government agencies, applying for the connection, receiving inspections of both the site and the internal wiring, purchasing material, getting the actual connection works and paying a security deposit<sup>7</sup> . Costs associated with electricity consumption are not included. Information from local experts and specific regulations and fee schedules are used as sources for costs. If several local partners provide different estimates, the median reported value is used. In all cases, the cost excludes bribes.

Cost estimates for 86 economies also include the present value of the lost interest earnings on the security deposits that utilities require as a guarantee against the possible failure of customers to pay their consumption bills. Instead of recording the full amount of the guarantee, we record the present value of the losses in interest earnings experienced by the customer because the utility holds the security deposit over a prolonged period. In most cases this is until the end of the contract (assumed to be after 5 years)<sup>8</sup> . To calculate the present value of the lost interest earnings, the end2010 lending rates from the International Monetary Fund’s International Financial Statistics are used. In cases in which the security deposit is returned with interest, the difference between the lending rate and the interest paid by the utility is used to calculate the present value.

In some economies the security deposit can be put up in the form of a bond wherein the company can obtain from a bank, or an insurance company, a guarantee issued on the assets it holds with that financial institution. In contrast to the scenario in which the customer pays the deposit in cash to the utility, in this scenario the company does not lose control over the full amount and can continue using it. In return, the company will pay the bank a commission for obtaining the bond. The commission charged may vary depending on the credit standing of the company. In any case, we assume that the best possible credit standing and thus the lowest possible commission. Where a bond can be put up, the value recorded for the deposit is the annual commission times the 5 years assumed to be the length of the contract. If both options exist, the cheaper alternative is recorded.

We also use, as an aggregate indicator, the relative rank of a given country on the process of obtaining an electricity connection. In order to calculate the rank, we aggregate the three sub-

> 7 Connection costs can be broadly divided into the following categories: often utilities charge a fixed connection fee that spreads the fixed costs of operating a distribution grid over all customers; in addition, customers are required to pay for the variable costs for each connection, including labor and material costs. Sometimes the costs also include fees for inspections that the utility or outside contractors are legally required to carry out. In many economies costs also include payments to other agencies for permits, inspections and approvals.

> 8 In the few cases in which the security deposit is used to cover the first monthly consumption bills, it is not recorded at all.

7
indicators – number of procedures, time and cost – in line with the methodology for other Doing Business indicators by averaging the percentile rankings on the component indicators.

Because we examine how far the process of obtaining an electricity connection differs depending on whether the responsible distribution utility is majority privately or publicly owned, we also collected data from the publicly available sources such as the websites of the surveyed utilities on the ownership composition of each utility in November 2011. We created a dummy variable for ownership which is 0 in the cases where more than 50% of the utility is government owned (this includes ownership by local government such as municipalities) and is 1 in those cases where more than 50% of the ownership of the utility is in private hands. As per this definition, 133 utilities in our sample are considered government owned and 50 are privately owned.

# _Other variables_

We are interested in the relationship between the new getting electricity indicators and three sets of variables.

First, we look at the relationship of the new indicator with outcome variables for the electricity sector. As outcome variables, we use data on the number of people with electricity access as a percentage of total population (“Electrification rate”) as collected by the International Energy Agency. We also examine the relationship with the percentage of electric power transmission and distribution losses as percentage of output<sup>9</sup> as collected by the International Energy Agency from the Energy Statistics and Balances of Non-OECD Countries and Energy Statistics of OECD Countries, and the United Nations’ Energy Statistics Yearbook. A substantial amount of energy is lost in the electricity transmission and distribution system by way of technical and non-technical losses such as electricity theft. In addition, shutdowns and breakdowns in the system also prevent the delivery of energy to end-consumers causing revenue loss. Successful utilities manage revenue by minimizing loss and maximizing energy delivery to end-consumers. Transmission and distribution losses are hence a useful measure of the technical efficiency of the electricity system. We also test the relationship between the new data and the “Perception of the Quality of Electricity” and the “Frequency of Bribe Payments to Public Utilities” as of the World Economic Forum’s Global Competitiveness Indicators. Lastly, we use data from the World Bank’s Enterprise Surveys on the percentage of sales lost due to power outages reported by managers. We also compare the new data with other data on the relative strength of regulatory institutions for

> 9 Technical loss is inherent in electrical systems, as all electrical devices have some resistance and the flow of currents causes a power loss. Every element in a power system (a line or a transformer) offers resistance to power flow and, thus, consumes some energy while performing the duty expected of it. The cumulative energy consumed by all these elements is classified as “Technical Loss.” Technical losses are due to energy dissipated in the conductors and equipment used for Transmission, Transformation, Sub-transmission and Distribution of Power. These occur at many places in a distribution and transmission system − in lines, mid-span joints and terminations transformers, service cables and connections, etc.

8
infrastructure services such as the “Electricity Regulatory Governance Index” developed by Andres et al. (2008) or the data on the degree of autonomy of different electricity regulators collected by Cubbin and Stern (2006). But the number of available observations for both data sets is so small (19 countries in the case of the “Electricity Regulatory Governance Index” and 28 in the case of the “Regulatory Autonomy” data) that we do not present the results here.

Second, we examine the relationship of the new indicator with other measures of government bureaucracy as reflected in a set of three Doing Business time and motion indicators: the number of procedures, time and cost to start a business, register a property and to deal with construction permits. For this analysis, we use the Doing Business data as of the Doing Business Report 2012 for all three indicators. The data cover 183 countries and the period from July 2010 to June 2011, just like for the Getting Electricity data. The Dealing with Construction Permits indicator records not only the process of obtaining construction permits and registering a new building, it also includes procedures, time and cost estimates related to connecting a new construction to utilities such as water, telecommunications and electricity. In order to avoid multi-collinearity with the Getting Electricity data, we adjust the data for the Dealing with Construction permits indicator by excluding all procedures, time and cost estimates related to utility connections from the original data set.

Third, we test the relationship of the new indicator with measures of enterprise performance. All data for this part of the analysis come from the Enterprise Surveys data set. This data set provides firms level data on firms’ characteristics and several measures of the business environment (for instance, trade, infrastructure and finance). The data is collected on a 3-year rotation, meaning that each developing country is visited every 3 years, but the cycle varies country by country (so the data collected for different countries is done in different years). For each country only one year of data was kept (the most recently available year). No data before 2006 were included since the new electricity data is as of 2010/11. Furthermore, only 108 countries of Enterprise Surveys match our sample of 183 economies. The 108 countries surveyed include 38 countries from Sub-Saharan Africa, 30 from Eastern Europe and Central Asia, 25 from Latin America and the Caribbean, 10 from East Asia and the Pacific, 4 from South Asia and one from the Middle East and North Africa region (i.e. Yemen). The richest countries in the Enterprise Survey sample are Slovenia and the Bahamas (as measured by real GDP per capita), the country with the lowest income is the Democratic Republic of Congo.

As outcome variables we use total sales and sales per worker. Nominal values are deflated using the GDP deflator from the World Development Indicators database. All values are presented in 2000 constant US dollars and the exchange rate is taken from the International Monetary Fund’s International Financial Statistics database. As dependent <mark>variables, we use past</mark> sales, average labor input (measured by the number of employees), capital input (proxied by investment), average international trade engagement (measured by the percentage of firms that export directly at least 10% of their sales, import directly at least 10% of their inputs or both of these in industry j country 9
c), average usage of bank finance (measured by the percentage of firms in industry j and country j that have a bank loan), and the average number of power outages reported by firms for a typical month.

# Table 1 describes all the variables used in this study.

# **III. Summary statistics**

There is a large variability in the number of procedures, the time and the cost for getting electricity (see Table 2). For instance, in order to get his business connected to electricity in 2010/2011, an entrepreneur in Ukraine had to go through 11 different procedures, which take at least 285 days and the connection will cost him a total of US$7,705.5 (equivalent to 275.2% of Ukraine’s GNI per capita). To do the same in Germany, an entrepreneur needs only 3 procedures, less than three weeks and the connection will cost him the equivalent of 51.9% of the GNI per capita. Appendix II details for illustration purposes the procedures, cost and time to obtain a new electricity connection in Baku, Azerbaijan.

Table 3 and 4 present the means in terms of the number of procedures, time and cost to get connected to electricity. In addition, the means for other Doing Business variables are provided, such as the procedure, time and cost to start a business, register a property or deal with construction permits by income group and region. Tables 5a, 5b and 5c compare the procedures, time and cost to obtain an electricity connection by the ownership structure of the utility connecting the new customer. Table 6 provides descriptive statistics for outcome variables such as the share of the population connected to electricity, transmission and distribution losses as a % of output and the value lost due to power outages as a percentage of sales by income group and region. Table 7 provides a correlation matrix between the new electricity data and the outcome variables. Several interesting findings emerge, as illustrated below.

# _Procedures to get connected to electricity_

The number of procedures to obtain an electricity connection in 2010/2011 varied from 3 procedures in countries like Germany, Japan, Sweden and Switzerland to 11 procedures in Ukraine. The average number of procedures required to obtain an electricity connection did not vary much across income groups and regions. A review drawn up for each country reveals that there tends to be fewer procedures in economies where<sup>10</sup> :

- Approvals that customers have to obtain from other agencies are consolidated with other approvals. In Romania the private contractor hired to complete the connection works must get a separate construction license for the distribution transformer needed for the connection. In

> 10 For a detailed review of the content of procedures and typical trends see (World Bank, 2010).

10
both Serbia and Montenegro the same construction license can be obtained from the municipality together with the main construction permit.

- The responsibility for safety compliance of the building’s internal wiring is transferred to private electricians and it is not customers who have to go through inspection procedures. In Denmark, Germany, Japan and Mauritius the customer's electrician is required to submit an installation wiring certificate with the service application. In other economies, like Ukraine, clients have to obtain multiple approvals from different agencies to comply with the same safety standards. In countries where responsibility for safety compliance are transferred to private electricians, a well regulated industry for electricians is usually in place, with a national accreditation system and professional licenses for electrical engineers.

- Utilities have the necessary material to connect customers in stock and readily available and they are not asked to obtain the required materials themselves. The latter is the case in countries like Bangladesh, Benin and the Central African Republic where customers are asked to provide the utility with such materials as poles, meter boxes or transformers because the utility does not have them in stock.

_Time to get connected to electricity_ Although there is no significant variation in the number of procedures across regions and income groups, the number of interactions customers have with the utility and other agencies is clearly associated with longer connection delays. In economies where businesses have to go through 7– 11 procedures to get connected, the process takes 141 days on average. In economies with 3–6 procedures, it takes only 104 days on average. It takes 66 days on average to get connected in the 8 economies with the smallest number of procedures (3 procedures), and 185 days in the 7 economies with the highest number of procedures (from 9 to 11 procedures).

The average number of days needed to obtain an electricity connection clearly decreases from the lowest income to the highest income countries with 163 days in low-income economies, 101 in lower-middle income economies, 107 days in upper-middle income economies and 92 days in high-income economies. Key observations also emerged depending on the regional classifications of countries, but they did not necessarily mirror the observations made across income groups. For instance, average connection delays are the longest in Eastern Europe and Central Asia, even though countries in the region fall mostly in the middle-income group category.

Furthermore, there can be striking differences within regions. For instance, within Sub-Saharan Africa, Mauritius makes it easy for businesses to get connected to electricity. It takes a total of 3 procedures and 91 days to get connected in Port Louis, the country’s capital. In the same region, the time it takes to obtain an electricity connection can range up to 450 days in Madagascar and 455 days in Guinea-Bissau.

11
For a few countries in the sample, connection delays are particularly long because a) connections are not granted at all or b) because once a connection has been granted the availability of electricity cannot be guaranteed afterwards.

# _Cost to get connected to electricity_

Average connection costs as % of income per capita across income groups show a clear trend of decreasing costs from low to high-income countries. While an electricity connection costs on average 7,903 percent of a low-income country’s income per capita, it costs only 108 percent in high-income countries and 620 percent in upper-middle income countries. The two regions with the lowest income, Sub-Saharan Africa and South Asia are also the regions where average connection costs are the highest with 6,099 percent and 2,115 percent of the income per capita, respectively.

But connection costs also vary significantly within income groups, suggesting room to reduce costs. In the 10 lowest-cost economies (all high-income economies except Panama), the average cost for a connection is no more than 8.3% of income per capita, which is 13 times lower than the average for all high-income economies (108% of income per capita). In contrast, in the 10 highestcost economies (all low-income economies), the average is 17,071% of income per capita, more than twice the average for the low-income group (7,903% of income per capita).

_Ownership of utilities_ Tables 5a and 5b and 5c present the data for the new Getting Electricity Indicator by ownership of the respective utility surveyed. The sample contains 133 utilities that are majority-publicly owned and 50 utilities that are majority-privately owned. Public ownership of distribution utilities is still the most common ownership model around the world. Moreover, in 2011, distribution utilities in 72 percent of all 183 countries surveyed were in public hands in 2011. Private ownership is most common in lower-middle and upper-middle income countries. Regionally, the share of private ownership is highest in Latin America and the Caribbean (Table 5b). Table 5a shows that the average number of procedures and the time utilities take to connect a customer to electricity differ little by ownership, nor do absolute connection costs. However, when connection costs are normalized by the average income per capita, majority publicly owned utilities charge their customers more than twice what the average private utility charges. Table 5c shows that connection costs as a share of income per capita are significantly lower in countries where utilities are privately owned and that these results are valid after controlling for income. The coefficient suggests that an increase in the connection cost equal to the income per capita is associated with a 7 times smaller cost increase when the utility is privately owned.

# _Comparing the new data with other measures of bureaucracy_

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Noteworthy observations emerge when comparing the data for the new Getting Electricity indicators with Doing Business Indicators that measure the complexity of bureaucratic processes such as Starting a Business, Registering Property and Dealing with Licenses.

First, the maximum number of procedures required to obtain an electricity connection is relatively small with 11 procedures compared to 18 for Starting a Business, 15 for Registering Property and 50 for Dealing with Construction Permits (table 3). Compared to the procedures number for other Doing Business indicators, the new indicator exhibits a much smaller standard variation in the number of procedures.

Second, the average cost for a new electricity connection is naturally much higher than the cost for starting a business, registering a property or obtaining a construction permit (table 3). While costs for these three indicators only include official processing fees and sometimes one-off taxes (like property transfer taxes), the cost for an electricity connection often includes significant material and labor costs (see section II on Data for a description of cost items covered). The average cost of an electricity connection is 2,158 percent of the income per capita, versus 42 percent for starting a company, 6 percent for registering property and 486 percent for obtaining a construction permit. The standard deviation for connection costs is highest in the low-income and lower-middle income group and average connection costs as % of income per capita across income groups show – like the other Doing Business indicators – a clear trend of decreasing costs from low to high income countries.

