rafmacalaba/data-use-mentions-extended huggingface dataset:
6.6% completed all 4 modules.

# **3.2 How Much Cost-Saving is there from Zoom Training?**

One motive for considering business training by Zoom is the potential to lower costs. We worked with CREA to collect cost data on provision of training and to compare it to their cost structure when offering in-person training. The estimated cost per participant in Zoom training was $50 in Mexico and $56 in Guatemala. This covers the cost of personnel for recruitment and training, technology costs such as Zoom license fees, data plans, paid Facebook advertising, and other recruitment costs. The costs of personnel, especially the trainer, are the main cost. While in principle these personnel costs could be lower than in person if the online trainers were able to teach more women at the same time, in practice the difficulty of recruiting large numbers of women who all wanted to start and attend sessions at the same time meant that class sizes were similar to an in-person class. As a result, the estimated cost of an in-person class is not that much higher: $62 in Mexico. In-person classes involve some costs for trainer transport and venue rental, and fewer technology costs, but even with in-person training the personnel costs are 79% of total cost. However, this is based on having microentrepreneurs show up in-person at places where CREA already operates. In contrast, if we were to take the geographic spread across all states of Mexico and also in Guatemala, it would be much more expensive for CREA to travel and set up new trainings in all of these locations.

8
consumption estimates for median and mean incomes), or 13-18% (using the income estimates); details are shown in Table 29. Given their standard errors, these estimates are not mutually inconsistent.

When borrowing leads to higher income, the economic benefit is easy to interpret, and so we begin here. Using mean costs (from the VF survey) and mean income (from the SES survey), Village Fund lending generates a net benefit of 8%; benefits are 18% of the loan, and costs are 10%. If instead we use the median values, which are less influenced by outliers, the net economic benefit is about 4%. An implicit assumption here is that the extra income did not require any additional effort on the part of the borrower; this is not entirely plausible, which would mean that the net benefits shown here may be overstated.

Although consumption is a more attractive measure of wellbeing than income, it is more difficult to interpret as a measure of benefit. If borrowing allows me to buy a sofa, my consumer spending may rise sharply, but the net contribution to economic wellbeing is likely to be more moderate – just some fraction of the total borrowing. Thus the net benefits based on the consumer spending impact – between 18% and 23% - are surely on the high side.

42
) (Brazil); ICETEX = Colombian Institute of Educational Credit and Technical Training Abroad ( _Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior_ ) (Colombia); PAA = Academic Aptitude Test ( _Prueba de Aptitud Académica_ ) (Dominican Republic); POMA = ( _Prueba de Orientación y Medición Académica_ ) (Dominican Republic); PRONABEC = National Program of Scholarships and Educational Credit ( _Programa Nacional de Becas y Crédito Educativo_ ) (Peru); Prof. = professional; SCP = short-cycle program; Tech. = technical. WBSCPS = data from the World Bank Short-Cycle Program Survey.

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# **5 Discussion**

The approach taken in this methodology combines several elements in the debt-default literature to not only predict possible default, but to also describe sources of vulnerabilities.

This is where the vulnerability index plays an important role. While looking at several single indicators is useful to understand potential vulnerabilities, a country-specific weighted index summarizes a large set of information. Dimension reduction techniques are well known for picking up signals in a large data group.

A key element of this analysis is a description of what is meant by default probability. There are several definitions of default probability that can be applied. However, here we use only two measures. The first relates to the ability of the government to maintain debt on a stable path. The second relates to investor perceptions regarding default. Monitoring both measures reveal interesting details regarding periods of weakened perceptions over and above the government’s own ability. Once the two default estimates diverge then it points to a panic.

Our methodology attempts to incorporate expert judgment in describing defaults. DSAs provide guidance on key thresholds of risk for several indicator variables. We use these thresholds along with historical data to codify judgment. We note that this judgment is subject to revisions as more information and better analytical tools help to bridge several gaps.

