rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10144

# **Abstract**

Can an implementation-driven analysis of project success be used as a more granular instrument for assessing the effectiveness of World Bank project? By focusing on how projects perform, this paper attempts to capture variation hitherto unexplored in the aid effectiveness literature. This offers greater precision for diagnosing implementation challenges throughout the project cycle, producing a cross-cutting instrument that reaches across country-, time-, and sector- based approaches. Using data from the Global Delivery Initiative’s “Delivery Challenges in Operations for Development Effectiveness” database and indicators from more than 5,000 lending projects (1995 –2015), the paper examines project performance and the achievement of development objectives across 42 specific delivery challenges. Bayesian model averaging is used for a holistic assessment of the relative impacts of each challenge alongside a battery of structural and contextual covariates. The findings show that issues of project design, ineffective monitoring, and weak organizational capacity have systematically hindered the World Bank’s performance and achievement of the indicators. Conversely, while financial instability and weaknesses in stakeholder engagement can hinder success, their identification and treatment ultimately improves project performance.

This paper is a product of the Governance 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 ahagh@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
(Co ̧sar & Fajgelbaum 2016, Fajgelbaum & Schaal 2020). We use on a general equilibrium model of an endowment economy to uncover the trade and income effects of a fall in the cost of trade that induces a reshaping of the geography of trade flows. This fall in the trade cost is the result of a combination of regulatory change (a regional trade agreement) and infrastructure investment.

Third, trade models have been used extensively to quantify the effects of trade agreements (see Lim ̄ao 2016 for a survey), including recent Structural Gravity General Equilibrium models (Anderson & Yotov 2016, Fontagn ́e & Santoni 2021, Fontagn ́e et al. 2023), New Quantitative Trade Models (Costinot & Rodr ́ıguez-Clare 2014, Caliendo & Parro 2015) and Computable General Equilibrium models.<sup>2</sup> While most papers examine the impact of infrastructure investment and trade reforms in isolation, we quantify their combined impact for Africa.

Finally, by combining GIS analysis and trade modeling, our work relates to a final stream of research that uses granular geographic data to shed light on the consequences of major infrastructure projects. The analysis of China’s Belt and Road Initiative is a recent and striking example of this (de Soyres, Mulabdic & Ruta 2020, Baniya, Rocha & Ruta 2020, Bird, Lebrand & Venables 2020, Lall & Lebrand 2020). We depart from the latter works in three ways: (i) we extend the transportation network developed by de Soyres, Mulabdic, Murray, Rocha & Ruta (2019) to the entire African continent for both regional and international transportation; this provides us with an ideal setup for quantifying the effect of a change in relative trade costs. (ii) We expand the types of transportation projects analyzed. (iii) We use a structural gravity general equilibrium approach

> 1A related but distinct line of research studies the impact of infrastructure development on allocative efficiency in developing economies. Asturias, Garc ́ıa-Santana & Ramos (2019
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a heavily populated African setting. Including data from districts across one Nigerian state, Figure 4 avoids the difficulties inherent in crosssection comparisons of countries with varying resource endowments, labor force defin'tions and policy environments.

# VI. MAGNITUDE OF THE FARM-NONFARM LINKAGES

As a prerequisite for estimating the magnitude of agricultural growth multipliers, as well as an aid in gaining a fuller understanding of the nature of farm-nonfarm linkages, it is useful to examine in some detail available evidence on the strength of intersectoral linkages in rural Africa. Five different linkages are important, two in factor markets and three in product markets. The factor market linkages involve capital and labor flows betweer. agriculture and nonfarm enterprises. Product markets include backward production linkages from agriculture to rural input suppliers, forward production linkages from agriculture to processors and distributors, and consumer demand linkages generated as a result of increasing farm incomes.

# A. Capital Flows

Host observers believe the outflow of capital from agriculture to be larger than the reverse flow from nonfarm activity to agriculture. Certainly at an aggregate level, a large accumulation of evidence suggests that surpluses have been consistently transferred out of agriculture through fiscal, crop pricing and trade policies (World Bank, 1981; Lee, 1983; ILD, 1982b; Sharpley, 1981). Marketing boards and export levies, for example, typically tax away 30 to 50 percent of farmers' cash crop prices (World Bank, 1981). Given the structure of government expenditures in most African countries, it is very likely that these agricultural surpluses have been transferred primarily to urban areas and into nonagricultural activities. Private investors have also channelled investment funds from
time activities.<sup>11</sup> Therefore, total income earnings for the reference year are divided by the months worked full-time.<sup>12</sup> Earnings distributions and deciles are defined for each of the groups of workers (full time, employers, regular employees and casual employees). Similar to the inflation adjustments applied to household disposable income, nominal earnings are converted into real values using decile-specific inflations rates computed using HBS data.

