rafmacalaba/data-use-mentions-extended huggingface dataset:
country and cross-project experience to inform project assessments and apply common standards. Moreover, since the 1990s most IEG evaluations have been public and the Group pays close attention to comments and criticisms of outside experts, civil society groups, and academia. On the other hand, IEG is primarily staffed by current and future Bank staff and there is some rotation in and out of IEG, although this turnover is considerably lower than in other parts of the Bank. There are also likely various informal channels of communication between IEG and World Bank staff which may affect the ratings process. While the full independence of IEG evaluations cannot be directly verified or contradicted, we can do no more than raise this as a potential question regarding the reliability of outcome measures.

A further qualification regarding these project outcomes ratings is that they are explicitly measured relative to the stated “development objective” of each project, rather than relative to some common standard across them. It could be the case that task managers set modest “development objectives” for projects undertaken in difficult countries, and set more ambitious ones in countries with good institutions, policies, and a good track record of implementing World Bank projects. This would understate the importance of country factors in determining the success of projects. At the same time, it could be the case that more experienced task managers set more modest development objectives, which are then more likely to be attained. This would in turn result in an overstatement of the importance of task manager effects in driving project success.<sup>5</sup> Our overall impression is that while the evaluation outcome data described here are far from perfect, arguably they meaningfully capture the experience and insights over the years of many World Bank staff on how well projects have fared. Of course, even well-measured individual project outcomes will not be perfectly informative about the overall aggregate development impact of aid, as there are there may be complementarities between projects, as well as the potential scope for aid-financed spending crowding out other sorts of public spending. Nevertheless
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underruns. About two thirds of projects either had cost underruns or were within +10% of the estimated cost, a finding supported by the analysis of 1984 supervision reports carried out by the Energy Department. More projects approved in the 1980s had substantial cost uriderruns than those approved In the second half of the 1970s because of the world recession during the early 1980s. Bqth International and local competitive bids came In lower than expected be4ause of fierce competitlon among contractors.

The above results Indicate that projects approved before and completed after the 1973 oil crisis were subject to serious cost overruns largely due to the completely unanticipated Inflation, particularly since some projects approved In the late 1960s did not have a provision for price contingencies In the cost estimates. However, It appears that once Bank staff had adjusted to the new Inflationary conditions, actual project costs were more In line with estimated costs. Apart from those projects Implemented during the oil crisis, the level of cost overruns appears to have been reduced In the 19709 and 1980s compared with those In the 19508 and 1960s. Available cost data in the OED surey (whlch Is given for each project component, not for the project as a whole) Indicates an average cost overrun of 13% compared to a negilgible overrun for projects approved for the decade from 1974. A significant part of these cost overruns were for power projects In Mexico which had very high local cost overruns - these overruns were especially significant because local costs made up 80% of total costs, a far higher proportlon than for other countries.

An analysis of cost overruns by region shows that those for Latin American countries have Increased over time. Durlng the 19506 and 1960s, average cost overruns were 18% compared with 42% for projects approved after 1967. LAC had the highest cost overruns for projects approved between 1967-78, as shown In the table below. Even with the 1973 oil crisis, South Asia and EMENA had cost underruns on average, while East Asia kept to within +1 0% of estimated project
# **1. Introduction**

There have been many attempts of policy makers to improve human capital through changes in the legislation requirements for teacher education in developing countries. In the case of Brazil, the national government strived to improve the profile of teachers through the requirement of tertiary education for teaching in basic education in 1996. They passed the Law of Guidelines and Standards of Education (Law No. 9394/96, LDB) that stablished the year 2007 as deadline for all Brazilian basic education teachers to achieve tertiary education level.<sup>1</sup> School census data of the Ministry of Education (MEC) suggest that, although this goal has not been fully achieved so far, this law appears to have influenced profoundly pre-service teachers’ education. From 1995 to 2007, the percentage of teachers of basic education with higher education increased from 49% to 68%, then reached 83% in 2015. For upper secondary school teachers, this percentage increased from 82% to 93% in the period between 1995 and 2007, remaining stable until 2015.