# _Other electricity measures_

Table 6 describes the outcome indicators used in the next section to analyze the relationship between the new electricity data and other variables of electricity sector performance, such as the share of the population connected to electricity (“electrification rate”), transmission and distribution losses as a % of output and the value lost due to electricity interruptions. In addition, we are using two perception based indicators from the World Economic Forum’s Global Competitive Indicator set, which capture the expert perceptions of the quality of electricity and the perceived incidence of bribe payments in connection with electricity. The sample size for all five indicators is smaller than the one for the new Getting Electricity Indicators. The sample for the electrification rate is by far the smallest with just 84 observations. As one would expect, electrification rates and the perceived quality of electricity increase with the income level of a country. Conversely for the transmission and distribution losses, the value lost of sales due to electricity interruptions, and the incidence of bribe payments; they all decrease with the income level of a country. Electrification rates range from an average of 23 percent in low-income countries to 99 percent in high income countries. The ratio of transmission and distribution losses averages 7 percent in high income countries and 20 percent in low and 18 percent in lower-middle income countries. And, the value lost due to power outages ranges from 1 percent of the sales of an average company in high income countries to 8 percent in low-income countries.

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Table 7 details correlations between the new Getting Electricity variables and the five outcome indicators. Correlations are positive and statistically significant between the cost to get a connection as of income per capita and distribution and transmission losses and the % of sales lost due to electrical outages. The time and cost to obtain an electricity connection are also negatively correlated with the electrification rate and the perception of electricity quality and bribes in electricity indicator. The procedures to obtain an electricity connection are positively correlated with the time and cost to obtain a connection and negatively correlated with the perception of electricity quality and bribes in electricity indicator. The highest correlation coefficients can be found between the Quality of Electricity score and the Bribes in Electricity score (0.83), the Quality of Electricity score and the electrification rate (0.65) the electrification rate and the cost to get an electricity connection (0.56), the value lost due to outages variable and the quality of electricity score (-0.57) and the Bribes in Electricity Score and the electrification rate (0.52).

# **IV. Results**

In order to establish the explanatory value of the new indicators, we examine the newly collected data in view of three questions. First, we examine how much explanatory power our new aggregated Getting Electricity index has for the general performance of the electricity sector. Second, we try to understand to what extent it moves in similar ways as other indicators of government regulation. To that end we compare the new indicator with other Doing Business measures of government regulation. And third, we look to establish if a less cumbersome and less costly process of obtaining an electricity connection is associated with better firm performance.

# **IV.1. Are electricity connections easier where the electricity sector performs better overall?**

In low-income countries where only 22 percent of the population is connected to electricity (Table 6), obtaining an electricity connection remains an important barrier for individuals and firms. But even where connections are accessible, the quality of electricity supply can weigh down on business activity. 41 percent of firms surveyed by the World Bank’s Enterprise surveys consider electricity either a very severe or major obstacle to the operations of their business (Table 15a). This is, especially true, for firms in South Asia and Sub-Saharan Africa, where firms are also more likely to own a generator and report the greatest number of power outages per month (Tables 15b to 15c).

To assess whether connection and other electricity sector problems go hand in hand, we regress our new measures of connection procedures, time and cost on various indicators that measure the overall performance of the electricity sector. Tables 8 to 12 present our main findings.

We use the share of the population connected to electricity as the dependent variable and the number of procedures, time and cost of obtaining an electricity connection as independent variables (Table 8). In an additional specification, we use the aggregated “Getting Electricity” indicator as independent variable. With only 84 observations for the electrification rate, the sample 14
is relatively small. The results show that the time and cost to obtain an electricity connection are negatively correlated with the electrification rate and statistically significant at the 1-5% level. The same is true for the aggregate Getting Electricity ranking that combines all three variables into one aggregate index. However, once we control for income levels, all specifications lose their statistical significance and the coefficients become significantly smaller suggesting that differences in electrification rates are much more driven by income levels of countries than by the complexity of the process for connecting new customers.

Second, we use transmission and distribution losses as a % of output as the dependent variable (Table 9). Here the results show that a greater number of procedures to obtain an electricity connection are associated with a higher percentage of losses in the distribution and transmission system. This result is statistically significant at the 1% level, but like for the electrification rate the result loses its significance once we control for income. The results for the relationship between the cost of obtaining a connection and the transmission and distribution loss ratio are the only ones that retain their statistical significance. This correlation might be driven by utilities having to recuperate losses through higher costs on customers.

Third, we use values lost due to outages (as % of sales) as reported by the World Bank’s enterprise surveys as the dependent variable (Table 10). We find a positive association of the variable with both the number of procedures and the cost associated with obtaining an electricity connection. However, only the positive correlation with procedures remains statistically significant after we control for income. We also find the aggregate rank on the ease of getting electricity is positively and statistically significantly correlated with the Value lost due to outages variable. In fact, the results show that a 10-position improvement on the ease of getting electricity is associated with a 4-percentage point increase of the value lost due to outages as a percentage of annual sales.

We obtain similar results in our specification with the WEF’s Perceived Quality of Electricity Supply variable as the dependent variable (Table 11). As in the previous specification out of the three new variables, the number of procedures to obtain a new electricity connection has the greatest predictive power for the dependent variable. While greater procedures, time and cost are associated with a lower quality of electricity supply in the specification without the income control, only the specification with procedures remains statistically significant after controlling for the income effect. And, again like in the previous specification, a better rank on the overall ease of getting electricity is associated with greater quality of electricity supply.

To test whether the ownership structure of utilities plays a role, we have also included in each one of the four specifications a dummy for the ownership structure of the utility, which does not enter significantly in any of the four specifications. Also, the inclusion of ownership dummy affects the significance level of the coefficients for our new Getting Electricity variables only marginally.

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Last, we use the Frequency of Bribe Payments to Public Utilities as a dependent variable (Table 12). This specification yields the strongest results so far. A greater number of procedures, as well as a longer connection time and greater cost are associated with a higher frequency of bribe payments to utilities and the results for procedures and cost both remain statistically significant after controlling for income. The most robust results are yielded for the procedures variable. A 10position improvement on the rank of the ease of getting electricity is associated with a 5 percentage-point decrease in the reported frequency of bribe payments. This finding is consistent with similar findings by Djankov et al. (2002) for the number of start-up procedures and corruption. More procedural complexity, i.e. bureaucracy, seems to precipitate or at least come hand in hand with corruption.

# **IV.2. Are electricity connections more cumbersome in more bureaucratic countries?**

Next, we examine whether complexities in electricity connection processes are merely a result of a general bureaucratic culture in the country. In order to answer this question, we look at how the new data on the process of obtaining an electricity connection relates to other indicators of regulatory efficiency. We use the Doing Business data on the number of procedures and the time for starting a business, for registering a property and for obtaining a construction permit as proxies of the bureaucracy in a given country.

# _Specification with procedures_

Table 13 presents our main findings for the specification focusing on the procedural complexity of different branches of the government bureaucracy. We use the number of procedures to obtain an electricity connection as the dependent variable and the three measures of number of procedures for starting a business, the number of procedures for registering property and for dealing with construction permits as independent variables separately. Our sample contains 180 to 182 observations. In this first simple specification, the procedures to transfer property and to deal with construction permits are positively correlated with the procedures for obtaining an electricity connection and the results are statistically significant at 1%. The results for the procedures to start a business are not. It is possible that the relationship with the Starting a Business procedures is less significant because of the greater convergence to international best practice in this particular area of business regulation (see World Bank, 2014). We then add the log of the GNI per capita as a control variable. The inclusion of the control variable does not lead to a sizeable change in the coefficients of the independent variables, but the significance level of the dealing with construction permits variable goes down to 5%.

# _Specification with time_

In a second set of specifications, we use the time component of the same Doing Business indicators instead of the number of procedures (Table 14). The first specification includes no controls. All time variables (starting, property, construction permits) are positively correlated with the time 16
needed to obtain a new electricity connection. However, only the coefficients for the time to register property and obtain a construction permit are statistically significant. The coefficient for the time to start a business is not; possibly for the same reason that the procedures were not correlated, due to a significant convergence to best practice. Once we control for income per capita, only the positive association between the time to obtain an electricity connection and the time to obtain a construction permit remains statically significant. This result seems intuitive given the obvious connection between the electricity connection and the construction permitting process. A review of individual procedure lists (see Appendix II for an example) for Getting Electricity reveals that the building office is the public office most likely to be involved in the connection process (e.g. to obtain right of ways for underground and overhead connections).

# **IV.3. Do firms perform better in countries with better electricity connection processes?**

As a last step, we examine to which degree the procedures, time and cost of obtaining an electricity connection matter for firm performance. In this paper we look at one major measure of firm performance: total sales as a measure of output. We do this analysis both at the firm level and aggregated to the industry level. It is important to note that the specifications analyzed here are based on cross section data and therefore no causal inferences can be made. We can only identify how the variables commove, but we are not able to affirm for sure that efficiency in the electricity connection process leads to better firm performance. In some specifications we include country fixed effects to address this issue.

The different specifications we use in the firm level analysis can be summarized in the following formula: _yijc_   0   1 _ec_   2 _Xijc_   3 _Ij_   5 _Cc_   6 _Ij_ * _ec_   _ijc_ Here _yijc_ represents either total sales in firm i, industry j and country c. _e_ stands for the electricity connection measure that is our variable of interest and varies at the country level. Here we use the overall percentile rank calculated using the average of the percentile ranks of procedures, time and cost to obtain an electricity connection. _Xijc_ represent firm characteristics which are as <mark>follows: log of past sales as a proxy for production capacity, log of firm labor input (measured</mark> by the number of employees), capital input (proxied by investment), firm’s international trade engagement (measured by either the firm exports directly at least 10% of its sales, imports directly at least 10% of its inputs or either the firms does both of these), firm’s usage of bank finance (measured by either the firm has a bank loan), and number of power outages in a typical month. _Ij_ stands for industry characteristics. They are captured with dummies defined at the 2-digit ISIC code. Cc represents country specific characteristics, which here are proxied by income per capita. Cc stands for country fixed effected and both our electricity connection measure and GDP per capita are excluded. However, to separate the income effect from the general country fixed effect we include dummies for four income groups (low, lower middle, upper middle and high). Finally 17
_Ij*ec_ represent the interaction term between industry and our electricity measure. The coefficients of these interactions are also variables of interest and measure whether firm performance in certain industries is more affected by the process of connecting to electricity. _Ejc_ is the residual term.

Output (in this case proxied by sales) is a function of the existing production capacity (proxied by past sales), variable inputs (labor and capital) plus other factors that can affect productivity levels. Identifying these determining factors has been the focus of a vast literature. Here we try to test if the efficiency in the electricity connection process can be one of these factors. To do so we use similar specifications to the ones used in papers such as Seker (2009) and Hallward-Driemeier, Wallsten and Xu (2003).

Given that our variables of interest vary at the country and industry level, the more appropriate specification is at the industry level. The firm level regression is more likely to produce statistically significant results just because the number of observations is much larger. Therefore, it is important to verify if the results hold once the data are collapsed at the industry level.

_yjc_   0   1 _ec_   2 _Xjc_   3 _Ij_   5 _Cc_   6 _Ij_ * _ec_   _jc_ (2) Here _yjc_ represents either total sales on average in industry j and country c. The other variables remain as in (1) except for _Xjc_ which now represent average industry characteristics. These are as follows: average past sales, average labor input (measured by the number of employees), capital input (proxied by investment), average international trade engagement (measured by the percentage of firms that export directly at least 10% of their sales, import directly at least 10% of their inputs or both of these in industry j country c), average usage of bank finance (measured by the percentage of firms in industry j and country j that have a bank loan), and average number of power outages in a typical month.

Here we follow a variation of the specification used by Klapper, Laeven and Rajan (2004). These authors study the effect of the business environment on firm creation by interacting industry characteristics with country characteristics. We adapt their specification by changing the dependent variable from firm entry to firm performance and consequently also altering the industry and country level controls. We also add the past sales variable as a proxy of existing production capacity, which would not apply to the question of firm entry. In these specifications, all the data except for _e_ (percentile rank of getting an electricity connection) come from the Enterprise Surveys data set.

# _Firm Level Specifications Results_

The firm level regression results are presented in tables 16a and b. We first describe table 16a which is based on the full sample. Column 1 shows the results under the basic specification where the covariates are kept to a minimum. We control for country characteristics, industry characteristics and some firm level characteristics (past sales, labor and capital inputs and power 18
outages). The coefficient of interest (Beta 1) is negative and significantly different from zero at the one percent level. That is, firms have lower sales where electricity connections are more difficult to obtain. Sales are also higher where labor input is higher. Once usage of bank financing is included (column 2), these results remain significant. The same is true when the trade variables are included (column 3). This specification also shows that sales are higher among firms that import at least parts of their production inputs. Finally, column 4 shows the specification with all the covariates included, plus the interaction terms between industry fixed effects and the electricity variable. These interactions identify which industries are more affected by the electricity connection process. When the interaction terms are included, it stands out the vast majority of the interaction terms are negative in particular for industries such as manufacturing refined petroleum, manufacture of tanning and dressing of leather and manufacturing of motor vehicles, indicating that these industries are more likely affected by an inefficient electricity connection process. These results are presented in Table 16b. We include only the industries that have enough observations for the estimation of coefficients to be possible.

The Enterprise Survey database covers many cities within a country while the Getting Electricity database covers only the major business city in the country. In countries where the ease of obtaining an electricity connection varies considerably across cities, including all the firms covered in each country in Enterprise Surveys can lead to imprecise estimates of the relationship between firm performance and the electricity connection process. We therefore try to improve the fit of our model by restricting the sample of firms to only those located in the major business city of the country. The results for the restricted sample are presented in Table 17a. The same coefficients are significant as in the full sample specifications but the coefficient of our new Getting Electricity variable become more negative and the overall R-squared increases.

# _Industry Level Specifications Results_

In a next step we collapse the firm data at the industry level as shown in every other column of Tables 16a and 17a. The industry level specifications use the same sets of covariates as in the firm level regressions. Compared to the firm level regressions, the same variables remain significant but the coefficients for past sales decrease slightly and those for labor input and the import dummy increase. The coefficient of our new Getting Electricity variable turns positive in all specification of the full sample. But once we restrict the sample to the Doing Business city only, the coefficient become negative again once enough controls are added to the regression (columns 2, 3 and 4). Moving up one position in the ranking from a more complex electricity connection process to a less complex one is associated with an increase of 0.2 in the log of firm sales and a 0.1 increase in the log of industry sales. Once the interaction terms are included in the restricted sample, as in the firm level regressions, there remain industries such as manufacture of tanning and dressing of leather and manufacturing of motor vehicles that also show a negative relation between the ranking in the ease of electricity connection and industry sales. These results are presented in Table 17b.

