rafmacalaba/data-use-mentions-extended huggingface dataset:
SOEs in a country (SOE registry) is built through a combination of (i) ownership trees run through the public authorities identified in the ORBIS ‘entities file’, (ii) ownership trees built on additional entities identified as government-owned firms through the review of the national legal form, (iii) ownership trees built on additional nodes identified through the global ultimate owner information, (iv) list of firms that satisfy the SOE definition in supplementary and official databases (e.g., MoF), and (v) the latter list of companies’ ownership trees (if the firms are in ORBIS).

# 4.1.2 Incorporating Financial, Economic Performance, and Governance Variables

Once the registry of SOEs is completed, the second stage consists of complementing the data with financial and corporate governance information. For this purpose, we used the ORBIS interface and the SOE Supplementary Databases. It is important to note that ORBIS presents information on consolidated and unconsolidated statements of firms. As explained by the database manual from Bureau van Dijk (ORBIS, 2011), consolidated accounts are composed of financial information for the mother company and all its subsidiaries. Unconsolidated accounts correspond to financial information of just the specific company, excluding the financials of its subsidiaries. (Cusolito, 2021) shows that some countries report only consolidated accounts, others report only unconsolidated accounts, and others report both in ORBIS.

Given that the BOS database provides the full ownership structures, unveils all subsidiaries, and presents the information where each company is the unit of observation, we focus on collecting unconsolidated financial accounts of domestic SOEs to avoid any double counting issues. Since the BOS database includes all separate legal entities including those that can be subsidiaries of other companies in the database, we

> 38 Close coordination with the regional and country-level experts across GPs and across EFI were key to identify these set of firms based on the national legal forms.

> 39 Some cases, the algorithm could not find the specific company because it referred to non-corporatized forms,
# 1. Introduction

The global economy regained some strength over the period between mid-2016 and 2018 but potential output growth—the rate at which an economy would grow when labor and capital are fully employed—has continued to remain weak (Figure 1). Post-crisis (201317), global potential growth fell short of its long-term average and was well below its precrisis average. This weakness was broad-based, affecting both advanced economies, where it was evident even before the financial crisis, and emerging market and developing economies (EMDEs), where there was a short-lived pre-crisis uptick.

Since the growth rate of per capita potential output is the overriding long-run force for sustained reductions in poverty, this trend is also cause for concern about the international community’s ability to meet broader development goals.<sup>1</sup> In some regions, especially commodity-exporting ones, such as Eastern Europe and Central Asia, and the Middle East and North Africa, the post-crisis slowdown in potential growth could set back per capita income convergence by more than a decade.

Against this backdrop, this paper addresses the following questions. First, how has potential growth evolved since the turn of the century? Second, what have been the drivers of potential growth? Third, what are the prospects for potential growth? Fourth, what policy options are available to lift potential growth?

To help answer these questions, the paper examines the evolution of potential growth in a large sample of countries, with a strong regional focus. Since potential output is not directly observable, economists estimate it from long time series of actual output, employment, capital stocks, and productivity. The paper uses a comprehensive database of potential output growth by Kilic Celik et al. (forthcoming).<sup>2</sup> For clarity, and in keeping with a longer-term focus, this study uses the production function approach.<sup>3</sup> This paper makes several contributions to the large literature on potential growth. First, the focus in
countries would not change classification immediately, but would only be placed in the new category for two or three years. But the biggest obstacle would appear to be that which affects all rankings: when one country becomes high income (for example), another country must become middle income. Some current users of the classification set policy on the basis of gaining a higher classification status; a ranking based on quartiles may be less fit for this particular purpose than the current methodology – though it can, of course, be argued that this use is not the intended analytic purpose of the income classification.

Another approach that would avoid these problems would be to rank countries by quartiles at a point in time (say for data relating to 2014, i.e. FY16), and set the upper bound of each group as the new graduation thresholds. Thresholds recalibrated in this way to the current period would initially include around 50 countries in each income category, and could then be updated for inflation every year. Changes in country classification would continue from 2016 as in the current system. Based on current data for the 2014 calendar year, the thresholds would need to be set around $2,000, $7,000 and $21,000.