At the same time, there was a significant increase in coverage of upper secondary education of 5.4 million to 8.1 million students from 1995 to 2015, which was achieved in detriment of improvements in quality. National Assessment of Basic Education (SAEB) results show a drop in the percentage of graduates of basic education with adequate levels of learning in both mathematics, from 11.6% to 7.3% of students, and in Portuguese, from 45.4% to 27.5% of students, from 1995 to 2015. It is possible that the massive incorporation into the educational system of out-of-school children from lower background families has negatively influenced the quality of elementary education at first and of secondary education years later. This seems consistent with the trajectory of the Portuguese learning indicator, which fell sharply up to 2005, showed a slight improvement until 2011 and stagnation afterwards, but does not seem to have had the same
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **B. Key indicators for the analysis of human mobility**

The analysis of patterns of human mobility during COVID-19 in The Gambia is based on a set of mobility indicators, which is calculated based on CDR aggregates. The indicators capture changes in population movements during the baseline, under COVID-19 and post-intervention periods, and results can be updated continuously as additional CDR data becomes available. We used the first two weeks of March to obtain indicators about routine baseline mobility. It could ideally be computed for a period of four weeks before the initial COVID-19 cases were announced, which was March 17, if the data before March were available.

The standardized indicators were proposed by the World Bank COVID-19 Mobility Task Force and build on a framework developed by Flowminder to support MNOs in producing basic indicators from telecom data (see Flowminder COVID-19 Resources - Mobility indicators). Methodologies for computing indicators were designed with the considerations that it is not excessively computationally intensive to produce even in resource scarce settings, fully anonymous and contain no information about individual subscribers, ensuring that the privacy of subscribers is maintained at all times, and robust to sparse tower distribution and to infrequent phone usage, both of which are common in low-and middleincome countries. It is composed of eleven key indicators that provide proxies for population, location of residence, distances traveled, and daily mobility across regions at different geographic and time levels. It also includes proxies to scale other indicators and as the input of epidemiological modeling. For this project, we selected four out of the eleven indicators. Table 1 summarizes the descriptions of the selected indicators based on information on the World Bank COVID-19 Mobility Task Force repository on GitHub.<sup>4</sup> <mark>Table 1.</mark> Summary of key indicators for the analysis

|Indicator ID|Frequency|Definitions|Use-case|
|---|---|---|-
# **1 Introduction and Reasons**

Infrastructure investment is a fundamental instrument to promote development and enhance capital accumulation. It reinforces network effects (Roller and Waverman 2001), regional comparative advantage (Estache and Fay 1997), education (Brenneman 2002), health (Agenor and Moreno-Dodson 2006) and other growth factors. Its importance is out of question, as the 1994 World Development Report on infrastructure shows (World Bank 1994), and every year more than 200 billion dollars are spent on this sector in developing countries, roughly a fifth of total investment.

As well as public authorities, also Multilateral Development Banks (MDBs) regard infrastructure investment as a priority and promote not only its financing, but also design and regulation. As Table 1 reports, the five largest MDBs lend more than USD 50 billion in infrastructure per year, with this single sector absorbing between 30 and 70 percent of their total lending.

However, pouring money is often insufficient to achieve results and, as this work shows, the governance of projects, their selection and implementation are the main factors on which authorities, both national and international, need to focus.

For this reason, in this paper we study the performance indicators of infrastructure projects and focus on the micro relations involved in them. Through a rigorous empirical analysis, we explain which variables are central in getting successful infrastructure projects and what actions can lead to high performance. The results of this work are mainly directed to Multilateral Development Banks, as we explore how to boost project effectiveness and what can be done toward this aim. We believe that this is an important research field, because in times of high uncertainty in economic prospects, especially for low-income countries, getting infrastructure investment right is central in cutting poverty, sparking growth and consolidating future fiscal positions.

The empirical analysis relies on a new database we assembled, which contains 1912 infrastructure projects financed by the World Bank between 1979 and 2008; these include detailed project performance evaluations on which we build this paper. Anticipating on the
Policy Research Working Paper 9038

# **Abstract**

This paper exploits a novel municipal-level data set to explore patterns of convergence in income and poverty in Mexico during 1992–2014. The paper finds that, despite a context of overall stagnant economic growth and poverty reduction, there is evidence of income and poverty convergence at the municipal level. The findings suggest that these convergence processes stem from a combination of considerable positive performance among the poorest municipalities and stagnant and deteriorating performance among richer municipalities. Re distributive programs, such as federal transfers to poor municipalities and cash transfers to poor households, seem to have played an important role in driving these results by bolstering income growth among the poorest municipalities, while also inducing progressive changes in the distribution of income.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at crodriguezc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
household data, 73 percent of these participating households in urban Djibouti fall within the bottom two quintiles of the asset index.

