rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9816

# **Abstract**

Proxy-Means Testing (PMT) and Community-Based Targeting (CBT) are among the most widely used methods for poverty targeting in low-income settings. This paper analyzes household data from nine programs implemented in the Sahel region using a harmonized approach to compare PMT and CBT selection as conducted in practice, once geographical targeting has been applied. The results show that the targeting performance measured depends critically on the definition of the targeting objectives, share of beneficiaries selected, and indexes used to evaluate targeting. While PMT performs better in reaching the poorest households based on per capita consumption, it differs little from CBT, or a random or universal allocation of benefits when distances to poverty lines are considered. When aiming to identify food insecure households, most PMT and CBT targeting schemes perform no better than a random allocation of benefits. On the other hand, targeting costs represent only a small share of budgets. Overall, the results emphasize the need to study programs as implemented in practice instead of relying on simulations of targeting performance, as widely used by practitioners and academics. Taken together, the findings suggest that while there may be a need to select households resulting from budget constraints, PMT and CBT contribute little to poverty or food insecurity reduction efforts in poor and homogeneous settings.

This paper is a product of the Social Protection and Jobs 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 pschnitzer@worldbank.org and quentin.stoeffler@um6p.ma.

_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
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# **Annex B. Literature on measuring public investment quality and the effects of public investment quality**

This annex reviews two strands of the literature on public investment quality that focus on how to measure public investment quality and on the economic effects of public investment quality.

_Measuring of public investment quality_ A seminal paper in the literature aimed at measuring public investment quality is Dabla Norris et al. (2012) <mark>. This paper introduces a multi-dimensional index—t</mark> he Public Investment Management <mark>—</mark> Index, PIMI <mark>aimed at assessing the effectiveness of public investment management processes across 71 emerging market and developing economies. PIMI measures the efficiency of these processes by aggregating 17 indicators across four critical stages: (i) Strategic Guidance and Project Appraisal; (ii) Project Selection and Budgeting; (iii) Project Implementation; and (iv) Project Evaluation and Audit.</mark><sup>33</sup> <mark>PIMI’s primary objective is to spotlight institutional features that serve to minimize risks and ensure the proficient management of public investment. D</mark> abla Norris et al. (2012) show that <mark>there are w</mark> ide variations in the efficiency and effectiveness public investment management across middle- and low-income countries.

An inherent limitation of the PIMI pertains to its reliance on qualitative data, rendering replication and updates challenging. Furthermore, its dependence on secondary data sources and proxies restricts its ability to comprehensively evaluate crucial institutions pertinent to public investment (IMF 2015).<sup>34</sup> To address these issues, IMF (2015) builds a novel Public Investment Efficiency Index (PIE-X) and a Public Investment Management Assessment (PIMA) tool.

Public investment efficiency relates to the correlation between the value of public capital stock and the quantified coverage and quality of infrastructure assets. The IMF Public Investment Efficiency Index PIE-X is the vertical distance from an efficiency frontier delineated
CFA and non-CFA sub-panels is also considered to capture regional effects of banking crises (see Table 3 and 4 in the Annex). The results show a negative association between real GDP per capita and nonperforming loans expressed as a percentage of loans loss provision. This implies that falling per capita income is associated with rising scope of nonperforming loans. To the extent that changes in per capita income is proxy for changes in economic growth, the negative association with nonperforming loans may reflect the impact of cyclical output downturns on the banking sector; a result that is expected in the literature [Gonzalez-Hermosillo (1999)]. The sign of the coefficient is consistent across state and private banks, though the magnitude of the correlation is stronger for state banks and financial institutions.

