rafmacalaba/data-use-mentions-extended huggingface dataset:
– ORBIS

Bureau van Dijk (BvD) provides two ways to access ORBIS data: the BvD-ORBIS online interface (Orbis.bvdinfo.com) and the BvD historic vintage files. The interface is the most straightforward way to access the data for each firm. By paying a subscription fee, a user can look up any company and if it exists, the interface will list all the information relevant to the search. For example, detailed information such as the list of shareholders and subsidiaries, sector of activity, balance sheets, and income statements among others can be viewed or downloaded online through ORBIS.

The platform is not designed for large volumes of data extraction because it puts a cap of 10,000 fields per export request. As a result, for data download requirements that exceed the cap limit, it is necessary to perform multiple downloads. Given the scope of the Global BOS database, the online download is also not a scalable option because the definition of SOEs requires both direct and indirect shareholding information and the interface lists only direct shareholders. To get the list of shareholders of the shareholders of a firm, the only option is to check each one of the first level shareholders and recover information manually.

Due to the challenges mentioned above, the best option for the construction of a large database such as the Global BOS database is to request access to the BvD historic vintage files, a copy of the entire raw 11
export booms lead to exchange rate appreciation, the government revenue base is eroded. In addition to limiting the naira value of petroleum revenues, Nigeria's currency appreciation lowered revenue from ad valorgm import duties and switched profits from the relatively taxable manufacturing sector to the untaxed commercial and distribution sectors. This caused federally collected revenue to fall from about 25 percent of GDP in 1981 to 20 percent of GDP in 1985. The revenue/GDP ratio did, however, improve sharply to 24 percent in 1986 as a result of the substantial devaluation experienced in the last quarter of the year, and efforts to widen the tax base. The sensitivity of government revenue to the exchange rate suggests that countries that manage to avoid overvalued exchange rates during booms will have a considerably easier time preventing fiscal problems from arising. This assumes, of course, that the government succeeds in limiting expenditure if revenlues remain intact.

Jamaica: Booms and Economic Stagnation The contrast in Jamaica's growth performance in the 1960s compared to the 1970s and first half of the 1980s is striking. In the 1960s, the economy experienced positive growth in every year except 1963 when it dropped slightly, before surging ahead at a 12.3 percent rate in 1964. In the 197085 period, on the other hand, real growth languished, dropping by as much as 5 or 6 percent in several terms. This stagnation has also been accompanied by considerably higher rates of inflation than those prevailing in the 1960s, or those prevailing in the industrial countries during the 1970s for that matter. (See the Jamaica Table in Appendix C.) The long-run trend in Jamaica's fiscal situation is equally unfavorable. The ratio of the fiscal deficit to GDP was generally 2-3 percent in the 30
country official statistical offices and other multilateral organizations (see Annex).<sup>3</sup> The annual GDP growth rate for the world as a whole is assumed to be 3.5% per year in the period 20112020, while that for MENA is 4.3%. Oil importing countries are assumed to grow at 3.5% per year, while developing oil exporters and GCC countries are assumed to grow at 4.6% and 4.5%, respectively.<sup>4</sup> Demographic trends are taken from the 2009 UN Urbanization Prospects.

**Table 4: Unit costs of infrastructure by sector (US$)**

|**Sector **|**Cost per unit**|**Unit**|**Depreciation rate (%)**|
|---|---|---|---|
|**Electricity generation**|2,000|kW|4|
|**Paved roads**|410,000|Kilometer|4.7|
|**Unpaved roads**|50,000|Kilometer|7.2|
|**Rails**|900,000|Kilometer|4|
|**Rural water and sanitation**|150|person|3|
|**Urban sanitation**|150|person|3|
|**Rural sanitation**|130|person|3|
|**Urban water**|80|person|3|
|**Main telephone lines***|127-580|line|8|
|**Mobile lines***|127-451|line|8|
|**Access to electricity**|195|person|4|
|**Ports**|348|TEU|4|
|**Wastewater treatment**|120|person|3|

Source: Yepes (2008)
the same time, changes in labor institutions and external conditions increased labor demand and wages, especially for the least qualified workers. Minimum wage had a real growth of more than 100% in the period, rising from 234 reais in 1994 to 555 reais in 2013.<sup>3</sup> The increase was faster in the 2000s, with a 62% rise between 2003 and 2012. Labor formalization increased fast in the 2000s, with the share of informal workers going from around half of the working population in 2002 to 38.3% by 2012. At the same time, the wage gap between formal and informal workers reduced by 12.6p.p., from 27.9% to 15.3%. The change in formalization was particularly high in the lower tail of the distribution, which potentially had a considerable impact on wage inequality, specially when coupled with the increase in real minimum wage in the period. Brazil also undertook a broad trade reform that dramatically reduced tariffs on imported goods in the early 1990s, with effective tariffs on imports decreasing from around 55% to 20%, while during the 2000s the commodity boom raised the terms of trade by around 35% between 2005 and 2011 in Brazil (de La Torre et al., 2015), whereas the real exchange

> 1Data from IPEA, available here, accessed in April 28, 2019.

