rafmacalaba/data-use-mentions-extended huggingface dataset:
challenges. These include factors related to project design, sequencing, socio-cultural issues, coordination, and a battery of other indicators related to the social, political, economic, geographic, and normative environments in which projects are implemented. However, each has been explored in relative isolation, outside the context of an overarching typology of the various paths to implementation failure.

In this paper, we use practitioner assessments of implementation challenges as a diagnostic tool for assessing the relative impact a nearly-comprehensive set of potential obstacles may have on a project’s ultimate outcome and the Bank’s overall performance. The inductively-generated taxonomy captures the set of issues project leaders identified as having an impact on the overall success of the disbursement. We test these alongside other potentially omitted correlates to gauge their substantive impact relative to one another. Iterated model averaging runs using a variety of different theoretical priors ensure that the standardized coefficients attributed to each challenge remain robust regardless of the impact other potentially exogenous factors may have. By taking a more holistic view, we attempt to examine a wider swath of the potential variation in project outcome, be it related to external conditions, the design of the project itself, or a function of the interaction between Bank staff and local actors. To achieve this end, the dataset of delivery challenges offers the most complete agglomeration of project-level indicators compiled thus far (Ortega Nieto and Agarwal, 2017). By approaching project challenges “from cradle to grave,” we offer a more complete view of the range of variation that has impacted project success over the last two decades. The next section will explore in greater depth the conceptual framework underlying our analysis, offering a brief overview of the delivery challenge framework as well as a review of the methodology employed to examine the determinants of project success.

6
percent). There was no World Bank Group engagement during this scale-up program. ONEE’s share of total investment financing was split approximately fifty-fifty with government borrowings, grants, and equity. These funds were raised in four tranches over the 15-year period, from 1996 to 2009. By 2009, consumers had provided about 25 percent of total investment, municipalities and communities provided about 20 percent (enabled by earmarked allocations sourced from value-added tax receipts and municipal development fund allocations), and ONEE provided 55 percent.<sup>51</sup> It is worth noting that the advent of IPPs gave ONEE the space to reallocate its financial resources to implement the vast rural electrification initiative.<sup>52</sup> Another reason for PERG’s success is that it was systematically and effectively managed by a single competent implementation agency, ONEE. The coherence of PERG in terms of having one plan and one implementer compares to earlier experiences of peer countries such as Tunisia, Thailand, Vietnam and the Lao People’s Democratic Republic.<sup>53</sup> Under ONEE's competent leadership, PERG had a clear vision –global electrification of rural households – fully aligned with the government's own developmental priorities.<sup>54</sup> This contrasted with previous unsuccessful attempts in the 1980s by MOI and local authorities. PERG at the outset was an analytically sound and comprehensive plan prepared by ONEE in a least-cost spatially optimized manner that was technologically neutral and that incorporated grid extensions for the most part, but also included mini-grids, Solar Household Systems (SHSs),<sup>55</sup> wind plants and micro power stations. ONEE performed optimal engineering design based on detailed surveys and developed an internal GIS database that captured almost all rural communities and villages in the country. This information assisted the utility and the government in opting for appropriate solutions to achieve assigned targets. PERG was flexible enough to integrate a broad range of technical approaches, enabling it to implement whichever solution
**TABLE A10: Capital market openness and development relationships without country fixed effects.** Table 8 is replicated without country fixed effects. Constant, R- squared, and number of observations not reported for brevity. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Reg|(5)<br>ression|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A of Table 8|||||||
|De-jure fnancial openness index|0.36|0.00|0.02|0.06|0.05|-0.07|
||(0.47)|(0.02)|(0.02)|(0.04)|(0.06)|(0.12)|
|Private sector credit (% of GDP)|-0.78*|0.00|0.02|0.01|0.03|-0.26*|
||(0.41)|(0.02)|(0.02)|(0.04)|(0.05)|(0.14)|
|Panel B of Table 8|||||||
|De-jure fnancial openness index|-0.03|-0.00|-0.04|-0.11*|-0.20**|
# **Table S20. Relationship between the WB Human Capital Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>||||0.005***|0.000|0.000||
|Score||||||||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||-0.001|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.
of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight—as when the Soviet Union collapsed—these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries—especially developing countries—have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous.

1 Of the 3,500 sources detailed in the overarching UN Global Migration Database, 1,107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably.