Based on our detailed time use data (not shown in Table 1), women devoted 50 percent of their time doing household chores and about 20 percent of their time caring for other household members in the 24-hour period prior to the baseline interview. Even with low levels of employment, they spend, on average **,** very little time in income-generating activities. Men, in contrast, spend half of their time in income-generating activities. Social activities within the neighborhood are important for both men and women, with men (women) spending 25 percent (16 percent) of their time (outside of personal care) socializing with neighbors and friends.

To capture the extent of women’s empowerment, we created two indices from questions related to decision making within the households. These questions touched on 10 areas related to spending: food; women/men/children’s clothes and personal items; and health consultations and medicines; education for sons; education for daughters; and taking out or repaying credit. The set of questions are drawn from the standard module administered in the Demographic Health Surveys. The first index measures whether the woman expressed her opinion the last time the household made this decision (“0” indicates “No” and “1” indicates “Yes” for each of the 10 areas). The second index captures whether the woman made the decision on her own the last time she made a decision in the given area (“0” indicates “No” and “1” indicates “Yes” for each of the 10 areas). For both indices, these 10 binary outcomes are summed and then normalized to 0. As to whether we might expect to see improvement in empowerment in this context, we were encouraged by Abdallah Ali et al.’s (2021) results showing that women’s access to microfinance results in a sense of increased
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE boreholes, while 12 percent of the wealthiest quintile of households rely on water from wells and boreholes.

Lower-income Sudanese use lower-end sanitation technologies. Around 54 percent of the poorest quintile of the population defecates in the open compared to 13 percent of the richest quintile. While none of the poorest quintile of the population uses flush toilets, almost 20 percent of the richest quintile do so. The most dominant sanitation technology across the board is pit toilets. Disparities are less glaring in the use of pit toilets—around 42 percent of the poorest quintile of the population and 56 percent of the richest quintile of the population use them.

The KWC’s poor performance, particularly due to inadequate collection of bills and network losses, also holds back service expansion. The

**Figure 15. Sanitation access is largely bimodal**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural<br>% population<br><!-- End of picture text --> _Source:_ Access figures calculated by AICD using data 2009 DHS data.

KWC fares poorly when its performance is compared to regional benchmarks. Nonrevenue water, at 40 percent of production, is twice that of a well-performing utility. At 90 connections per employee, labor productivity is less than half the average productivity of a utility in middle-income countries, which is 200 connections per employee. On average, Sudan’s water utilities recover only 62 percent of the total billing, which is comparable to the revenue collection of utilities in resource-rich countries, but is a very poor track record in absolute terms. Finally, cost-recovery tariffs are not in place, creating a constant financial drain.

The poor performance can be quantified as a percentage of revenue, giving a sense of the
for integration of continuous data like precipitation and temperature. However, as we are aiming to assess the added value of the more complex calculations in this context, both are evaluated. For polygon features we extract values using a zonal mean, or average of all cells overlapped by the polygon.

# **2.3.3 Combining Blinded Data**

As mentioned in the introduction and in the pre-analysis plan, the authors divided themselves into two groups in order to blind the Data Analysis Group from the identity of the remote sensing data (Michler et al., 2019). The entire team participated in the development and registration of the pre-analysis plan, which included defining the remote sensing products to be used and the extraction methods to be employed. At that point, the Data Generating Group accessed the publicly available remote sensing data for use in the study. They also used the privately available household coordinate data to generate the ten different sets of extraction methods to be used. The actual GPS household location is not part of the publicly available LSMS-ISA data and is known only to a limited number of individuals at the World Bank.

12
; data governance; cybersecurity; and intermediary liability regulations.

To guide the analysis, the study draws on a pilot database for regulation on digital trade in 40 countries.<sup>1</sup> The data are based on the review of laws and regulations relating to electronic documentation and signature; online consumer protection; data governance; cybersecurity; and intermediary liability regulations in MENA countries as well as over 20 comparator countries. This provides information on the regulatory stances of the different countries on a selected set of key provisions relevant to trade policy. At the same time, the codification of the regulatory frameworks allows to translate that regulatory information into dummy variables or into a Digital Trade Regulatory Readiness Index to be used in economic analysis.