The study also investigates the association between nonperforming loans and, domestic credit, broad money supply (M2) and inflation. Though the magnitude of the coefficient of correlation between inflation and nonperforming loans is low, the sign is negative; unexpected rise in inflation under cyclical downturns is likely to negatively affect the performance of the banking sector and recovery of loans to private operators and investors. In the extreme case, hyper-inflation may erode banks assets and equity and weaken banks position through the interest rate channel. However, the magnitude of the coefficient is relatively low, and may reflect the general context of declining inflationary pressures in the nineties, especially in the sub-panel of CFA countries.<sup>30</sup> The results also show a positive association between real exchange rate appreciation and nonperforming loans. The magnitude of this association is particularly strong in the sub-panel of CFA countries, which underwent a devaluation of their currency in the early nineties. Real

> 29 Bankscope collects data on individualized banks portfolio and assets structure, including nonperforming loans. 30 With few exceptions, the average CPI inflation rate was relatively low in the CFA monetary zone, which offers a limited room for independent monetary policy, and hence little scope for inflation through money supply [Husain and Faruqee (1994)
||||<sup>3</sup>||56<br><sup>3</sup>||<sup>3</sup>|||<sup>2</sup>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||journal|% not|_I_ =0|Rank|_I_ =0.5|Rank|_I_ =1|Rank|_h_-index<br>|_I_|Rank|Normalized|Rank|
|||found in|||||||(_I_<sup>1</sup>)|||_h_-index||
|||Google|Publication||||Total|||QIF||||
|||Scholar|count||||citations|||||||
|93|Journal of Urban Economics|5%|21|188|94|108|636|107|12|0.289|107|35%|167|
|93|Journal of the European Economic Association|0%|16|277|90|111|598|111|12|0.273|112|38%|154|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is
|
|Variable|Construction|Maintenance|Administration|Decentralization|
|fiscal|4.33|-<br>.63|0.44|9|
|decentralization|(0.74)|.0)|(1 .3_5)*_*|(1|
|fiscal<br>2|-0.03|-0.03|3|-0.19|
|.<br>decentralization|(-0.26)|(-1 .59)*|(-1|.57)*|
|fiscal<br>|0.001|0.0001|--|0.001|
|decentralization<sup>3</sup>|(0.32)|_(1.15)_|(3.80)|(1.12)|
|R-squared|0.72|0.82|0.81|0.76|
|Observations|261|261|261|261|

Fiscal decentralization is measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

22 The Deutsche Strassenliga brings together professionals working in different areas on roads. Members come mainly from the private sector but the agency is in close cooperation with the state and federal governments. It promotes the development of roads in Germany.

16
and disabled leaders could be incorporated into the school curriculum to help foster a more inclusive culture.

4. **Funding and policy action to support the identification and service provision for children with physical disabilities** **<mark>:</mark>** <mark>The Indonesian education system and curriculum is not currently meeting the needs of disabled children. This lack of adequate facilities and training combined with social stigma contributes to children with disabilities falling behind and dropping out at high rates when they do enroll. Teachers also often lack the appropriate training necessary to accommodate specific disabilities. Refining the curriculum for children with disabilities and providing teachers with training on appropriate strategies to teach students with disabilities would help to improve access to and the quality of inclusive education. This could include</mark> _<mark>implementing</mark>_ <mark>the following measures at the district and provincial levels:</mark><sup>36</sup>

- a. <mark>Improve and harmonize data systems (Dapodik, EMIS) to capture a higher level of detail regarding disabled students and the services that they are accessing, as well as teachers trained on inclusive education and in delivering special education services.</mark>

- b. <mark>School buildings and other education facilities, such as teacher training institutions, should be designed to enable an accessible and inclusive learning environment. Existing structures can be upgraded to be more accessible.</mark>

- c. <mark>All teachers should receive initial training on inclusive education during pre-service teacher education; availability of continuous professional development (CPD) of teachers on inclusive education.</mark>

- d. <mark>School leaders should be trained regarding their responsibilities for enhancing the inclusion of all students, including students with disabilities and removing barriers to their inclusion.</mark>

- e. <mark>School leaders should support teachers in enriching or adapting their strategies to students with a different readiness
province to identify the eligible population. This exercise started with a wealth ranking in 100 villages performed by CHA in partnership with village leaders, yielding a population of 26,957 households that were split into four community-defined categories: well-off (6%), better-off (16%), poor (34%), and ultra-poor (44%). This was followed by a verification survey, administered by CHA, to measure the six qualifying criteria for being eligible for the TUP program listed above. This removed 85% of the households identified as UP from the wealth ranking exercise. MISFA then completed a final verification exercise, removing 28% of this group whose initial eligibility was overturned due to reporting inconsistencies. This resulted in an eligible group of 1,235 households, or slightly under 5% of the population in our study area.

