rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7789

# **Abstract**

Analysis of household expenditure surveys since 2008 in 22 Sub-Saharan African countries shows that one-third of all people use electricity. As expected, users are disproportionately urban and rich. In communities with access to electricity, lack of affordability is the greatest barrier to household connection. Lifeline rates enabling the poor to use grid electricity vary in availability, with six countries allowing 30 kilowatt-hours or less of electricity usage a month at low prices. Affordability challenges are aggravated by sharing of meters by several households—denying them access to lifeline rates—and high connection costs in many countries, made worse by demands from utility staff for bribes in some countries. Collection of detailed information on residential schedules enabled calculation of the percentage of total household expenditures needed for electricity at the subsistence and other levels. Affordability varied across countries, with grid electricity even at the subsistence level being out of reach for the poor in half the countries and even more so once connection charges are considered. Examination of the gender of the head of household shows that female-headed households are not disadvantaged in electricity use once income and the place of residence (urban or rural) are taken into account. However, female-headed - households tend to be poorer, making it all the more impor tant to focus on helping the poor for the goal of achieving universal access. Installing individual meters and subsidizing installation, encouraging prepaid metering so as to avoid disconnection and reconnection charges, reformulating lifeline blocks and rates as appropriate, and stamping out corruption to eliminate bribe-taking can all help the poor.

This paper is a product of the Energy and Extractives Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mkojima@worldbank.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
tax rate on income, profits and capital gains, compared to other countries, even those at similar income levels (Figure 3a). However, Value Added Tax (VAT) rates, which range from 5 percent to 20 percent across the continent, are comparable to other countries with an average rate of 15 percent (Figure 3b).

Taxpayers in Africa face higher tax compliance costs, even compared to countries at similar income levels. Figure 4 presents how much time, measured in hours per year, firms spend preparing and paying three major types of taxes: the corporate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions. African countries spend on average 273 hours per year on taxes, a greater burden than the average of 220 hours in other regions (World Development Indicators, 2018).<sup>2</sup> Tax administration patterns in African countries feature lower reliance on technology and greater reliance on manual systems and in-person interactions between taxpayers and tax collectors. In African countries, 72 percent of firms report being required to meet with tax officials, and for those affected, 3.2 meetings are held on average each year (World Bank Enterprise Surveys, 2010-2020). In contrast, 37 percent of firms in Europe and Central Asia are required to meet with tax officials and of those affected, 2.1 meetings are held on average. The high frequency of meetings is also correlated with corruption indicators in taxation. On average, 17 percent of firms in Sub-Saharan Africa report that they are expected to give gifts in meetings with tax officials. However only 7 percent report the same in Europe and Central Asia. Similarly, 28 percent of firms in Sub-Saharan Africa cite tax administration as a major constraint, compared to 16 percent in Europe and Central Asia (World Bank Enterprise Surveys, 2010-2020).

Along the same lines, the use of information technology in tax administration, such as e-filing, increases with national income. For example, as of 2016, 85 percent of high-income countries, 65 percent of middle-income countries and 32 percent of low-income countries had adopted e-filing (World Bank, 2016). Its prevalence has since continued to grow rapidly.

> 2Data from the most recent year available between 2010 and 2020 is used to calculate the regional averages.

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20 discoveries are more likely (an informational probing effect).<sup>22</sup> The coefficient associated with the square term in the stock of cumulative discoveries is negative suggesting that the effect is non-linear. In other words, bigger stocks of cumulative discoveries eventually turn out to be associated with a lower likelihood of discovery as the easiest available and cheapest deposits have been discovered (“running out of resources” effect). We interpret this as a trade-off between the initially reduced costs of exploring close to a known deposit with the eventually increased cost due to geological depletion.