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Only the industries with enough observations for the estimation of coefficients to be possible are included.

In general the results at the firm and industry levels are similar. The differences emerge when we compare the full sample and the restricted sample to the Doing Business city only. The significance of results increases with the restricted sample because the two data sets (Doing Business and Enterprise Surveys) become more comparable.

# **V. Conclusion**

This paper presents basic statistical relationships between the number of procedures, time and cost to obtain an electricity connection and other variables using a new data set on the process of obtaining an electricity connection for 183 economies. The statistical relationships presented in this paper suggest that the new indicators not only add value by providing high-frequency data for a wide range of countries on connection processes and costs, but that they could also serve as a useful proxy for the performance of the electricity sector overall. Using the new data, we find electricity to be of greater quality and bribe payments to electric utilities to be less frequent in countries where it is easier and less costly to obtain an electricity connection. We also find that connection costs are higher in countries where technical and non-technical losses in the transmission and distribution system are high, and that privately owned utilities provide new connections at a lower cost than publicly owned utilities. Our analysis also suggests that the general procedural environment in a country influences the complexity of connection processes; they tend to involve more procedures in countries where other regulatory processes are also procedureintensive. Last, the analysis indicates that firms perform better in terms of sales in countries where it is easier and less costly to get an electricity connection. This seems to be especially true for firms in the manufacture of tanning and dressing of leather industry and in the manufacturing of motor vehicles industry.

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# **VI. References**

Andres, Luis, Jose Luis Guasch and Sebastian Lopez Azumendi. 2008. "Regulatory Governance and Sector Performance: Methodology and Evaluation for Electricity Distribution in Latin America. " Policy Research Working Paper 4494. World Bank, Washington, DC.

Calderón, César, and Luis Servén. 2003. “The Output Cost of Latin America’s Infrastructure Gap.” In William R. Easterly and Luis Servén, eds., The Limits of Stabilization: Infrastructure, Public Deficits, and Growth in Latin America. Washington, D.C.: World Bank.

CEER (Council of European Energy Regulators). 2008. 4th Benchmarking Report on Quality of Electricity Supply 2008. Brussels. http://www.energy-regulators.eu/.

Cubbin, John and Jon Stern. 2006. “The Impact of Regulatory Governance and Privatization on Electricity Industry Generation Capacity in Developing Economies.” The World Bank Economic Review Advance Access, World Bank, Washington, DC.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2002. “The Regulation of Entry”, Quarterly Journal of Economics 117(1):1-37.

Dollar, David, Mary Hallward-Driemeier, and Taye Mengistae. 2003a. “Investment Climate and Firm Performance in Developing Countries.” In Economic Development and Cultural Change, 54:1–31, October 2005 Eifert, Benjamin. 2007. " Infrastructure and Market Structure In Least-Developed Countries", http://beifert.googlepages.com/ Estache, Antonio and Martin Rossi. 2004. “Have Consumers Benefited from the Reforms in the Electricity Distribution Sector in Latin America?” Policy Research Working Paper 3420. World Bank, Washington, DC.

Estache, Antonio and Martin Rossi. 2005. Do regulation and ownership drive the efficiency of electricity distribution? Evidence from Latin America. Economic Letters. Volume 86, Issue 2, February 2005, Pages 253–257 Foster, Vivien, and Steinbuks Jevgenijs. 2009. “Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.” Policy Research Working Paper 4913, World Bank, Washington, DC.

Gassner, Katharina, Alexander Popov and Nataliya Pushak. 2009. “Does Private Sector Participation Improve Performance in Electricity and Water Distribution? Trends and Policy Options No. 6, World Bank, PPIAF, Washington, DC.

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Hallward-Driemeier, Mary & Wallsten, Scott & Lixin Colin Xu, 2003. "The investment climate and the firm : firm-level evidence from China," Washington D.C.. World Bank Policy Research Working Paper Series 3003.

Iimi, Atsushi. 2008. "Effects of Improving Infrastructure Quality on Business Costs: Evidence from Firm-Level Data", http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1149084.

Jamasb, T., R. Mota, D. Newbery, and M. Pollitt. 2005. ‘‘Electricity Reform in Developing Countries: A Survey of Empirical Evidence on Determinants and Performance.’’ World Bank Policy Research Working Paper 3549. Washington, D.C.: World Bank.

Kirkpatrick, Colin, Parker, David, and Yin-Fang Zhang. 2002. "Electricity Sector Reform in Developing Countries: An Econometric Assessment of the Effects of Privatisation, Competition and Regulation." Centre on Regulation and Competition (CRC) Working paper no. 30593.

Kumbhakara, Subal C. and Lennart Hjalmarsson. 1998. "Relative performance of public and private ownership under yardstick competition: electricity retail distribution", European Economic Review Volume 42, Issue 1, January 1998, Pages 97-122.

Klapper, Leora, Laeven, Luc, and Raghuram Rajan. 2004. "Business Environment and Firm Entry: Evidence from International Data". World Bank Policy Research Working Paper 3232. Washington, D.C.: World Bank.

La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert Vishny. 1999. “The Quality of Government”, Journal of Law, Economics and Organization 15(1): 222-279.

Lee, Kyu Sik. 1985. "An Evaluation of Decentralization Policies in Light of Changing Location Pattems of Employment in The Seoul Region." Urban Development Discussion Paper, UDD-60. The World Bank.

Lee, Kyu Sik. 1989. "The Location of Jobs in a Developing Metropolis : Patterns of Growth in Bogota and Cali, Colombia." Oxford University Press.

Lee, K.S., Anas, A. and Oh, G.T., 1996. “Cost of infrastructure deficiencies in manufacturing in Indonesia, Nigeria and Thailand,” Policy Research Working Paper Series 1604, Washington DC: World Bank.

McCotter, Brian. 2005. “Best Practices in Consumer Services. A Comparative Study.” USAID, Washington, DC.

Reinikka, Ritva, and Jakob Svensson. 1999. “Confronting Competition: Investment Response and Constraints in Uganda.” Washington, D.C.: World Bank Policy Research Working Paper Series 2242.

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Seker, Murat. 2009. "Importing, exporting and innovation in developing countries," Washington D.C.. World Bank Policy Research Working Paper Series 5156.

World Bank. 2004. World Development Report 2005. A Better Investment Climate for Everyone. Oxford: Oxford University Press.

World Bank. 2010. Getting Electricity – A pilot indicator set from the Doing Business Project. <u>http://www.doingbusiness.org/Documents/GettingElectricity_pilot_indicator_project.pdf.</u> World Bank. 2014, _Doing Business 2014: Understanding regulations for small and medium-size enterprises._ Washington, DC: World Bank Group.

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# **<mark>VII. T</mark> ables**

# **Table 1: Description of variables used**

|**Variable**|**Source**|**Year**|**Definition**|
|---|---|---|---|
|Procedures<br>Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures required to complete a<br>connection to electricity of a newly built construction<br>with a 140 kVA load demand.|
|Time Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Time in days required to connect a newly built<br>construction to electricity starting from the first<br>procedure required for the connection and until<br>electricity starts flowing.|
|Cost in USD<br>Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost in USD paid to a utility, electrical contractors and<br>other agencies for completion of an external electricity<br>connection of a newly built construction with a 140<br>kVA load demand.|
|Cost as % of GNI<br>per capita Getting<br>Electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost as % of GNI per capita paid to utility, electrical<br>contractors and other agencies for completion of an<br>external electricity connection of a newly built<br>construction with a 140 kVA load demand. 2008 GNI<br>in local currency units from the World Development<br>Indicators is used as a denominator.|
|Country rank on<br>the ease of getting<br>electricity|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Rank of countries based on the procedures, time and<br>cost of obtaining an electricity connection where 1<br>indicates the highest rank.|
|GNI per capita<br>2008|World Bank (World<br>Development Indicators 2009)|2011|The gross national income, converted to U.S. dollars<br>using the World Bank Atlas method, divided by the<br>midyear population.|
|Private/public<br>utility|Author's classification: review<br>of utility’s web-sites and other<br>sources on the structure of the<br>electricity sector|2011|A dummy variable that identifies the ownership of a<br>utility providing the electricity connection. The<br>ownership is classified as public if more than 50% of a<br>utility is government owned.|
|Procedures<br>Starting a<br>Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures that entrepreneur needs to carry<br>out to begin legally operating a firm involved in<br>industrial or commercial activity.|
|Time Starting a<br>Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of days it takes an entrepreneur to carry out all<br>the procedures necessary to begin legally operating a<br>firm involved in industrial or commercial activity.|
|Cost as % of GNI<br>per capita Starting<br>a Business|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost of starting a business includes all official fees and<br>fees for legal or professional services which have to be<br>paid to start up and formally operate an industrial or<br>commercial business..|

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|Procedures<br>Registering<br>Property|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures necessary for a business (buyer)<br>to purchase a property from another business (seller)<br>and to transfer the property title to the buyer’s name.|
|---|---|---|---|
|Time Registering<br>Property|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of days it takes to complete all the procedures<br>necessary for a business (buyer) to purchase a property<br>from another business (seller) and to transfer the<br>property title to the buyer’s name.|
|Cost as % of<br>property value<br>Registering<br>Property|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost of registering property includes official costs<br>required by law to transfer the property title, including<br>fees, transfer taxes, stamp duties and any other payment<br>to the property registry, notaries, public agencies or<br>lawyers.|
|Procedures<br>Construction<br>Permits (net of<br>procedures related<br>to utility<br>connections)|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of procedures required for a business in the<br>construction industry to build a standardized<br>warehouse. These procedures include submitting all<br>relevant project-specific documents (for example,<br>building plans and site maps) to the authorities;<br>obtaining all necessary clearances, licenses, permits and<br>certificates; completing all required notifications; and<br>receiving all necessary inspections.|
|Time<br>Construction<br>Permits (net of<br>time related to<br>utility<br>connections)|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Number of days necessary for a business in the<br>construction industry to complete all the formalities to<br>build a standardized house.|
|Cost as % of GNI<br>per capita<br>Construction<br>Permits|Doing Business Report 2011<br>(www.doingbusiness.org)|2009-<br>2010|Cost of obtaining construction permits includes all the<br>fees associated with completing the procedures to<br>legally build a warehouse, including those associated<br>with obtaining land use approvals and preconstruction<br>design clearances; receiving inspections before, during<br>and after construction; getting utility connections; and<br>registering the warehouse property.|
|Electric power<br>transmission and<br>distribution losses<br>(% of output)|International Energy Agency,<br>Energy Statistics and Balances<br>of Non-OECD Countries and<br>Energy Statistics of OECD<br>Countries, and United Nations,<br>Energy Statistics Yearbook.|2010|Electric power transmission and distribution losses as<br>percentage of output include losses in transmission<br>between sources of supply and points of distribution<br>and in the distribution to consumers, including<br>pilferage.|
|Electrification<br>rate (% of<br>population)|International Energy Agency|2009|Number of people with electricity access as a<br>percentage of total population.|
|Value lost due to<br>electrical outages<br>(% of sales)|World Bank, Enterprise<br>Surveys|2006-<br>2010|Value lost due to electrical outages is the percentage of<br>sales lost due to power outages.|

25
|Quality of<br>Electricity Supply<br>Score|World Economic Forum –<br>Executive Opinion Survey,<br>Global Competitiveness<br>Indicators|2010 and<br>2011<br>weighted<br>average|How would you assess the quality of the electricity<br>supply in your country (lack of interruptions and lack of<br>voltage fluctuations)? [1 = insufficient and suffers<br>frequent interruptions; 7 = sufficient and reliable]|
|---|---|---|---|
|Frequency of<br>Bribes to Public<br>Utilities Score|World Economic Forum –<br>Executive Opinion Survey,<br>Global Competitiveness<br>Indicators|2011|In your country, how common is it for firms to make<br>undocumented extra payments or bribes connected with<br>public utilities? Answers ranges from 1 (very common)<br>to 7 (never occurs).|
|Firms’ current<br>sales|World Bank, Enterprise<br>Surveys|2006-<br>2010|Firm total annual sales in local currency in 2000<br>constant US dollars.|
|Firms‘ labor input|World Bank, Enterprise<br>Surveys|2006-<br>2010|Number of firms’ full time workers.|
|Firms’<br>productivity|World Bank, Enterprise<br>Surveys|2006-<br>2010|Total annual sales in 2000 constant US dollars per full<br>time worker.|
|Firms’ capital<br>input (investment)|World Bank, Enterprise<br>Surveys|2006-<br>2010|Total annual expenditure in 2000 constant US dollars<br>for purchases of machines, equipment and buildings.|
|Direct Export<br>10%|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm exports directly<br>at least 10% of its sales.|
|Direct Import|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm imports at least a<br>part of its inputs.|
|Two-way Trader<br>10%|World Bank, Enterprise<br>Surveys|2006-<br>2010|Measure of firms’ international trade engagement:<br>Dummy variable equal to 1 if the firm exported at least<br>10% of its inputs AND exports at least 10% of its sales.|
|Firms’ usage of<br>bank finance|World Bank, Enterprise<br>Surveys|2006-<br>2010|Dummy variable equal to one if the firm uses banks or<br>other financial institutions to finance its investments or<br>working capital.|
|Number of power<br>outages per month<br>experienced by<br>firms|World Bank, Enterprise<br>Surveys|2006-<br>2010|Average number of power outages observed by the firm<br>in a typical month.|