As well as the analytical classification, the World Bank also classifies countries for operational lending purposes; this classification is published as part of Operational Policy 3.10,<sup>17</sup> and categorizes borrowing countries according to their lending eligibility: IDA, IBRD, and Blend. A further operational classification used by the World Bank is the list of countries in Fragile and Conflict Affected Situations.<sup>18</sup> Aggregates and groupings for these categories are already available in the _World Development Indicators_ database for analytic purposes and may be more appropriate for analyzing policy questions of interest, such as the multiple facets of fragility and resilience of countries. One option is to align the _income_ thresholds for low income and IDA graduation (US$1,
may facilitate their acceptance for their daughters, wives, mothers, and sisters to engage in market work (Cunningham et al 2010).

To invest in girls’ health, emerging evidence is finding that unconditional cash transfers can change girls’ behaviors (mostly by delayed marriage and childbearing, see Baird et al 2010), but supply-side interventions may also be effective. Re-orienting health delivery systems to provide adolescent girls with services that are accessible, customized, confidential, and nonjudgmental could provide girls with information to make good sexual health choices, better support them to prevent unwanted circumstances, and screen for reproductive and sexual health risks such as domestic violence and unintended pregnancy (Cunningham et. al. 2008b).

# **Count girls**

Providing programs is not sufficient since entry to programs often depends on recognition that girls exist. This requires an effort by governments to register all newborns and provide birth certificates to ensure access to health services and education (Cunningham et al, 2008b). And once the girls (and boys) are older, it is necessary to furnish them with government-issued identification cards so that they may continue to access educational opportunities, jobs, and health services.

To monitor program success, it is necessary to collect data on adolescents and disaggregate it by age and gender to assess whether programs are reaching adolescent girls. By tracking program beneficiaries by age, gender, marital status, location, family income and school enrollment status in all programs and sectors, program managers and governments can better assess whether programs are reaching adolescent girls— especially the most vulnerable. Regularly reporting results will increase accountability, share learning, target solutions, demonstrate success, and catalyze more resources.

# **Advocate for girls**

Finally, governments could better advocate for girls at two levels. First, governments could make the law work better for adolescent girls by repealing laws that discriminate against girls in the workplace, schools, or family and ensure equality of access to health services, education, jobs and earnings, credit, and
than 75<sup>th</sup> percentile countries. In this case, Sierra Leone would aim for the average growth of spending in low-income countries by increasing its public education spending as a share of GDP from 1.6 to 1.7 percent over five years (Figure 14a). These two potential pathways provide a plausible range of targets within which Sierra Leone could increase its public education spending over the next five years.

Using school-age population projections, these possible spending paths translate into a range of per child funding of between $166 and $218 in 2020/21 compared to $126 per child in 2015/16 (Figure 14b). Are these spending increases worth pursuing for Sierra Leone? Based on the past record of low spending in inefficient countries (see Table 8, Share of total government budget as % of GDP), spending increases in this range have on average resulted in an increase of approximately one-third to two-thirds of one year of learning-adjusted years of schooling (Figure 14c). This is not to say that just by increasing spending these improvements in education outcomes are automatic, only that this is the average conversion of spending into resources for countries like Sierra Leone.

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instruments.<sup>32</sup> These variables are either related to a location’s agricultural potential and/or the constraints its geography poses on urban expansion (ruggedness, elevation). Especially when focusing on the urban workers in our sample only (see the next sub-section), these instruments should plausibly satisfy the exclusion restriction.

