rafmacalaba/datause-encoder-data huggingface dataset:
domestic credit to the private sector to GDP from the International Monetary Fund’s International Financial Statistics (IMF-IFS). Second, as a proxy for capital market development we use the ratio of stock market capitalization to GDP from the World Bank Development Indicators (WDI). Since the stock of credit and market capitalization only indirectly captures the effect of investor protection, as an alternative direct measure we use the Accounting Standards, AccStan, in a country. This is available for only a subset of countries in our data set and measures the number of items disclosed by the largest corporations in a country (see La Porta et al. (1998) for details).

For the industry variable, External Financial Dependence, ExtFin, is a measure of dependence on external finance and equals the industry-level median of the ratio of capital expenditures minus cash flow over capital expenditures. The numerator and denominator are summed over all years for each firm before dividing. This variable measures the portion of capital expenditures not financed by internally generated cash. Cash flow is defined as the sum of funds from operations, decreases in inventories, decreases in receivables, and increases in payables. Capital expenditures include net acquisitions of fixed assets.<sup>41</sup> In Table 10 columns (iv) and (v) we find as predicted that entry is higher in more financially intensive industries in countries that have higher financial development. We find similar results when using private credit to GDP or stock market development individually as measure of financial development (not shown).<sup>42</sup>

# _3.6.4 Human Capital_

Next, while we have just examined the effects of access to capital, one might also consider access to specialized human capital. In industries that need skilled human capital, there

> 41 Cash flow is calculated using Compustat item 110, if available, and otherwise by the sum of Compustat items 123, 125, 126, 106, 213, and 217, plus the change in working capital (the sum of Compustat items 302, 303, and 304). Capital expenditure is calculated as the sum of Compustat items 128 (capital expenditure) and 129 (net acquisitions).

> 42 For robustness, we replace FinDev with Trade Credit, the average ratio of accounts payable to total assets.  We find that industries with higher dependence on trade credit financing exhibit higher entry rates in countries with relatively weak financial institutions.  This finding is similar to Fisman and Love (2003) who use a similar methodology, but a different dataset for a larger number of countries, and find that industries with higher dependence on trade credit financing exhibit higher growth rates in countries with relatively weak financial institutions.

29
where _I jst_ is the number of firms in a location-sector for a given time period.

Location is measured at the province level. The census data divides the country into 70 provinces. Tables 1 and 2 provide some descriptive statistics for these agglomeration variables. From Table 1 it can be seen that the average firm is located in a province-sector with an average of 212 competitors and 17294 workers, representing just over 17% of the total workers in the province. Comparing the values of the agglomeration variables across new entrants, surviving firms and exiting firms in Table 2, it can be seen that new firms tend to locate in province-sectors which are generally smaller and where competition is comparatively weak (as evidenced by the fewer number of firms and the lower level of the Competition HHI).

Determining the effect of firm turnover on the agglomeration variables introduced above presents some practical difficulties. Similar to the productivity decompositions, the agglomeration variables will be affected by within (e.g. a firm experiences a positive sales shock and so raises employment and ceteris paribus, the values of the associated agglomeration variables) and between firm effects (e.g. the production of one firm falls in response to the competition of another firm), in addition to those changes stemming from firm turnover. A question of key importance is how we can isolate the effect of firm turnover on these variables from the other effects mentioned. Establishing a reliable counter-factual for the other effects in these circumstances is difficult. For example, if there was no firm turnover in a given province-sector, what would be the level of production and employment of the existing firms? Given that any method employed to estimate the effect of firm turnover on the agglomeration variables is potentially flawed, we opt for the simplest procedure capable of providing an insight into the effect of firm 23
# **2.5 Comparison of Additional Human Capital across Interventions**

Table 2 compares the additional human capital available under the four different treatments, which the entrepreneur can use to help build skills in her firm (either personally or organizationally): (i) business trainer; (ii) business consultant; (iii) insourced worker; and (iv) outsourced professional. This comparison uses data collected from a monitoring survey of the insourcing and outsourcing treatments, and from requests for details from the training agency (EDC) and the consulting advisory company (Growbridge). An important caveat is that these comparisons measure formal schooling, experience and certified skills, but do not capture any possible advantage that younger specialists may have in using new digital marketing tools such as social media.

The four treatments differ considerably in the skill level of the additional human capital and the intensity with which the intervention provided it. The common approach of training the entrepreneur lies at one extreme (higher experience; lower intensity). The business trainers all have some form of skill certification, average over 15 years of work experience, and have an average age of 49 years. 82% have post-graduate education, and they are paid just over 1 million Naira (US$2,960) per month. However, the trainers do not work directly in the firms – instead, firms only receive 12 days (84 hours) of their time in a group classroom setting.