2
# **1 Introduction**

The persistence in productivity differences among firms, even within the same narrowlydefined industry, is striking and puzzling. For example, within four-digit industries in the United States, labor productivity in manufacturing plants at the 90th percentile is four times as high as it is in plants at the 10th percentile (Syverson, 2011). A growing literature suggests that management practices play a key role in explaining the large variation in productivity across industries and countries (Bloom and Van Reenen, 2007; Bloom et al., 2012b). In a world facing climate change, patterns of energy use and associated greenhouse gas emissions have taken center stage in discussions on firm performance. To this end, methodologies have been adapted to study the relationship between management practices and energy demand at the firm level, albeit mostly in developed countries (Bloom et al., 2010a; Boyd and Curtis, 2014; Martin et al., 2012; Schweiger and Stepanov, 2019). Our paper builds on this nascent literature to explore the link between firms’ management practices and energy use.

Despite the implications of business decisions for climate change, and climate change for business, the firm as a unit of analysis is not common in the literature on energy efficiency. Energy use by firms is a major driver of greenhouse gas emissions worldwide. Direct emissions from manufacturing and construction account for 20% of global CO2-equivalent emissions from fossil fuel combustion (World Bank Data, 2020), and is much larger once indirect (e.g., electricity and heat) emissions are included. Firm energy use decisions not only have implications for firms’ private costs, but also affect the global and local environmental impacts of economic activity. Prior studies focus heavily on individual decisions, or aggregated outcomes at the industry or country level, such as total production, given that energy data at the firm level are often aggregated into a firm’s materials inputs or not reported at all.

Management practices could interact with energy decisions via several channels
A number of indicators have been developed for measuring statistical capacity (PARIS21 2017c). Most of these measures are based on data collected directly from the staff of national statistical offices (NSOs) or local experts. Although this procedure may provide more in-depth analysis and uncover finer details about the organization of an NSS, it is more expensive and far more timeconsuming than measures that are publicly available and easy to access.<sup>2</sup> In addition, these laborintensive assessments are conducted at the request of the NSS. As such, they are infrequent and cannot be used to track country-specific or global trends in statistical capacity.

To overcome these deficiencies, the World Bank launched the Statistical Capacity Building Indicator (SCBI) in 2004. The SCBI is based on objective indicators that can be updated annually for all emerging market and developing countries (but not for developed countries). International and national agencies have adopted it to measure progress in statistical capacity building and related investments (World Bank 2020b).

Since the launch of the SCBI, its methodology and coverage have remained unchanged despite the marked changes in the data landscape. Open data agendas have propagated; both high- and lowcapacity NSSs are modernizing. The SCBI does not capture these advances. Its lack of coverage of high-capacity NSSs means that it is not possible to track advances across the full spectrum of countries.

This paper uses the World Bank’s Statistical Performance Index (SPI), designed to assess a country’s statistical capacity, to identify areas for improvement in NSSs and to monitor the progress of reforms in statistical capacity building. This index was developed to address the limitations in the SCBI and facilitate intertemporal and intercountry comparisons.<sup>3</sup> The SPI uses publicly available data on a set of readily observable and verifiable indicators. It provides internationally comparable, objective, country-level assessments across the globe. It views statistical capacity as the range of products and processes an NSS uses to produce and disseminate data.

> 2 Even the best
Another set of approaches for obtaining poverty estimates in a fragile context consists of modeling consumption, or poverty, based on a set of observable covariates and then projecting estimates using cross‐survey imputation. In this manner, infrequent bulky consumption surveys can be combined with more frequent surveys that collect information on the covariates necessary for imputing poverty (for example labor force surveys as in Douidich et al., 2013; or SWIFT<sup>19</sup> ). However, this methodology is problematic in contexts where there is no consumption survey to underlie the estimation, or where there may have been deep structural change that changes the relationship between covariates and poverty across time (Beegle et al., 2016; Christiaensen et al., 2010). This is most likely the case in South Sudan, where the last full consumption survey was conducted in 2009 and which had experienced a period of rapid development leading up to independence in 2011 and until the breakout of the current conflict in 2013.

Within‐survey imputation can alleviate some of these concerns because the assumption of similar covariate distribution between the data used to estimate poverty and that used to project is more likely to hold, or the differences may not be as great. One approach consists of administering a full consumption module to a subset of respondents, generally in more secure areas where time‐constraints are not binding, and then impute consumption for less secure areas based on a smaller set of covariates (Fujii and Van der Weide 2013). However, safer areas where the full consumption module can be administered may still systematically differ from insecure areas where only the covariates are collected, thus violating the assumption of equally distributed covariates.

