rafmacalaba/data-use-mentions-extended huggingface dataset:
to reach desired levels because employers have limited flexibility to replace incumbent workers with new hires who have the requisite skills.<sup>25</sup> Temporary or even persistent skill gaps can arise with constraints in the local supply of certain skills and with technology and demand shocks that increase the demand for skills. Employer assessments of whether the education and skills of their workforce are adequate and whether skill shortages pose an obstacle to the firms’ operations are used to measure the presence and severity of these skill deficits. To study their determinants, we analyze whether these skill deficit measures are related to a set of control variables for firm characteristics, actual skill mix, firm productivity attributes, and demand from exports and innovation.

- _Skill strategy equations_ . Employers may respond to identified skill deficits in different ways. The survey asked about strategies employers may be using to improve workforce

> 24 Implementing the skill share of total payroll specification of the skills demand model requires detailed wage data for each skill dimension in the firm, as well as the prevailing wage rates of different skills groups in the open labor market. The TESS did not elicit data on mean wages of the workforce by level of education; while it asked for mean wages by occupation, these data were incomplete and were not deemed reliable. The challenging data needs have prompted other researchers to rely instead on specifications using the skill shares of total labor (for example, see Almeida, 2010).

> 25 Abowd et al. (2007) exploit the availability of matched employer-employee panel data to study the responses of employers to gaps between desired and actual skills mix, using a partial adjustment model.

17
# **An Anatomy of Urbanization in Sub-Saharan Africa**

**Pierre-Philippe Combes, Clément Gorin, Shohei Nakamura, Mark Roberts and Benjamin Stewart**<sup>*****</sup>

## **JEL codes:** R12, R23, O55

**Keywords:** urbanization, Sub-Saharan Africa, dartboard approach, satellite imagery, population density

**Acknowledgments:** The authors gratefully acknowledge comments provided by participants at the 2021 and 2022 North American Urban Economics Association meetings and the 2021 SMU Urban & Regional Economics conference, as well as additional comments provided by Yue Li and Harris Selod. They further acknowledge the generous assistance provided by Andrew Tatem, Maksym Bondarenko and Alessandro Sorichetta from the University of Southampton’s WorldPop group in generating constrained WorldPop gridded population data for 2015. This paper is part of a set of two Policy Research Working Papers that have been produced under the World Bank’s “A Move to Consistency – Integrating the Consistent Measurement of Urbanization into Global Poverty Measurement” Advisory Services and Analytics project (P175622). The accompanying paper – entitled “Where is Poverty Concentrated? New Evidence Based on Internationally Consistent Urban and Poverty Measurements” – is available as Policy Research Working Paper No. WPS10620.  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.

* Pierre-Philippe Combes is affiliated with the Department of Economics, Sciences Po, and CEPR, and Clement Gorin with the University of Paris 1. Shohei Nakamura, Mark Roberts, and Ben Stewart are affiliated respectively with the World Bank’s Poverty & Equity Global Practice; Urban, Resilience, and Land Global Practice; and Geospatial Operations Support Team. Shohei Nakamura and Mark Roberts are the World Bank Task Team Leaders for the project. **Corresponding author
(Singh et al. (2020)). Given area politics, we may expect similarities in displacement between Syria and Iraq, where those topics which generated the most buzz prior to displacement were related to politics, insecurities, and infrastructure (Martin and Singh (2018)).

Barring local conditions, violence is often considered the key driver of displacement (Davenport et al. (2003), Zolberg et al. (1989)). In a 2020 CARE survey conducted through interviews with Syrian IDPs, 99% said they had been displaced due to violence or fighting (Hoffman and Makovsky (2021)). The type and prevalence of violence also matters, in that state sponsored violence and genocide are more likely to lead to refugees whereas civil wars are associated more highly with internal displacement (Steele (2019), Moore and Shellman (2006)). Individuals who witness, but do not necessarily directly experience, violence are more likely to delay leaving if they receive community support and experience post-traumatic growth (Schon (2019)).