26
# **Table 2: Getting Electricity indicator data (2010/2011)**

||||Private<br>or|Procedures<br>Getting|Time<br>Getting|Cost in<br>USD<br>Getting|Cost as % of<br>GNI per capita<br>Getting|
|---|---|---|---|---|---|---|---|
|Economy|City|Name of the Utility<br>|Public|Electricity|Electricity|Electricity|Electricity|
|||Kabul Electricity Department,<br>Da Afghanistan Breshna||||||
|Afghanistan|Kabul|<br>Sherket(DABS)|Public|4|191|18,040.0|3,711.1|
|Albania|Tirana|CEZ Shperndarje|Private|6|177|24,290.4|615.0|
|Algeria|Algiers|SONELGAZ (via filiale<br>Société de Distribution de<br>l'électricité et gaz d'Alger<br>(SDA))<br>|Public|6|159|78,431.8|1,774.5|
|Angola|Luanda|Empresa de Distribuicao de<br>electricidade(EDEL)|Public|9|69|44,618.7|1,278.5|
|Antigua and<br>Barbuda|St. John's<br>|Antigua Public Utilities<br>Authority|Public|4|42|16,037.5|132.2|
||Buenos Aires|||||||
||(Ciudad|||||||
|Argentina|<br>autonoma de)|EDESUR<br>|Private|6|91|3,567.1|46.9|
|Armenia|Yerevan|Electric Networks of Armenia<br>(ENA)CJSC|Private|6|242|8,645.8|278.9|
|Australia|Sydney|EnergyAustralia|Public|5|75|4,317.5|9.9|
|Austria|Vienna|Wien Energie Stromnetz<br>GmbH|Public|5|23|52,955.2|113.0|
|Azerbaijan|Baku|Bakielektrikshebeke OJSC|Public|9|241|37,732.3|779.6|
|Bahamas||Bahamas Electricity||||||
|,<br>The|Nassau|<br>Corporation|Public|5|67|24,645.8|114.5|
|Bahrain|Manama|Ministry of Electricity and<br>Water (MEW) (Electricity<br>Distribution Directorate, EDD<br>at the MEW)|Public|5|90|13,031.9|67.0|
|Bangladesh|Dhaka|Dhaka Electric Supply<br>CompanyLtd.(DESCO)|Public|8|142|32,898.7|5,576.1|
|Belarus|Minsk|Minskenergo|Public|7|179|76,619.7|1,383.0|
|Belgium|Brussels|Sibelga|Public|6|88|43,803.3|96.7|
|Belize|Belize City|Belize ElectricityLtd(BEL)|Private|5|66|14,941.5|369.4|
|Benin|Cotonou|Société béninoise d'énergie<br>électrique(SBEE)|Public|4|172|115,890.3|15,452.0|
|Bhutan|Thimphu|Bhutan Power Corporation Ltd|Public|6|101|25,473.3|1,261.1|
|Bolivia|La Paz|Electropaz|Private|8|42|21,146.1|1,297.3|
|Bosnia and<br>Herzegovina|Sarajevo|Elektroprivreda BiH|Public|8|125|24,271.6|516.4|
|Botswana|Gaborone|Botswana Power Corporation|Public|5|121|31,005.9|495.3|
|Brazil|São Paulo|AES Eletropaulo|Private|6|57|12,192.8|151.1|
|||Department of Electrical||||||
|Brunei|Bandar Seri|<br>Services (at the Ministry of||||||
|Darussalam|Begawan|<br>Energy)|Public|5|79|12,891.4|49.0|

27
|Bulgaria|Sofia|CEZ Distribution Bulgaria AD|Private|6|137|22,915.0|397.1|
|---|---|---|---|---|---|---|---|
|||Société Nationale d’Electricité||||||
|Burkina Faso|Ouagadougou|<br>du Burkina(SONABEL)|Public|4|158|75,996.8|14,901.3|
|Burundi|Bujumbura|Régie de Production et<br>Distribution d'Eau et<br>d'Electricité(Regideso)|Public|5|188|55,045.1|36,696.7|
|Cambodia|Phnom Penh|Electricité du Cambodge|Public|4|183|23,279.9|3,581.5|
|Cameroon|Douala|AES SONEL|Private|4|64|21,598.8|1,846.1|
|Canada|Toronto|Toronto Hydro|Public|8|168|64,084.2|153.0|
|||Empresa de Electricidade E||||||
|Cape Verde<br>|Praia|<br>Agua(Electra)|Public|6|58|36,646.0|1,217.5|
|Central||||||||
|African<br>Republic|Bangui|Enerca|Public|7|210|59,842.1|13,298.3|
|Chad|N’Djamena|Société Tchadienne d'Eau et<br>d'Electricité(STEE)|Public|6|67|91,262.9|14,719.8|
|Chile|Santiago|Chilectra|Private|6|31|7,808.9|82.6|
|China|Shanghai|Shanghai Municipal Electric<br>Power Company|Public|5|145|27,339.0|755.2|
|Colombia|Bogota|CODENSA|Public|5|165|58,542.4|1,182.7|
|Comoros|Moroni|MaMWE|Public|3|120|20,476.7|2,353.6|
|Congo, Dem.<br>Rep.|Kinshasa|Société Nationale d'Electricité<br>(SNEL)<br>|Public|6|58|51,894.5|32,434.1|
|Congo,Rep.|Brazzaville|Société Nationale d'Electricité<br>(SNE)<br>|Public|6|135|140,902.4|7,699.6|
|Costa Rica|San José|Compania Nacional de Fuerza<br>yLuz|Public|5|62|19,822.8|316.7|
|Côte d'Ivoire|Abidjan|Compagnie Ivoirienne<br>d'Electricité(CIE)|Private|8|55|43,851.8|4,137.0|
|Croatia|Zagreb|HEP ODS|Public|5|70|45,227.1|327.5|
|Cyprus<br>|Nicosia|Electricity Authority of<br>Cyprus(EAC)|Public|5|247|26,339.9|88.9|
|Czech<br>Republic|Prague|(PRE)/ PREdistribuce a.s.|Private|6|279|32,408.7|187.2|
|Denmark|Copenhagen|DongEnergy|Public|4|38|75,559.1|128.2|
|Djibouti|Djibouti Ville|Electricité de Djibouti(EDD)<br>|Public|4|180|112,853.9|8,816.7|
|Dominica|Roseau|Dominica Electricity Services<br>Ltd.(DOMLEC)|Private|5|61|43,801.6|893.9|
|Dominican<br>Republic|Santo<br>Domingo|EdeSur|Public|7|87|18,359.5|405.3|
|Ecuador|Quito|Empresa ElectricaQuito|Public|7|89|35,436.6|899.4|
|||South Cairo Electricity||||||
|Egypt, Arab<br>Rep.|Cairo|Distribution Company<br>(SCEDC)|Public|7|54|10,555.0|509.9|
|El Salvador|San Salvador|AES El Salvador|Private|7|78|18,199.5|540.0|
|Equatorial<br>Guinea|Malabo|Segesa|Public|5|106|103,492.1|833.3|

28
|Eritrea|Asmara|Eritrean ElectricityAuthority|Public|5|59|15,084.5|4,156.7|
|---|---|---|---|---|---|---|---|
|Estonia|Tallinn|Eesti Energia|Public|4|111|32,208.0|229.1|
|Ethiopia|Addis Ababa|Ethiopia Electric Power<br>Corporation|Public|4|75|12,083.7|3,661.7|
|Fiji|Suva|Fiji ElectricityAuthority|Public|4|81|80,416.0|2,035.9|
|Finland|Helsinki|Helsinki Energy|Public|5|47|15,495.0|33.9|
|France|Paris|Electricité de France(EDF)|Public|5|79|17,436.7|39.6|
|||Société d’Energie et d’Eau du||||||
|Gabon|Libreville|<br>Gabon(SEEG)|Private|6|160|38,326.1|520.0|
|Gambia,The|Banjul|National Water and Electricity<br>Company (NAWEC)|Private|4|178|28,715.7|6,526.3|
|Georgia|Tbilisi|Telasi JSC<br>|Private|5|96|21,783.4|861.0|
|Germany|Berlin|Vattenfall Europe Distribution<br>Berlin GmbH|Private|3|17|22,107.5|51.9|
|Ghana|Accra|ElectricityCompanyof Ghana<br>Dimosia Epicheirisi<br>Ilektrismou (DEI) (also known<br>as the Public Power|Public|4|78|16,964.2|1,425.6|
|Greece|Athens|<br>Corporation(PPC)of Greece)<br>|Public|6|62|16,450.2|57.5|
|Grenada|St. George's|Grenada Electricity Services<br>Ltd.|Private|6|49|20,659.3|370.2|
|Guatemala|Guatemala<br>City|Empresa Electrica de<br>Guatemala,S.A|Private|4|39|17,239.7|655.5|
|Guinea|Conakry|Electricite de Guinee(EDG)|Public|5|69|49,119.0|13,275.4|
|Guinea-<br>Bissau|Bissau|Electricidade e Águas de<br>Guinea-Bissau(EAGB)|Public|7|455|10,880.7|2,133.5|
|Guyana|Georgetown|Guyana Power and Light Inc.<br>(GPL)|Public|7|109|14,623.0|556.2|
|Haiti|Port au<br>Prince|Electricité d’Haiti(EdH)|Public|4|66|24,527.1|3,345.3|
|Honduras|Tegucigalpa|Empresa Nacional de Energia<br>Electrica(ENEE)|Public|8|33|20,206.4|1,110.2|
|Hong Kong<br>SAR,China|HongKong|CLP Power HongKongLtd.|Private|4|93|556.6|1.9|
|||Budapesti Elektromos Művek||||||
|Hungary|Budapest|<br>Rt.(ELMű Rt)|Private|5|252|16,421.3|126.5|
|Iceland|Reykjavik|Orkuveita Reykjavíkur|Public|4|22|2,837.8|6.6|
|India|Mumbai|Bhrihan Mumbai Electricity &<br>Transport Undertaking|Public|7|67|4,687.0|400.6|
|Indonesia|Jakarta|PT PLN|Public|7|101|30,105.8|1,350.0|
|Iran, Islamic<br>Rep.|Tehran|Tehran Regional Electric<br>Company (TREC)|Public|7|140|50,210.0|1,108.4|
|Iraq|Baghdad|Ministryof Electricity|Public|5|47|14,599.9|660.6|
|Ireland|Dublin|Electricity Supply Board<br>(ESB)<br>|Public|5|205|38,387.0|86.6|
|Israel|Tel Aviv|The Israel Electric Corporation<br>Ltd.|Public|6|132|3,252.8|12.6|

29
|Italy|Rome|Acea|Private|5|185|116,772.1|332.9|
|---|---|---|---|---|---|---|---|
|||Jamaica Public Service Co||||||
|Jamaica|Kingston|.<br>Ltd.|Private|6|96|22,333.3|444.9|
|Japan|Tokyo|Tokyo Electric Power<br>Company (TEPCO)<br>|Private|3|105|-|0.0|
|Jordan|Amman|The Jordanian Electric Power<br>Co.,Ltd.(JEPCO)|Private|5|47|12,109.5|323.8|
|Kazakhstan|Almaty|Alatau Zharyk|Public|6|88|7,504.4|111.3|
|Kenya|Nairobi|Kenya Power and Lighting Co.<br>Ltd|Public|6|163|11,258.9|1,462.2|
|Kiribati|Tarawa|Public Utilities Board|Public|6|97|100,547.1|5,320.0|
|Korea,Rep.|Seoul|Korea Electric Power Corp|Public|4|28|8,387.5|42.3|
|||Kosovo Energy Corporation||||||
|Kosovo|Pristina|<br>(KEK)<br>|Public|7|60|33,566.6|1,036.0|
|Kuwait|Kuwait City|Ministry of Electricity and<br>Water(MEW)|Public|7|42|19,969.8|63.4|
|Kyrgyz<br>Republic|Bishkek|Severelectro|Public|7|247|18,364.5|2,110.9|
|Lao PDR|Vientiane|Elictricite du Laos|Public|5|134|24,061.9|2,734.3|
|Latvia|Riga|Sadales Tikls|Public|5|193|50,203.1|405.2|
|Lebanon|Beirut|Electricité du Liban(EDL)|Public|5|75|10,281.0|129.0|
|Lesotho|Maseru|Lesotho Electricity Company<br>(Pty)Ltd|Private|5|140|27,173.3|2,664.1|
|Liberia|Monrovia|Liberia Electricity Corporation<br>(LEC)|Public|4|585|8,470.5|5,294.1|
|Lithuania|Vilnius|AB Lesto|Public|5|128|7,305.2|64.0|
|Luxembourg<br>|Luxembourg|Creos LuxembourgS.A.|Public|5|120|49,209.5|66.1|
|Macedonia,||||||||
|<br>FYR|Skopje|EVN Macedonia<br>|Private|5|151|39,078.8|888.2|
|Madagascar|Antananarivo|Jiro sy rano malagasy<br>(JIRAMA)<br>|Public|6|450|38,055.4|9,236.4|
|Malawi|Blantyre|Electricity Supply Corporation<br>of Malawi(ESCOM)|Public|6|222|32,771.0|11,703.9|
|Malaysia|Kuala<br>Lumpur|Tenaga Nasional Berhad|Public|5|46|4,678.5|64.7|
|Maldives|Malé|State Electric Co. Ltd|Public|6|101|22,290.3|576.0|
|Mali|Bamako|Energie du Mali(EDM)|Private|4|120|26,369.7|3,877.9|
|Marshall<br>Islands|Majuro|Marshalls EnergyCompany|Public|5|67|30,200.0|986.9|
|Mauritania|Nouakchott|Société Mauritanienne<br>d'Electricité(SOMELEC)|Public|5|75|86,370.7|8,997.0|
|Mauritius|Port Louis|Central Electricity Board<br>(C.E.B.)|Public|4|91|25,169.4|347.6|
|Mexico|Mexico City|CFE|Public|9|114|39,068.9|436.0|
|Micronesia|Island of|Pohnpei Utilities Corporation||||||
|,<br>Fed. Sts.|Pohnpei|<br>(PUC)|Public|3|105|11,542.3|519.9|

30
||(Palikir/Kolo<br>nia)|||||||
|---|---|---|---|---|---|---|---|
|Moldova<br>Mongolia|Chisinau<br>Ulan Bator|ICS RED Union Fenosa S.A.<br>Ulaanbaatar Electricity<br>Distribution Network<br>(UBEDN)|Private<br>Public|7<br>8|140<br>126|12,655.9<br>21,101.5|796.0<br>1,294.6|
|Montenegro|Podgorica|Elektroprivreda Crne Gore<br>(EPCG)|Public|5|71|35,551.2|542.8|
|Morocco|Casablanca|Lydec|Private|5|62|76,040.2|2,725.5|
|Mozambique|Maputo|Electricidade de Mozambique|Public|7|87|11,105.0|2,523.9|
|Namibia|Windhoek|Cityof Windhoek|Public|7|55|24,849.9|576.6|
|Nepal|Kathmandu|Nepal ElectricityAuthority|Public|5|74|10,431.2|2,370.7|
|Netherlands|Amsterdam|Liander|Public|5|143|14,544.9|29.5|
|New Zealand|Auckland|Vector Limited|Public|5|50|22,025.0|80.8|
|Nicaragua|Managua|DISNORTE - DISSUR|Private|6|70|17,861.3|1,768.4|
|Niger|Niamey|Société Nigérienne<br>d’Electricité(NIGELEC)|Public|4|120|26,814.6|7,886.7|
|Nigeria|Lagos|Eko Distribution Company|Public|8|260|13,455.3|1,180.3|
|Norway|Oslo|Hafslund|Public|4|66|6,300.7|7.3|
|Oman|Muscat|Muscat Electricity Distribution<br>Company (MEDC)|Public|6|62|11,941.6|66.3|
|Pakistan|Karachi|Karachi Electricity Supply<br>CompanyLtd<br>|Private|6|266|13,826.5|1,355.5|
|Palau|Koror|Palau Public Utilities<br>Corporation|Public|5|125|11,864.5|190.8|
|Panama|Panama City|Unión FENOSA|Private|5|35|1,078.6|16.0|
|Papua New<br>Guinea|Port Moresby|PNG Power Ltd|Public|4|66|875.5|74.2|
|Paraguay|Asuncion|Administracion Nacional de<br>Electricidad(ANDE)|Public|4|53|6,554.3|287.5|
|Peru|Lima|Luz del Sur|Private|5|100|20,800.0|500.0|
|Philippines|Quezon City|MERALCO|Private|5|50|16,023.0|895.1|
|Poland|Warsaw|RWE Stoen|Private|6|186|26,000.0|212.1|
|Portugal|Lisbon|Energias de Portugal (EDP) -<br>Distribuicao|Private|5|64|12,002.1|57.3|
|Puerto Rico|San Juan|Puerto Rico Electric Power<br>Authority (PREPA)|Public|5|32|67,800.0|428.6|
|||Qatar General Electricity and<br>Water Corporation||||||
|Qatar|Doha|<br>(Kahramaa)|Public|4|90|4,066.3|5.8|
|||ENEL (former Electrica||||||
|Romania<br>|Bucharest|<br>Muntenia Sud(EMS))|Private|7|228|44,865.6|538.6|
|Russian<br>Federation|Moscow|MOESK|Private|10|281|386,548.9|4,125.4|

