rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

It is estimated that non-communicable diseases (NCDs) cause over 70% of deaths worldwide (WHO, 2022). Among these, an estimated 7.9 million deaths and 187.7 million disability-adjusted life-years (DALYs) were attributable to dietary risk factors in 2019 (Qiao et al., 2021). Unhealthy diets - linked to leading NCDs like diabetes, heart disease, stroke, and cancer - are characterized by low consumption of whole grains, fruit, vegetables, nuts and seeds, fiber, polyunsaturated fatty acids, omega-3 oils, calcium, or milk, and/or high consumption of sodium, trans-fats, processed and red meat, and sugary drinks (as classified by Global Burden of Disease studies) (PAHO, 2023).

Increased dietary risk has contributed to an unprecedented rise in overweight and obesity. The first report from the Lancet Commission on Obesity described this rise, together with undernutrition and climate change, as a “Global Syndemic”, a synergy of epidemics sharing common societal drivers including food and agriculture, transportation, urban design, and land use systems (Swinburn et al., 2019). Due to factors such as limited political leadership, coordination, and public demand, and strong commercial lobbies, no country has managed to reverse the rise in obesity prevalence over the last four decades, despite three decades of World Health Assembly commitments and the UN designation of 2016-2025 as the “Decade of Nutrition” (Swinburn et al., 2019). The Americas is the World Health Organization (WHO) region with the highest prevalence of overweight and obesity, estimated at 62.5% and 28% respectively in 2016 (PAHO, 2023).

In addition to contributing to obesity and overweight (which in turn increases NCD risk), ultra-processed foods have been independently linked to a range of negative health outcomes, informing policy efforts to reduce their consumption to promote healthy diets and reduce the burden of diet related NCDs, as recommended by the Framework for Action from the Second
a spatial generalequilibrium model to produce counterfactuals for more domestic and regional integration through future infrastructure investments. Section 5 concludes.

# **2 Data and descriptive statistics**

## **2.1 Data**

This paper uses household survey data that have been georeferenced, new spatial infrastructure data, and district characteristics in order to study links between access to infrastructure and the structure of local economies as well as the complementarities between types of infrastructure.

**Infrastructure** We collected new information on road network expansions, access to the electricity network, and access to Internet fiber backbone from various sources (Table 1).

> 3See Redding and Turner (2015) and Redding (2016) for surveys of the literature on the impacts of transport infrastructure and the growing use of models to quantify these impacts.

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0.1. Unit of observation are ethnic groups. The dependent variable is the natural logarithm of night light per capita. Night light data are gathered form the National Oceanic and Atmospheric Administration (NOAA). “Conflict incidence” is a dummy defined through more than 25 fatalities in ethnic conflicts. Dummies “First 5 years” and “First 10 years” take a value of 1 if the ethnic group is experiencing respectively one of the first five or ten years after the conflict episode it was involved in. Dummies “Remaining 5 years” and “Remaining 10 years” control the remaining years with no war. “Fragile peace” is a dummy that equals one if is experiencing one of the first five year of recovery but will relapse into conflict within the next five years.

selection problem built into the idea of studying long periods of recovery which we turn to next.

# **3.2 Unstable Peace as a Central Selection Problem**

In the analysis of recovery provided above we focus on groups, countries or regions that did not face the resurgence of violence. This is obvious in the existing literature which is focused on countries like Japan, Germany and Vietnam. It is also true for the data we use as the dummies in our regressions condition on the length of the recovery period. In column (1) of Table 4 this implies, for example, that we focus on the recoveries that have lasted longer than 5 years. Economic performance in the other cases cannot be studied because they have entered renewed conflict. This selection problem means we are drawing conclusions from countries that have managed to stay peaceful.

This is a serious concern as most groups do not stay in peace. In Figure 5 we show the distribution of recovery lengths at the group level. The figure shows that 151 20
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_Note_ : Each column reports the estimated total number of households using a coping strategy in response to COVID-19-relaated income loss and average household-level incidence of using a coping strategy. Adoption of a strategy is condition on experiencing a COVID-19 related loss of income per Table S8. Data come from the most round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Standard errors on the estimates are in parenthesis.

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women may be less likely to own property and other assets, for example, differences can vary across and within countries, by region, institutional context, age, marital status, and other individual, household and local factors. Multi-topic household surveys are an ideal source of information on these different dimensions, and to better highlight the socioeconomic and demographic profiles of asset owners for accurate policy targeting. When included in surveys, however, variables on wealth (as well as other welfare aggregates such as consumption, on which national measures of inequality are based), are often collected at the household level (De Vryer and Lambert, 2021; OECD, 2013), or by a single member reporting for others (Hasanbasri et al., 2021b). Household-level measures of inequality, however, can inherently mask unequal resource allocation and welfare outcomes among individuals (Chiappori and Meghir, 2015; Haddad and Kanbur, 1990). Improved data in this area that allows for a disaggregated profile of asset owners and wealth is instrumental in constructing accurate estimates of wealth inequality, and in turn, shaping effective, distributionally-sensitive policies around asset ownership and rights — whether in such areas as land reform, expanding access to finance, or taxation.