At the other extreme lies insourcing (lower experience; higher intensity). The insourced workers hired by firms are much younger (average age 29 years), with 3.5 years of work experience on average. And only 24% of these workers have a formal skill certification, while just 32% have post-graduate education. Firms paid these employees 50,000 Naira (US$136) per month on average, but had them working inside the firm for an average of 42 hours per week (1,455 hours in total if they stayed on for the full subsidy period).

Between these two extremes lies consulting (high experience; medium intensity) and outsourcing (medium experience; high intensity). The interventions differ considerably in the amount of contact time with firms: an outsourced professional spent a total of 678 hours on average versus only 88 total hours by a business consultant. That said, despite having similar years of work experience (12.6 and 12.7), it is the business consultants who tend to be more senior than the outsourced professionals. Consultants are five years older on average, and they are more likely to have post-graduate education (88% versus 59%) as well as a formal skill certification (80% versus 67%). However, the median monthly pay is similar for these two types of specialists at 130,00013
- 6 -

# 2. VXflD= ON KNIM-_UL MMSTIMuZOTI

> In order to **assess the** *conomlc viabillty of pursuing a fuel swLtching program it is necessary to havo some assurance that lt li possible to change the propertlons of different fuels used **as** lnputs to dlfferent processes. Some aggregate evldence can be obtalned by examining country statlitics dLiaggregated by **use** sector and over tLr-w

The most comprehensive energy statletlcs avaLlable are those for tho twenty-flve countrles covered by O.3.C.D. publlcatlons. These exlet on a comparable basis for a lengthy perLod of time (more than twenty yaars) and have the greatest degree of dLeaggregatLon. AccordLngly these are taken as a benchmark for lnvestlgatlon of fuel swltchlng ln a wider context. The O.E.C.D countrLes are the highest lncome and most industrialized natLons and many are rlch ln natural sources of energy so that thelr patterns of energy use are not likely to be typlial of that of the rest of the world. Nevertheless the varLatLons shown both between countrLes and for a given country over tlme will indicate the potentlal at high income standards for variatLons in fuel use. FUad **Use** in **the** O.3.C.D.

The startlng polnt for the analysls i$ the overall breakdown of fuel use by sector for the whole of the O.3.C.D. lr a recent year. Table 2.1 shows the use of varlous forms of fuel as lnputs lnto dlfferent sectors of the economy. Each fuel li converted lnto tons of oll equLvalent (to compare the amount of fuel to provlde the same energy output) uslng standard conversLon factors. These flgures for a z.Acent year, well after the effects of the two oil shocks have had time to take effect ln producing aubstitution away from oil, lndlcate several important features of the overall energy market.

- (i) In transport and agrlculture oll is stlll the domlnant fuel. SubstitutLon between fuels Ln these sectors has not responded much to the enormous price shlfts of the past. Clearly the technological advantage of oll ln these sectors is such that fuel substitution will not respond eaelly to prlce LncentLves, although the growth of the sector as a whole and hence their use of **energy** may respond to fuel prlcee.
# **D. Access policy framework index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Regulation of New Connections**|**68%**|**88%**|**57%**|**14%**|**65%**|
|Roles of regulator, utility, rural electrification agency<br>clearlydefined||||NAV|93%|
|Utilities have regulatory obligation to connect new<br>customers|||||94%|
|Regulatory entity has authority to approve connection<br>charges for new customers|||||71%|
|Connection charges are set usingshallow entry||||NAP|58%|
|Governmentprovides subsidyfor new connections|||||53%|
|Connection has to beprovided in a specified time|||||94%|
|Regulatory entity monitors tiem taken to provide new<br>connections|||||44%|
|Regulator has authority to levy penalties for not<br>connectingcustomers on time|NAP||NAP|NAP|71%|
|Time taken toprovide connectionspubliclyavailable|||||24%|
|There are connection charges||||NAV|94%|
|Customerpays limited connection charges||||NAP|43%|
|Connection charge ispubliclyavailable|||||71%|
|**Regulation of solar home systems**|**100%**|**50%**|**100%**|**0%**|**66%**|
|Minimum technical standards and post-installation<br>warrantyrequirements for solar home systems|||||71%|
|Regulator reviews and approves prices of surplus SHS<br>sales of electricityto thegrid operator|NAV|||NAV|62%|
|**Regulation of mini-grids**|**33%**|**33%**|**67%**|**NAV**|**44%**|
|Privatelyowned mini-grids legallyallowed to operate||||NAV|81%|
|Clear options for mini-grid operator when the<br>interconnected grid reaches the area, including<br>compensation||||NAV|7%|
|Subsidy or other mechanism to help mini-grid<br>operators recover their costs||||NAV|47%|