A baseline survey was conducted among all eligible households from February to April 2016, before a public lottery to identify TUP participants took place in May 2016. The randomization was stratified by PRA group by holding separate lotteries for each. Most villages (51) had one PRA group, while larger villages were split into multiple PRA groups which were typically defined by the feeder area of a masjid (mosque). PRA groups with one TUP-eligible household were removed from the study due to the infeasibility of a lottery in these cases, effectively dropping 16 study households from 16 PRA groups. This resulted in 133 PRA groups and subsequent lotteries across our study sample. Then 1,219 UP households in 80 villages were randomly assigned into one treatment group (491 households) and one control group (728 households). Starting in May 2016, the treatment group received the full TUP package as described above, and the control group did not receive any of the components. The program lasted for 12 months from the time of asset transfer. A follow-up survey was conducted from July to October 2018, approximately 1 year after program completion and 2 years after the asset transfer. In this study, we
Data Set of Educational Attainment in the World, 1950-2010. _Journal of Development Economics, 104_ , 184-198.

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- Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2009). Fertility, female labor force participation, and the demographic dividend. _Journal of Economic Growth_ , _14_ (2), 79–101. https://doi.org/10.1007/s10887-009-9039-9

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make the budget constraint variable. These technical features are further described in the following sections, which describe IPF step‐by‐step.

# _Constructing the IPF Indices_

The first step of IPF is to identify the set of indicators that will be combined to construct the social‐environmental and financial‐economic indices. The selection of variables – or project comparison indicators – may differ amongst application contexts based on government policy goals (e.g., particular sectoral, economic, social, and environmental aims) and stakeholder consultation. For explanatory purposes, however, we will discuss the IPF index construction as performed in the first conceptualization of IPF in Vietnam and initial pilot test in Panama.<sup>10</sup> The selection of variables seeks to preserve the principle of parsimony. Accordingly, this methodology relies on and requires a minimum level of relevant information to compare expected outcomes of alternative infrastructure investments. Multiple variables are selected to reflect different aspects of expected performance in two composite indicators, the Social and Environmental Index (SEI) and the Financial and Economic Index (FEI), each built on quantitative and transformed qualitative variables combined via an additive model.<sup>11</sup> To condense dissimilar data types and scales of measurement into indices, three data transformations are required. One must (a) transform qualitative data and ordinal quantitative data into usable scalar data, wherein the intervals between values reflect degrees of difference; (b) standardize criteria measurements to a common scale; and (c) establish weights for each criterion in the additive model.

The transformation of categorical and ordinal qualitative and quantitative data into usable numerical data may be done using the Alternating Least Squares Optimal Scaling (ALSOS) algorithm, a widely accepted transformation approach. Within a quantified categorical variable, the numbers assigned by the ALSOS algorithm to each category reflect the distance _between_ categories, revealing the implicit metric of the variable (Perreault & Young, 1976).<sup>12</sup> Numerical values are thereafter standardized
9 The problem arises from the combination of poor transport infrastructure and high cost transport services. The arid countries with low population density, such as Mauritania and Niger, have the severest dearth of roads, with approximately 1 km of road for each 100 km<sup>2</sup> of area, against the low SSA average of about 20 km. While a few SSA countries have extensive networks of paved roads, only 25 percent of road distance is paved, on average. Poor transport infrastructure poses particular problems for the 17 landlocked African countries that face distances of 200-1500 km from their capital cities to the nearest seaports. All-season rural road access appears a key means of overcoming spatial poverty traps, not least of which by encouraging greater market participation, input use, and agricultural specialization (Stifel and Minten 2008, Calderon 2009, Jacoby and Minten 2009, Gollin and Rogerson 2010). Indeed, transport costs are so considerable in land-locked regions, that global oil price shocks have a greater impact on local maize prices than do global maize price shocks (Dillon and Barrett 2016). Rail systems hold promise. However, World Bank data indicate that the modest total distance of active railway lines actually declined by about 30 percent between 1986 and 2009 (Barrett and Upton 2013).