# 4.1.2. Main Sample

In estimating equation (1), we follow De la Roca and Puga (2017) as closely as possible, and restrict attention to working age (15 - 64) males that report a non-zero income in the August 2014 round of SAKERNAS. This initial sample consists of 116,156 workers. Next, we further exclude workers employed in agriculture, forestry, livestock and fishing, mining and quarrying, public administration and defense, education, health and social work. These activities are typically rural or much more controlled / regulated by the national and local governments in Indonesia. Also, we only focus on workers who have worked at least 32 hours (four working days) during the last week. These restrictions reduce the sample to 56,577 workers. In several robustness checks to these choices, we also show results when extending the sample to females; workers who have worked at least 24 hours (3 working days) in the previous week; workers in public administration and defense, education, health and social work; and/or non-production occupations in agriculture, forestry, livestock, fishing and mining and quarrying. We also perform robustness checks based on restricting the sample to employees or prime age (25-54) males only, or measuring the hourly wage using cash income only.

# _4.2. Baseline results_

Table 2 and Figure 4 show our main results using De la Roca and Puga’s (2017) preferred measure of metro/urban size as our main independent variable of interest – i.e. the number of people within 10 km of the average person in the metro area or district. Table
a bad approximation based on the World Bank HCI, see Appendix Figure 3). FF MM (6) hLL<sup>B</sup> **3.2 Calculating the full GEGI in the data: Employment ratios and the HCI** ωω= hLL<sup>B</sup> +LL<sup>OOBB</sup>

**3.2 Calculating the full GEGI in the data: Employment ratios and the HCI** Often it is convenient to work with employment variables as shares of the working-age population—rather than in terms of the number of people.  Here I rewrite the building blocks of the full GEGI— G , G and — in terms of ratios. These expressions are equivalent to those in Equations 3, 4 and 6 above, and are used to calculate GG GG BBEE OOEE ωω the full GEGI empirically (see Appendix 3 for derivations). The first ratio is the Better Employment Rate (BER), which is defined better employment as a share of the working age population (Pennings 2020). The second ratio is the Other Employment Rate (OER) as other employment as a share of the working-age population. The other employment rate can be calculated as the employment-to-population ratio ( , less the better employment rate ( ). This allows the Better Employment Gap to be expressed as (equivalent to Equation 3): EE) B −B (7) OOEEOO= EE−BBEEOO G = × 100% B +B RRMM RRFF And the Other Employment Gap becomes (equivalent to Equation 4): BBEE GG RRMM RRFF (8) G = × 100% OOBBRRMM −OOBBRRFF The calculation of the GDP weights OOEE GG OOBBRR requires data on human capital per worker MM+OOBBRRFF h , which requires taking a stand on timing.  Because we are usually interested in evaluating the effect of today’s policy changes in the future ωω (forward looking) we want a measure on human capital of the next generation: which is what the
**Table 2: Program expenditures per capita across states**

||Expenditure p<br>2009/10|er capita (Rs)<br>2010/11|
|---|---|---|
|Andhra Pradesh|749<br>|896<br>|
|Assam|406|358|
|Bihar|214|309|
|Chhattisgarh|723|884|
|Gujarat|214|226|
|Haryana|87|128|
|Himachal Pradesh|936|837|
|Jammu and Kashmir|221|445|
|Jharkhand|586|539|
|Karnataka|742|683|
|Kerala|186|276|
|Madhya Pradesh|734|707|
|Maharashtra|54|60|
|Orissa|281|455|
|Punjab|89|98|
|Rajasthan|1133|647|
|Tamil Nadu|555|744|
|Uttar Pradesh|389|365|
|Uttarakhand|406|539|
|West Bengal|335|399|
|All India|464|477|

_Notes and sources_ : Cumulative expenditures (including wage and non-wage spending) in current prices during the 2009/10 and 2010/11 FY were obtained from the Ministry of Rural Development website (http:\\nrega.nic.in). To calculate expenditure per capita the authors used the population projections for 2009 and 2010 done by Registrar General of India.

**Table 3: Coverage of MGNREGS across consumption quintiles of the rural population of India, 2009/10**

|Quintiles|Participation<br>rate|Demand<br>rate|Rationing<br>rate|Excess<br>demand<br>rate|Share of<br>HHs with<br>jobcard|Mean person<br>days amongst<br>participating<br>HHs|<br>Mean<br>person days<br>among all<br>rural HHs|
|-
2019) show that both predictions hold in their data.