> 2Data from IPEA/PME. Only for the metropolitan areas of Recife, Salvador, Belo Horizonte, Rio de Janeiro, S ̃ao Paulo and Porto Alegre.

> 3Using 2011 Brazilian reais. In nominal terms, these values are respectively 70 and 622 reais. Using 2011 PPP, they are respectively 240 and 377 dollars. To compute real values, we use price data from IPCA, provided by IPEA here. For data on PPP, we use data from the World Bank, provided here. Both were accessed on June 6, 2019.

4
Economic segregation can be thought to affect educational attainment through two distinct channels (Mayer 2002): through the distribution of resources between schools, and through the composition of students within schools. In this paper, I use a pupil level data-set for sixth graders in Argentina to explore the second mechanism. The effect of the composition of classmates will be identified using the variations across classes within schools, so that the first set of factors – related to the political economy of school financing, and quality of monitoring and institutions – will be controlled for rather than tested. The identification strategy follows closely that of Ammermueler and Pischke (2009). The aim is to understand the impact of increased socioeconomic segregation on both the achievement gap between poor and non-poor and the average academic achievement of children at school. To do this, I need to depart from the standard peer-group effects approach, allowing for heterogeneity in the effects both in terms of the socioeconomic background of the recipient student and in terms of the distribution of socioeconomic characteristics within the classroom.

I find that, first, peer effects are indeed heterogenous; wealthier pupils are more sensitive to peer effects overall. Second, poor children do best with peers who are richer, but not too rich. This result is consistent with hypotheses of social distance and competition; people that are too far away in the social spectrum have lower (or even negative) effects on others (Akerlof 1997). The combined prediction of these two effects implies a strong equityefficiency tradeoff from school integration. Increasing socioeconomic segregation raises both the overall average test score and the gap between the rich and poor pupils, where the latter is appreciably more sensitive to the allocation of students than the former.

The paper proceeds as follows. The next section describes the data employed and presents basic stylized facts on the relation between test scores and segregation within provinces and localities. Section 3 presents the standard approach to estimating peer-group effects and the alternative model proposed. Section 4 describes the empirical specification and the estimation strategy chosen. Section 5
Policy Research Working Paper 7786

# **Abstract**

Recent evidence suggests only modest social and economic impacts of microfinance. Favorable cost-benefit ratios then depend on low costs. This paper uses proprietary data on 1,335 microfinance institutions between 2005 and 2009, jointly serving 80.1 million borrowers, to calculate the costs of microfinance and other elements of the microfinance business model. It calculates that on average, subsidies amounted to $132 per borrower, but the distribution is highly skewed. The median microfinance institution used subsidies at a rate of just $26 per borrower, and no subsidy was used by the institution at the 25th percentile. These data suggest that, for some institutions, even modest benefits could yield impressive cost-benefit ratios. At the same time, the data show that the subsidy is large for some institutions. Counter to expectations, the most heavily-subsidized group of borrowers is customers of the most commercialized institutions, with an average of $275 per borrower and a median of $93. Customers of nongovernmental organizations, which focus on the poorest customers and women, receive a far smaller subsidy: the median microfinance nongovernmental organization used subsidy at a rate of $23 per borrower, and subsidy for the nongovernmental organization at the 25th percentile was just $3 per borrower.

This paper is a product of the Finance and Private Sector Development Team, Development Research 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 rcull@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
found no effect on access to credit. This could suggest that formal firms created just after the change in the law were not credit constrained and may have decided to start up as formal for other reasons.

Curiously, the same journal published another evaluation of the same tax simplification program that arrived at different conclusions. Unlike Fajnzylber et al. (2011), Monteiro and Assunção (2012) found a positive and significant impact on formalization rates but only among firms in the retail sector. The authors report no results on firm performance indicators and are, in fact, very skeptical about the impact of SIMPLES on formalization rates and firm performance.

Looking carefully at both studies, a few differences stand out. For instance, Monteiro and Assunção (2012) use firms that are 0-20 months old, while Fajnzylber et al. use firms that are 3-20 months old, with a kernel function with one month bandwidth to assign higher weights to observations close to the threshold (the date the law was passed). Another difference may have to do with the sample composition. Fajnzylber et al. (2011) considered entrepreneurs aged 20-65 without college degrees while Monteiro and Assunção (2012) apparently used the whole sample.

