rafmacalaba/data-use-mentions-extended huggingface dataset:
of these correspond to exactly one of the 226 countries and territories in the master list. Others pertain to localities that tend to be obscure territories, islands, or regions, such as the Isle of Man or Ceuta.

- _Aggregate geographic regions:_ These correspond to two or more countries or territories in the master list. They can be continents (such as Africa), parts of continents (such as South Asia), political alliances (European Union), or other classifications (such as Other Ex-French Africa; Algeria, Tunisia, and Morocco; and Melanesia). The data for these aggregate regions need to be allocated to the 226 countries in the master list. The details of the procedures are discussed below.

- _Miscellaneous categories:_ These include refugees, stateless, and born at sea. There are generally no geographic correspondences for these.

Thousands of geographic regions and categories emerged from the more than one thousand individual destination country sources chosen for the analysis. The vast majority of these are repetitions that refer to identical geographic locations using different 14 The United Nations (1998) has developed recommendations aimed at promoting standardized recording practices across countries. Until such practices are followed uniformly, harmonization will remain a key issue in understanding and comparing migration statistics.

10
2 reform.<sup>4</sup> Another contribution of the paper is the modeling of the effects of corruption in the sector and their interactions with reform policies. These interactions add useful policy results to Wallsten’s inclusion of a governance variable as a driver of performance.<sup>5</sup> Finally, we also look into the relevance of investment risk and its interactions with reform policies for telecom performance.

The paper is organized as follows. Section 2 offers background on the state of reforms in the sector. Section 3 discusses the performance indicators available and presents basic statistics. Section 4 summarizes previous cross-country academic literature on the impact of telecom reforms. Section 5 presents the econometric analysis resulting from this larger database on telecom performance. Section 6 concludes.

# **2. THE MAIN “MACRO” REFORMS IN TELECOM**

The reforms of the 1990s brought major changes to the traditional structure of the telecommunications sector. Until then, the typical operator was a monopolistic stateowned company. In most countries, this monopoly was self-regulated or regulated by the government rather than by an independent agency. The main drivers of regulation tended to be fiscal or employment concerns instead of service oriented concerns. Indeed, prices were often intended to generate fiscal revenue rather than to reflect costs and complex cross-subsidy schemes.

This situation changed with the major technological shocks of the 1990s. Besides the unbundling of the sector stimulated by the technological revolution, the first common element of reforms across countries may be the opening to competition. In most countries, this materialized by an increased role of the private capital in the sector. Indeed, state-owned operators were usually separated in multiple companies and in most cases they were sold, given in concession or licensed to private operators or investors which in turn started to compete with the incumbents and other entrants. Because there is no reliable measure of the degree of competition for a large sample of countries, the existence of private capital investment in telecoms may be the best proxy to document the commitment
https://s3.amazonaws.com/ndpcstatic/CACHES/PUBLICATIONS/2016/04/16/Women+&+Men+in+Ghana+A+Stitistical+Compendium-+2014.pdf Gutierrez, Federico H. “Acute morbidity and labor outcomes in Mexico: Testing the role of labor contracts as an income smoothing mechanism.” _Journal of Development Economics_ 110 (2014): 1-12.

Heath, Rachel, Ghazala Mansuri, Bob Rijkers, Will Seitz and Dhiraj Sharma. “Measuring Employment in Developing Countries: Evidence from a Survey Experiment in Ghana” (2019) _mimeo_ .

<mark>Heltberg, Rasmus, and Niels Lund. “Shocks, coping, and outcomes for Pakistan's poor: health risks predominate.”</mark> _<mark>The Journal of Development Studies</mark>_ <mark>45.6 (2009): 889-910.</mark> Honoré, Bo E. “Trimmed LAD and least squares estimation of truncated and censored regression models with fixed effects.” _Econometrica_ (1992): 533-565.

