rafmacalaba/data-use-mentions-extended huggingface dataset:
for utility _s_ ; are the efficient total losses from the II Chilean sector; IICC<sup>II</sup> is the estimated asset base for water production of size _m_ for utility _s_ in the IBNET database (calculated using weighted average of the unit water production asset base TTCCTT<sup>II</sup> TTQQTT for small, medium, and large utilities); and KK<sup>II,PPrrCCdd,II</sup> C I I are the reported water customers of utility _s_ in the IBNET database.<sup>rr</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> The estimated asset base for water production is calculated using the weighted average water production asset base per customer from the Chilean model firms of similar size: 3,572, 2,165, and 952 USD per customer for small, medium, and large utilities respectively.

The model firm developed by SISS in Chile defines an efficient level of employees per customer for each firm. The efficient ratio of employees per customer<sup>21</sup> we adopt is based on a weighted average of our sample of 15 Chilean model firms by size: 5.2, 4.4, and 2.4 employees per 1,000 customers for small, medium, and large utilities respectively. In general, most WSS providers, particularly in developing countries, have staff numbers that are substantially higher.

Overstaffing implies higher O&M costs. To account for this cost differential, we estimate the incremental cost associated with the difference between (i) the efficient ratio of employees per customer and (ii) the level reported for each utility in IBNET, using the following equation:

> 21 A generally accepted benchmark for staff efficiency is 5 employees for every 1,000 customers, although we refrain from using this, since it does not reflect economies of scale and thus may result in the illusion that large companies have efficient staffing levels.

11
The World Bank has an independent Operations Evaluation Department (OED) that reports directly to the shareholders (not the management) and that makes an ex post assessment of each project financed by the Bank. OED makes a number of different evaluations; we use its most fundamental “thumbs up, thumbs down” measure of whether a project was satisfactory in meeting its stated objective. We take as our universe all of the countries in which the Bank carried out at least three projects in the 1990s (taking the view that if there were only one or two projects in a country then we do not have a very reliable estimate of the expected success rate of donor-financed projects). For each country, we thus calculate the proportion of World Bank projects, conducted between 1990 and 1999, that were determined to have been successful by the OED. In practice, this success rate varies considerably: in the largest borrowing country, China, 90% of projects succeeded; contrast this with Pakistan’s 67% success rate or Nigeria’s 47%.

We consider a number of hypotheses about why this success rate varies across countries. First, in line with the discussion in the introduction, we consider that the overall quality of the recipient country’s institutions and policies may affect the likelihood of project success. We use several different measures of institutions/policies. From ICRG we take the Rule of Law Index. This variable "reflects the degree to which the citizens of a country are willing to accept the established institutions to make and implement laws and adjudicate disputes" (Knack and Keefer 1995). Higher scores indicate: "sound political institutions, a strong court system, and provisions for an orderly succession of power." Lower scores indicate: "a tradition of depending on physical force or illegal means to settle claims." We take this as a key economic institution that indicates whether agents are secure in making transactions. It has been found in other work to be an important determinant of growth and to be correlated
for micro firms. However, the Enterprise Survey’s Power Outage measure is associated with higher productivity for relatively larger firms—a surprising result. Given that two of the three sets of indicators of the quality of the electricity system suggest that good provision of electricity tends to be enabling for relatively small firms, and the Power Outage measure likely does not capture electricity provision as fully as the other two indicators, we tentatively conclude that electricity provision is enabling for relatively small firms.

_Human Capital_ . A better human capital environment also appears to level the playing field for smaller firms. Reducing the Enterprise Survey’s Skills Obstacle measure is associated with higher productivity only for micro firms. The World Bank’s HLO is associated with higher productivity only for micro and small firms; an increase of one standard deviation implies higher productivity of 30 percentage points for micro firms and 22 percentage points for small firms. Similarly, the Global Competitiveness Quality of Education System measure is most strongly and positively (though insignificantly) related to productivity for micro firms.