A novel addition to default prediction is to incorporate more structural and theoretical models. We utilize a standard general equilibrium framework to compute debt thresholds. This is different compared to empirical relationships between the size of debt and default to define thresholds. In this example, debt thresholds are determined by the maturity structure, the probability of a panic, and the size of the government. These measures provide insights on whether countries will do well to reduce debts to reduce risks. Two thresholds provide three regions of risk. There is a debt to GDP ratio where the risk of a default is very low. At the same time, there is a debt to GDP ratio where default is very likely or imminent. Finally, there is a region between the no
Policy Research Working Paper 7675

# **Abstract**

In many countries safety nets consist predominantly of universal subsidies on food and fuel. A key question for policy makers willing to shift to targeted safety nets is under what conditions middle-class citizens would be supportive of redistributive programs. Results from a behavioral experiment based on a nationally representative sample in Jordan reveal that increasing transparency in benefit delivery makes middle-class citizens (particularly among the youth and low-trust individuals) more willing to forgo their own welfare to benefit the poor. Moreover, increasing transparency enhances the relative support for cash-based safety nets, which have greater impact on poverty compared with in-kind transfers, but may be perceived as more prone to elite capture.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Social Protection and Labor Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jsilva@worldbank.org, mmorgandi@ worldbank.org and vlevin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 12. Financial inclusion reform undergoing**

<!-- Start of picture text --> 72%<br>62%<br>57%<br>51% 50% 49% 50% 49% 52%<br>45%<br>42% 37% 43% 41%42%41%<br>38% 38%<br>34%<br>28%<br>Consumer Financial capability Facilitating SME Enabling micro Facilitating rural<br>protection access to finance financial access<br>OIC countries Developing countries OIC‐GCC low‐income countries<br><!-- End of picture text --> Source: CGAP (2010)

**Financial inclusion gap in OIC countries can also be reflected by governments’ heavy investment in SME policy reforms.** According to the CGAP financial access database (Table ), OIC countries are more likely to monitor the level of lending to SMEs through specific dedicated agencies than developing countries (73% vs. 48%). Also, 62% of OIC countries designated an agency to promote SME access to finance specifically, compared to the figure of 49% in developing countries. However, the SME lending as proportion of GDP in OIC countries is still far behind the OECD level (7.43% vs. 24.47%), which indicates that there is a huge gap in terms of SME funding between the two groups (See **Figure 13** ) .

37
revision of the International Standard Classification of Occupations (ISCO-68). To make the classification consistent, I first convert the national classification into the ISCO-68. Following, I remap the ISCO-68 occupations to ISCO-88 following the scheme in Ganzeboom and Treiman (1996). Again, I do not find any unusual kinks in the share of occupations as a result of these transformations.

**El Salvador:** The country dollarized its economy on January 1, 2001. Hence, labor income in the preceding survey years, was divided by the exchange rate to convert it to dollars.

**Panama:** From 2011 to 2013 occupations in the national surveys match the major groups of the ISCO-88. For previous years, Panama has its own classification of occupations (NCO-2000). The system follows the ISCO-88 taxonomy and it is straightforward to construct correspondence between the two.  The NCO2000 classification was used from 2001 to 2010. Before 2001, a different classification was in use (NCO90). The working manual from the National Institute of Statistics and Census (1999) provides the correspondence tables between the NCO-1990 and the NCO-2000 taxonomies. Using these, I harmonize the occupations across the survey years.

# **A2. Calibration details**

**Parameters invariant over time:** There are seven parameters in the model that are invariant over time. Barring the elasticity of substitution across occupation-specific labor inputs , all time-invariant parameters are calibrated for each country separately. For the baseline results, I assume and report the responsiveness of the results by considering other values of . σσ σσ= 4 **a.   Preference parameter :** The parameter captures the trade-off between consumption and time σσ dedicated in accumulating human capital. To calibrate the parameter, I match the Mincerian returns to ββ schooling. The first step is to collect for year using the following regression ������� ii ln�wψψ i tt i i where variables under bars represent the average for a group ggee ii�= ααii + ψψii ss
|30|
|---|