Enabling return for the displaced requires a good understanding of how the displaced view their possibilities and options around return (Metivier et al. (2018), Camarena and Hägerdal (2020)) as well as the broader effects of such return on postwar politics (Fabbe et al. (2019)) or on security (Van Leeuwen and Van Der Haar (2016), Camarena (2016), Schwartz (2019)). Willingness to confront danger on the path to return may also be impacted by refugees’ experiences of violence prior to return and belief in their ability to adapt to circumstances on the ground (Ghosn et al. (2021)). As a result, refugees who spent more time in their home country before fleeing, and were thus more likely to have been victims of violence, are more likely to return. In the Syrian context, refugees who are single and male are more likely to return since family ties often play a large role in
nets in Africa during the pandemic can inform future programing and responses to future crises.

This paper serves two purposes: first we use recent data to estimate and reassess the impact of the pandemic on country-level poverty in Africa for 2020. For this purpose, we combine national accounts data – particularly real per capita GDP growth and High-Frequency Phone Surveys (HFPSs or phone surveys henceforth) for several countries. The use of both macro and micro-level sources of data allows us to uncover potential heterogeneities across countries, geographies, and households. We assemble the World Bank’s country-level pre-pandemic growth projections, national account data on overall per capita GDP growth and sectoral growth for 2020. Whenever possible, we merge these data with the HFPS collected by the World Bank in collaboration with National Statistical Offices (NSOs). Second, we assess and synthesize empirical evidence on the role of social protection systems to mitigate the adverse impact of the pandemic in Africa. For this purpose, we compile recent studies on the potential impact of social protection programs in cushioning against the adverse impacts of the pandemic. We particularly aim to draw important lessons in the delivery, targeting and impact of various social protection programs launched or expanded in Africa in response to the pandemic.

3
|36|
|---|

|Topic|Year|Title|First author|Journal / Edited volume|Citations|Rank|
|---|---|---|---|---|---|---|
|Economic evaluation|1994|Uncertainty in the economic evaluation of health<br>care technologies: the role of sensitivity analysis|A. Briggs|Health Economics|293|204|
|Economic evaluation|1988|Outcome Measurement: Concepts and Questions|Kathleen N. Lohr|Inquiry|286|213|
|Economic evaluation|1993|The Validity and Reproducibility of a Work<br>Productivity and Activity Impairment Instrument|Margaret C. Reilly|PharmacoEconomics|277|227|
|Economic evaluation|1998|Health Care Contingent Valuation Studies: A<br>Review and Classification of the Literature|Alan Diener|Health Economics|272|239|
|Economic evaluation|1997|Is There a Role for Benefit-Cost Analysis in<br>Environmental, Health, and Safety Regulation|Kenneth J. Arrow|Environment and Development Economics|249|277|
|Economic evaluation|1996|Health-Related Quality of Life in Inflammatory<br>Bowel Disease|Glenn M. Eisen|PharmacoEconomics|243|288|
|Economic evaluation|1998|Confidence Intervals or Surfaces? Uncertainty on<br>the Cost-Effectiveness Plane|Andrew Briggs|Health Economics|236|299|

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. The 300 most-cited publications were then assigned either one of the 12 topics listed in the appendix or a ―topic not assigned‖ code. The latter included publications that were too general (some were actually textbooks) some had no economic content despite being in EconLit, and some had no health content despite the JEL code.
# **Tables and Figures**