_Access to Finance_ . Access to finance has most positive associations with firm growth for small and medium firms, and this is true based on both the Enterprise Survey Overdraft indicator and the Global Competitiveness Financial Development indicator. In contrast, the positive association between access to finance (as measured by the Enterprise Survey Overdraft measure) and productivity is relatively stronger for larger firms. The results suggest that access to finance does different things for firms of various sizes: allowing smaller firms to grow faster, while allowing larger firms to enjoy higher productivity.

_Barriers to Entry_ . Finally, land access is likely particularly important for relatively small firms. The Enterprise Survey Land Access Obstacle measure is negatively and significantly associated with productivity for micro firms and small firms.

To summarize, particularly important BE elements for relatively small firms are corruption control, both basic and modern infrastructure, the human capital environment, and land access. Access to finance is also important for relatively smaller firms in terms of firm growth. For relatively
the international economy and affect different agents in society.

On the empirical front, this relational approach to GVC activity has largely focused on case studies and, in a few cases, on more representative datasets that provide some information on the ownership decisions of firms (see Antràs, 2015). A burgeoning literature is attempting to build more systematic measures of relational GVC activity, and later, this paper will build on this literature to suggest a measure of relational GVC participation based on transaction-level customs data from the World Bank’s Export Dynamics Database (EDD). It will also be discussed how one can build on transaction-level trade data to construct country- and industry-level narrow GVC participation measures analogous to those developed in the literature embracing the broad approach to GVC activity.

Beyond expositing these alternative conceptualizations of GVCs, this paper will put them to work to provide some guidance for the empirical work in various chapters of the 2020 World Development Report. First, it will delineate various key determinants of GVC participation and will present hypothesis regarding the relative importance of these determinants depending on the type of GVC activity (broad or relational) one focuses on. Second, it will also build on the conceptual framework to highlight several notable implications of GVC participation for economic performance at the firm level, but also for growth, poverty, inequality, and market structure at the country level.

3
.0|0.2|
||**Syria***|10|2.3|1|1|1.5||16||2.7|4.2|4.6|4.0|
||**UAE***|6|3.6|2.3|1.7|3.3||35||4.1|5.8|5.5|-0.7|
||**Yemen***|32|1.9|1.3|2|1.7||19||2.9|4.0|3.9|3.8|
||**MENA**|**14.3**|**2.6**|**1.5**|**1.7**|**2.6**|**26.0**|**29.5**|**11.5**|**2.5**|**4.8**|**5.9**|**3.6**|
|_Source:_WB|**Global Average**<br>Database of Politic|**7.4**<br>al Institutions|**2.5**<br> (2010) (i),Af|**1.6**<br>D Instutional|**2.2**<br>Profiles Datab|**2.8**<br>ase(ii,iv,v),I|**39.7**<br>nsititute(vi),|**50.9**<br>Freedom H|**8.0**<br>ouse
survey carried out by UCLG in 2009 shows that local capital expenditures have actually increased in some countries because municipalities have participated in national economic stimulus programs that have targeted local infrastructure projects (United Cities and Local Governments 2009). However, in a great variety of countries, including Bulgaria, Mexico, Nicaragua, Philippines, Russia, and Ukraine, municipalities have cut back on capital spending. In Senegal, the reduction in capital transfers to cities has led to a cutback in local investments since these transfers are used to fund the municipal matching share of specific projects for which financing is also received from a national fund supported by multinational institutions (Paulais 2012).