|Topic|Year|Title|First author|Journal / Edited volume|Citations|Rank|
|---|---|---|---|---|---|---|
|Public health|1982|The potential for using excise taxes to reduce<br>smoking|E. M. Lewitt|J Health Econ|311|176|
|Public health|1991|<br>Rational Addiction and the Effect of Price on<br>Consumption|Gary S. Becker|American Economic Review|306|186|
|Public health|1986|<br>Evaluating the Effects of Optimally Distributed<br>Public Programs: Child Health and Family<br>Planning Interventions|Mark R. Rosenzweig|American Economic Review|298|199|
|Public health|1982|The Effect of Liquor Taxes on Heavy Drinking|Philip J. Cook|Bell Journal of Economics|281|221|
|Public health|1984|A Model of the Optimal Use of Liability and<br>Safety Regulation|Steven Shavell|RAND Journal of Economics|280|224|
|Public health|1997|<br>Price, Tobacco Control Policies and Smoking<br>among Young Adults|Frank J. Chaloupka|Journal of Health Economics|273|235|
|Public health|1999|<br>Willingness to Pay for Health Protection:<br>Inadequate Sensitivity to Probability?|James K. Hammitt|Journal of Risk and Uncertainty|246|282|
|Public health|2002|<br>Age, Health and the Willingness to Pay for<br>Mortality Risk Reductions: A Contingent<br>Valuation Survey of Ontario Residents|Alan J. Krupnick|Journal of Risk and Uncertainty|245|285|
|Public health|2006|<br>Do Cigarette Taxes Make Smokers Happier?|Jonathan Gruber|Happiness and Public Policy: Theory
# **Figure 10. Average markup differences relative to non-exporters by export status**

<!-- Start of picture text --> 50<br>45 41.6<br>40<br>35<br>30 25.8<br>25<br>20<br>15 12 11.9<br>10<br>5<br>0<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set.

_Note:_ Numbers reflect estimated coefficients (multiplied by 100) for R&D dummy category—relative to non-R&D performer firms, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**Figure 11. Average markup differences relative to non-R&D performers by R&D status**

<!-- Start of picture text --> 50 46.0<br>45<br>40<br>34.0<br>35<br>30 27.3<br>25<br>20 16.0<br>15<br>10<br>5<br>0<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for export dummy category—relative to non-exporters, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the
The average upward mobility gap for the 1980s birth cohort varies from 0.8 (in Uzbekistan, with respect to fathers) to 9.1 (in Tunisia, with respect to mothers). When looking at the 1950s birth cohort, the upward mobility gap varies from 0.7 (in Mauritania, with respect to fathers) to 8.3 (in Viet Nam, with respect to mothers). While the upward mobility gap calculated with respect to fathers and the one calculated with respect to mothers are correlated (figure 1), the values with respect to mothers are, in most cases, above those calculated with respect to fathers. This results from the lower average educational attainment among mothers.

High values of the upward mobility gap usually occur when the share of children having more education than their parents is high (the horizontal axis in figure 2), and the increase in years of education among children who are more educated than their parents is large (the vertical axis in <u>figure 2). For the 1980s birth cohort, the median share of upward movers (calculated relative to</u> their mother’s education) is 61 percent (58 percent relative to their fathers’). The median increase in years of education is 5.7 relative to mothers’ education and 5.3 years relative to fathers’.

<u>Figure 3</u> plots the values of upward mobility for every birth cohort, calculated with respect to mothers’ education (a similar pattern is found when mobility is calculated with respect to fathers’ education). For Europe and Central Asia, we plot the values ranging from the 1930s birth cohorts to the 1990s birth cohorts, using all the information available from LiTS. For the remaining regions of the world, for which we use information from GDIM, we plot the values from the cohorts born between the 1950s and the 1980s. In almost every country, upward mobility is lowest for the younger generations. The patterns of
Our main outcome of interest is mental health, which was measured in the baseline and third waves of the survey. All adult respondents responded to the PHQ-9 in the baseline, but only the main respondent answered the MHI-5 questions in wave 3. Another difference is that in the baseline wave, the Patient Health Questionnaire (PHQ-9) was used, but in wave 3, the Mental Health Inventory 5 (MHI-5) was used instead. As we describe in section A.1, although the instruments are different, both of these measures can be used to categorize a respondent as having severe depression, moderate depression, mild depression, or no/minimal depression. We therefore use indicators for different categories of depression as our outcome variables. This has the added advantage of avoiding the cardinal use of an ordinal variable, shown to be problematic by Bond and Lang (2019).

We use several other variables in our analysis. Food security questions were included in waves 1 to 4 of the survey. Because only one question was asked in wave 1 (“In the past 7 days, did you or any other family member eat fewer meals in a day because of lack of food?”), we restrict our attention to this question in all waves, even though waves 2 through 4 included a more detailed set of food security questions (described in section A.2). Income and employment (for which we report baseline levels in Table 1) were collected in all waves of the survey. We also examine an indicator for whether the household reported receiving remittances. This is obtained from the wave 3 survey, during which respondents are asked two separate questions: whether they were receiving remittances before the lockdown in March, and whether they had received any remittances since the lockdown in March. We assign the answer to the first question to the baseline wave and the answer to the second question to the third wave.