<mark>Islam, Asadul, and Pushkar Maitra. “Health shocks and consumption smoothing in rural households: Does microcredit have a role to play?”</mark> _<mark>Journal of Development Economics</mark>_ <mark>97.2 (2012): 232-243.</mark> <mark>Jalan, Jyotsna, and Martin Ravallion. “Are the poor less well insured? Evidence on vulnerability to income risk in rural China.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>58.1 (1999): 61-81.</mark> Jayachandran, Seema. “Selling labor low: Wage responses to productivity shocks in developing countries.” _Journal of Political Economy_ 114.3 (2006): 538-575.

Kochar, Anjini.”Smoothing consumption by smoothing income: hours-of-work responses to idiosyncratic agricultural shocks
approval to hire additional staff. It remains to be seen how effective this re-centralization of the wage bill is, and the extent to which it will ensure that these expenditures match local needs and address any prevailing disparities.

# **4. Evaluating Transfer Allocation Scenarios**

Various trade-offs inherent in different transfer distributions can be quantified by comparing allocation simulations. Formula-based allocations for fiscal transfers have the advantage of being more transparent and predictable. Such allocations help regional governments plan their expenditure better (Bird and Smart 2002). Since they are open to scrutiny they may promote greater accountability. However, formula-based allocations rely on the availability of a timely and reliable sub-national data. Moreover, even if good proxy data for expenditure needs and fiscal capacity are available, the exact design of a formula may be open to question.

A number of considerations may see central governments deviate from a pure formula-based allocation. The policy principles of predictability argue that allocations should be smoothed over time. For example, existing civil servant and establishment costs may be subject to significant rigidities. Hold-harmless arrangements allow for a transition period. Indonesia’s decentralization did ensure that localities were at least held harmless relative to previous years as formula based allocations become increasingly pronounced. Another form of hold-harmless may be due to a mix of omission and political wrangling. For example, during the first 2001 DAU allocation natural resource sharing revenues (SDA) were not considered as a part of the fiscal capacity in the formula. When they were introduced in the subsequent year, this would have meant that natural resource rich would have gotten significantly less DAU. Richer regions, in part 13
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management system improvements, civil service censuses, functional reviews of whole governments or selected ministries, diagnostic studies of overall employment problems, and other specific aspects of employment and pay policy.

# Civil Service Censuses

Civil service censuses are often viewed as a preliminary step to generating information crucial to making necessary changes in public employment and pay policy. Such surveys have been carried out in 6 countries where the Bank has been active in pay and employment reform. They are intended as a head count of civil servants actually working in government. They should be able, in principle, to distinguish those actual employees from fictitious or fraudulent names (ghosts) on the staffing lists of the civil service ministry or the individual technical ministries. This can be achieved by comparing the results of the census with the payroll. In 3 cases, this comparison was made and ghosts were eliminated. Such follow-up comparisons are not always performed, however, calling the usefulness of the census itself into question. Civil service censuses have generally been carried out by government functionaries, often teamed with
# **5. Conclusion**

This paper is an initial attempt to assess the effect of accessibility to transport on rural development in Sub-Saharan Africa. To the best of our knowledge, our paper is the first to investigate the relation between access to markets from road improvements and the spatial expansion of cultivated land using geo-referenced panel data. Our analysis was carried out for a 35-year span when roads were constructed and cropland area expanded in the region quantifies the link between the two. In accordance with the theory, we find suggestive evidence that improved market access leads to more land put into cultivation. The effect, however, is quite modest as a doubling of our measure of market access leads to an increase in cropland by approximately 2 to 6 percent. This, however, seems driven by increases in local population much more than by improved accessibility from better roads, which is suggestive of agriculture mainly responding to local demand. Complementary to this result, we also find that cropland expansion only marginally increases local GDP, which is consistent with the idea that a significant part of cropland expansion in Sub-Saharan Africa is not accompanied by the development of commercial agriculture and that trade of agricultural goods is to a certain extent limited to the immediate vicinity of production. In the wake of our study on the extensive margin of agriculture (cropland expansion), further research will be needed to assess whether market access has an impact on the intensive margin (yields), which, however, are known to have been stagnating in the region. More generally, research will also be needed to identify the enabling environment that could allow transport investment to better support rural development in the region.