Surprise or double billing is a situation whereby health care providers charge the insurance company the reimbursable amount, but then levy additional (and illegal) top-ups from patients. Rather than providing an administratively mandated price for a procedure, insurance reimbursements are then better regarded as a partial subsidy for the service, with pricing determined both through the usual considerations of supply and demand elasticities, but also possibly price discrimination and the special characteristics of health care markets. Again, there are no nation-level studies of double-billing, since it requires surveys of insurance beneficiaries in addition to claims data. One of the few studies to combine administrative data and household surveys is Jain (2021) who studies double-billing in the Indian state of Rajasthan. We will discuss this study further below.

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Further, the unweighted average of all dimensions pertaining to a particular pillar yields the score for that pillar, whether enablers or safeguards. The average scores for dimensions 1–3 provide an overall sense of how enablers are performing, while dimensions 4–7 do the same for safeguards. No attempt is made, however, to average the scores obtained for enablers and safeguards into an overall country score. This avoids the creation of country rankings, as the purpose of the exercise is not to rank countries but rather to help them identify gaps in their respective regulatory frameworks.

Under the safeguards pillar, data governance regulatory environments for personal data and non-personal data are assessed differently. The personal data dimension collects information on the regulatory practices adopted to protect data that convey information specific to a known (or knowable) individual. Personal data protection that takes a human rights approach is considered good practice—emphasizing the rights of data subjects, including the right to challenge the accuracy of data and obtain corrections. An independent data protection authority also plays an important role in enforcing compliance of those requirements. With nonpersonal data, a balance must be struck between data sharing and protecting intellectual property rights. Cybersecurity and cybercrime are additional dimensions covered under the safeguards pillar. The cost of malicious cyberactivity in the US economy was estimated in 2016 at $57 billion to $109 billion (Council of Economic Advisers 2018). For the sustainable development of the data economy, there needs a regulatory framework that criminalizes illegal cyber-activities, specifies cybersecurity measures and requires the establishment of organizations such as national Computer Security Incident Response Teams (CSIRT). Finally, the safeguards pillar, includes cross-border data flows, the global volume of which increased twentyfold between 2007 and 2017, with a further threefold increase expected in the 2017–22 period (Cisco 2018). How cross-border data flows are regulated may shape a country’s competitiveness and opportunities in international trade. For instance, data localization conditions may deter some firms from opening offices in some countries.

The enablers pillar begins
satellite images for fishery management. In short, the VBD algorithm detects spikes in the illumination from offshore areas, at the same time controls for background noise radiance due to moonlight, and filters out lighting and energetic particles in the ionosphere.

For the purpose of this analysis, we use the monthly-aggregate VBD products published by NOAA for the period between 2013 and 2016.<sup>13</sup> We then aggregate the number of monthly boats detected into 10-mile-square geo-grid cells that, together, spans Vietnam’s entire Maritime Exclusive Economic Zone.<sup>14</sup> The chosen resolution of 10-mile-square grid allows us to better capture the effect for different fishing grounds across the entire country’s coastal area. It covers a marine space granular enough to detect micro changes in fishing activity’s patterns (e.g. within-province fishing grounds’ migration). Nevertheless, this footprint level still spans a sufficiently large sea segment, which allows us to be less concerned with issues about spatial autocorrelation or spurious boat detection.