# **Reduced Financing Capacities**

As cities have struggled to regain stability after the crisis, so too have the various external sources of capital on which cities usually rely. Perhaps most importantly, the cost of borrowing has increased during recession, as creditors are wary of buying emerging-market bonds during bad times. This, in turn, prevents emerging-country governments from smoothing the cycle by borrowing, as their rich counterparts can often afford to do. Generally speaking, fiscal policy is procyclical in developing countries, in contrast to high-income countries where it is countercyclical. This is supported by strong evidence in Ilzetzki & Vegh (2008) and Kraay (2012). This being said, even developed nations have been struggling. In the U.S. tightened credit markets have made it increasingly difficult for cities to maintain debt-funded projects, particularly for infrastructure, and have resulted in higher debt costs. Additionally, in many EU countries like Greece, Spain and Portugal, cities have had difficulties to borrow due to legal constraints (including consolidated public sector limits arising from the Maastricht Treaty). The downturn has also reduced foreign direct investment in developing country cities.

The ability to weather this crisis, as in previous crises, has been different for central and subnational governments. While the former can incur large deficits financed by debt, the latter (outside of a number of generally federal or
The size of the informal sector is of particular interest to economic policy makers concerned to promote the development of a micro-entrepreneurial sector. This concern arises because of its perceived contribution to dynamic economic efficiency, possibly as a response to growing competitive pressures brought about by trade liberalization. On the other hand policy makers may be concerned that significant numbers, perhaps even a majority of workers in developing countries, have little or no social security provision. This means that they have little on which to fall back in the event of illness, unemployment or old age, beyond personal wealth and extended family support. A narrower but nevertheless important concern may be to reduce informality in order to widen the base of direct taxation.

This discussion of alternative perspectives on informality brings into sharp focus the question of the most appropriate way to define and measure the informal sector. This is a question which has attracted little or no detailed attention in the literature. The purpose of the present paper is to attempt to redress this. This lack of attention may arise from the paucity of data on a sector which by its nature is problematic to define. Thus researchers tend to fall back on the pragmatic and judicial use of data on employment status and sectoral affiliation. There is little discussion of the sensitivity of any conclusions drawn to issues of measurement and definition. An implicit assumption is that international comparisons of the scale of informality, which may be based on very different empirical definitions, are valid. Over the last 20 years or so the growing availability of household and living standards surveys has improved the evidence base for many developing economies. However such surveys are typically not sufficiently rich to permit an evaluation of alternative definitions of informality.

4
communication. To this end, I use data on fixed telephone network penetration and construct a dummy variable equal to 1 for countries where the penetration rate at the baseline is below the median and 0 for those above the median. I then interact the dummy with the various measures of mobile phone penetration to explore the differential effect of mobile penetration across these groups. Table 6 presents interesting findings. First, similar to the results on the interaction with the income grouping, the effect of 2G network expansion on economic growth is statistically significant only in countries with low fixed telephone penetration at the baseline. This finding suggests that access to 2G mobile networks is a plausible substitute to fixed line telephony, hence the expansion in 2G networks enabled countries that hitherto had no/low access to telephone services to reap the economic gains associated with digital connectivity. The effects of 3G/4G connectivity are however present in countries with low as well as high fixed line telephone penetration. However, once again the effect in locations with low prior fixed line telephone penetration is relatively higher. Overall, the results indicate that access to mobile phones induces greater economic footprints in countries with previously low digital communication capacity.

# **5 Robustness Checks**

To assess the robustness of the results, I perform several robustness checks: First, by restricting the data to the period between 1998 and 2013, I show that the results hold for both the harmonized and non-harmonized nightlights data. Specifically, Table A4 shows the results from estimating the main IV equations using the restricted data on harmonized and non-harmonized nightlights data. In fact, the results in Table A4 show that estimates based on the harmonized nightlights data are slightly lower than using the non-harmonized lights data. This gives assurance that using the harmonized nightlights data does not lead to an overestimation of the impact of mobile phone coverage on economic activities.