31
|Rwanda|Kigali|EWSA|Public|4|30|25,362.5|5,513.6|
|---|---|---|---|---|---|---|---|
|Samoa|Apia|EPC|Public|4|34|25,044.6|881.9|
|São Tomé<br>and Principe|São Tomé|Empresa de Agua e<br>Electricidade(EMAE)<br>|Public|4|89|16,383.6|1,437.2|
|Saudi Arabia|Riyadh|Saudi Electricity Company<br>(SEC)<br>|Public|4|61|3,494.7|21.5|
|Senegal|Dakar|Société Nationale d'Électricité<br>du Sénégal(SENELEC)|Public|8|125|62,378.0|5,997.9|
|Serbia|Belgrade|Elektroprivreda Srbije (EPS):<br>Elektrodistribucija Beograd<br>plc|Public|4|131|33,821.6|564.6|
|Seychelles|Victoria|Public Utilities Corporation|Public|6|147|47,962.1|565.6|
|Sierra Leone|Freetown|National Power Authority|Public|8|137|9,907.8|2,914.1|
|Singapore<br>|Singapore|SP PowerGrid Ltd.|Public|4|36|12,585.3|33.8|
|Slovak<br>Republic|Bratislava|Zapadoslovenska Energetika|Public|5|177|31,858.8|197.5|
|Slovenia|Ljubljana|Elektro Ljubljana,d.d.|Public|5|38|28,910.0|122.9|
|Solomon<br>Islands|Honiara|Solomon Islands Electricity<br>Authority|Public|4|160|23,432.3|2,575.0|
|South Africa|Johannesburg|ESKOM|Public|5|214|102,727.1|1,780.4|
|Spain|Madrid|Iberdrola|Private|5|101|75,513.8|236.9|
|Sri Lanka|Colombo|Ceylon ElectricityBoard|Public|4|132|33,207.0|1,668.7|
|St. Kitts and<br>Nevis|Basseterre|St. Kitts Electricity<br>Department<br>|Public|4|18|38,275.4|377.1|
|||St. Lucia Electricity Services||||||
|St. Lucia|Castries|<br>Ltd.(Lucelec)|Private|4|25|11,032.3|212.6|
|St Vincent||||||||
|.<br>and the<br>Grenadines|Kingstown|St. Vincent Electricity<br>Services Ltd.(VINLEC)|Public|3|52|14,399.6|280.7|
|Sudan|Khartoum|National Electricity<br>Corporation|Public|5|70|50,319.8|4,091.0|
|Suriname|Paramaribo|N.V. Energiebedrijven<br>|Private|4|58|45,134.9|795.3|
|Swaziland|Mbabane|Swaziland Electricity Board<br>(SEC)|Public|6|137|34,596.4|1,472.2|
|Sweden|Stockholm|Vattenfall|Public|3|52|10,653.5|21.8|
|Switzerland|Zurich|EKZ|Public|3|39|47,794.5|70.7|
|||Public Establishment for<br>Distribution and Exploitation||||||
|Syrian Arab<br>Republic|Damascus|<br>of Electrical Energy<br>(PEDEEE)|Public|5|71|25,205.9|1,045.9|
|Taiwan,<br>China|Taipei|Taiwan Power Company|Public|4|24|9,798.1|55.1|
|Tajikistan|Dushanbe|Barki Tojik|Public|9|193|10,232.5|1,461.8|
|Tanzania|Dar es<br>Salaam|Tanesco|Public|4|109|12,014.5|2,402.9|

32
|Thailand|Bangkok|Metropolitan Electricity<br>Authority|Public|4|35|3,245.1|86.3|
|---|---|---|---|---|---|---|---|
|Timor-Leste|Dili|Electricidade de Timor-Leste|Private|3|39|23,416.3|1,159.2|
|Togo|Lomé|Togo Electricité|Private|4|74|26,491.0|6,020.7|
|Tonga|Nukualofa|Tonga Power Ltd.|Public|5|50|3,753.3|115.1|
|Trinidad and<br>Tobago|Port of Spain|Trinidad and Tobago<br>ElectricityCommission|Public|4|61|1,214.8|7.3|
|Tunisia|Tunis|Societe Tunisienne de<br>l'Electricite et du Gaz(STEG)|Public|4|65|39,535.7|1,062.8|
|Turkey|Istanbul|Boğaziçi Elektrik Dağıtım<br>A.Ş.(BEDAŞ)|Private|5|70|62,357.5|714.3|
|Uganda|Kampala|Umeme|Private|5|91|26,520.9|5,765.4|
|Ukraine|Kiev|JSC Kievenergo|Public|11|285|7,705.5|275.2|
|United Arab||Dubai Electricity And Water||||||
|Emirates|Dubai|<br>Authority (DEWA)|Public|4|55|9,270.4|22.1|
|United<br>Kingdom|London|UK Power Networks|Private|5|109|17,966.2|43.3|
||New York|||||||
|United States|City|Con Edison|Private|4|68|7,966.4|16.9|
|||Administración Nacional de<br>Usinas y Transmisiones||||||
|Uruguay|Montevideo|Eléctricas(UTE)|Public|5|48|1,447.6|15.4|
|Uzbekistan|Tashkent|Uzbekenergo|Public|9|117|24,808.1|2,255.3|
|Vanuatu|Port Vila|Unelco|Private|5|257|33,170.7|1,266.1|
|Venezuela,<br>R.B.|Caracas|Electricidad de Caracas<br>(ELECAR)|Public|6|158|148,938.1|1,460.2|
|Vietnam|Ho Chi Minh<br>City|Ho Chi Minh City Power<br>Company|Public|6|115|30,141.2|2,984.3|
|West Bank<br>and Gaza|Ramallah|Jerusalem District Electricity<br>Company (JDECo)|Private|5|63|24,251.3|1,560.6|
|Yemen,Rep.|Sanaa|Public Electricity Corporation<br>(PEC)|Public|4|35|52,718.3|4,973.4|
|Zambia|Lusaka|Zambia Electricity Supply<br>Corporation Ltd.(ZESCO)|Public|6|117|12,130.2|1,250.5|
|||Zimbabwe Electricity Supply||||||
|Zimbabwe|Harare|<br>Authority (ZESA)|Public|6|125|24,405.5|6,511.9|

33
# **Table 3: Number of Procedures, Time and Cost by Income Group**

|||Pro<br>Starting<br>|cedures<br>|||<br>|Time<br>||C|ost as%o<br>Startin<br>|f GNIper ca<br>|pita|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Getting|a|Registeri||Getting|Starting|Registeri|||g a|Registerin||
||<br>Electricit<br>y|Busines<br>s|ng<br>Property|Constructi<br>on Permits|<br>Electrici<br>ty|<br>a<br>Business|ng<br>Property|Constructi<br>on Permits|Getting<br>Electricity|<br>Busine<br>ss|g<br>Property|Construction<br>Permits|
|**Entire **|**Sample**||||||||||||
|Obs|183.0|183.0|180.0|182.0|183.0|183.0|180.0|182.0|183.0|183.0|180.0|182.0|
|Mean|5.4|7.8|5.9|15.7|111.4|34.0|57.9|192.7|2,158.0|42.2|6.0|486.0|
|Std.|1.5|3.3|2.3|6.5|80.0|58.7|64.9|130.1|4,514.7|74.3|4.9|1,073.1|
|Min|3.0|1.0|1.0|6.0|17.0|1.0|1.0|26.0|0.0|-|-|0.9|
|Max|11.0|18.0|15.0|50.0|585.0|694.0|513.0|1,179.0|36,696.7|735.1|27.9|7,553.7|
|**Low-In**|**come**||||||||||||
|Obs|32.0|32.0|32.0|31.0|32.0|32.0|32.0|31.0|32.0|32.0|32.0|31.0|
|Mean|5.3|8.5|6.5|14.8|163.1|40.4|87.1|252.3|7,902.5|128.4|8.5|1,354.3|
|Std.|1.5|3.7|2.3|5.0|123.7|43.3|80.4|218.7|8,206.8|132.4|4.9|2,078.5|
|Min|3.0|2.0|3.0|8.0|30.0|3.0|5.0|75.0|1,461.8|3.7|0.4|47.9|
|Max|9.0|17.0|12.0|29.0|585.0|216.0|301.0|1,179.0|36,696.7|735.1|18.5|7,553.7|
|**Lower**|**Middle Inc**|**ome**|||||||||||
|Obs|54.0|54.0|51.0|54.0|54.0|54.0|51.0|54.0|54.0|54.0|51.0|54.0|
|Mean|5.7|8.3|6.1|16.4|101.1|37.5|70.4|177.8|1,962.1|46.0|6.2|545.5|
|Std.|1.7|2.8|2.4|5.9|62.6|31.5|75.9|85.3|2,061.5|43.4|6.0|830.3|
|Min|3.0|3.0|1.0|6.0|33.0|3.0|2.0|51.0|74.2|3.1|-|22.4|
|Max|11.0|17.0|15.0|34.0|285.0|161.0|513.0|475.0|8,997.0|169.9|27.9|3,758.4|
|**Upper**|**Middle Inc**|**ome**|||||||||||
|Obs|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|50.0|
|Mean|5.6|8.0|6.3|17.0|107.3|40.6|38.8|201.9|620.4|15.6|5.7|257.9|
|Std.|1.4|3.2|2.0|7.8|61.2|98.3|30.5|103.1|664.6|19.9|4.3|397.4|
|Min|3.0|3.0|2.0|7.0|18.0|3.0|2.0|46.0|15.4|1.0|-|5.4|
|Max|10.0|17.0|13.0|50.0|281.0|694.0|197.0|483.0|4,125.4|119.9|16.9|1,690.0|
|**High In**|**come**||||||||||||
|<br>Obs|<br>47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|47.0|
|Mean|4.8|6.4|4.9|14.2|92.4|18.5|44.7|160.8|107.6|7.3|4.4|87.6|
|Std.|1.0|3.5|2.3|6.3|64.3|24.1|58.7|109.2|144.1|15.0|3.6|141.9|
|Min|3.0|1.0|1.0|6.0|17.0|1.0|1.0|26.0|0.0|-|-|0.9|
|Max|8.0|18.0|11.0|33.0|279.0|135.0|298.0|677.0|833.3|100.9|12.7|589.4|

34
# **Table 4: Number of Procedures, Time and Cost by Region**

|||Pro|cedures||||Time|||Cost as%|of GNIper cap|ita|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Getting<br>Electricit|y<br>Starting a<br>Business|Registering<br>Property|Construction<br>Permits|Getting<br>Electricity|Starting a<br>Business|Registering<br>Property|Construction<br>Permits|<br>Getting<br>Electricity|Starting a<br>Business|Registering<br>Property|Construction<br>Permits|
|**East Asia a**|**nd Pacifi**|**c**|||||||||||
|Obs|24.0|24.0|21.0|24.0|24.0|24.0|21.0|24.0|24.0|24.0|21.0|24.0|
|Mean|4.8|8.0|5.0|17.8|93.7|40.3|86.8|158.8|1,208.8|26.5|3.9|96.3|
|Std. Dev.|1.2|3.5|1.8|7.6|56.0|30.6|120.6|126.3|<br>1,369.4|38.8|3.3|118.8|
|Min|3.0|3.0|2.0|6.0|24.0|3.0|2.0|26.0|1.9|0.7|-|4.8|
|Max|8.0|17.0|10.0|33.0|257.0|110.0|513.0|652.0|5,320.0|150.5|12.9|518.2|
|**Europe an**|**d Central**|**Asia**|||||||||||
|Obs|24.0|24.0|24.0|24.0|24.0|24.0|24.0|24.0|24.0|24.0|24.0|24.0|
|Mean|6.6|6.3|5.9|20.1|162.4|17.0|38.6|249.5|880.7|9.1|2.9|636.3|
|Std. Dev.|1.9|2.3|2.8|8.6|70.8|15.5|33.2|130.8|897.0|10.2|3.3|887.9|
|Min|4.0|2.0|1.0|9.0|60.0|3.0|2.0|74.0|64.0|1.0|-|19.0|
|Max|11.0|12.0|15.0|50.0|285.0|64.0|118.0|677.0|4,125.4|36.9|13.9|3,758.4|
|**High Inco**|**me: OEC**|**D**|||||||||||
|<br>Obs|<br>31.0|<br>31.0|31.0|31.0|31.0|31.0|31.0|31.0|31.0|31.0|31.0|31.0|
|Mean|4.8|5.6|4.7|13.5|100.9|13.1|32.5|154.5|92.6|5.2|4.4|78.3|
|Std. Dev.|1.1|2.9|2.3|6.1|69.3|9.9|39.0|73.3|83.0|6.0|3.3|118.8|
|Min|3.0|1.0|1.0|6.0|17.0|1.0|1.0|26.0|0.0|-|-|3.3|
|Max|8.0|15.0|11.0|33.0|279.0|47.0|152.0|301.0|332.9|20.7|12.7|589.4|
|**Latin Ame**|**rica and**|**Caribbean**|||||||||||
|Obs|32.0|32.0|32.0|32.0|32.0|32.0|32.0|32.0|32.0|32.0|32.0|32.0|
|Mean|5.5|9.2|6.9|14.0|67.3|56.4|65.6|225.0|609.4|37.3|5.9|169.3|
|Std. Dev.|1.4|3.2|1.7|6.1|34.9|120.8|63.8|201.5|670.2|49.4|3.9|166.4|
|Min|3.0|5.0|4.0|7.0|18.0|7.0|7.0|46.0|7.3|0.7|0.9|4.0|
|Max|9.0|17.0|13.0|33.0|165.0|694.0|301.0|1,179.0|3,345.3|250.9|13.8|587.3|
|**Middle Ea**|**st and No**|**rth Africa**|||||||||||
|Obs|18.0|18.0|18.0|18.0|18.0|18.0|18.0|18.0|18.0|18.0|18.0|18.0|
|Mean|5.1|8.4|6.0|16.3|77.7|20.8|35.6|140.5|1,385.4|36.6|5.7|325.7|
|Std. Dev.|1.1|2.7|2.4|4.0|41.0|18.3|21.2|80.9|2,238.9|48.5|6.6|456.4|
|Min|4.0|5.0|2.0|10.0|35.0|7.0|8.0|43.0|5.8|0.8|-|0.9|
|Max|7.0|14.0|10.0|24.0|180.0|77.0|75.0|320.0|8,816.7|169.9|27.9|1,828.1|
|**South Asia**|||||||||||||
|Obs|8.0|8.0|8.0|8.0|8.0|8.0|8.0|8.0|8.0|8.0|8.0|8.0|
|Mean|5.8|7.3|6.0|16.1|134.3|24.8|103.3|205.0|2,115.0|27.0|7.5|1,094.2|
|Std. Dev.|1.4|2.7|2.3|8.5|66.6|13.3|92.8|65.1|1,748.7|18.0|4.1|1,790.9|
|Min|4.0|4.0|3.0|8.0|67.0|7.0|5.0|115.0|<br>400.6|7.2|4.8|<br>13.6|
|Max|8.0|12.0|9.0|34.0|266.0|46.0|250.0|334.0|5,576.1|56.5|16.9|5,183.9|
|**Sub-Sahar**|**an Africa**||||||||||||
|Obs|46.0|46.0|46.0|45.0|46.0|46.0|46.0|45.0|46.0|46.0|46.0|45.0|
|Mean|5.4|8.8|6.5|14.8|141.0|44.8|67.3|202.6|6,098.6|100.7|9.6|1,075.7|
|Std. Dev.|1.4|3.4|2.2|4.1|109.0|44.9|51.8|107.0|<br>7,501.6|116.7|5.2|<br>1,726.0|
|Min|3.0|2.0|4.0|8.0|30.0|3.0|9.0|75.0|347.6|2.2|0.4|23.1|
|Max|9.0|18.0|13.0|29.0|585.0|216.0|295.0|614.0|36,696.7|735.1|20.9|7,553.7|