> 1 Based on calculations from Fischer and Shah (2010), Deininger et al. (2011) report figures for suitable land in nonforest areas under different population density criteria: 68 million hectares (respectively 201 million hectares) for areas of less than 5 people (respectively 25 people) per km<sup>2</sup> (see Table A2
Boddy et al. 2005; Maffeis 1999), these factors and their nonlinearities need to be accounted for in the analysis.

# III. DATA

This study uses historical CO and meteorological data to assess the effect of PYP on CO during two-year symmetrical time intervals centered on the start of each phase (August 18, 1997, through August 17, 1999, and February 7, 2008, through February 5, 2010); see table 1 for descriptive statistics. Allowing the analysis to span two years for each phase ensures accounting for seasonal variation and lessens the effect of possible confounding factors on CO levels (see Davis 2008). The first time window is used to evaluate the effect of moderate restrictions (pre-PYP versus moderate phase), and the second to assess the effect of drastic restrictions (moderate versus drastic phase).

Data on hourly CO levels and meteorological variables were taken from the Air Quality Monitoring Network of Bogotá (RMCAB). The RMCAB is a system of continuous and automatic monitoring of air quality dating back to August 1997<sup>13</sup> that measures ambient concentrations of CO, NOx, nitrogen monoxide (NO), nitrogen dioxide (NO2), PM10, sulfur dioxide (SO2), and ozone (O3), as well as meteorological variables such as wind speed, wind direction,<sup>14</sup> relative humidity, superficial temperature,<sup>15</sup> and rainfall. Monitoring stations with CO data for more than 75 percent proxy for car use. To the best of my knowledge, this is the first paper assessing driving restrictions that includes this factor.

13. At present, the RMCAB consists of 15 point stations and a mobile station. Equipment is in compliance with the US Environmental Protection Agency’s (EPA’s) regulations (Secretaría de Ambiente 2015).

14. To allow for meaningful interpretations, this reading was converted from azimuth bearings to a set of dummy variables corresponding to the eight-point compass international convention.
# 1. Introduction

The assessment of banking system stability in emerging and developing economies (EMDEs) is often impaired by the lack of reliable and complete financial data. Most systemic risk measures, such as the Conditional Value at Risk (Adrian & Brunnermeier, 2016), the Marginal Expected Shortfall (Acharya et al., 2017; Acharya et al., 2017), or the Expected Capital Shortfall (Brownlees & Engle, 2017) rely on detailed market data which are not readily available in most EMDEs. In addition, market data primarily cover large listed commercial banks and thus may not capture challenges at unlisted banks (including mutual banks or state-owned banks), whose potential distress could still be relevant for systemic banking sector resilience.

In this paper, we develop a simple and transparent country-level metric, the Consolidated Distance to Breakpoint (CDBP), which provides a basic approximation of system-wide bank resilience to credit shocks. We start from a simple “reverse stress test” framework and apply it to basic bank-level accounting data. We then focus on the tail of the most vulnerable banks in the system and propose a new aggregate metric of systemic distress.

Our new metric quantifies the rise in non-performing loans (NPLs) necessary to trigger undercapitalization in a subset of the most fragile banks that collectively represent at least 20 percent of banking system assets (“Banks at Risk”), a threshold commonly associated with a systemic banking crisis (see Reinhart, 2021a, for a taxonomy of financial crises). The larger the necessary rise in NPLs, the more resilient the banking system appears to be.