The sample for the analysis of real minimum wages is restricted to employees who work full time. For each year, earnings in cash are compared to the value of the official minimum wage to identify workers who are paid the minimum wage. However, this approach comes with a caveat. The identification of minimum wage earners is measured with error due to the self-reporting nature of the income data in SILC, in particular the rounding of earnings and months of work in addition to the likely reluctance of some individuals to report their full income to avoid losing social benefits that are tied to the value of the minimum wage. In fact, the empirical distribution of earnings amongst formal workers shows bunching right below the minimum wage threshold. In contrast, the distribution of earnings for informal workers does not display any discontinuities around the same threshold.

# **5. Results**

## **5.1** **_The incidence of inflation_**

As expected, there is significant variation in the composition of the consumption baskets of Turkish households across the income distribution. Expenditure data for 2019 classified by the 12 COICOP groups presented in Figure 2 shows that food, non-alcoholic beverages and housing make up a larger share of overall consumption for the bottom of the distribution compared to the upper deciles. While food and non-alcoholic beverages account for 41.1% of the total expenditures for households from the bottom of the distribution, this share is a third (13.1%) for the top decile –and also smaller for the median household (26.
(OD), introduced in Reijnders and de Vries (2018), and extended with a set of lower income Asian economies by Bertulfo et al. (2022), see Appendix Table C2 for the sources. The database provides information on the number of workers by occupation-industry pair as well as their labor earnings. For each country national data has been harmonized using mappings from national occupation classifications to an international classification of thirteen occupational groups based on 2-digit codes in the ISCO88 (International Standard Classification of Occupations), see Appendix table C3. While some countries have adopted the ISCO in their administrative statistics, most follow their own classification and bridge tables needed to be developed to have consistent coding. Furthermore, national industry classifications were mapped to a common set of 35 industries covering the overall economy. These are mostly 2-digit industries in the ISIC (International Standard Industrial Classification) revision 3.1, see Appendix table C4.

Previous studies have compiled large-scale data sets to compare changes in occupational structures across countries and over time. The _Jobs of the World Project_ is a prominent example (Bandiera et al., 2022). We elaborate by adding a cross-classification of occupations by industry. This is an important step to account for the variety in tasks required from workers of the same occupational group in different industries. Compare for example the so-called work activities performed by machine operators in the textile manufacturing industry and similar workers in the electronics manufacturing industry. Both occupations fall under ISCO 1-digit code 8. Data from O*NET indicates that “getting information” is a very important work activity in both industries, ranking first in electronics and second in textiles (out of forty-one work activities that are distinguished). But whereas “evaluating information to determine compliance with standards” and “making decisions and solving problems” are also very important activities in electronics (ranking third and second), they are much less important in textiles (11<sup>th</sup> and 10<sup>th</sup> ). Conversely
.2|3.3|2.4|na|2.0|
|Freight (US cents/tonne-km)|3.3|5.5|4.4|na|5.8|

Source: Bullock 2009, derived from AICD railways database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# **Challenges**

The crisis that erupted in 2002 severely affected rail freight traffic volumes, and by 2003 traffic volumes had fallen to 100 million tons (figure 5). Following special security measures, traffic volumes recovered quickly even before the peace accord of 2007, and since 2006 they have exceeded their earlier peak, which was reached in 2001.

More recently, theSitarail concession has entered into a dispute with the authorities, due to the failure of the concessionaire to finance anticipated track rehabilitation. As a result, only half of the planned five year investment program of $12.4 million has been delivered. This problem has been widespread across Sub-Saharan African rail concessions. Due to the relatively low volume of freight traffic and competition from the road sector, it is rarely possible for rail networks to earn sufficient revenue to finance track rehabilitation. In the case of Sitarail, it is likely that the original forecasts made at the time of the award of the concession overestimated the likely freight traffic flows and underestimated the extent of the investment need. More recently, spending needs for the concession for 2008 to 2020 have been estimated to be $132 million for track rehabilitation and $99 million for rolling stock—a total of $231 million. This is roughly three times the revenue of the concession in 2009 and can therefore only be met with public finance.