# **B.   Verifying the Mechanism: Exploration Efforts**

So far, we have focused on the relationship between market orientation and major discoveries. To examine the underlying mechanism, we explore whether exploration efforts rise following shifts in market orientation. Our hypothesis is that more market-oriented and open economies are able to attract more exploration investment and thus have more resource discoveries. Oil and gas exploration, as well as mineral exploration, are capital intensive and thus costly. Nowadays, over a hundred billion dollars are spent on resource exploration annually according to Rystad and SNL Metals and Mining. And while exploration is a very risky activity<sup>23</sup> , in which “luck is obviously a major factor” (Harbaugh, Davis, and Wendebourg, 1995), exploration efforts ought to be a key determinant of discoveries. To verify that proposition for our data, we first estimate the following equation where _yitk_ and _sitk_ are the number of discoveries and the logarithm of exploration spending in millions of constant (2010) U.S. dollars<sup>24</sup> , respectively, of resource _k_ in country _i_ at time _t_ and  _i_ , _t_ and  _k_ are country, year and resource fixed effects. ���� is a _p_ -th order lag operator.

> 22 Cavalcanti et al (2016) used well-level data on oil drilling for Brazil to show that, after a first wild-cat discovery, follow-up exploration activity and additional discoveries increase significantly in following years.

> 23 An oil exploration well (wildcat well – a well drilled a mile or more from an area of existing oil production) can have a probability as low as 10% of yielding viable oil, while a rank wildcat (a well drilled in an area where there is no existing production) has an even smaller chance of finding oil. Elf was drilling in 1971 for offshore oil in Norway and found nothing. Recently, it found a huge new field just 3 metres away from the original drilling. Drilling outcomes are therefore highly uncertain.

> 24 Exploration expenditures data are deflated using the US GDP deflator. Using alternative deflators gives similar results.
low to high-income countries. Table 12 provides a view of this. For each of our region-toregion links it calculates the percentage change in average gross national income (GNI) per capita for the average migrant. That is, for each bilateral link it calculates the change in current dollar GNI per capita and then weights these together into regional summaries by the number of migrants reported for the link.<sup>21</sup> The GNI is generated in $US using the World Bank Atlas method of conversion and divided by the mid-year population and is taken from the World Development indicators for 2001. The matrix is best thought of in terms of blocks. With the Northern groups reported first, the (3x3) matrix in the top left corner refers to North-North movements. The differentials are small here, but they show the North Americans always "losing", for example. The (3x6) matrix in the top right is North to South and it shows massive "income loses" for Northern people migrating to developing countries. The (6x3) matrix in the bottom left is South-North and shows huge gains. The final sub-matrix – (6x6) in the lower right - is South-South. It has pluses and minuses as one would expect, given the spread of incomes from essentially middleincome regions LAC and EAP to the very poor in AFR and SAS.

> 21 The GNI refer to 2001, not the (unknown) years in which migrants moved, but the basic argument still holds.

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8 shareholders with a distribution rule allocating 20 percent of the shares to the state;<sup>7</sup> 20 percent to Slovenian citizens (each citizen received an allotment of free certificates that they could exchange for shares in former state enterprises); 20 percent to enterprise employees; and the remaining 40 percent to bid. The enterprise employees could acquire these shares at a 50% discount payable over four years, so there was a built in bias favoring internal ownership. The process of transferring ownership from state to private hands was completed by 1995.

Unsurprisingly, the ownership pattern which emerged immediately upon the completion of privatization programs of individual enterprises corresponded very well to the conditions imposed by the privatization law. Based on a 1994/95 survey of 183 former state enterprises, Simoneti et al (2001) found that internal owners controlled 44 percent of the shares in these firms. Even in firms with a majority of internal owners, managers only controlled 5% of the shares so the shares were broadly distributed among the current and former firm workers and not the managers. The state retained about 30% of the shares. Privatization funds (essentially a mutual fund with a portfolio of former state owned enterprises) owned about 19 percent of the shares. Over time, these relatively diffuse ownership patterns became more concentrated. By 1999, 40 percent of initial shareholders had sold their shares, and the 5 largest owners held, on average, 62 percent of the stock. Managers and large outside investors increased their holdings, while small shareholders and the state reduced their holdings.