The second panel (Panel II) in Table 1 presents fundamental statistics of two dependent variables related to fishing intensity that are made possible by VIIRS: (1) _fishing prevalence_ , i.e. boat detection likelihood, which is the probability that at least a boat was detected inside a particular grid in a given month and, thus, provides information regarding the _extensive_ margin of the disaster’s impact; and (2) _fishing density_ , i.e. the number of boats detected in each grid, which captures the _intensive_ margin. The three-year baseline gridmonth statistics from VIIRS 2013-2015 suggests that fishery actions are considerably more intense in the central coast. Near-shore ( _≤_ 20 nautical mile) fishery activity has a 59 percent average monthly coverage, while off-shore fishing covers over two-third of the marine pixels.<sup>15</sup> The respective boat-detection likelihoods in the control provinces are 40 (near-shore) and 55 (off-shore) percent. On average, there
identifies the (partial) border effect, i.e. how much trade within national borders is different from trade with other countries, controlling for other bilateral determinants of trade and for multilateral resistance terms. We control for a standard set of other time-invariant determinants of bilateral trade. The variable _DIST_ is the the log of the population-weighted bilateral distance (Mayer and Zignago, 2011). _CONTIG_ is a dummy equal to one if the two countries in the pair share a border, _COLONY_ equals one if the two countries share colonial history, _LANG_ equals one if the two countries share an official language, and _LEGAL_ is a dummy for common legal origin. These variables are sourced from CEPII (Mayer and Zignago, 2011).

The other determinants of trade costs are variables that measure participation in trade agreements, with a focus on trade policies related to government procurement. The _TANOPROC_ indicator equals one if the two countries in the pair are part of a TA at time _t_

> 1 _−σk_

> 16Given our structural interpretation of the model, the normalization implies that � _Pj,t_<sup>_k,s_</sup> � = _Ej,t_<sup>_k,s/E_</sup> _USA,t_<sup>_k,s_exp</sup> � _m_<sup>_k,s_</sup> _j,t_ �. It follows that _PUSA,t_ = 1.

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Tuungane was implemented with a variation in treatment, which allows us to explore, in addition to the program’s overall effect, the impact of having women in leadership positions. Fourth, Congo provides a good setting because CDD programs often take place in the context of weak state capacity.

We collected outcome data in 735 of a targeted 781 villages, which makes this one of the largest CDD field experiments to date. As a comparison, Fearon et al. (2009) examine 83 villages, Casey et al. (2013, 2019) survey 236 villages, Beath et al. (2013, 2015) study 217 villages, while Avdeenko and Gilligan (2015) investigate 24 communities. This study builds on data from village chief and household surveys and a carefully-designed facility audit to explore the longer term impacts of the Tuungane program on eight outcome families that we pre-registered prior to data collection: service provision (in the health and education sector), health, education, economic welfare, women’s empowerment, governance, intra-village and inter-village social cohesion.<sup>4</sup> We find evidence for the physical endurance of infrastructure built by the program. Treatment villages are served by primary schools and hospitals that have higher quality infrastructure, and their hospitals are better stocked. However, we find no evidence that the program had an impact on other dimensions of service provision, health, education, economic welfare, women’s empowerment, governance, and social cohesion. These findings are broadly consistent with findings from Casey et. al (2019); the only other study, we know of, that investigates the longer term impacts of CDD programs. Thus, while CDD programs appear to effectively deliver public infrastructure in the short and longer term, their impact on economic development and social transformation appears to be limited, even in the longer term.

The remainder of this paper proceeds as follows. Section 2 discusses the Tuungane program and the experimental design. Sections 3 and 4 discuss the data collection and present
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 20. Diesel and super gasoline retail prices**

a. Prices of diesel and super gasoline in Cameroon <!-- Start of picture text --> a. Prices of diesel and super gasoline in Cameroon b. Retail price of diesel in Central Africa<br>180 180<br>160 160<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Diesel Super Gasoline<br>US cents per liter US cents per liter<br><!-- End of picture text --> _Source_ : GTZ 2009. Diesel retail prices as of mid-November 2008 from GTZ Survey carried out November 15–17, 2008.

**Figure 21. Power prices in Sub-Saharan Africa**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US cents per KWh<br>Liberia Chad Senegal Mali Uganda Niger Kenya Rwanda Benin Namibia Cameroon ... Ghana Botswana Lesotho Tanzania Zimbabwe Ethiopia Botswana Nigeria Malawi DRC Zambia<br>Cape Verde Burkina Faso Congo, Rep. Cote d'Ivoire Madagascar Mozambique South Africa<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia, and Shkaratan 2010.

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