The study offers an overall assessment of how comprehensive and up to date the regulatory framework for digital trade is. The different regulatory areas under review have in common that, taken together, they offer a set of basic rules that create tools for remote transactions (edocument and e-signature) or clarify conditions of digital transactions that enhance consumers’ (online consumer protection; data governance and cybersecurity) and business’ (intermediary liability) trust. Importantly, however, there are a number of additional policies relevant to digital trade that are not covered in the current study: regulations related to competition policy, taxation, e-payments, as well as the conditions for business licenses, which can also make or break a conducive framework for digital businesses.

> 1 See Annex 1 for details.

2
definition, we construct explanatory variables at the location-field level. To study the responsiveness of SCP supply to changes in local economic activity, an challenge is the lack of a direct mapping from higher education fields of study to economic activity sectors. We overcome it by constructing two measures of the local demand for graduates from a given field, GDP by field and field relative employment. Below we describe the measures; in Section 4 we justify their exogeneity.

**GDP by field** . We construct a Bartik-style (Bartik, 1991), shift-share variable using OLE and DANE data to measure shocks to the local economic activity associated with a field of study. For a given year, we interact sector-level aggregate activity at the national level with local time-invariant employment shares by field and economic sector as follows:

10
As indicated in Figure 4, there are three different approaches that can be adopted for the treatment of operational risk: Basic Indicator, Standardized and Advanced Measurement Approach. Under the Basic Indicator approach, capital requirements for operational risk are simply 15% of Gross Income. Under the Standardized Approach capital requirements are the weighted sum of the gross income stemming from different sources with the weights ranging from 12% for gross income from the retail portfolio to 18% for gross income from corporate finance and trading. Under the Advanced Measurement Approach the regulatory capital will be given by a risk measure generated by a bank’s own model of operational risk. This model must be calibrated on the banks own experiences with regard to operational risk related losses and hence the bank must have a methodology for measuring such losses and the relevant historical data<sup>35</sup> .

Banks opting for the Simplified Standardized Approach will adopt the Basic Indicator methodology for Operational Risk. Banks adopting the Standardized Approach for Pillar 1 capital, in theory, can choose between any of the methodologies but are likely to adopt either the Basic Indicator or the Standardized Approach and only those banks adopting Pillar 1, IRB are likely to adopt the Advanced Measurement Approach for Operational Risk. Indeed banks that adopt IRB are certainly expected to opt at least for the Standardized Approach and to graduate to an AMA approach over time.

An explicit capital charge for operational risk is clearly new. It is also undoubtedly relevant for developing countries. While the Basel Core Principles require a bank to consider all risks including operational risk, to date there has not been a formal benchmark laid down in an international standard.

It is likely that most banks in developing countries will not have developed operational risk models and will therefore adopt the Basic Indicator or the Standardized Approach. The inclusion of an explicit operational-risk, capital charge should not in theory imply a significant increase in required supervisory resources, as supervisors should be assessing operational risk anyway. However, as the BCP compliance data above makes clear in practice
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<mark>Pazarbasioglu, Ceyla; Byskov, Steen; Bonomo, Marco Antonio Cesar; Carneiro, Igor Andre; Martins, Bruno; Azevedo Hernandez Perez, Adriana. (2017). Brazil - Financial intermediation costs and credit allocation. Discussion paper for workshop. Washington, D.C. World Bank Group.</mark> <mark>Petersen, M.A. and Rajan, R.G., 1994. The benefits of lending relationships: Evidence from small business data.</mark> _<mark>The journal of finance</mark>_ <mark>, 49(1), pp.3-37.</mark> <mark>Petersen, M.A. and Rajan, R.G., 1995. The effect of credit market competition on lending relationships. The Quarterly Journal of Economics, 110(2), pp.407-443.</mark> <mark>Rajan, R.G., 1992. Insiders and outsiders: The choice between informed and arm's‐length debt.</mark> _<mark>The Journal of finance</mark>_ <mark>, 47(4), pp.1367-1400.</mark> <mark>Rajan, R. and Zingales, L., 1998. Financial Dependence and Growth, American Economic Review.</mark> 88 (1998): 559-586 Ru, H., 2018. Government Credit, a Double‐Edged Sword: Evidence from the