The HFS employed a method of within‐survey imputation, but instead of imputing the totality of consumption in certain areas based on data from other areas it imputed a randomly different fraction of consumption across all enumeration areas covered in the survey (Pape and Mistiaen, 2018, 2015). Food and non‐food consumption items were first into
A well-known and important caveat, already mentioned in Section 1.1 above, needs to be recalled with regard to aggregating provider information on the number of accounts. The same individual, household or enterprise may have multiple deposit or loan accounts whether within a single intermediary or with many intermediaries. Furthermore, inactive accounts may not always be closed. While a well-managed intermediary should be able to identify the number of its distinct clients, and also distinguish between active and inactive accounts, this is not the case for all intermediaries in developing countries. (Furthermore, if complex multi-firm corporate structures are present, it is not obvious how the customer should be defined). A good credit registry can help uncover multiple borrowing relationships, but multiple deposit relationships will generally be difficult to discover from the providers’ side. All of this implies that the total number of accounts exceeds the number of banking relationships by an unknown factor.<sup>24</sup> Of the list of provider-sourced information, pricing of services is worth highlighting, given its importance as mentioned in determining whether poor households will buy the services in question. The underlying costs are not the only determinants of pricing. Traditional banking practice and lack of management interest in reaching a poor clientele can result minimum opening balance and minimum service fees for transactions accounts being set at fairly arbitrary levels which, though of little concern to the bank’s present customers, are a prohibitive barrier to those at low incomes (in some low income countries minimum balance requirements can equal several months of average per capita income). Pricing deserves much more attention, not only in the context of changing technology that can dramatically alter the unit cost for small transactions, but also given the non-competitive environment which is often observed.

# _Published information for specialized MFIs_

In the case of specialized microfinance providers, the situation is somewhat different, given their dual mandate of operating efficiently and reaching many target clients. A large number of country studies and studies of multi-country MFI networks throw light on the scale of
-0.09|-0.16|0.15|-0.11|-0.24<sup>_∗∗_</sup>|0.06|-0.07|
||(0.12)|(0.12)|(0.11)|(0.12)|(0.11)|(0.12)|(0.12)|
|Observations|2407|2408|2419|2403|2408|2303|2413|
|ITT + ITT X Child of Leader = 0|0.88|0.82|0.06|0.71|0.09|0.12|0.91|

_Note:_ All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Five villages without leaders were excluded from the sample. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 69
comes, the timing and duration of migration episodes, workers’ savings levels, and their post-return employment outcomes. We show how the savings accumulated abroad enable workers to overcome the credit constraints they face for entrepreneurship at home. We estimate our dynamic model using data from the BRMS and the nationally representative Household Income and Expenditure Survey. Using our estimated model, we then explore the effects of changes in exogenous parameters, such as the cost of migration, and the interest rates on loans. We examine how these changes simultaneously affect migration and investment decisions, and lifetime welfare.

Our analysis of simulated policy changes shows that a decline in migration costs would lead to a rise in emigration and a reduction in the duration of migration which, in turn, contribute to an increase in savings and self-employment. By contrast, a reduction in interest rates for entrepreneurial investment reduces migration and savings levels, undercutting part of the positive effect on self-employment. We find that providing workers with information that corrects widely held, inflated expectations about earnings abroad would reduce the rate and duration of emigration. The result is a decline in repatriated savings and in the rate at which new businesses are created. Though individual welfare benefits from betterinformed choices, overall business creation is reduced by lowering of workers’ exaggerated expectations. Throughout these simulations, we observe changes in both the extensive and intensive margins of migration and investment decisions.

These results confirm that policy makers must take such dynamic externalities into account when designing their policy interventions. Given Bangladesh’s similarities with other migrant sending, lower income countries, the findings of this paper have relevance for lowskilled temporary migrant workers from many countries of origin.

52
Moreover, the associated production data are often inaccurate (especially for vegetables and tree products). I thus select _major field crops_ according to the criteria that they cover at least 1 percent of total cropped area nationally _or_ that at least 5 districts had no less than 10 percent of their cropped area planted to them in 2003-4. These 18 crops, listed in Table 2 in descending order of planted area, comprise some 92 percent of area devoted to field crops in 2003-04 in the major states of India.<sup>22</sup> Table 2 also reports national average log-price changes (weighted by the state share of total production) _relative to rice_ . Thus, in the first row, the relative price change for rice is zero, quite negative for several important crops (e.g., cotton, gram, groundnut, mustard/rapeseed) and highly positive for pulses (Urad, Moong, and Arhar).