Monteiro and Assunção (2012) used ineligible firms that started business after December 1996 as a counterfactual. In the RD framework, the counterfactual is less clear, as the data do not allow one to distinguish between genuine new entrants and informal firms that decided to formalize with the program. A key difference between these two studies is the date each used for the implementation of the program. Monteiro and Assunção (2012) used December, while Fajnzylber et al. (2011) used November 1996 as 2
0|(0.0)|0.0|(0.1)|
|District FE|Yes||Yes||Yes||
|IHS round FE and Season FE|Yes||Yes||Yes||
|Observations|35627||24643||24643||

Note: FE stands for fixed effects. The dependent variables are per capita consumption without aid (food and cash) in columns 1 and 2 and per capita food consumption (without transfers) in column 3. In Column 2, we restrict the sample to households included IHS 3 and 4 so that we could measure food consumption without transfers in a similar way. Due to lack of access to Stata dofile used to construct consumption aggregates in IHS2, we could not 19
The details of the construction of the base year data for the power sector and the base case projections of the various generation sources is given in Appendix B. We discuss the impact of using this electricity projection for the base case in section 5.

# **Figure 1. Structure of electricity sector**

<!-- Start of picture text --> Electricity<br> σED=0.7<br>Transmission<br>Generation<br> See Fig A2b σ GE =1<br>Base load Intermit.<br> σ BL =4 Renewables<br> σ RE =4<br>Wind Solar<br>Coal Gas Nuclear Other<br>Hydro Coal-CCS Gas-CCS<br> σM =Coal σM =Nuclear Fig A2b σM =CoalCCS<br><!-- End of picture text -->

# **3. The Base Case Scenario**

GDP growth in China between 1978 and 2007, the eve of the Global Financial Crisis, was 9.9% per year. With the stimulus to fight the effects of the Crisis in 2008 and 2009, the growth rate between 2007 and 2011 was maintained at a high 9.6%, however, with the end of the stimulus and the continuing weakness in the world economy, it decelerated to about 7.5% during 2012-14.<sup>7</sup> In the 13<sup>th</sup> Five-year plan announced in March 2016, the goal was to reduce the energy per unit GDP by 18% between 2015 and 2020, and reduce CO2 per unit GDP by 20%.

> 7 Cao and Ho (2014) discuss the sources of growth in China for the past 30 years – aggregate productivity and industry productivity performance – and discuss how these might inform projections of future growth. They relate this growth accounting of China to the literature about the middle-income trap and growth slowdowns.

‐7‐
# **Table 3. Percentage Borrowing and Reasons for not Borrowing by Credit Source**

|||Credit S|ource|Relatives|
|---|---|---|---|---|
||Commercial<br>Bank|MFI|Informal<br>Lenders|and<br>Friends|
|Percent borrowing from [source] in 2006|5.07|83.43|33.77|21.12|
|The main [reason] for not borrowing from [source]?|||||
|Do not like/need to borrow|52.55|81.39|70.81|74.69|
|Inadequate collateral|18.50|1.98|11.94|6.77|
|Lender's procedures are too cumbersome|14.49|9.31|6.70|5.82|
|Lender's loan terms are unfavorable|5.48|0.59|3.35|0.91|
|Lender is too far away|3.18|0.40|0.78|1.35|
|Need to pay bribes|2.02|0.79|2.18|0.08|
|Past default with lender|1.96|0.79|0.89|1.15|
|Members not willing to lend to me|1.25|4.55|2.51|6.53|
|Bad credit history|0.56|0.20|0.84|2.69|

Notes: Data come from the baseline survey (November 2006)
into account the supplies of these countries while estimating the demand for imports from Bangladesh.

Finally, the bulk of the literature estimates import demand functions using highly aggregated data. We use disaggregated data by exploiting the information available on MFA imports into the United States. These data are readily available from the International Trade Commission (ITC) publications by the country of origin. A major advantage of using the disaggregated data is that unit-value indices which must inevitably be used to represent prices are far more meaningful in these data than in aggregated data.<sup>7</sup> Compositional changes are far less likely to pollute unit values when data are highly disaggregated. To highlight the level of disaggregation, we note that there were as many as 148 MFA product categories in the United States in 1994. Cotton shirts alone are divided into four separate categories: cotton knit shirts for men & boys, cotton knit shirts for women & girls, cotton nonknit shirts for men & boys and cotton nonknit shirts for women & girls.<sup>8</sup> Additionally, since quotas are closely monitored, these data are also more reliable than aggregate trade data used by most investigators.