35
# **Table 5a: Getting Electricity Indicators by Utility Ownership**

|Variable Name|Obs|Mean|**Public U**<br>Std.<br>Dev.|**tility**<br>Min|Max|Obs|**P**<br>Mean|**rivate Util**<br>Std.<br>Dev.|**ity**<br>Min|Max|**T-test**<br>Public vs.<br>Private|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Procedures Getting||||||||||||
|Electricity|133|5|2|3|11|50|5|1|3|10|0.4670|
|Time Getting||||||||||||
|Electricity|133|112|82|18|585|50|111|74|17|281|0.0527|
|Cost as % of GNI<br>pc Getting||||||50|1,185|1,622|0|6,526|1.7978|
|Electricity|133|2,524|5,161|6|36,697|||||||
|Cost in USD||32,13||||||||||
|GettingElectricity|133|1|28,683|876|148,938|50|33,331|55,124|0|386,549|-0.1917|

# **Table 5b: Number of Observations for Getting Electricity Indicators by Utility Ownership, Income Group and Region**

||||Incom<br>|e Group<br>|||||Region<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Utility<br>Ownership|Entire<br>Sample|Low-<br>Incom<br>e|Lower<br>Middle<br>Income|Upper<br>Middle<br>Income|High<br>Income|East<br>Asia<br>&Pacific|Europe<br>&<br>Centra<br>l Asia|High<br>Income:<br>OECD|Latin<br>America<br>&Caribbean|Middle<br>East &<br>North<br>Africa|South<br>Asia|Sub-<br>Sahara<br>n<br>Africa|
|Public|133|28|37|32|36|20|15|21|17|15|7|38|
|% of total|73%|88%|69%|64%|77%|83%|63%|68%|53%|83%|88%|83%|
|Private|50|4|17|18|11|4|9|10|15|3|1|8|
|% of total|27%|13%|31%|36%|23%|17%|38%|32%|47%|17%|13%|17%|

# **Table 5c: Time and Cost of Getting Electricity controlling for income**

||Time Gett|ingElectricity|Costas % ofGN|Ipc GettingElectricity|
|---|---|---|---|---|
|Private utility|-0.702|1.798|-1338.251**|-1039.477**|
||(12.6)|(12.5)|(503.1)|(381.0)|
|Ln GNI per capita||-13.914**||-1662.882***|
|||(4.9)||(320.2)|
|Constant|111.602***|226.523***|2523.627***|16257.891***|
||(7.2)|(44.3)|(448.3)|(2,975.1)|
|Degrees of Freedom|181|180|181|180|
|R-squared|0.00<br>0.07|0.07|0.02|0.33|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

36
# **Table 6: Electricity Sector Outcome Variables by Income Group**

|Variable Name|Sample|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
||**Entire Sample**|**129**|**13**|**11**|**2**|**83**|
|Electric power<br>|Low Income|13|20|15|2|58|
|transmission and<br>distribution losses<br>|Lower Middle<br>Income|33|18|14|6|83|
|(% of output)|Upper Middle<br>Income|39|15|8|6|56|
||High Income|44|7|3|3|17|
||**Entire Sample**|**125**|**5**|**5**|**0**|**27**|
|Value lost due to|Low Income|28|8|6|0|27|
|electrical outages<br>(% of sales)|Lower Middle<br>Income|44|5|4|0|18|
||Upper Middle<br>Income|44|3|3|0|14|
||High Income|9|1|1|0|2|
||**Entire Sample**|**84**|**67**|**33**|**9**|**100**|
||Low Income|18|22|12|9|44|
|Electrification rate<br>(% of population)|Lower Middle<br>Income|30|63|25|16|100|
||Upper Middle<br>Income|25|89|20|34|100|
||High Income|11|99|1|98|100|
||**Entire Sample**|**139**|**5**|**2**|**1**|**7**|
|GCI Quality of<br>Electricity Score|Low Income|21|3|1|1|4|
|<br>(1="insufficient and<br>suffers frequent|Lower Middle<br>Income|34|3|1|1|5|
|interruptions" to 7=<br>"sufficient and reliable")|Upper Middle<br>Income|39|5|1|1|6|
||High Income|45|6|1|5|7|
||**Entire Sample**|**139**|**5**|**1**|**3**|**7**|
|GCI Bribes in Electricity|Low Income|21|4|1|3|6|
|<br>Score (1= "very<br>common" to 7 = "never|Lower Middle<br>Income|34|4|1|3|6|
|occurs")|Upper Middle<br>Income|39|5|1|3|6|
||High Income|45|6|1|4|7|

37
**Table 7: Correlation Matrix for Getting Electricity Indicators and other measures of the performance of the electricity sector**

|Variable/Variable|No. of<br>Procedures<br>Getting<br>Electricity|Time<br>Getting<br>Electricity<br>(in days)|Cost<br>Getting<br>Electricity<br>in USD|Cost<br>Getting<br>Electricity<br>(% of<br>GNIpc)|Electric<br>power<br>transmission<br>and<br>distribution<br>losses<br>(% of<br>output)|Value<br>lost due<br>to<br>electrical<br>outages<br>(% of<br>sales)|Electrification<br>rate<br>(% of<br>population)|GCI Quality of<br>Electricity<br>Score<br>(1="insufficient<br>and suffers<br>frequent<br>interruptions"<br>to 7=<br>"sufficient and<br>reliable")|GCI<br>Bribes in<br>Electricity<br>Score (1=<br>"very<br>common"<br>to 7 =<br>"never<br>occurs")|
|---|---|---|---|---|---|---|---|---|---|
|No. of Procedures Getting<br>Electricity|**1**|||||||||
|Time Getting Electricity (in<br>days)|**0.2857***<br>(0.0001)|**1**||||||||
|Cost Getting Electricity in|**0.2003***|**0.1999***|**1**|||||||
|USD|(0.0066)|(0.0067)||||||||
|Cost Getting Electricity (%|**0.0129**|**0.1738***|**0.2744***|**1**||||||
|of GNIpc)|(0.8619)|(0.0186)|(0.0002)|||||||
|Electric power transmission<br>and distribution losses<br>|**0.1155**|**0.0633**|**0.1311**|**0.1896***|**1**|||||
|(% of output)|(0.1923)|(0.4763)|(0.1386)|(0.0314)||||||
|Value lost due to electrical<br>outages|**0.1135**|**0.0178**|**0.0409**|**0.4231***|**0.3698***|**1**||||
|(% of sales)|(0.2074)|(0.844)|(0.6508)|(0)|(0.0005)|||||
|Electrification rate<br>|**-0.0319**|**-0.3512***|**-0.1864**|**-0.5611***|**-0.3634***|**-0.3892***|**1**|||
|(% of population)<br>GCI Quality of Electricity|(0.7735)|(0.0011)|(0.0896)|(0)|(0.0016)|(0.0014)||||
|Score (1="insufficient and<br>suffers frequent|**-0.3116***|**-0.2212***|**-0.1271**|**-0.4569***|**-0.4968***|**-0.5663***|**0.6524***|**1**||
|interruptions" to 7=||||||||||
|<br>"sufficient and reliable")|(0.0002)|(0.0089)|(0.136)|(0)|(0)|(0)|(0)|||
|GCI Bribes in Electricity<br>Score (1= "very common"|**-0.4133***|**-0.2692***|**-0.2143***|**-0.3860***|**-0.3826***|**-0.3346***|**0.5204***|**0.8269***|**1**|
|to 7 = "never occurs")|(0)|(0.0014)|(0.0113)|(0)|(0)|(0.0009)|(0)|(0)||

P-values in parentheses 38
# **Table 8:  Regression Results: Electrification Rate (as % of population)**

||Electrification|Rate (as % of population)|||
|---|---|---|---|---|
|Procedures Getting Electricity|-0.791|-0.793|0.996|0.993|
||(2.568)|(2.588)|(1.917)|(1.926)|
|Private utility||<br>1.581||<br>3.271|
|||(8.057)||(5.503)|
|Ln GNI per capita|||19.362***<br>|<br>19.393***<br>|
||||(1.44)|(1.43)|
|Constant|71.002***|70.621***|<br>-90.646***|<br>-91.687***|
||(15.135)|(15.238)|(16.611)|(16.485)|
|R-squared|0.001|0.001|0.612|0.614|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Electrification|Rate (as % of population)|||
|Time Getting Electricity (Log)|<br>-21.030***|<br>-21.665***|-7.335|-7.098|
||(5.107)|(5.239)|(3.843)|(4.112)|
|Private utility||<br>-4.134||<br>1.323|
|||(7.655)||(5.716)|
|Ln GNI per capita|||18.273***<br>|<br>18.317***<br>|
||||(1.55)|(1.57)|
|Constant|159.813***|163.656***|<br>-44.177|<br>-45.905|
||(22.514)|(23.587)|(24.925)|(26.684)|
|R-squared|0.132|0.135|0.625|0.625|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Electrification|Rate (as % of population)|||
|Cost as % of GNI pc|-12.342***|-12.340***|-2.395|-2.297|
|<br>Getting Electricity (Log)|(1.042)|(1.045)|(2.151)|(2.210)|
|<br>Pi ili||<br>1119||<br>2938|
|rvate utty||.<br>(5900)||.<br>(5596)|
|Ln GNI per capita||.|16.253***|.<br>16.404***|
||||(3.33)|(3.41)|
|Constant|148.059***|147.765***|<br>-45.02|<br>-47.585|
||<br>(6.726)|<br>(6.963)|<br>(38.900)|<br>(40.304)|
|R-squared|0.516|0.516|0.615|0.617|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||<br>Electrification|<br>Rate (as % of population)|||
|Country rank on the ease of getting|-0.329***|-0.331***|-0.062|-0.058|
|<br>electricity|(0.051)|(0.049)|(0.055)|(0.057)|
|Private utility||-2014||2532|
|||.<br>(7132)||.<br>(5565)|
|L GNI it||.|17888***|.<br>17989***|
|n per capa|||.<br>(201)|.<br>(204)|
|Constant|98.245***|98.896***|.<br>-67.735***|.<br>-69.493***|
||(4.737)|(4.546)|(19.577)|(20.064)|
|R-squared|0.267|0.268|0.617|0.618|
|<br>Observations|84|84|84|84|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

39
# **Table 9:  Regression Results: Transmission and Distribution Losses (% of output)**

||Log of Transmi|ssion and Distribution Losses||
|---|---|---|---|
|Procedures Getting Electricity|0.095**|0.095**<br>0.020|0.020|
||(0.032)|(0.033)<br>(0.030)|(0.030)|
|Private utility||<br> <br>0.015|<br>0.038|
|||(0.105)|(0.086)|
|Ln GNI per capita||-0.234***<br>|-0.234***<br>|
|||(0.04)|(0.04)|
|Constant|1.850***<br>|1.845***<br>4.300***<br> <br>|4.293***<br>|
||(0.197)|(0.206)<br>(0.427)|(0.435)|
|R-squared|0.052|0.052<br>0.311|0.311|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi<br>|ssion and Distribution Losses<br> <br>||
|Time Getting Electricity (Log)|0.168|0.168<br>0.079|0.077|
||(0.089)|(0.090)<br>(0.068)<br>|(0.069)<br>|
|Private utility||-0.006|0.03|
|||(0.112)|(0.088)|
|Ln GNI per capita||-0.235***<br>|-0.236***<br>|
|||(0.04)|(0.04)|
|Constant|1.632***|<br>1.633***<br>4.077***|<br>4.080***|
||(0.399)|(0.400)<br>(0.492)|(0.493)|
|R-squared|0.027|0.027<br>0.314|0.315|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi|ssion and Distribution Losses||
|Cost as % of GNI pc|0.104**|0.106**<br>0.030*|0.032*|
|Getting Electricity (Log)|(0.036)|(0.035)<br>(0.013)|(0.013)|
|<br>Private utility||<br> <br>0.079|<br>0.054|
|||(0100)|(0088)|
|Ln GNI per capita||.<br>-0.204***|.<br>-0.203***|
|||(0.04)|(0.04)|
|Constant|1.796***|1.765***<br>3.991***|3.958***|
||(0.213)|(0.207)<br>(0.387)|(0.402)|
|R-squared|0.191|0.194<br>0.318|0.319|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||
||Log of Transmi|ssion and Distribution Losses||
|Country rank on the ease of getting|0.005***|0.005***<br>0.001|0.001|
|<br>electricity|(0.001)|(0.001)<br>(0.001)|(0.001)|
|Private utility||0.001<br>(0102)|0.032<br>(0087)|
|Ln GNI per capita||.<br>-0.215***|.<br>-0.216***|
|||(004)|(004)|
|Constant|1.974***|.<br>1.974***<br>4.148***|.<br>4.146***|
||(0.089)|(0.094)<br>(0.428)|(0.432)|
|R-squared<br>|0.151<br>|0.151<br>0.315<br> <br>|0.316<br>|
|Observations|129|129<br>129|129|
|Standard errors in parentheses|*** p<0.01, **|p<0.05, * p<0.1||