**Figure 1. Sample selection**

<!-- Start of picture text --> Citations identified<br>1. Jstor (n = 450)<br>Rexer and<br>2. Scopus (n = 80) CCDRs<br>Sharma(2024)<br>3. Core.ac.uk (n = 320) n = 37<br>n = 324<br>4. Index studies (n = 3093)<br>Excluded:<br>1. Duplicates (n = 2640)<br>Abstract review<br>Excluded:<br>n = 1303<br>1. Reviewer<br>Excluded: screening for<br>outcomes of<br>1. Field and journal<br>interest (n = 304)<br>screening (n = 518)<br>2. AI screening (n = 424)<br>3. Reviewer screening<br>for outcomes of<br>interest (n = 278)<br>Full text review Full text review Full text review<br>n = 89 n = 20 n = 37<br>Excluded: Excluded: Excluded:<br>1. Reviewer 1. Reviewer 1. Reviewer<br>screening for screening for screening for<br>outcomes of outcomes of outcomes of<br>interest (n = 64) interest (n = 12) interest (n = 0)<br>Studies Studies Studies<br>included n = 25 included n = 8 included n = 37<br>Included in the meta-analysis: 70 Studies and 701 estimates<br><!-- End of picture text --> _Note_ : CCDR = _Country Climate and Development Report_ . Rexer and Sharma (2024) document 324 relevant climate change adaptation studies. Among 324 relevant climate change adaptation studies, eight document either the poor’s exposure to
Access to financial services, however, is not synonymous to the use of financial services. Economic agents might have access to financial services, but might decide not to use them, either for socio-cultural reasons, or because opportunity costs are too high. Therefore, it is necessary to carefully distinguish between two different concepts when discussing the outreach of the banking system – (i) access and the possibility to use financial services and (ii) actual use of financial services.<sup>5</sup> This paper introduces two classes of indicators that correspond to the different concepts of access to and use of financial services. Specifically, we present data on the number of branches and ATMs relative to population and area, to capture the geographic and demographic penetration of the banking system. Higher branch intensity in demographic and geographic terms would indicate higher possibilities of access and the opportunity to use financial services by households and enterprises. To measure the actual use of deposit and credit services, we present indicators on the number of loan and deposit accounts relative to population and average loan and deposit size relative to GDP per capita. Higher ratios of the number of loan and deposit accounts per capita and lower average loan and deposit amounts relative to GDP per capita would indicate use of deposit and credit services by a greater share of the population and “smaller” clients.

Our sample of 99 countries is comprised of financially and economically developed economies as well as emerging markets and transition economies. The first part of our empirical analysis shows the predictive power of our indicators by relating them to user-based household and firm surveys. In particular, we show that our loan and deposit indicators are good predictors of the share of households with bank accounts and the share of small firms with bank loans. In the absence of user-based survey measures on the use of deposit and loan services for a broad

> 5 Also see the discussion in Beck and de la Torre (2005).

4
for 2021. Data Blog (blog). 11 January. https://blogs.worldbank.org/opendata/updated-estimates-impact-covid19-global-poverty-looking-back-2020-and-outlook-2021.

- Lakner, Christoph, Daniel Gerszen Mahler, Mario Negre, and Espen Beer Prydz. 2022. How Much Does Reducing Inequality Matter for Globa Poverty? _Journal of Economic Inequality._

- Mahler, D.G., N. Yonzan, and C. Lakner. 2022. The Impacts of COVID-19 on Global Inequality and Poverty. Mimeograph. The World Bank.

- Mahler, D. G., C. Lakner, A. Castaneda Aguilar, and H. Wu. 2020b. Updated Estimates of the Impact of COVID-19 on Global Poverty. Data Blog (blog). 08 June. https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty.

- Mahler, Daniel Gerszon; Castaneda Aguilar, R. Andres; Newhouse, David. 2021a. Nowcasting Global Poverty. Policy Research Working Paper;No. 9860. World Bank, Washington, DC.

- Mahler, D. G., N. Yonzan, C. Lakner, R. A. C. Aguilar, H. Wu. 2021b. Updated estimates of the impact of COVID-19 on global poverty: Turning the corner on the pandemic in 2021? World Bank, Washington, DC.

- Mahler, Daniel G., Christoph Lakner, R. Andres Castaneda Aguilar, and Haoyu Wu. 2020. The Impact of COVID-19 (Coronavirus) on Global Poverty: Why Sub-Saharan Africa Might Be the Region Hardest Hit. World Bank Data Blog. https://blogs.worldbank.org/opendata/impact-covid-19- <u>coronavirus-global-poverty-why-sub-saharan-africa-might-be-region-hardest</u>

- McCord, A., Cherrier, C., Both, N. and Bastagli, F. 2021. Official development assistance financing for social protection lessons from the Covid-19 response. ODI Working Paper. London: ODI

- McDermott, J., and J. Swinnen. 2022. COVID-19 and global food security: Two years later
however to baseline dropouts only. Behrman, Parker and Todd (2009) find that _Progresa_ decreases likelihood to migrate for boys and delays the age of marriage.