Secondly, I conduct sensitivity analysis by investigating whether the baseline results are driven by a particular group of countries, i.e. if the results are sensitive to the country
between the resource groups of countries in linkage rates and changes in these rates, both sets of indicators vary substantially across countries within each group _._ This is observed in Figures 3, 4 and 5. The first of these presents four country cases within the non-resource rich group and shows, for example, that Rwandan

> 6 As a rule, extreme outliers observed in all three indicators are dealt with by winsorizing at the (1, 99) percentiles. The exception is indicators in the country-sector data, in which case the proportionate share of DVX in gross exports and GVC participation rate are each winsorized at the (1, 90) percentiles.

12
invest in accurate direct reporting._ As DMOs engage with external agents to produce debt reports, they may see it as a form of outsourcing their reporting responsibilities. In fact, indirect statistics may reduce DMOs’ incentives to produce direct statistics. This effect may be amplified by the fact that, when discrepancies arise, MDBs’ statistics are usually trusted more than national ones. As an example, a simple exercise was conducted to measure the relevance of direct versus indirect reporting sources. By inspecting The Economist<sup>41</sup> articles from 1998 to 2021 that mention country-specific public debt statistics of 10 LIDCs that disclose their own data,<sup>42</sup> the analysis showed that 93% of the sources reported were indirect. The IMF is most cited (30% of the cases), followed by the World Bank (10%). Direct sources were referred to in only 7% of the articles and included National Statistics Offices, Central Bank Statistics and DMOs. Although the study is limited in scope, it supports the hypothesis that indirect reporting is more commonly relied upon, at least at the international level.

Against these scenarios, this paper calls for: 39 World Bank loans and financing for countries failing to meet basic DRS reporting requirements cannot be presented to the Board unless the country provides an acceptable plan of action for reporting its external debt. 40 The IMF’s Articles of Agreement establish an obligation for every IMF member to provide a minimum set of data to the Fund for its activities, as set forth in Article VIII, Section 5. “In addition, the IMF can request debt information beyond the above-mentioned minimum set of data and can decide not to lend if such critical information is not forthcoming” (WB IMF, 2020). For instance, the IMF interrupted its program in Mozambique in 2017 when cases of “hidden debt” were identified.

> 41 The Economist was selected due to its international coverage and the availability of structured and consistent information on sources
dataset derived from DMSP-OLS and VIIRS from 1992 to 2018; DMSP-OLS data from 1992–2013 is used, and VIIRS is used to simulate DMSP-OLS-like data from 2014 to 2018. Urban and cropland categoriescome from the ESA Globcover dataset. The table shows values in 1996 (first year of road data available), 2013 (last year of DMSP-OLS data available), and 2016 (last year of road data available, and where VIIRS is used to simulate DMSP-OLS-like data).

# **4 Empirical Strategy**

As shown in the previous two sections, Ethiopia experienced substantial changes in its road network (Figure 1) and in economic activity (Table 1). In this section, we exploit the temporal and spatial variation in roads and economic activity to estimate the effect of road upgrades.

We rely on three complementary empirical approaches to estimate the impact of road upgrades on economic activity and changes in land cover (urban land and cropland). First, we rely on a difference-indifference design that focuses on the impact of road upgrades on surrounding areas. Second, we rely on a long-difference approach that focuses on the benefits of the program for incidentally treated areas compared to the areas that did not benefit from the program. Third, we examine how improvements in market access 12
The median quoted internediation spread between deposit and lending rates remained broadly constant during 1960-1980 but has risen rather sharply since then. A numlber of interpretations are possible. For one thing there could have been an increase in the market power of banks, possibly associated with the relaxation of interest rate controls. Another factor could be the deterioration in loan-loss experience in the latter part of the sample: equilibriurm spreads should have widened to take account of the credit risk. Finally, the degree to which the quoted rates are representative will have varied over time, with large depositors and first-rate borrowers beginning to have new non-bank opportunities.