40
# **Table 10: Regression Results: Value lost due to outages (% of sales)**

||Log of Va|lue lost due to outages|||
|---|---|---|---|---|
|Procedures Getting Electricity|0.116*|0.112*|0.121**|0.120**|
||(0.056)|(0.056)|(0.045)|(0.045)|
|Private utility||-0272||-0078|
|||.<br>(0.224)||.<br>(0.186)|
|Ln GNI per capita|||-0.502***|<br>-0.497***|
||||(0.07)|(0.07)|
|Constant|0.4<br>|0.513<br>|4.281***<br>|4.277***<br>|
||(0.353)|(0.362)|(0.660)|(0.666)|
|R-squared|0.025|0.038|0.309|0.31|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Time Getting Electricity (Log)|0.200|0.193|0.026|0.026|
||(0.146)|(0.144)<br>|(0.122)|(0.123)<br>|
|Private utility||-0.28||-0.098|
|||(0.223)||(0.187)|
|Ln GNI per capita|||-0.498***|-0.492***|
||||(0.08)|(0.08)|
|Constant|0.137|0.265|<br>4.807***|<br>4.798***|
||(0.698)|(0.704)|(0.974)|(0.979)|
|R-squared|0.013|0.027|0.282|0.284|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Cost as % of GNI pc|0.373***|0.367***|0.162|0.162|
|Getting Electricity (Log)|(0.051)|(0.051)|(0.087)|(0.088)|
|<br>Private utility||<br>-0.134<br>||<br>-0.095<br>|
|||(0.200)||(0.188)<br>|
|Ln GNI per capita|||-0.332*|-0.327*|
||||(0.13)|(0.13)|
|Constant|-1.502***|-1.421***|2.525|2.521|
||(0.362)|(0.370)|(1.540)|(1.548)|
|R-squared|0.256|0.259|0.299|0.3|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Log of Va|lue lost due to outages|||
|Country rank on the ease of getting|0.009***|0.008***|0.004*|0.004*|
|electricity<br>|(0.002)|(0.002)<br>|(0.002)|(0.002)<br>|
|Private utility||-0.21||-0.087|
|||(0.214)||(0.185)|
|Ln GNI er caita|||-0427***|<br>-0423***|
|p p|||.<br>(0.09)|.<br>(0.09)|
|Constant|0.149|0.237|<br>3.937***|<br>3.932***|
||(0.228)|(0.243)|(0.822)|(0.827)|
|R-squared<br>|0.14<br>|0.148<br>|0.31<br>|0.311<br>|
|Observations|124|124|124|124|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

41
# **Table 11: Regression Results: Perceived Quality of Electricity Supply**

||Perceived Qu|ality of Electricity Supply|||
|---|---|---|---|---|
|Procedures Getting Electricity|-0.333***|-0.330***|-0.135*|-0.130*|
||(0.089)|(0.089)|(0.058)|(0.058)|
|Private utility||017||0266|
|||.<br>(0.252)||.<br>(0.148)|
|Ln GNI per capita|||0.828***|<br>0.831***|
||||(0.04)|(0.04)|
|Constant|6.361***<br>|6.291***<br>|-1.818**<br>|-1.956**<br>|
||(0.518)|(0.527)|(0.607)|(0.600)|
|R-squared|0.097|0.1|0.694|0.699|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Time Getting Electricity (Log)|-0.687***|-0.690***|-0.149|-0.152|
||(0.188)|(0.189)|(0.114)|(0.116)|
|Private utility||<br>0.253||<br>0.296|
|||(0.262)||(0.152)|
|Ln GNI per capita|||0.843***|0.844***|
||||(0.04)|(0.04)|
|Constant|7.624***|7.563***|<br>-2.012*|<br>-2.101**|
||(0.849)|(0.855)|(0.783)|(0.795)|
|R-squared|0.075|0.08|0.682|0.689|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Cost as % of GNI pc|-0.367**|-0.366**|-0.053|-0.046|
|Getting Electricity (Log)|(0.137)|(0.137)|(0.037)|(0.038)|
|<br>Private utility||<br>0.083<br>||<br>0.27<br>|
|||(0.210)||(0.152)<br>|
|Ln GNI per capita|||0.794***|0.805***|
||||(0.06)|(0.06)|
|Constant|6.664***|6.633***|<br>-1.954**|<br>-2.176**|
||(0.841)|(0.821)|(0.688)|(0.713)|
|R-squared|0.381|0.382|0.683|0.689|
|Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||
||Perceived Qu|ality of Electricity Supply|||
|Country rank on the ease of getting|-0.018***|-0.018***|-0.005*|-0.005*|
|<br>electricity<br>|(0.002)|(0.002)<br>|(0.002)|(0.002)<br>|
|Private utility||0.208||0.279|
|||(0.228)||(0.150)|
|Ln GNI er caita|||0756***|<br>0760***|
|p p|||.<br>(0.07)|.<br>(0.07)|
|Constant|6.153***|6.086***|<br>-1.511*|<br>-1.639*|
||(0.185)|(0.195)|(0.727)|(0.726)|
|R-squared|0.367|0.371|0.695|0.701|
|<br>Observations|139|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1|||

42
# **Table 12: Regression Results:  Frequency of Bribe Payments to Public Utilities**

|||Frequency of Bribe P|ayments|
|---|---|---|---|
|Procedures Getting Electricity|-0.281***||-0.176***|
||(0.050)||(0.040)|
|Ln GNI per capita||0.479***|0.439***|
|||(0.039)|(0.040)|
|Constant|6.398***|0.762*|2.066***|
||(0.30)|(0.35)|(0.50)|
|R-squared|0.171|0.523|0.586|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
|||Frequency of Bribe P|ayments<br>|
|Time Getting Electricity (Log)|-0.494***||-0.203|
||(0.115)||(0.103)|
|Ln GNI per capita||0.479***|<br>0.456***|
|||(0.039)|(0.043)|
|Constant|7.085***<br>(0.54)|0.762*<br>(0.35)|1.866*<br>(0.72)|
|R-squared|0.096|0.523|0.538|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
|||<br>Frequency of Bribe P|ayments|
|Cost as % of GNI pc Getting Electricity (Log)|-0.220**||-0.060*|
||(0.075)||(0.029)|
|Ln GNI per capita||0.479***|0.406***|
|||(0.039)|(0.053)|
|Constant|6.138***|0.762*|1.728**|
||(0.47)|(0.35)|(0.58)|
|R-squared|0.340|0.523|0.536|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
||-0012***|Frequency of Bribe P|ayments<br>-0005***|
|Countr rank on the ease of ettin electricit|.||.|
|y gg y|(0.001)||(0.002)|
|Ln GNI per capita||0.479***|0.365***|
|||(0.039)|(0.055)|
|Constant|5.909***|<br>0.762*|<br>2.209***|
||<br>(0.13)|<br>(0.35)|<br>(0.59)|
|R-squared|0.382|0.523|0.572|
|Observations|139|139|139|
||*** p<001 **|<005 * p<01||
|Standard errors in parentheses|.,|., .||

43
**Table 13: Regression Results: Procedural Complexity**

|||Procedu<br>|res GettingElectricity<br>|Private Utilities<br>|
|---|---|---|---|---|
|||Full Sample|Public Utilities only|only|
|Procedures Starting a Business|0.060|0.046|0.052|0.033|
||(0.032)|(0.028)|(0.034)|(0.051)|
|Ln GNI per capita||-0.125|-0.136|-0.068|
|||(0.066)|(0.072)|(0.162)|
|Constant|4.918***|6.066***|6.130***|5.610***|
||(0.274)|(0.673)|(0.756)|(1.505)|
|R-squared|0.018|0.035|0.042|0.014|
|Observations|183|183|133|50|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

|||Procedu<br>|res GettingElectricity<br>||
|---|---|---|---|---|
|Procedures Transfer Property|0.185***|Full Sample<br>0.169***|Public Utilities only<br>0.195***|Private Utilities only<br>0.089|
||(0.045)|(0.047)|(0.057)|(0.053)|
|Ln GNI per capita||-0.095|-0.092|-0.086|
|||(0.064)|(0.070)|(0.162)|
|Constant|4.316***|5.196***|5.042***|5.549***|
||(0.287)|(0.647)|(0.726)|(1.517)|
|R-squared|0.086|0.095|0.118|0.035|
|Observations|180|180|131|49|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

|||Procedu|res GettingElectricity||
|---|---|---|---|---|
|Procedures Dealing with|0.063***|Full Sample<br>0.060**|Public Utilities only<br>0.04|Private Utilities only<br>0.085***|
|Construction Permits|||||
||(0.016)|(0.018)|(0.023)|(0.023)|
|Ln GNI per capita||-0.131*|-0.146*|-0.105|
|||(0.064)|(0.071)|(0.131)|
|Constant|4.397***|5.529***|6.005***|4.785***|
||(0.275)|(0.648)|(0.742)|(1.209)|
|R-squared|0.078|0.096|0.052|0.304|
|Observations|182|182|132|50|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

44
# **Table 14: Time to deal with public service providers**

||Time Getti<br>|ngElectricity (Log)<br>||
|---|---|---|---|
||Full Sample|Public Utilities only|Private Utilities only|
|Time to Start a Business (Log)|0.034<br>-0.030|0.021|-0.138*|
|Ln GNI per capita|(0.050)<br>(0.051)|(0.063)|(0.064)|
||-0.124***|-0.139***|-0.032|
|Constant|(0.034)|(0.039)|(0.076)|
||4.402***<br>5.622***|5.592***|5.172***|
||(0.157)<br>(0.374)|(0.447)|(0.681)|
|R-squared||||
|Observations|0.003<br>0.078|0.132|0.041|
|Standard errors in parentheses|*** p<0.01, ** p<0.05, * p<0.1|||

|||Time G<br>|ettingElectricity (Log)<br>||
|---|---|---|---|---|
|Time to Transfer Property (Log)|0.094*|Full Sample<br>0.035|Public Utilities only<br>0.056|Private Utilities only<br>0.001|
||(0.046)|(0.045)|(0.056)|(0.080)|
|Ln GNI per capita||-0.110**|-0.130***|-0.01|
|||(0.035)|(0.038)|(0.087)|
|Constant|4.174***|5.300***|5.383***|4.594***|
||(0.171)|(0.385)|(0.441)|(0.897)|
|R-squared|0.023|0.082|0.138|0|
|Observations|180|180|131|49|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

|||Time G<br>|ettingElectricity (Log)<br>||
|---|---|---|---|---|
||||||
|Time to Obtain a|0.228**|u ampe<br>0.166|uc es ony<br>0.173|rvae es ony<br>0.152|
|Construction Permit (Log)|(0.081)|(0.087)|(0.103)|(0.159)|
|Ln GNI per capita||-0.107**|-0.134***|0.013|
|||(0.035)|(0.039)|(0.076)|
|Constant|3.343***|4.554***|4.741***|3.611**|
||(0.418)|(0.625)|(0.744)|(1.118)|
|R-squared|0.042|0.102|0.162|0.018|
|Observations|182|182|132|50|
|Standard errors in parentheses|*** p<0.01,|** p<0.05, * p<0.1|||

45
# **Table 15a: Percentage of firms considering X a constraint to their business operation (out of 47,179 observations)**

||Type of Obstacle|no obstacle|minor<br>obstacle|moderate<br>obstacle|major obstacle|very severe<br>obstacle|
|---|---|---|---|---|---|---|
||Electricity|28.4|16.5|14.0|19.2|22.0|
||Informal sector competitors|31.4|17.4|21.2|17.3|12.8|
||Access to finance|29.1|18.4|23.1|18.0|11.5|
||Crime and Theft|33.5|22.2|17.4|15.7|11.3|
|How much of an obstacle is... to|Inadequately educated workforce<br>|31.3|19.7|21.4|18.4|9.2|
|your business operations?|Transportation|38.8|19.9|19.0|13.8|8.5|
||Access to Land|49.4|14.6|14.6|13.8|7.7|
||Zoning restrictions|58.8|12.6|11.7|10.0|7.0|
||Customs and Trade Regulation|49.2|17.9|16.5|10.5|5.8|
||Labor Regulations|45.2|20.9|19.8|9.3|4.7|

# **Table 15b: Percentage of firms considering electricity a constraint to their business operation –by region**

||no obstacle|minor<br>obstacle|moderate<br>obstacle|major<br>obstacle|very severe<br>obstacle|
|---|---|---|---|---|---|
|Sub-Saharan Africa|17.5|15.4|13.6|24.5|29.0|
|South Asia|8.1|9.4|16.7|38.4|27.3|
|Latin America and Caribbean|29.7|17.9|15.0|15.2|22.3|
|Eastern Europe and Central Asia|39.4|14.9|12.0|14.9|18.8|
|East Asia and Pacific|40.7|24.0|14.9|14.4|5.9|

# **Table 15c: Percentage of firms owning a generator in the last fiscal year (by region)**

|% of firms owning a generator in|last fiscal year|
|---|---|
|South Asia|33.0|
|Sub-Saharan Africa|28.3|
|Latin America and Caribbean|26.1|
|East Asia and Pacific|23.1|
|Eastern Europe and Central Asia|6.9|