Little is also understood as to which assets drive wealth inequality within countries, given data gaps on individual ownership of different asset classes, particularly in national surveys that could allow for a deeper understanding of inequality within populations. And even when these topics are covered in surveys, household members’ asset ownership and control are typically asked only of one person considered by survey enumerators to be knowledgeable about these issues. Recent evidence has shown that this more common survey approach, as opposed to surveying individuals directly and in private, leads to significant gender differences in reporting — in a study examining agricultural land ownership in Malawi, Kilic, Moylan and Koolwal (2021) find that asking one household member to report on others’ ownership, compared to having all members reporting themselves, leads to lower reporting of women’s land
dimension of our methodology. Unfortunately, there is no ideal way to do so. The model estimates may be closer to the truth than direct computations from the CFUWBES, and sector classifications differ from WBES to the CMMHH. These issues, combined with small sample sizes for certain sectors in the WBES, suggest extra caution when using WBES data to derive sector-specific job loss estimates.

# **5.4.1 Jordan**

We first compare wave 1 results for modeled- versus survey-measured approaches. As shown in Table 5-8, the rate of job losses varies a great deal, for these sector categories between 5 and approximately 20 percent ( _chemicals_ and _wholesale, retail, and other services_ , respectively). The job loss projections from the surveyestimated projections deviate somewhat from our modeled projections, particularly for sectors with fewer observations, including _Chemicals, Garments,_ and _Metals and Minerals_ . However, the model broadly predicts which sectors report the most severe reductions in workforce as well as those with less than average declines.

A comparison of wave 2 predictions for all firms (permanently closed and operational) with surveymeasured job loss projections (Table 5-9) shows a reasonable match for some sectors (Chemicals, Hotel, restaurant, and transportation, Machinery, electronics, and construction, wholesale, retail, and other services). The greatest disparity in terms of sector ranking is for Food, Drink, and Tobacco and Wood, Paper, Publishing, and Printing. On the whole, the model, which is based on wave 1 data produces a closer alignment to wave 1 surveymeasured projections than we obtain when we use the same model to predict sector-level job losses forward to wave 2.

_Table 5-8 Jordan: Model-Predicted versus Survey-Measured Projections of Sector-Level PFPS Jobs Losses, Wave 1 / Summer 2020 (with permanently closed firms)_

||Model-<br>Predicted|Survey-<br>Measured|No. of<br>Observations|
|---|---|---|---
imposing caps on rates. Surprisingly, data on lending rate ceilings show that low and middle income economies are as likely as their high income peers to choose a benchmark market rate (both at 27 percent of those with IRCs). As far as caps on deposit rates are concerned, only Sri Lanka and the United States have set their maximum rate relative to a market rate, while Belgium and Morocco set theirs in relation to their central bank policy rate.

IRCs are typically applied to deposits and loans denominated in local currency. In the case of deposit rate floors, all but one country (Belgium, where a floor is set for rates on deposits 10
matrix ( _BMca_ ) which links commodities to sectors: The total regional output, as sum over regions, activities, and commodities is equal to €3,159.8 billion.

# **3.2 Total Demand**

The commodity row represents the total demand (or total supply of commodities for use in the economy). Its sum provides a measure of aggregate welfare. It can be formulated as the sum of the following accounts:

- Intermediate Consumption calculated as:

where AFLQ is the Augmented Flegg’s Location Quotient, a measure for considering the concentration of a sector and the region’s dimension when there is a lack of data or adjustment is required. The location quotients are here calculated on employment data. _NatIOac_ represents the national input output account, while _V Ara_ is the regional value added by activity sector. Finally, the total national intermediate consumption is equal to €1,637 billion;

- Margins calculated for the commodities trade, transport, and energy based on the national demand for the respective commodity. The total national margins results equal to €309.5 billion;

- Households final consumption from the estimation of interregional flows through administrative micro-firm VAT data DF estimation. The estimation results show a total national final consumption by households equal to €1,019.5 billion;

- Government Consumption calculated starting from 10 COFOG commodities and obtaining 20 commodities through a bridging matrix. The results are reproportioned on the national demand for government consumption. The total government consumption is equal to €282.3 billion;

- Local Government consumption follows the same procedure as the national government consumption. The local government consumption account is diagonal in the SAM and

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followed by the end of the multi-fiber agreement (MFA), meant greater competition in the European market overall and especially in textiles and apparel (Lopez-Acevedo & Robertson, 2012).