1.2 The developing countries catch up: annual data from 1975

Data for the early years in Table 1 are sparse: the early years included vety few observations. Only from about 1980 on is there data for at least several dozen countries in each case. Table 2 and Figures 1 to 4 provide more detail for the period since 1975, distinguiishing between industrial and developing countries. These show mean and percentile figures in addition to the median on an annual basis. Along with rnoney market and treasury bill rates, we show bank deposit rates and the quoted intermediation spread, i.e. the difference between quoted deposit rates and quoted lending rates for as many countries as have sufficient annual observations included in _International Financial Statistics._<sup>_5_</sup> The general trend is summarized by three-yearly averages of the medians shovn in Table 2.

Table 2: _Median Ex-post Real World Interest Rates: 1970s to 1990s_

|%|Money<br>Industrial|Market<br>Developing|Treas<br>Industrial|ury Bill<br>Developin|De<br>g Industrial|posit<br>Developing|Lending l<br>Industrial|ess deposit<br>Developing|
|---|---|---|---|---|---|---|---|---|
completion of the community works, a URRAP contractor engaged by the woreda carries out the remainder of the construction.<sup>7</sup> We focus on rural roads developed between 2010 and 2014. During the initial four years of the URRAP program, the Ethiopian government developed rural roads in about 4,000 communities, reaching nearly 20,000 km in total (Table 1). The number of communities in which any segment of newly developed or upgraded roads was 1,850 (or 13 percent of the total number of rural communities) between 2010 and 2012 and 2,205 (15 percent) between 2012 and 2014.<sup>8</sup> Among the nine regions analyzed, Oromia has the largest number of communities (around 2,500) that received rural roads between 2010 and 2014. The total length of the rural roads developed in Oromia during the period was about 13,000 km, accounting for 64 percent of the total length in the nine regions.

A recent report (World Bank 2018b) shows that connectivity and accessibility of rural Ethiopians have substantially improved since 2010. The average travel time to the nearest town decreased by nearly 30 minutes between 2010 and 2016. An additional 6 percent of rural Ethiopians were connected to rural roads during the same period, as the rural accessibility index (RAI) increased from 46 in 2010 to 52 in 2016.<sup>9</sup> As a result, market accessibility improved except for some lagging regions.<sup>10</sup> Although a glance of Figure 1 gives the impression that rural road development has taken place all around the country, a comparison of baseline community characteristics illustrates that recent rural road development occurred in communities that were already relatively well connected prior to the placement (Table 2). For example, the communities that have received rural roads (i.e., treated communities) since 2010 were located closer to the nearest towns (91 minutes), compared to the other
to December. There were no long‐lasting, nationwide power outages reported, and although there was an increase in the number of cells with no cloud free days, it cannot explain a deviation of this magnitude. The most likely explanation is some noise in the underlying VIIRS data causing the November 2016 data to deviate from other months.

Regardless of the November data, there does not seem to be a big drop or anything else that causes the mean of the cells struck by the earthquake to deviate from the mean of the non‐ affected cells. This could be due to aid and relief efforts, but given the noise already seen in the data, it is more likely that VIIRS has been unable to capture the effects the Aceh Earthquake had on the local economy.

The second set of analysis is the fixed effect regression. The above regression was run with a number of different specifications. The key assumptions were related to the following areas:

1. Distinction between rural and urban areas to decide which vulnerability curve was applicable

2. Treatment of VIIRS values below 0 With regards to deciding between rural and urban areas, no national data are available to the authors’ knowledge. Furthermore, it would be preferable to use the same definition across all countries to have comparable numbers. As such, the regressions were run with numerous specifications ranging from the most extreme cases where the countries were assumed to be all rural or all urban to cases where any cell with less than 10, 50 or 100 people were assumed rural. Overall, this assumption did not affect the results much in terms of significance, but there were some minor shifts in coefficients. Due to the lack of impact from this assumption, it is in the following assumed that any VIIRS cell (450m by 450m) containing fewer than 10 people is considered rural, while any cell with more is considered urban.

As for the treatment of VIIRS values below 0 ‐ or close to 0 for that matter – there is no clear consensus in literature. The methodology