# **3.3 Wages**

Wage data are derived from the NSS Employment-Unemployment Survey (EUS), normally conducted every five years. The most recent round, the 66th, collected in 2009-10, is the first conducted in the wake of the food price “crisis” of 2007-08, whereas the 61st round of 2004-05 most closely preceded it. Once again, in the spirit of the theoretical model, I focus on _manual_ labor, which constitutes nearly 83 percent of days of paid employment in rural areas.<sup>23</sup> The first-stage of the estimation takes individual log daily wages in the last week and regresses them on district fixed effects as well as a quadratic in age interacted with gender. Thus, I estimate the respective log-wage district fixed effects, _<u>wd,</u>_ 09 and _<u>wd,</u>_ 04, separately for each round, removing, via the constant terms, year effects due to, e.g., general inflation.

> 22One could assume that
<!-- Start of picture text --> African Growth Incidence Curve (fractile groups), 1993-2008<br>2<br>3<br>B (ES<br>5<br>oa<br>a<br>oO<br>I a<br>(=) a<br>Sw<br>2<br>£<br>s p 5<br>=<br>2<br>3)<br>o O<br>=)<br>=<br>=<br><x<br>-.5<br>10 20 30 40 50 60 70 80 90 100<br>Percentile groups of African distribution of consumption expenditure<br>Annualised growth rate in average consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Growth incidence evaluated at ventile groups (e.g. bottom 5%). The horizontal line shows the growth rate in the mean of .92%.<br><!-- End of picture text --> <!-- Start of picture text --> African Growth Incidence Curve (fractile groups), over time<br>se 10<br>fa}<br>”/<br>=SL /<br>a 5 “ao re /<br>= ai ke?<br>©= eo cuieeet age "s<br>=} .<br>(Si e<br>(oe:<br>Lito<br>10 20 30 40 . 50. . 60 70 . 80 90 100<br>Percentile groups of African distribution of consumption expenditure<br>——— 1993-1998 ———-— 1998-2003 ---------- 2003-2008 -—— ——: 1998-2008<br>Annualised growth rate in average consumption expenditure of the fractile group (
.2|$35.9|
|(2)Average Transfer|$1,946.5|$1,301.7|$1,112.4|$345.5|$1,302.4|$1,112.8|$1,201.8|$1,568.3|$708.6|$664.8|
|Beneficiaries|137,071|204,973|239,848|772,197|204,856|239,772|222,010|165,599|376,548|401,350|
|Ex-antepoor|0.28|1.00|1.00|1.00|0.60|0.65|0.27|0.22|0.71|0.75|
|Ex-ante non-poor|0.13|0.00|0.07|1.00|0.17|0.22|0.33|0.25|0.45|0.49|
|Ex-ante Vulnerable|0.17|0.00|0.08|1.00|0.25|0.33|0.35|0.26|0.53|0.58|
|Post-shockpoor|0.26|0.87|1.00|1.00|0.54|0.59|0.36|0.29|0.75|0.78|

Note: impacts are color-coded from dark red (highest impacts) to dark blue (lowest impact). Not all households declare labor income.

Under this shock, results and optimal responses change substantially. In both Moldova and North Macedonia, covering the pre-shock poor either through perfect means testing or using a PMT indicator (columns 2 and 5) becomes a suboptimal response (again, using the poverty gap squared as a metric). If the post-shock poor could be
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

# **5.5**

**VII. Macro-Level Indicators**

|**Focus area**|**Indicator**|**Data Source (e.g.**<br>**UK)**|**Source/reference**<br>**(example)**|
|---|---|---|---|
|_? GDP_|? GDP per capita|OECD|Bacon and Besant-<br>Jones (2002)|
|_? Energy intensity_|? Electricity use per unit of GDP<br>? Energy use per unit of GDP|IEA Statistics:<br>World Energy<br>Statistics|See above|
|_? Economic_<br>_liberalisation_|**? Has liberalisation been implemented in**<br>**other infrastructure industries? (no/yes – e.g.**<br>**natural gas, telecom, transport, railway)**||See above|
|_? Country risk_|**? Country risk index**||Bacon and Besant-<br>Jones (2002)|
|_? Institutional_<br>_endowment_|**? Institutional and political strength – e.g.**<br>**Judiciary independence, economic**<br>**management, corruption indices**||Bergara et al. (1997)<br>Kaufmann et al.<br>(1999a, 1999b)|
|_? Foreign aid and_<br>_economic dependence_|? Foreign aid share of GDP (ratio)<br>**? Degree of economic freedom (index)**||Bacon and Besant-<br>Jones (2002)<br>Zhang (2002)|
|_