Djankov and Murrell (2002) report that the only type of ownership concentration that negatively affected privatized firm performance in the transition economies was when workers own the shares. If those results hold for Slovenia, the initial concentration of shares among workers would have hampered firm efficiency, but the later move toward more concentrated ownership among either insiders or outsiders should improve the efficiency of privatized firms.
Why might aid be more effective coming from a single or dominant donor? In a recipient with many donors, each of which is responsible for only a small part of development assistance, responsibility for success or failure is diffused, and any single donor will rarely have much of a stake in the country’s economic and social development (Belton 2003). Aid involves a set of collective action problems when there are multiple donors, each concerned with development in the recipient country, but with their own national goals as well, that sometimes detract from development objectives. Donor countries all have their own commercial and security objectives, and their aid agencies additionally have the objective of maximizing aid budgets, which requires satisfying key domestic constituencies in parliament and among aid contractors and advocacy groups. This latter objective often requires making the results of aid programs visible, quantifiable, and directly attributable to the donor’s activities – even when doing so reduces the developmental impact of aid. From the perspective of a recipient country’s welfare, incentives for any one donor to shirk on activities that maximize overall development in favor of activities that contribute to donor-specific goals strengthen as the number of donors increases.

Because supplier cartels are typically created to raise the price of their products, donor coordination may be viewed as something inimical to the development of aid recipient countries. However, donors have a common interest in development, as well as their separate “private” goals, which lead to practices such as tying aid, hiring away key government staff to run their projects, etc. Collusion by suppliers in this setting often can imply reducing rather than increasing the “price” of aid, and some forms of competition among donors can increase its price. Admittedly, donor cooperation sometimes takes the form of imposing unwelcome policy conditions, although government resistance to them is typically motivated more by concern over the rent-reducing effects of these policies than by any concern over adverse effects on poor people’s welfare.

Costs associated with a proliferation of donors can be grouped into two broad categories. Some costs are primarily of a short-term and reversible nature, “merely” wasting resources unnecessarily. For example, tying aid to the employment of donorcountry contractors has been estimated in an OECD study to reduce its real value by between 15 percent and 30 percent (Jepma 1991). Transactions costs associated with numerous and diverse donor rules and procedures for managing aid projects and programs, different languages and fiscal calendars, etc. (see Berg 1993: 81; UNDP 2003: 148) can also be viewed as detracting from aid’s value.<sup>2</sup> There is much duplication in “country analytic work” such as poverty assessments, public expenditure reviews, 2 Untying aid and standardizing and coordinating donor procedures would not necessarily increase the real resources to recipients, however, as political support for aid in donor countries might drop in response.

2
We include several variables to account for country-level variation. From the WDI database, we define access to credit as measured by the ratio of private sector credit to GDP and skills as measured by the percent of the workforce with tertiary education. Quality of infrastructure is measured by an index taken from the Global Competitiveness Report,<sup>24</sup> a survey of business leaders published by the World Economic Forum. Foreign direct investment inward and outward stock are measured as the ratios of stock to GDP for manufacturing and for service sectors, from the Eurostat SBS.

To assess the regulatory environment within each country, we employ the World Bank’s Doing Business<sup>25</sup> (DB) database. Using principal component analysis, we construct a comprehensive index of all regulatory policies, _all_DB_ .<sup>26</sup> A second variable, _DB_business_startup_ , indexes barriers to entry and exit, including the costs of starting a business, registering property, and closing a business. _DB_business_operations_ indexes the difficulty of operating a firm, including securing construction permits, paying taxes, trading across borders, and employing workers. Finally, _DB_institutional_environment_ is an index of the quality of the legal and institutional framework for enterprises, including the level of protection for minority shareholders, the quality of the credit information systems, and the cost and speed of contract enforcement. All indices are coded such that higher values indicate better regulation.<sup>27</sup> Summary statistics are provided in Appendix XII.