# **_Labor Market Outcomes_**

Growth of Morocco’s working age population<sup>5</sup> has outpaced job creation. The rural working-age population increased 14.2 percent from 2000 to 2018. The urban working age population, however, increased 49.9 percent. The same trend emerged for both genders (Figure 2.6). Rural employment increased 1.9 percent and urban employment grew 45.8 percent, mainly driven by jobs for men (Figure 2.7). More drastically, while the working-age women population increased 3.4 million, just 117,800 new jobs were created. The gap between male and female employment grew dramatically.

**Figure 2.6: Working age population has increased, especially in urban areas** . Millions of persons

<!-- Start of picture text --> Rural - Female 10<br>Urban - Female<br>Rural - Male<br>Urban - Male 8<br>6<br>4<br>Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force<br>Survey)<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

**Figure 2.7: Employment growth has not kept the same growth pace** Millions of workers

<!-- Start of picture text --> Rural - Female<br>6<br>Urban - Female<br>Rural - Male<br>Urban - Male 4<br>2<br>0<br>Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force<br>Survey)<br>
Seats for intercontinental travel<br>(seats per year)|165,776|297,891|832,895|1,230,000|147,095|2,437,702|
|Seats available per capita|0.06|0.06|0.08|0.23|0.03|0.09|
|Herfindahl index – air transport<br>market (%)|11.75|9.75|6.28|11.64|22.89|11.28|
|_Quality_|||||||
|Percent of seat-km in newer aircraft|95.6|90.9|96.8||93.4|71.42|
|Percent of seat-km in medium or<br>smaller aircraft|54|52.3|15.7|39.3||29.6|
|Percent of carriers passing<br>IATA/IOSA audit|0|0|0|50.0|0|28.6|
|FAA/IASA audit status|No audit|Fail|Fail|No audit|No audit|No audit|

Source: Bofinger 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Note: All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Seabury Aviation Data Group. This captures 98 percent of worldwide traffic, but a higher percentage of African traffic is not captured by the data. The HerfindahlHirschmann Index is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly. The lower the HHI, the more competitive the market is.

Recent investments on several local airports—including refurbishment of Bamako Airport—total over 30 billion FCFA (US$
found that women from certain communities would forego wage work, saying it was mostly due to family responsibilities and social norms (IAMR and ILO 2013). On average, working women spent 4–6 hours a day either attending to household duties or taking care of the children or elderly. Some women with young children preferred to work from home, when there was no one from the family to help with child care. Based on interviews with female engineers, an article in _Quartz_ cites the “maternal wall” as the main barrier for Indian women to advance in their careers (Madhok 2015), while in a 2015 poll of G20 countries, female respondents from India stated that balancing work with home was the main issue they dealt with in their working lives, followed by few opportunities for flexible work (Thomson Reuters Foundation 2015).

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assessment or IRT-linking methodology while demonstrating consistent estimates where there is overlap.

We contribute to the literature in several ways. This is the most current and expansive cross-country dataset on education quality, including the most developing countries. We include, for the first time, the Early Grade Reading Assessment (EGRA) in addition to TIMSS, PISA, PIRLS, SACMEQ, LLECE, PASEC and MLA. This enables us to extend our database substantially, including 48 countries with recent data in the last ten years, all of which are developing economies. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries that are often excluded from large international assessments. We introduce a series of methodological updates, including measures of uncertainty, fixed conversion factors for greater comparability over time, and year-by-year data. All data are derived directly from test score data. In contrast to black-box and complex imputation, this enables methodological transparency. Moreover, it produces a clear policy lever: when countries perform better on RSATs and ISATs they can be certain the HLO will improve.

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transaction, the gender and age of the customer who executed the transaction as well as the gender and the age of the agent who assisted with the transaction. The data also include information on the type of transaction (cash deposit, withdrawal, funds transfer, or school fees payment) and the currency used (USD or CDF). Market level information such as the branch, the town and the dominant financial institution where the branch was located was also available.

About 61% of the FINCA DRC customers are males, a distribution similar to that of many other institutions in developing countries (Chamboko et al., 2018). In addition, males comprise 77% of FINCA DRC agents. This limited participation of females as agents may be explained in part by the fact that FINCA targets former or current clients as prospective agents (Cull et al., 2018). Targeted clients are individuals for whom FINCA has a reliable financial activity record and who have an existing shop which can double up as an agent (Cull et al., 2018).

Figure 1 shows the distribution of male and female agents across the 21 FINCA branches (henceforth referred to as markets). The representation of female agents ranges from 7% in Boma to 34% in Matete, indicating that there are both male and female agents in every market. Thus, while in every market the majority of agents were men, clients always had a number of male and female agents to choose from.