# **E. Corporate governance- accountability index for Vietnam and comparators, 2015**

||**Vie**|**tnam**|**Philip**|**pines**|**Col**|**ombia**|**P**|**eru**|**International**|
|---|---|---|---|---|---|---|---|---|---|
|**Corporate governance**||||||||||
||NPC|HCMPC|MERALCO|BENECO|EPM|CODENSA|Luz del Sur|Hidrandina|**benchmark**|
|**Accountability**|**17%**|**17%**|**100%**|**67%**|**75%**|**92%**|**92%**|**58%**|**60%**|
|Private orpublic shareholders appoint board|||||||||36%|
|Transparentprocess exists for Board selection|||||||||36%|
|Board members cannot be removed at will|||||||||29%|
|Chairperson & CEO are separatepositions|||||||||75%|
|Function of CompanySecretaryexists|||||||||82%|
|Board Sub-Committees for different issues|||||||||68%|
|Audit committee of the Board|||||||||71%|
|Board Code of Conduct exists|||||||||64%|
|Requirement to declare conflicts of interest|||||||||75%|
|Utility has carried out any third party<br>transactions in last fiveyrs|||||||||46%|
|Minorityshareholders' rights areprotected|||||||||39%|
|Utility publishes an Annual Report|||||||||93%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 45
information on the type, number and location of projects; key dates (of application, approval and completion); size of FISE transfer; amount of community-level counterpart funding (if any); and the name of implementing agency (contractor, NGO, the community itself, etc.).<sup>19</sup> MIS data covering all projects that were applied for between May 1993 and January 1996 - and that were granted - serve as our source of project-level information. Information is available on a total of 2,876 projects. As foreshadowed in the previous section, the MIS data reveal that many _parroquias_ applied for and were granted more than one FISE project. An important limitation of the data set is that MIS did not keep records of unsuccessful applications.

For the purposes of this study, the key variables of interest are the type of project chosen by each community, and the name and location of the beneficiary community. Table 2 documents the percentage breakdown of projects across types. Just over a third of projects (34%) comprise the acquisition of school equipment and materials.<sup>20</sup> Another 32% of projects involved new construction of school rooms or school buildings. While projects supplying school equipment involved the delivery of goods in kind, construction projects involved transfers of funds which were used to finance contractors for the construction work.

A third, sizeable, category of projects comprises construction of latrines (13% of all projects). These projects are of central importance to the analysis in this paper, since they embody the two main properties of the _x_ -good projects ( _π1_ ) discussed in Section 3. First, latrines are used largely by the poor in rural Ecuador (see Table 1). Evidence from household surveys indicates that non-poor households are far more likely to use other forms of sanitation infrastructure – such as toilets with connections to a networked water supply, or septic tanks. Second, the latrines constructed by the FISE were intended as private goods delivered to households with no previous sanitation infrastructure. Project documents indicate that beneficiary households obtaining such latrines had to provide the

> 19 Unfortunately, the project funding data seems to be unreliable for use in our empirical analysis.  For example, for projects with in-kind transfers, such as equipment and materials, the funding is usually entered as ‘zero’ in the MIS database.  Furthermore, sometimes the total amount of funding the community or the applicant received seems to have been entered under one project line and the rest of the projects again register ‘zeros’.  For this reason, we refrain from using MIS funding amounts data in our analysis. 20 FISE project documents indicate that equipment included such items as blackboards and desks, but the school equipment and materials projects explicitly did not allow for the acquisition of school books.

17
use the systematic, consistent estimates provided in Table 1 to illustrate the potential impact of recent regulatory changes on national banking systems. Also, to continue our narrative on ten particular countries, we focus the discussion on Argentina, France, Indonesia, Japan, Korea, Malaysia, the Philippines, Russia, the United Kingdom, and the United States, even though other countries have frequently undertaken the biggest regulatory reforms, which will be illustrated in the figures. Finally, for each regulatory index and for each country, we show which individual regulations changed by documenting changes question-by-question. Thus, readers can readily identify which individual regulatory reforms produce the changes in the indices that we use when conducting the simulations.

# IV.D.1 _banking crises_

Figure 5 presents the estimated changes in the probability of a crisis for each country resulting from the change in regulatory restrictions on bank activities from Survey I (1997) to Survey III (2007). In presenting the simulations, we use terms such as “increased fragility” or “enhanced stability” to describe increases or decreases, respectively, in the estimated probability of a systemic banking-system crisis in a particular country. Crucially, we examine the impact of a country’s changing bank regulations on the probability of a systemic crisis in that country. We do not examine contagion. Nor do we aggregate regulatory changes across individual countries and weight the resultant fragility effects by the financial importance of each country to derive an estimate of a world financial system crisis. These are valuable extensions.