# **4. Methodology**

We use the following specification to analyze productivity growth in Europe.

The variable _∆_ ln(Prodi)03-08 is the annualized growth rate of labor productivity (defined as value-added per employee) for firm _i_ from 2003 to 2008.<sup>28</sup> _Size_ , is expressed in terms of number of employees on the company’s payroll, and is divided into five previously mentioned categories. Microenterprises are mostly family-owned and have a limited division of tasks. Flexibility in labor usage and minimal overhead costs allow microenterprises to reach a baseline level of efficiency. However, limited access to capital

> 24 World Economic Forum. _Global Competitiveness Report_ . http://www.weforum.org/issues/global-competitiveness.

> 25 World Bank. _Doing Business_ . http://doingbusiness.org/.

> 26 For a discussion of principal component analysis, see Appendix V.

> 27 Given that the principal component analysis is built on the basis of indicators, it shares the indicators’ methodological limitations. To verify the quality of the principal component analysis indicator, we compare it with an alternative measure of the quality of business regulation, the Product Market Regulation indicator constructed by the Organisation for Economic Co-operation and Development. The correlation between the comprehensive principal component analysis index of _Doing Business_ indicators and the economy-wide Product Market Regulation indicator is very high (-0.74) using 2008 data (World Bank 2009) for the 39 countries for which both indicators are available.

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interest rates are then reported using an average weighted by loan volume. Doing so puts greater weight on larger loans and ensures that our results are economically meaningful.

The sixth data set consists of the monthly balance sheet information of 22 commercial banks representing more than 98 percent of commercial bank-lending in the country.<sup>23</sup> Data are provided by the Bank of Mexico (Banxico) and variables in this data set include the total credit volume of banks, both to private firms and public entities, as well as their respective interest rates.

Finally, the seventh main data set is Orbis, a _firm-year_ level data set compiled by Bureau van Dijk, containing information on the balance sheets and income statements of a large set of Mexican firms. The data set reports information on assets and revenues of firms as well as their total and bank-specific liabilities by type of financing. As shown in Morais et al. (2019) this sample of firms is representative of the universe of sectors and locations in Mexico, albeit skewed towards larger firms.

The summary statistics of our data set are shown in Table 1, with the definitions of all variables listed in Table A1 in the Appendix. In Panel A, we display summary statistics of macrolevel data from states and municipalities. We start by presenting the ex-ante indebtedness of state governments, measured by the main indicator _DebtStates,16Q1_ , which is the ratio of states’ public debt to freely disposable income one month prior to the introduction of the FD Law.<sup>24</sup> This ratio is on average 86 percent. However, there is large variation across states (Figure 1). While states in the bottom decile have a ratio of less than 20 percent, the indebtedness ratio of states in the top decile is 225 percent.<sup>25</sup> Furthermore, the average maturity of debt to states is of 14.6 years, with bottom and top deciles having a maturity of 8.3 and 19.3 years, respectively. Still in Panel A, we also present statistics on state-level GDP and employment growth rates, as well as poverty and inequality ratios. The average quarterly growth rate is 0.8 percent, while the employment quarterly growth rate is on average 0.6 percent, with growth rates at the bottom and top deciles of -2.1 and 3.3 percent, respectively. Regarding poverty rates, 43 percent of the population of the average state is considered poor. More concretely, 34 percent of the population is considered to be in moderate

> 23 To guarantee the comparability of our results across banks, and given our focus on commercial lending, we exclude from our analysis banks that specialize in consumer lending as well as niche banking.

> 24 Of the 32 Mexican states, we only have local public debt data on 30. Both Distrito Federal (i.e. Mexico City) and Tlaxcala do not report this information.

> 25 We also present data on the debt of municipalities, which corresponds to less than 10 percent of local debt.

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