In addition, crossing borders to reach ports leads to high costs beyond the burden of poor physical infrastructure. Border crossings often entail fees—both official and unofficial—that increase transaction costs and delay deliveries. Non-infrastructure related transaction costs also add a lot to the price of transporting goods within countries. Teravaninthorn and Raballand (2009) report, for example, that Africa’s transportation costs along four major transport corridors in four different parts of the continent are no higher than in other developing countries, such as China. Nonetheless, transportation prices are much higher, not only because of informal payments along the roads, but importantly also because of trucking companies’ market power. Many African countries restrict entry of new companies, enabling incumbents to earn
regression specification, weighting grid-cells jointly by country population and the inverse of the error on our wealth measure, indicate that a 1 standard deviation increase in the wealth index is associated with a 0.04 standard deviation increase in ambient PM2 _._ 5. This result is robust to a variety of alternative weighting schemes (Table SI-1) and the relationship is further strengthened by trimming our wealth data at the 5<sup>_th_</sup> and 95<sup>_th_</sup> percentile (Table SI-2).

This positive correlation appears to be driven by urban-rural differences in both income and pollution. The shading in Figure 1b-c highlights that both grid-cells with the highest pollution levels and grid cells with the highest wealth levels are the most urban in our sample. More generally, being in or near larger urban areas is associated with higher levels of wealth (Figure SI-2). Wealth increases with urbanization both within and across categories (urban, peri-urban, rural) and the differences are stark enough that on average the wealthiest rural areas are poorer than the poorest urban areas. Exposure to ambient air pollution follows similar patterns albeit less dramatically (Figure 1c). Nonetheless, average pollution in urban areas is 12% ( _t-stat:_ 47.7) higher than in peri-urban areas and 50% ( _t-stat:>_ 100) higher than in rural areas.

Our pooled estimate hides substantial heterogeneity across countries. Country specific estimates indicate that ambient pollution increases with wealth in the majority of LMICs. However, a substantial minority of countries demonstrate the opposite relationship. While wealthier individuals live in more polluted locations in countries like Nepal, pollution decreases strongly with wealth in other countries like Nigeria (Figure 2a). When we map the country level relationship (Figure 2b-c) one immediate pattern becomes clear: countries with strong negative correlations between ambient pollution and wealth are highly concentrated in West or Central Africa. In most of the rest of the world, particularly Asia, wealthier areas have higher ambient pollution.

One reason
Policy Research Working Paper 9932

# **Abstract**

Pandemic shocks disrupt human capital accumulation through schooling and work experience. This study quantifies the long-term economic impact of these disruptions in the case of COVID-19, focusing on countries at different levels of development and using returns to education and experience by college status that are globally estimated using 1,084 household surveys across 145 countries. The results show that both lost schooling and experience contribute to significant losses in global learning and output. Developed countries incur greater losses than developing countries, because they have more schooling to start with and higher returns to experience. The returns to education and experience are also separately estimated for men and women, to explore the differential effects by gender of the COVID-19 pandemic. Surprisingly, while the study uncovers gender differences in returns to education and schooling, gender differences in the impact of COVID-19 are small and short-lived, with a loss in female relative income of only 2.5 percent or less, mainly due to the greater severity of the employment shock on impact. These findings might challenge some of the ongoing narratives in policy circles. The methodology employed in this study is easily implementable for future pandemics.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 aislam@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