# 4. Data

To identify causal effects, we rely on an RCT methodology, exploiting the initial randomized rollout of the program at the barangay level. We collect the data on individuals who are currently in the age group of 23-24.5 years old, and compare young adults who were residing in treatment barangays during adolescence (and thus were eligible to receive the 4P transfer) to young adults who were residing in control barangays during adolescence (and were not eligible to receive the transfer at any point in their life).

To identify and track these young adults, we use data from _Listahanan_ (2008-09). Using the _Listahanan_ , we were able to identify households who had children in the age-group 12.5-14 years during the time of initial targeting and whether a household was identified to be eligible to receive the 4P transfers. To ensure that we did not pick up any spillovers across siblings within the same household, we limited the sample to families where the child aged 12.5 years or older was the youngest child in the family when the program was expanded to control barangays. We provided the data collection firm with the list of eligible households for each of the 71 barangays included in analysis.

The firm randomly drew a required subsample of households, stratified by gender in each barangay, and validated that the respondent households were indeed residents of the barangay with local officials. If the household was not recognized by the local official, the household was replaced. Among the replacements, 81% are from the same barangay, and 19% are from a different barangay.

After the validation with barangay officials, the field team visited households and administered a short tracking questionnaire with the objective of (i) confirming that at the time of initial 4P rollout there was a child aged 12.5 to 14 in the household; (ii) collecting information on where
: *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

Table 2 complements these insights by providing a more complete picture of Egypt’s poor in 2015. Poverty affects 29 percent of children, 25 percent of adults, and 17 percent of the elderly. Hence, children are more affected by poverty than adults. On average, poor households have a larger size (5.6 members) than non-poor households (3.9 members). The former also have more dependents (2.7 dependents) than the latter (1.6 dependents). Poor households are more often headed by a person with low education than non-poor households. Poor households are more likely to have a head who is working. Finally, poor households are less likely to own welfare-relevant assets, such as private cars, washing machines, or smartphones. This unequal distribution of welfare-relevant assets is not surprising but confirms that the poverty measurement methodology adequately identifies households with fewer assets.

_Table 2: Profile of the poor in 2015 (total expenditure)_

|Profiles among poor households<br>(total expenditures)|Poor<br>household|Non-poor<br>household|
|---|---|---|
|Fraction<br>of<br>poor<br>among<br>Children<br>Elderly<br>Men<br>Women|29%<br>17%<br>26%<br>24%||
|Average HH size|5.60|3.93|
|Average number of dependents|2.67|1.64|
|Fraction of poor HHs whose head has low education|95%|81%|
|Fraction of poor HHs whose head is not currently working|25%|31%|

6
|-0.27|-0.02|0.77|-0.02|
|Constant|6.19||6.19|3.05||3.05|1.62 *||1.62|
|Dependent Variable||||||||||
|Log Nat Expenditure||6.61|6.27||6.59|6.61||7.45|7.50|
|Evaluated in pesos||745.90|<br>528.66||726.01|<br>740.81||1725.7|1807.44|

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

***** Significant at 5%, ** Significant at 10%

**Table 6. Household Expenditure on Educational Public Service by Type of Education**

|||**Pre Prim**|**ary**||**Pnnma**|**ry**|**Lo**|**wer Seco**|**ndary**|**U**|**pper Seco**|**ndary**|**Tec**|**hnical Ed**|**ucation**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Explanatory vaiables**|**Coe.**|**Mean**|**M.W. to P**|**. Cod.**|**Mesn**|**M.W. to P
(MFA) - that we address in Section 4 - was dismantled in 2004. A number of studies have focused on different aspects of the MFA (Brambilla, Khandelwal and Schott, 2010; Harrigan and Barrows, 2009; Dean, 1995; Khandelwal, Schott, Wei, 2013), and especially the implications of its expiration.