Panel A of Table 1 reports agent characteristics from the transaction data set. To complement these data, the authors were able to obtain the paper-based registration forms of the first 404 FINCA agents that signed up. These registration forms contain additional information that could be matched for 131 agents in the transaction data. Columns 1 and 3 of Table 1 report data on the sample of 131 matched agents, while Columns 2 and 4 report data from the remainder of the 1,119 unmatched agents. Column 5 reports the p-values for group differences between the sample
227|-0.002|
|Mean canopy cover 2000|42.433|50.909|0.200|
|Percent indigenous in municipality|0.161|0.328|0.289|
|Deforestation risk (INE)|0.057|0.055|-0.025|
|Obs|1977|862|2839|

> 1 Table shows mean values in columns (1) and (2), and the normalized differences between these means in columns (3).

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household toilet ownership, subsidy receipts and official VG classification status (henceforth referred to as ‘SBM data’); (ii) household survey data on toilet ownership, open defecation practice and various household socio-economic characteristics as well as borrowing data that were collected for the experimental impact study of the micro-credit intervention (‘survey data’); and (iii) administrative MFI and credit bureau data.

## **SBM data**

The SBM data were downloaded from the SBM data portal, a Management Information System (MIS) developed by India’s Ministry of Drinking Water and Sanitation (MDWS) to monitor progress towards its ODF mission. In 2012-2013, prior to the launch of the SBM on 2 October 2014, the MDWS conducted a nationwide survey to assess SBM baseline levels of toilet coverage across the country and to identify households eligible for SBM subsidies (BPL households and vulnerable APL households, see Section 2.2). The SBM data portal encompasses these baseline data. In addition, States are required to update toilet ownership information on a continuous basis, the latest by April every year (SBM, 2017). Among the information uploaded is household level information on toilet ownership, subsidy receipts (since SBM

> 11There are 5 branches in total in the study area and village size could be either ‘large’ (villages with more than 480 households) or ‘small’. A further 39 GPs received another intervention. In on-going work we study the impacts of this intervention separately.

> 12SBM had just been announced when the randomization of GPs was conducted.

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above the age of 64 years. Overall, 10 percent of the population lives in an inter-generational household. Nevertheless, this type of household is disproportionately represented among the poor, ranging from 14 to 17 percent in the three poorest deciles. This type of household is also more prevalent in the South-East region, followed by the North-West and South-West regions.

# **3. Methodology**

Using a methodology similar to that employed by Ajwad, Aran, Azam and Hentschel (2013), this paper employs a hybrid approach that combines the results of macroeconomic projections of a sectoral slowdown over the whole year of 2020 of the Tunisian economy with micro-simulation techniques. Combining information on GDP growth projections by sector and employment elasticity of growth of each sector (to capture how elastic employment is to changes in sectoral GDP), we estimate the loss of employment in each sector. Using household survey data, we then identify individuals who are likely to lose their jobs based on regression analysis and simulate the impact on consumption under various assumptions. With this, we can identify a new distribution of post-pandemic consumption and assess 10
<mark>This does not include other considerations such as production losses due to supply chain disruptions, or welfare implications of lost access to critical services and supplies.</mark> To calculate the baseline travel delay cost, we first estimated the travel time for each commuter trip (8,866 total) under five flood scenarios using the walking, the drive and the public transit networks based on travel mode information reported in the JICA survey. Next, we calculated travel delay for each flood scenario by subtracting the travel time under dry condition from the estimations. This result was then combined with the personal monthly income information provided in the JICA survey. For each OD trip, we multiplied the delay in travel time due to flooding by the income per unit of time assuming that each worker works eight hours a day, five days a week, and on average 22 days a month. The corresponding output was then doubled to account for return trips from work. To adjust for the difference between sample size and survey population, the cost was multiplied by respective weighting factors for each trip. This same process was repeated for five flood return periods namely: 10-year, 50-year, 100-year, 500-year and 1000-year and the economic cost for all 8,866 trips were later aggregated to the TAZ level. Table 7 shows the number of commuters’ trips affected by five flood scenarios under different delay thresholds. Figure 17 illustrates maps of the averaged economic cost due to travel delay for each TAZ under all flood scenarios. We observe that areas towards the center of Kinshasa city experience relatively higher cost than peripheral suburbs in the south-east and northeast with the exception of the smaller TAZs situated in the heart of the city. It should be noted that the averaged cost aggregation at the TAZ level is more representative in the central regions of the city. Due to the uneven spatial distribution of sampling locations, the peripheral TAZs only contain a few sampling points. For example, the Kinsielele region towards the south of the city shows up with relatively high