Because we will be examining changes in depression rates over time, we restrict our sample to households with
Fund (IMF) and the International Finance Corporation (IFC) also increasingly pay attention to this debate. The IMF has launched a new database on financial inclusion,<sup>1</sup> and the IFC together with Consultative Group to Assist the Poor (CGAP) and AFI have been leading the G-20 discussion around financial inclusion for households and SMEs. The Bill & Melinda Gates Foundation has just pledged $500 million per year over the next five years to expand access to saving services.

Against this backdrop, the World Bank Group (WBG) has initiated the _Financial Access_ indicators and reports to respond to a higher demand for data and measurement of financial inclusion. This initiative aims to fill the gap in the data landscape by collecting supply-side data on financial access as well as data on financial inclusion policies and regulations from financial regulators around the world. The pilot round of surveys was implemented and published as _Banking the Poor_ in 2008, which was based on data from both regulator and bank surveys, covering 54 countries around the world with a strong focus on Sub-Saharan African countries. Subsequently, CGAP and the WBG launched _Financial Access 2009_ , the first in an annual series of reports to inform policy debate and monitor statistics of financial access worldwide. _Financial_

> 1 http://fas.imf.org.

2
University of Massachusetts Amherst) to use remote sensing, machine learning, and big data to map connected populations and the systems that support them. This group created an algorithm for estimating the location of medium-voltage infrastructure based on nighttime lights and the location of roads assuming that medium-voltage lines are more likely to follow (or be followed by) main roads.<sup>7</sup> Figure A5 in the appendix shows the grid for the Horn of Africa using the AICD grid and using the most recent grid.

Internet infrastructure is proxied by access to the fiber broadband backbone network. We obtained data for all Africa for 2009–20 from Africa Bandwidth Maps, which provides the exact location of fiber nodes along the backbone network (see Figure A1). We constructed a proxy for access to the fiber backbone that is equal to one if there is a node of the backbone in the location of interest. Each node has a year attribute, which allows us to build a panel for access

> 5An example of mapping rural electrification based on night-time lights can be found at http://india.nightlights.io/. 6The World Bank reports access to electricity (percent of population) for most countries for a long period at https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS.

> 7More details can be found on the blog https://blogs.worldbank.org/energy/using-night-lights-map-electricalgrid-infrastructure and in the paper Arderne et al. (2020)

7
TradeLens (container tracking data) TradeLens, started in 2018 and discontinued in 2023, was a data and document sharing data platform bringing together shipping lines and other participants in container logistics. The project was a collaboration between IBM and GTD Solution, a division of shipping conglomerate Maersk. TradeLens brought together over 1,000 entities involved in the global supply chain, including over 200 ports and terminals, over 15 custom authorities and, by mid-2022, was facilitating the information exchange of about 65% of containerized trade.

# <u>TradeLens data model</u>

TradeLens used a data model with three related classes: Consignments, Transport Equipment and Shipments. It tracked consignments, transport equipment (containers) and shipments, while managing the identifiers and relationships between them. The TradeLens data structure allowed a consignment to be sequentially in multiple transport equipment (ship, rail, barges, truck), along with other consignments. It also allowed transport equipment to be part of multiple consignments.

The LPI 2023 dataset retrieval for this report was defined by the data provider for a period of 6 months between May 1, 2022, and October 31, 2022. The sample contained timestamps for 11 events for four transport modes (Ocean, Road, Barge, and Rail) and two load statuses (Full or Empty), associated with over 3 million unique tracked consignments (container voyages) and over 30 million observations in total. The dataset covers over 11,000 UN/LOCODEs<sup>14</sup> (including destinations, origins, and live locations (locations of specific event’s timestamps).

# <u>Process framework and definitions of phases</u>

The conceptual framework for processing the data is presented in Figure 4 and with more detail in Appendix 1. The dataset contained a sequence of events and their timestamps for each consignment. On average, there were 9.8 events associated with each consignment. Additional static information on each consignment includes UN/LOCODEs for origin and for destination