# **3.3 Price undertakings and voluntary export restraints**

A second form of quantitative restriction is a voluntary agreement by exporters to raise their prices and/or restrain their export volumes. These policies are referred to as price undertakings or voluntary export restraints (VERs), and while they share many common economic features; they are currently treated in different ways under the WTO. For while VERs were supposedly banned in the Agreement on Safeguards established in 1995, price undertakings are encouraged as an outcome in the 1995 WTO Agreement on Antidumping.<sup>60</sup> Not surprisingly, given the high frequency of antidumping use across countries, price undertakings are also a relatively common outcome of the investigations. Consider, for example, the data on antidumping outcomes for the European Union. Overall, approximately 20 percent of EU antidumping investigations that found evidence of dumping by foreign exporters over 1989-2011 resulted in a negotiated price undertaking. These arrangements typically consist of a minimum import price and a market share allotment.<sup>61</sup> Thus, the impact of an undertaking, like that of a quota, will depend on the competitive structure of the industry with considerable scope for losses to consumers if the market is imperfectly competitive.

Table 7 summarizes the EU’s application of different _forms_ of import barriers arising as the outcomes of antidumping investigations over 1989-2011. Each entry is the percent of total antidumping measures, by export origin, implemented in the form listed.<sup>62</sup> The EU imposed almost two thirds of antidumping measures as ad valorem duties and roughly 10 percent as specific duties; as noted earlier, these specific duties can also
the technology could affect product weight as it may allow to design new structures that are lighter and use less material.

With these caveats in mind, Figure 11 presents the results on the impact of 3D printing on exports and imports as a function of product weight. Panel A shows that the impact of 3D printing on exports decreases with product weight. Exports of lighter products such as spectacles increased the most after the technology became available while the impact becomes insignificant for heavier product such as aircraft and spacecraft parts. Results in Panel B suggest that the availability of 3D printing led to a statistically significant decrease in import of heavy printable products, mainly machineries. The results suggest that 3D printing is more likely to lead to fragmented production for products that are light and, hence, cheaper to trade. On the other hand, there is some evidence that the technology could be used to produce goods closer to consumers for products subject to high transport costs.

18 We calculate unit weights with HS 6-digit data, instead of SITC 4-digit, as the information is provided on a more detailed degree of disaggregation.
# **7. Conclusion and Policy Insights**

**This paper applies an innovative approach to estimate the population's vulnerability to poverty in El Salvador and analyzes potential underlying drivers** . We apply  a novel ex-ante approach to vulnerability developed by the World Bank to the context of El Salvador to measure ex-ante vulnerability and its components. In addition, we take advantage of household-level data from 2016 to 2019 to show trends over time. We also analyze the components behind these trends, distinguishing between poverty- and risk-induced vulnerability and idiosyncratic and covariate vulnerability. Our analysis is based on an income measure of welfare.

**Our main results show that ex-ante vulnerability to poverty decreased over time, in parallel to the poverty reduction observed in the country.** This development comes hand in hand with an increase in the importance of risk-factors relative to factors related to assets driving vulnerability. The importance of risks in overall vulnerability increased over time. Although the vulnerability rate decreased since 2016, this might be a hint for shocks playing a more significant role in the overall development path of El Salvador. The analysis at hand finds that risk-induced vulnerability is mainly driven by idiosyncratic (household-level) shocks and not covariate (municipality-level) shocks. Consequently, shocks occurring to individual households (such as crime, unemployment, or illnesses) should receive special attention from policy makers. Still, it is essential to note that covariate shocks (such as a drought or flood at the municipality level) can lead to idiosyncratic shocks (an illness or employment loss of an individual household). While about half of the vulnerable also suffer from poverty, the vulnerable do not coincide entirely with the poor.

**Furthermore, our work reveals that vulnerability rates are higher in rural than urban areas, and risk factors have become relatively more important in rural areas over time.** Our paper demonstrates that, while vulnerability was mainly poverty-induced in rural areas in 2016, this dynamic shifted over the last few years. In 2019, risk factors played