12
Licensing & royalties balance of payments, 2009 _Includes entire World Bank dataset, not just case study states; also uses different definitions of income categories Table 2. Licensing and Royalties Balance of Payments, 2009_

|**Country Group**|**Royalty, License**<br>**Fees Received**|**Royalty, License**<br>**Fees Paid**|**IP Balance of**<br>**Payments**|**Payments as a**<br>**% of Receipts**|
|---|---|---|---|---|
|**High income**|$177,835,372,041|$156,372,095,047|$21,463,276,994|88%|
|**Middle income**|$3,766,598,542|$32,421,925,901|-$28,655,327,359|861%|
|**Low income**|$33,678,498|$67,097,779|-$33,419,281|199%|

(Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files)

# Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files **3. The Monetization of Intellectual Property**

## **Intellectual Property and Technology Commercialization**

The institutionalization of property rights is a constitutive element of a market economy. Intellectual property is typically conceived as a means of reducing market failures associated with knowledge assets that are non-excludable and non-rivalrous in consumption (Merges 1994). The introduction of property rights over intangibles renders embedded and tacit knowledge codifiable, functional and manageable. In general, the introduction of property rights enables the generation of surplus value. People can still extract ‘use value’ from land,
This empirical approach allows us to compare districts that are similar in observable characteristics with the only dierence being the exposure to violence while dealing with non-observable variables with the use of panel data.

We run several models where the dependent variable is always FLFP rates expressed as a number between "zero" and "one". The variable of interest at the right hand side switches between dierent proxies of violence measured as ows at the district level for each year. In the Robustness section we provide alternative specications with the aim of shedding light on the non-linearity of the relation by using (i) a single dummy variable equal to "one" if there was an attack in a year in a given area; (ii) a set of dummy variables for dierent ranges of attacks number; (iii) number of attacks lagged one time period.

# 4.2 Quantile Regressions

Violence may have dierent eects depending on the initial level of FLFP in each district. For instance, the occurrence of a terrorist attack could reduce participation relatively more in areas with higher initial participation or, alternatively, have more impact in areas with lower rates. Or there might be no dierence in the eect along the distribution of rates. To formally test this possibility, we estimate a set of quantile regressions which basically look for estimates of parameters by minimizing the dierence between the explanatory variables and a given quantile of the dependent variable (as opposed to OLS that minimizes with respect to the mean). We run the estimations using ve points of the FLFP rates distributions: quantiles 10, 25, 50, 75 and 90; and use our three measures of violence (attacks, people wounded, or people killed).

# 4.3 Tobit Estimation

An empirical approximation to equation 6 consists of estimating a Tobit model augmented by the exposure to violence in each district. Due to the widely missing data on wages for each worker, we use "education" as
abounds on how Internet access prices evolved following the arrival of major submarine cables (Appendix A). For instance, Nigeria experienced the arrival of five SMCs between 2010 and 2015.<sup>3</sup> These new SMCs have accelerated the growth of international Internet bandwidth used in the country, from an annual rate of 48 percent between 2010 and 2014 to 70 percent between 2015 and 2020.<sup>4</sup> Such an expansion in bandwidth was accompanied by a 5 percentage point drop in the price of mobile broadband over the same period.<sup>5</sup> Cameroon also experienced the arrival of three major SMCs during the same period, among the five which arrived in Nigeria: ACE, WACS and NCSCS. These arrivals were also associated with an ex-

> 1Some submarine cables may have some terrestrial segments.

> 2Telegeography, 2022.

> 3Glo-1 and MainOne in 2010; Africa Coast to Europe (ACE) and West Africa Cable System (WACS) in 2012, and the Nigeria Cameroon Submarine Cable System (NCSCS) in 2015.

> 4Based on bandwidth data from Telegeography.

> 5Price of 2GB of mobile broadband data in percentage of monthly income per capita - based on data from the ITU.

2
impact of time invariant productivity shifters such as producers' endowments of physical assets, human capital, and their access to public services. Both the structural parameters for specific inputs and the productivity shifters enable us to derive more precise policy recommendations.

Instead of relying on a survey that was specifically fielded for this purpose and the small sample that is generally associated with such efforts, we use data on farm production from a panel of 4853 farm households from the Central Statistical Office's Post Harvest Surveys for 1993/94 and1994/95. The data were collected sufficiently long after initiation of the reforms for them to have shown at least some impact. While the time period covered is rather short, it coincides with significant variability in the economic and agro-climatic environment such as the final closure of official credit institutions, and a drought in 1994/95. As a result, there is sufficient "within" household variation in input use for panel data methods to be meaningful and allowing us to
consumption than HFCE.<sup>14</sup> The second factor is the recovery from the 2008 financial crisis, which is distinctly visible in the 2009 national accounts data. Survey-based consumption, however, may have taken longer to recover. Both factors could have contributed to the unusually large increase in mean consumption observed between 2009-10 and 2011-12, which counteracted the usual tendency for HFCE growth to exceed growth in mean consumption. We know of no reason to believe, however, that growth in HFCE will accurately reflect growth in mean consumption going forward. Turning back to the model predictions, we consider three alternative specifications that impose more restrictive assumptions. These estimate a slower poverty decline, with estimated headcount rates ranging from 17.4 to 19.1 percent. In contrast, the traditional line-up method shows a much more rapid decline in poverty, from 21.6% in 2011-12 to 10.9% in 2014-15. This shows that the choice of modeling assumptions matters, at least in this case.