46
# **Table 16a: Specification with Log of annual sales as dependent variable – Full sample**

|**Full sample**|**(1)-B**|**asic**|**(2) = (1) +**<br>**finan**|**access to**<br>**ce**|**(3) = (2) +**<br>**firms' int**<br>**trade eng**|**degree of**<br>**ernational**<br>**agement***|**(4) = (**<br>**interact**|**3) plus**<br>**ion terms**|
|---|---|---|---|---|---|---|---|---|
|Variables|Firm|Industry|Firm|Industry|Firm|Industry|Firm|Industry|
|Log of past sales|0.675***<br>(0.02)|0.485***<br>(0.04)|0.719***<br>(0.02)|0.490***<br>(0.05)|0.739***<br>(0.04)|0.483***<br>(0.04)|0.740***<br>(0.04)|0.482***<br><br>(0.04)|
|Rank on the ease of Getting<br>Electricity|-0.00191***|0.00604**|-0.00184**|0.00762*|-0.00143**|0.00|0.00631***|0.0102*|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|
|Number of power outages per<br>month|-0.0001|0.0038|-0.0001|0.0059|0.0003|0.0036|0.000533*|0.0034|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|
|Log of Labor Input|0.359***|0.601***|0.314***|0.588***|0.257***|0.508***|0.253***|0.509***|
|Capital Input|(0.03)<br>-0***|(0.05)<br>-0***|(0.03)<br>-0***|(0.06)<br>-0***|(0.04)<br>-0***|(0.07)<br>-0***|(0.04)<br>-0***|(0.06)<br>-0***|
||0.00|<br>0.00|0.00|0.00|<br>0.00|<br>0.00|<br>0.00|<br>0.00|
|Access to finance Dummy|-|-|0.0535**|0.10|0.0685**|0.24|0.0647*|0.25|
||-|-|(0.02)|(0.13)|(0.03)|(0.17)|(0.03)|(0.19)|
|Direct Export 10% Dummy|-<br>-|-<br>-|-<br>-|-<br>-|0.0552<br>(0.05)|0.0148<br>(0.17)|0.0477<br>(0.05)|-0.0013<br>(0.19)|
|Direct Import Dummy|-|-|-|-|0.151***|0.271**|0.155***|0.265*|
||-|-|-|-|(0.03)|(0.14)|(0.03)|(0.14)|
|Two-way Trader Dummy|-|-|-|-|(0.02)|0.10|<br>(0.02)|0.09|
||-<br>|-<br>|-<br>|-<br>|(0.05)<br>|(0.17)<br>|(0.05)<br>|(0.18)<br>|
|Log of real GDP per capita<br>Income Group Dummies|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|#N/A<br>Yes|
|Country Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interaction Terms:<br>Industry*Getting Electricity|No|No|No|No|No|No|Yes|Yes|
|Constant|3.784***|5.870***|3.222***|5.791***|3.202***|6.612***|2.159***|5.984***|
||(0.34)|(0.54)|(0.34)|(0.69)|(0.49)|(0.78)|(0.49)|(0.85)|
|Observations|8,767|1,574|7,355|1,112|3,829|816|3,829|816|
|R-squared<br>Robust standard errors in paren<br>* measured by whether the firm|0.89<br>theses *** p<<br>exports directly|0.88<br>0.01, ** p<0.05<br>at least 10% of|0.91<br>, * p<0.1<br>its sales, impor|0.88<br>ts directly at|0.93<br>least 10% of i|0.91<br>ts inputs or do|0.93<br>es both of th|0.91<br>ese.|

47
# **Table 16b: Industry interaction terms – Full sample**

|||<br>Firm|Full Samp|le|<br>Industry||
|---|---|---|---|---|---|---|
|ISIC2 Code|Rank on the<br>Ease of GE|Industry Code<br>(ISIC2) -<br>coefficient|Industry*Rank<br>GE|Rank on the<br>Ease of GE|Industry<br>Code (ISIC2) -<br>coefficient|<br>Industry*Ran<br>k GE|
|Other mining and quarrying|0.00631***|-0.940***|0.00|0.0102*|-1.620***|0.00|
|Manufacture of food products and<br>beverages|<br>0.00631***|0.549***|-0.00776***|0.0102*|-0.59|-0.00581***|
|Manufacture of tobacco products|0.00631***|1.054***|-0.0117***|0.0102*|-0.10|-0.00825***|
|Manufacture of textiles|0.00631***|0.23|-0.00587***|0.0102*|-1.052***|-0.00365***|
|Manufacture of wearing apparel;<br>dressing and|0.00631***|0.08|-0.00588***|0.0102*|-1.165***|-0.00396**|
|Tanning and dressing of leather;<br>manufacture|0.00631***|0.864**|-0.0103***|0.0102*|-0.37|-0.0109***|
|Manufacture of wood and of<br>products|0.00631***|0.44|-0.00881***|0.0102*|-0.884**|0.00|
|Manufacture of paper and paper<br>products|0.00631***|0.390**|-0.00588***|0.0102*|0.00|-0.00882***|
|Publishing, printing and<br>reproduction of recorded|0.00631***|0.386**|-0.00705***|0.0102*|-0.54|-0.00728***|
|<br>Manufacture of coke, refined<br>petroleum products|0.00631***|3.459***|-0.0170**|0.0102*|0.27|0.00|
|Manufacture of chemicals and<br>chemical products|0.00631***|0.534***|-0.00706***|0.0102*|-0.29|-0.00655***|
|Manufacture of rubber and<br>plastics products|0.00631***|0.461***|-0.00712***|0.0102*|-0.55|-0.00513***|
|Manufacture of other non-metallic<br>mineral products|0.00631***|0.396**|-0.00746***|0.0102*|-0.35|-0.00736***|
|Manufacture of basic metals|0.00631***|0.571**|-0.00732***|0.0102*|-0.80|0.00|
|Manufacture of fabricated metal<br>products, except|0.00631***|0.31|-0.00683***|0.0102*|-0.670*|-0.00610***|
|Manufacture of machinery and<br>equipment n.e.c.|0.00631***|0.42|-0.00730***|0.0102*|-0.560*|-0.00804***|
|Manufacture of electrical<br>machinery and apparatus|0.00631***|0.38|-0.00595**|0.0102*|-0.43|-0.00658***|
|Manufacture of radio, television<br>and communication|0.00631***|-0.04|-0.00446**|0.0102*|-0.41|-0.01|
|Manufacture of medical, precision<br>and optical|0.00631***|-0.04|0.00|0.0102*|-0.42|-0.01|
|Manufacture of motor vehicles,<br>trailers and|0.00631***|0.897***|-0.0132***|0.0102*|-0.77|0.00|
|Manufacture of other transport<br>equipment|0.00631***|0.47|-0.00883***|0.0102*|-0.84|0.00|
|Manufacture of furniture;<br>manufacturingn.e.c.|0.00631***|0.05|-0.00471**|0.0102*|-0.811*|-0.00532***|
|Recycling|0.00631***|0.551**|-0.0127***|0.0102*|-0.02|-0.0114**|
|Construction|0.00631***|-1.045*|0.00|0.0102*|-0.57|0.00|
|Sale, maintenance and repair of<br>motor|0.00631***|0.653***|-0.00955***|0.0102*|-0.35|0.00|
|Wholesale trade and commission<br>trade, except|0.00631***|1.42|-0.0209**|0.0102*|-0.30|-0.00524**|
|Retail trade, except of motor<br>ehile|0.00631***|0.72|-0.00954*|0.0102*|-0.46|-0.00643**|
|vcs<br>Hotels and restaurants|0.00631***|0.565***|-0.0126***|0.0102*|-0.625**|-0.00514**|
|Land transport; transport via<br>pipelines|0.00631***|0.23|0.00|0.0102*|-1.266***|0.00|
|Supporting and auxiliary transport<br>activities;activities|0.00631***|1.858***|-0.0190***|0.0102*|-0.39|0.00|
|<br>Post and telecommunications|0.00631***|0.210*|0.00|0.0102*|-0.908***|0.00|
|Computer and related activities|0.00631***|0.404***|0.00|0.0102*|-0.830***|0.00|
|Other business activities<br>Robust standard errors in parenthe|0.00631***<br>ses *** p<0.0|1.464***<br>1, ** p<0.05, * p<0.|0.00 <br>1|0.0102*|-0.31|0.00|

48
**Table 17a: Specification with Log of annual sales as dependent variable – Doing Business City only**

|**DB City only**|**(1)-**|**Basic**|**(2) = (1) +**<br>**fina**|**access to**<br>**nce**|**(3) = (2) +**<br>**firms' inte**<br>**trade eng**|**degree of**<br>**rnational**<br>**agement***|**(4) = (**<br>**interacti**|**3) plus**<br>**on terms**|
|---|---|---|---|---|---|---|---|---|
|Variables|Firm|Industry|Firm|Industry|Firm|Industry|Firm|Industry|
|Log of past sales|0.687***<br>(0.04)|0.450***<br>(0.06)|0.710***<br>(0.05)|0.410***<br>(0.07)|0.683***<br>(0.07)|0.334***<br>(0.09)|0.690***<br>(0.06)|0.328***<br>(0.09)|
|Rank on the ease of Getting<br>Electricity|-0.0307***|0.0637***|-0.0344***|-0.03|-0.218***|-0.0913***|-0.231***|-0.0908***|
||(0.01)|(0.01)|(0.00)|(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|
|Number of power outages per<br>month|0.0012|0.0048|0.0010|0.0068|0.0009|0.0037|0.0010|0.0051|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.00)|(0.01)|
|Log of Labor Input|0.346***|0.676***|0.318***|0.745***|0.309***|0.790***|0.297***|0.779***|
||(0.05)|(0.10)|(0.05)|(0.11)|(0.06)|(0.12)|(0.06)|(0.13)|
|Capital Input|0***|-0***|0***|-0***|0**|0.00|0*|0.00|
||0.00|<br>0.00|<br>0.00|0.00|<br>0.00|<br>0.00|0.00|<br>0.00|
|Access to finance Dummy|-|-|0.05|0.12|<br>0.07|<br>0.27|0.06|<br>0.28|
||-|-|(0.03)|(0.12)|(0.05)|(0.18)|(0.05)|(0.19)|
|Direct Export 10% Dummy|-|-|-|-|0.1260|0.3160|0.1070|0.2980|
||-|-|-|-|(0.12)|(0.32)|(0.12)|(0.35)|
|Direct Import Dummy|-|-|-|-|0.219***|0.417***|0.237***|0.469***|
||-|-|-|-|(0.06)|(0.14)|(0.06)|(0.14)|
|Two-way Trader Dummy|-|-|-|-|(0.13)|0.04|(0.13)|(0.02)|
|L f l GDP i|-<br>#N/A|-<br>#N/A|-<br>#N/A|-<br>#N/A|(0.12)<br>#N/A|(0.27)<br>#N/A|(0.12)<br>#N/A|(0.32)<br>#N/A|
|og o rea per capta<br>Income Group Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interaction Terms:<br>Industry*Getting Electricity|No|No|No|No|No|No|Yes|Yes|
|<br>Constant|4.873***|4.551***|4.802***|7.550***|43.70***|10.09***|45.57***|9.980***|
||(0.44)|(0.54)|(0.45)|(0.73)|(2.76)|(1.21)|(2.71)|(1.40)|
|Observations|3,043|875|2,749|679|1,270|468|1,270|468|
|R-squared<br>Robust standard errors in paren<br>* measured by whether the firm|0.93<br>theses ***<br>exports direc|0.86<br>p<0.01, ** p<0.<br>tly at least 10%|0.94<br>05, * p<0.1<br>of its sales, im|0.87<br> <br>ports directly|0.95<br>at least 10%|0.90<br>of its inputs o|0.95<br>r does both o|0.91<br>f these.|

49
# **Table 17b: Industry interaction terms – Doing Business city only**

|||Idt Cd<br>Firm|Doing Busine|ss city only|Idt<br>Industry||
|---|---|---|---|---|---|---|
|ISIC2 Code|Rank on the<br>Ease of GE|nusry oe<br>(ISIC2) -<br>coefficient|Industry*Ran<br>k GE|Rank on the<br>Ease of GE|nusry<br>Code (ISIC2) -<br>coefficient|<br>Industry*Ran<br>k GE|
|Manufacture of tobacco products|-0.231***|0.00|0.00|-0.0908***|-0.47|0.00|
|Manufacture of textiles|-0.231***|-0.664***|0.00418**|-0.0908***|-0.581*|0.00|
|Manufacture of wearing apparel;<br>dressing and|-0.231***|-0.746***|0.00353**|-0.0908***|-0.57|0.00|
|Tanning and dressing of leather;<br>manufacture|-0.231***|0.11|-0.00322*|-0.0908***|0.36|-0.00750*|
|Manufacture of wood and of<br>products|-0.231***|-0.39|0.00|-0.0908***|0.12|0.00|
|Manufacture of paper and paper<br>products|-0.231***|-0.22|0.00248**|-0.0908***|0.90|-0.01|
|Publishing, printing and<br>reproductionof recorded|-0.231***|-0.282**|0.00|-0.0908***|0.14|0.00|
|Manufacture of coke, refined<br>petroleumproducts|-0.231***|0.505***|0.00|-0.0908***|0.800***|0.00|
|Manufacture of chemicals and<br>chemicalproducts|-0.231***|-0.217*|0.00179*|-0.0908***|0.12|0.00|
|Manufacture of rubber and<br>plastics products|-0.231***|0.04|0.00|-0.0908***|0.04|0.00|
|Manufacture of other non-metallic<br>mineralproducts|-0.231***|-0.39|0.00|-0.0908***|-0.42|0.01|
|Manufacture of basic metals|-0.231***|-0.33|0.00|-0.0908***|-0.54|0.01|
|Manufacture of fabricated metal<br>products, except|-0.231***|-0.756***|0.00529***|-0.0908***|-0.50|0.00|
|Manufacture of machinery and<br>equipment n.e.c.|-0.231***|-0.29|0.00|-0.0908***|-0.24|0.00|
|Manufacture of electrical<br>machineryandapparatus|-0.231***|-0.25|0.00|-0.0908***|0.21|0.00|
|Manufacture of radio, television<br>and communication|-0.231***|-1.512***|0.0111***|-0.0908***|-2.450***|0.0184**|
|Manufacture of medical, precision<br>and optical|-0.231***|-0.993***|0.00|-0.0908***|-0.47|0.00|
|Manufacture of motor vehicles,<br>trailersand|-0.231***|0.809**|-0.00897***|-0.0908***|0.29|0.00|
|Manufacture of other transport<br>equipment|-0.231***|-0.537*|0.00689***|-0.0908***|-0.03|0.00|
|Manufacture of furniture;<br>manufacturingn.e.c.|-0.231***|-0.41|0.00|-0.0908***|0.09|0.00|
|Recycling|-0.231***|-0.916**|0.01|-0.0908***|-1.14|0.02|
|Construction|-0.231***|-1.598***|0.0174*|-0.0908***|-0.21|0.01|
|Sale, maintenance and repair of<br>motor|-0.231***|-0.292***|0.00|-0.0908***|0.60|0.00|
|Wholesale trade and commission<br>trade, except|-0.231***|0.75|-0.01|-0.0908***|0.01|0.01|
|Retail trade, except of motor<br>vehicles|-0.231***|-0.73|0.01|-0.0908***|0.06|0.00|
|Hotels and restaurants|-0.231***|-0.13|0.00|-0.0908***|0.36|0.00|
|Supporting and auxiliary transport<br>activities; activities|-0.231***|-0.11|0.00|-0.0908***|-0.728***|0.00|
|Computer and related activities|-0.231***|-0.30|0.01|-0.0908***|-0.25|0.00754***|
|Robust standard errors in parenthe|ses *** p<0|.01, ** p<0.05, * p|<0.1||||

50