We only focus on credit risk, the most common source of financial instability in EMDEs, although banks often face a wide range of other risks, including liquidity, foreign exchange, and market risks. Other relevant factors are also not considered, including contagion and second-round effects, implications of contingent liabilities, and the strength of the bank resolution framework. Therefore, our framework is inherently limited in scope and should ideally be complemented with additional
6|
|Dominican Republic|10.3|Canada|5.2|
|Netherlands|10|Germany|5.2|
|United Arab Emirates|10|Finland|5.1|
|Mexico|9.7|New Zealand|5.1|
|Ireland|9.6|Israel|4.9|
|Singapore|9.6|United Kingdom|4.7|
|Spain|9.4|Italy|4.7|
|Morocco|9.3|Denmark|4.4|
|Czechia|9.2|Sweden|4.3|
|India|8.8|Japan|3.7|

Source: Statista Global Consumer Survey. Note: Statista reported that the survey contains between 1,000-4,000 respondents per country and that the samples are representative of the online population. These figures should be interpreted with caution as data gaps remain significant.

9
||
|Extreme vulnerable|from 17 -<br>26|Host or displaced > 3<br>months|4,868|45%|
|Extreme - Mid vulnerable|from 17 -<br>26|Host or displaced|6,239|58%|
|All vulnerable group|9<br>and<br>above|Host or displaced|6,491|60%|
|Non<br>vulnerable<br>/<br>not<br>qualified|below 9|Host or displaced|4,327|40%|
|MoLSA-PMT|Ln||||
|Bottom 15% nationally|4.6|100000|2,124|20%|
|Bottom 18% nationally|4.7|110000|2,325|21%|
|Bottom 25% nationally|4.9|135000|4,356|40%|
|Bottom 30% nationally|4.9|140000|4,644|43%|
|Bottom 35% nationally|5.0|150000|5,375|50%|

Note: The unit of PMT score of SEVAT is Log10, the unit for legacy VA score is an index between 0 and 50, and the unit of MolSA is the natural log.

# 4.3 The referral rate using the Full PMT

The share of households referred (i.e., cross-eligible households) was analyzed using Equation 1 described in the methodology section. We are interested in calculating the proportion of households eligible (or 15
shock ( _εy_ ).<sup>11</sup> Realization of the income shock occurs after the fertility decision is made. Therefore, pregnancy decisions are based on expected income.

# 2.4 Perceived Infection Hazard

A woman's perceived exposure to HIV infection is modeled as a hazard process.<sup>12</sup> Let _h_ ( _t_ ) be the probability a woman assigns to getting infected at period _t,_ conditional on being HIV-negative until then. The perceived hazard rate for period _t_ is given by where _x_ ( _t_ ) is a vector containing the woman's characteristics, the duration of her marriage, her age and age squared, and a constant. The parameters related to the constant, age and age squared are allowed to dier for unobserved types.<sup>13</sup> The perceived unconditional probability of getting infected at period _t_ , _P_ ( _t_ ) _,_ is given by

# 2.5 Survival Expectations

A woman is assumed to know the mortality processes associated with HIV infection (for both adults and children). Survival to each additional period provides her with information about her HIV status. Specically, she reduces the probability of having gotten infected in each past period. Given these probabilities and the mortality process, the woman assigns probabilities to survival to each future period. The woman also updates expectations about the survival of children of ages zero to three in future periods. Conditional on the probabilities she assigns

> 11The Malawi Multiple Indicator Cluster Survey 2006 estimates that 27.5 percent of children aged 5 to 11 are involved in at least one hour of economic work or 28 hours of domestic work per week. A more natural specication of the income function might take into account the ages of the children; however, I do not keep track of the ages of children above age 3 in the state space to reduce computational burden.