Having laid out our claims in strong terms, we must also note some of the limitations of our analysis. First, like other investigators, we make use of separability in the utility function. Without this assumption, it is not possible to estimate a demand equation unless we 7Even Ghose and Kharas (1993) who take into account the prices of competitors work with very aggregated data.

8The very intent of the MFA being to protect domestic producers, there has been a great temptation to define product categories tightly and to multiply them. Detailed specifications are provided, for example, to define what constitutes a cotton knit shirt for men and boys.

5
Policy Research Working Paper 8582

# **Abstract**

As many as 50 million people in Pakistan may still live without connection to the electric grid. Pakistan also has some of the world’s worst power outages. Using data from a nationally representative two-period panel survey, this paper presents the first empirical evidence on the cost of unreliable electricity supply to households in Pakistan. The results show that lack of connectivity and poor reliability may be costing the country at least $4.5 billion (1.7 percent of gross domestic product) a year. Addressing the problem requires energy sector reforms to correct regulatory and institutional distortions in the gas and electricity sectors.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://www.worldbank.org/research. The authors may be contacted at zhang1@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
135|0.006|0.789|0.954|0.135|0.010|0.790|0.961|0.289|0.130|0.388|0.427|
|Endline control mean|2.4|65|2.7|89|2.4|65|2.7|89|0|.167|0.|227|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.034|0.503|0.172|0.545|0.006|0.094|0.023|0.075|0.006|0.282|0.053|0.310|
|Observations|1,616|1,616|388|388|1,616|1,616|388|388|1,616|1,616|388|388|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FD stands for field day. FD- and non-FD samples consist of actual field day participants respectively non-participants in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

17
appendix). Attendance is formally free of charge from first grade up to high school completion,<sup>7</sup> and below tertiary education there are few private schools.

There is a steep socioeconomic gradient in educational achievement <u>(Walker 2011), which</u> some worry might increase due to migration, not least due to widespread informal (and often illegal) payments to schoolteachers and other officials. The institutional causes of these are twofold: teachers’ wages are low and often delayed, and, socially, there is public tolerance of corruption and insufficient critical input of mass media. According to the 2013 Global Corruption Barometer, 37% of households in Moldova that came into contact with education authorities paid bribes in the 12 months before the survey and 58% of respondents perceived the education system to be corrupt or highly corrupt (Transparency International <u>2013). Similarly, in</u> the 2011 Citizen Report Card study, corruption is cited to be the most common difficulty when requiring services from public educational institutions, and paying bribes is the second most common way of solving problems after insistence, joint with using personal contacts. Another form of corruption in the education system is the acquisition of unnecessary tutoring from a child’s teacher <u>(Carasciuc 2001). This means that tutoring is often in a gray area between a</u> productive investment in students’ cognitive achievement and paying teachers informally. Besides seeking individual gains for one’s own child, there is an important social component to making illicit payments to teachers resulting from the interaction of parents, teachers, and school principals (ESP/NEPC <u>2010).</u><sup>8</sup> The less frowned upon kind of these payments are monetary transfers or in-kind “gifts” that are often collected by informal parental committees. Typically, they either supplement teachers’ wages or finance maintenance spending in schools. These expenditures face some of the
Importantly, the data set we use allows one to track what fiat currency is used for payment (which on-chain transactions do not) and to show that a considerable fraction of crypto vehicle transactions involve cross border flows.

Our laboratory is the world’s largest centralized peer-to-peer exchange market, that includes more than four years of extremely high frequency data for 135 currencies. The data cover all trades made over that window, including currency used, the size of the Bitcoins purchased (down to units of Satoshi – one hundred millionths), and a precise time stamp to the second. By matching identical-size (to eight digits) transactions that take place within a short window, we are able to identify with high probability trades out of one fiat currency into another, as well as transactions that very likely represent domestic payments.<sup>3</sup> We estimate that at least 7.4% of all trades in our data are crypto vehicle transactions; this is only a lower bound, as we only include trades we can identify as likely transactions with very high probability. Of these, around 20% represent international currency movements. These are average numbers; for some currencies (notably the ruble and the renminbi), the share of purely domestic trades is much higher. For others, most notably countries with significant restrictions on international capital flows, the share of international trades is much higher. Our international payments and transfer estimate is especially likely to understate the true flows, as when trades involve countries with relatively inefficient financial systems, the long time lags required to clear fiat currency payments can elude our methodology, which focuses on short time windows. We show, for example, that a much larger share is suggested by the “natural experiment” embodied in the March 7, 2019 widespread power outage in the República Bolivariana de Venezuela that turned out to have massive spillovers on Bitcoin markets in several other Latin American currencies.

The first part of the paper describes our novel core data set, with details provided in