The final step is to use the estimates for 2014-15 from the econometric model to generate the poverty estimate for India for 2015 used in the global estimates. This was done by estimating an appropriate fraction, or “pass-through rate”, to apply to growth in HFCE, thereby assuming that only a portion of the HFCE growth is passed through to growth in mean welfare in the household survey. Pass-through rates were selected to match the $1.90 headcount poverty estimates from the survey-to-survey imputation model for 2014-15, which were 10% in urban areas and 16.8% in rural areas. These were then used to calculate estimates for 2015, based on the scaled down growth rate of HFCE.<sup>15</sup> The rest of this paper is organized as follows. The next section reviews the data used for the analysis. Section III turns to the methodology. It discusses the estimation method and the process used to select the set of predictor variables, and examines the results of four alternative
hazard measures generated are in levels as well as standardized anomalies (z-scores).<sup>13</sup> The latter assume that differences in the level of the hazard from the historic average (i.e., level changes) matter not in an absolute sense but in proportion to an area’s usual variation. Welfare measures are only partly affected by the relation between hazards and crop yields and are the outcome of various ex ante and ex post risk-coping strategies developed by households over the years for the purpose of reducing income fluctuations and/or protecting their level of welfare (e.g., consumption). Hence the extent to which the experienced hazard deviates from what households expect (i.e., the historic mean value of the hazard) appears to be a better specification than the actual level of the hazard. Thus it is anticipated that the specification of shocks as standardized anomalies (in standard deviation units or z- scores) is likely to yield more sensible welfare impact estimates than those obtained using the level of the hazard (i.e., rainfall in millimeters). In the case of droughts, a positive anomaly means the hazard experienced was less severe than the historical mean, and a negative anomaly means it was more severe.

When analysis of the specification of the hazard level as a level includes enough years of welfare data, controlling for fixed effects at some spatial level (e.g., district level) transforms the hazard and all other control variables as deviations from the mean (Dell, Jones, and Olken 2014). In cases such as ours, where the sample of welfare data contains a limited number of observations over time, this is not the case, and it is preferable to use a full time series of hazard data prior to regression analysis to generate a standardized anomaly.<sup>14</sup> 13 Some hazard measures such as the WRSI cannot be expressed as a standardized anomaly because by construction they are top coded (
Andr ́ee

# **4.1 Linear analysis of March 22 cases per 100,000 inhabitants**

To analyze the relationship between spatial variation in particulate matter concentrations and COVID-19 incidence, a number of regressions are estimated that control for possible spatial autocorrelation (Anselin, 1988).<sup>13</sup> Importantly, the spread of SARS-CoV-2 manifests itself in hot-spots that result from contact with infective subjects from areas that are in close proximity, and it can show strong geographical patterns that are not structurally related to air pollution levels. The fact that the infection started at different times in different areas together with the exponential and geographical nature of case spread, may lead to spurious associations between the spatial distribution of case hot-spots and pollution levels, particularly if the initial cases occurred in polluted regions by mere chance and then spread to nearby regions.<sup>14</sup> To account for the issue, spatial models include neighboring values of the dependent variable and/or residuals as additional variables. These spatial averages control for the clustering that results from geographical spillovers.<sup>15</sup> These models can be understood as spatial equivalents to the models that are commonly used to analyze time series in which observed values are in part explained by recent observations. While the household composition and population density terms capture more dense social links, the spatial regression components capture the likelihood of contact with infective subjects. In particular, within a hot-spot, neighboring areas have high numbers of cases per 100,000 inhabitants, and the spatial regression terms capture the increased likelihood of having contact with infective subjects within the region. The important empirical question that these models thus seek to answer is whether pollution and case incidence are associated after controlling for the geographical relationships in disease spread.

First, Model 1 estimates a linear regression using all 22 covariates and possible confounders of interest that are summarized in table 3. These include population density, gender, age groups, marital status and household composition, the share of