> 12Perceived and actual exposures to HIV infection are treated as exogenous to women's behavior
respectively.<sup>17</sup> These results are remarkably consistent with previous studies of registration reforms in two different OECD countries, as discussed in the introduction (see Branstetter, et al., 2010, Bruhn, 2008, Bruhn 2010).<sup>18</sup> We perform several robustness tests to ensure that our main results are not driven by omitted variables. First, previous research noted that the rate of new firm formation is related to industry structure (e.g. Reynolds, et al., 1994 and Baptista, 2008). For example, an economy consisting of mainly self-employed microenterprises has a different structure than one where large-scale manufacturing enterprises account for a large share of employment. Clearly, our sample includes countries with very different industrial composition. However, all level differences between countries are already accounted for in our model with country-specific fixed effects. Nevertheless, we also verify that time-varying differences in industry composition are not affecting our results. In separate regressions (available upon request) we control for industry composition by including annual data on the value added of the manufacturing sector (as a percentage of GDP) and the service sector (as a percentage of GDP) (World Development Indicators, 2012). The results remain robust to these additional controls.<sup>19</sup> Second, differences in the level of economic development have also been found to affect the rate of new firm entry (eg. Wennekers, 2005). To test whether the level of economic development is driving our results we add lagged GDP per capita to our model. We find our

> 17 As an additional robustness check, we also evaluate the impact of “negative” reforms, which refers to instances in which starting a business measures _increase_ . Though the sample size for these reforms is very small (generally less than 10, even at 20 percent), we do observe a significant relationship between increases in procedures and decreases in entry density (results available upon request).
# 1. Introduction

In many low-income countries, children learn little from attending school (World Bank 2018). For example, four out of five students in Mozambique and Nigeria cannot read a simple word of Portuguese and English, respectively, after more than three years of compulsory language learning. In India, only one in four fourth grade students manages tasks—such as basic subtraction—that are part of the curriculum for the second grade. In Uganda, roughly half of the students cannot order numbers between 0 and 100 after three years of mathematics teaching.<sup>1</sup> A growing body of evidence—based on teacher value-added and experimental studies—suggests that teacher quality, broadly defined, is a key determinant of student learning.<sup>2</sup> Less is known, however, about what specific dimensions of teacher quality matter. In this paper we focus on teacher subject content knowledge, noting that there is a dearth of well-identified studies on the impact of this knowledge on learning outcomes in low- and middle-income countries (see Glewwe and Muralidharan 2015).

In particular, we investigate the role of teachers’ knowledge of the subject they teach, using unique cross-sectional data collected from nationally representative surveys of schools in Kenya, Nigeria, Mozambique, Senegal, Tanzania, Togo, and Uganda (these countries, together, represent close to 40 percent of the region’s total population). The data quantify teacher quality along three core quality dimensions: time spent teaching, teachers’ knowledge of the subject they are teaching, and teachers’ pedagogical skills (see Bold et al. 2017). Unlike many other large-scale data collection efforts, which assess students and teachers using multiple choice items and thus introduce additional chance variation in test scores, the data we use are collected using one-on-one tests for students, and test scores for teachers that are derived from mock student tests marked by the teachers.

Our first contribution is methodological. We show that data on test scores of the student
# Minding the Gap: Aid Effectiveness, Project Ratings and Contextualization

Diana Goldemberg<sup>1</sup> , Luke Jordan<sup>2,</sup> , Thomas Kenyon<sup>1</sup> _Keywords:_ aid effectiveness, machine learning, World Bank projects JEL Codes: O12, O15, O19

> _⋆_ The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> _⋆⋆_ The authors would like to acknowledge the valuable contributions of Lily Chu, St ́ephane Guimbert, Jed Friedman, Michael Woolcock, Lily Tsai, Daniel Honig, Justin Shenk, Jos Vaessen, Christopher Nelson, Stephen Francis Pirozzi, as well as seminar participantsat the MIT GOV/Lab Seminar and the RMES Results Peer Learning Series. All errors are our own. _Email addresses:_ `dgoldemberg1@worldbank.org` (Diana Goldemberg), `lukej@mit.edu` (Luke Jordan), `tkenyon@worldbank.org` (Thomas Kenyon)

> 1World Bank Group 2MIT