By intensifying regulatory restrictions on bank activities, many countries increased banking-system fragility according to our simulations. The simulations suggest that Argentina and Russia imposed additional restrictions on bank activities and these reforms will increase the probability of a systemic crisis by between 20 and 40 percent. Other countries relaxed restrictions on bank activities, allowing banks to diversify income flows with positive effects on banking-system stability. According to our estimates, Mexico’s reduction in regulatory impediments to banks engaging in non-lending services will have a large stabilizing effect on Mexico’s banking system. On a much smaller level, Japan, the U.K. and the U.S. also reduced activity restrictions, with corresponding boosts to stability.

16
generate net gains in the labor market. Grosh et al. (2015) conducted a randomized experiment involving a labor market matching service in Jordan. This experiment aimed to reduce search costs by offering jobmatching services based on educational backgrounds and psychometric assessments. However, the study concluded that what appeared to be frictional unemployment was in fact the result of the explicit preferences of job seekers, 28 percent of whom turned down the opportunity for an interview; among those who received job offers, 83 percent rejected the offer or quit the job shortly after taking it.

The situation in India may be different, as unemployment is often attributed to an insufficient number of job opportunities. A set of randomized control trials is currently being designed in an effort to reduce information asymmetry between job seekers and employers. The trials will focus on the role of noncognitive skills among job seekers. In an earlier study, Indian employers hiring recent engineering graduates rated behavioral skills such as teamwork, reliability, leadership and willingness to learn, creative thinking and problem-solving skills, and specific knowledge and technical skills as important qualifications. Two out of three of these employers reported that most of these skills were “very” important, but that they were only “somewhat” satisfied with graduates’ skills levels (Blom and Saeki 2011).

Though reducing the frictional unemployment caused by limited information is an important economic objective, there is not yet sufficient evidence to inform effective policies. In the area of non-cognitive skills, a growing number of empirical studies have incorporated “big-five traits”, a psychological instrument for the classification of personality traits, to explain employment probabilities and wages in the US and European countries,<sup>24</sup> contributing to the international knowledge of how non-cognitive traits are correlated with labor market outcomes. The Skills Toward Employment and Productivity (STEP) surveys, initiated by the World Bank, also use the big-five-based instrument to capture personality traits (Pierre et al. 2014). While these studies assessed the contribution of non-cognitive skills to employment outcomes and wages, information on how employers view non-cognitive skills is scarce. The proposed experimental study adds to the growing literature on non-cognitive skills and labor market outcomes by supplying new knowledge on how the availability of non-cognitive skills information contributes to employment outcomes.

Further studies should strive to identify the types of information employers are attempting to acquire about potential hires and the extent to which skills-matching improves when information gaps are narrowed. Specific attention should be paid to employers’ demand for information about the non-cognitive skills of

> 24See, e.g., Heineck and Anger (2010); Mueller and Plug (2006); Nyhus and Pons (2005); and Wichert and Pohlmeier (2010). See Almlund et al. (2011) for a comprehensive review of the literature.

24
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 20. Funding gaps by sector**

<u>$ millions</u>

||**ICT**|**Irrigation**|**Power**|**Transport**|**WSS**|**Total**|
|---|---|---|---|---|---|---|
|Spending needs|(92)|(66)|(184)|(139)|(266)|(747)|
|Existing spending|31|10|56|62|66|225|
|Efficiency gains|0|1|16|28|17|62|
|Funding gap|**(62)**|**(54)**|**(112)**|**(50)**|**(183)**|**(460)**|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. Total assumes complete fungibility across sectors. WSS = water supply and sanitation; ICT = information and communication technology.

# What else can be done?

The funding gap can be addressed by raising additional finance, adopting lower-cost technologies, or extending the time horizon.

In the case of Niger, there may be realistic prospects for _increasing the flow of resources_ from alternative sources, such as private players. When compared to other African peers, Niger has failed to attract a significant amount of private finance for infrastructure (figure 27). Over the early 2000s, Niger captured private investment commitments worth only 0.8 percent of its GDP, predominantly in the ICT sector. Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of their GDP. For instance, as the ICT market is still growing, there is plenty of room for private sector companies to invest in Niger. On the other hand, attracting private sector investment into Niger’s infrastructure sectors might be constrained by low purchasing power.

**Figure 27. Niger has captured relatively low private sector participation**

Average per year between 2002 and 2007 <!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Water Transport Power ICT<br>Percentage of GDP<br>Guinea-Bissau Senegal Kenya Uganda Nigeria Liberia Sierra Leone Mozambique Tanzania Rwanda Ghana Zambia Burkina Faso Mali Benin Seychelles South Africa Cameroon Niger Lesotho Gabon Madagascar Guinea Malawi Mauritania Cote d'Ivoire Zimbabwe Angola Burundi Congo, Rep. Mauritius Chad Botswana Togo Swaziland Central African ... Namibia<br>DRC Congo, Dem. ... Equatorial Guinea<br><!-- End of picture text --> _Source:_ AICD calculations. _Note:_ GDP = gross domestic product; ICT = information and communications technology.

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