rafmacalaba/data-use-mentions-extended huggingface dataset:
_Sources_ : Original estimation based on updated educational attainment data from the updated Barro-Lee Educational Attainment dataset (Barro and Lee, 2013) and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure plots the increase in share of the adult (15+) population with tertiary education in the vertical axis against GDP growth between 1960 and 2020 in the horizontal axis. The line shows a linear fit of the relationship across all countries. Low-income countries were among the bottom third in 1960 in terms of per capita GDP in purchasing power parity (PPP) terms; middle-income countries were among the middle third; high-income countries were in the top third. Countries whose GDP declined are omitted from the plot. Data labels use International Organization for Standardization (ISO) country codes.

A higher level of education is associated with higher income in the origin country, which makes migration less attractive.<sup>17</sup> The disincentive to migrate to low-return destinations that do not offer much better prospects for the migrants could be especially strong. This could reduce migration to low-return destinations as a low-income country develops and poor people attain higher levels of education and better income opportunities domestically (figure 3, panel a).

Increased education reduces barriers to migrate, particularly to high-return destinations. As discussed, higher education reduces _financing_ constraints through higher incomes, particularly for high-cost, high-return destinations. Moreover, better educated workers may experience lower _frictions_ in the destination labor market due to better match of skills and improved ability to signal their ability to potential employers. They may also face lower policy barriers to emigrate to high-income countries due to the high demands for highskilled workers. Together, these factors lead to an increase in the skill profile of migrants as countries develop.

To observe these effects in the data, however, is challenging. Comprehensive data on the skill level of migrants, particularly time series data spanning several decades, do not yet exist. To provide some insight
that the recently developed URCA global data set is an important step forward, especially if complemented with studies, to understand how spatial relationships operate in a variety of social, economic, and developmental contexts. Similarly, the global delineation of metropolitan areas (Moreno-Monroy, Schiavina, & Veneri, 2020) also provides new entry points for informing development policy, particularly for urban planning.

Although not covered in this paper, we acknowledge that, with ICT, rural–urban linkages may become partly decoupled from travel time and distance to an urban center. This trend has, to some extent, increased with the COVID-19 pandemic. In fact, while the pandemic has exacerbated long-standing inequalities – including spatial inequalities along the rural–urban continuum – it has also induced innovation. The consequential health crisis, for example, pushed physicians and health systems worldwide to adopt virtualized treatment approaches between patients and health providers (Webster, 2020). The challenge will be to sustain these innovations on a permanent basis after the pandemic. Rural residents can greatly benefit from enhanced quality-of-life provided by better ICT connectivity for access to e-government, health care, education, etc. However, small towns and rural communities often lack the basic ICT infrastructure needed to prosper in the digital age, namely internet access (Lai & Widmar, 2020), as well as access to digital goods and services such as e-health (Webster, 2020) and online education. This is an interesting avenue for future research that will surely affect social and economic development along the rural–urban continuum.

Another unaddressed, though very important area, is that of natural resource management and, specifically, of ecosystem services – the benefits that people obtain from nature – that also differ along 26
The frequency distribution allows policy makers to see the distribution of risks they face. The distribution indicates the level of damage and its chance of occurring. A more intuitive description of frequencies can be seen in the return rate (RR) function of storms causing damage (D). The return rate is defined: The return rate describes the average amount of time between storms causing each level of damage. Examining the tail of the return rate or frequency distribution is particularly helpful to calculate the amount of catastrophic insurance needed to cover large damage.

Damages are also broken down by region within the United States. We examine five regions: Gulf, Florida, Southeast, Mid Atlantic and New England<sup>2</sup> . State specific estimates are presented in Appendix A.

# III. Hurricane Integrated Assessment Model

The integrated assessment model has been constructed to project hurricane risks given different climates. The analysis integrates an emission path, the resulting climate change, the impact on hurricanes, and the resulting damage. The analysis relies on the A1B SRES emissions scenario generated by the Intergovernmental Panel on Climate Change (IPCC 2000). The scenario assumes that mitigation is gradually tightened over time so that greenhouse gas concentrations finally peak and stabilize at 720 ppm.

We rely on four climate models: CNRM (Gueremy et al. 2005), ECHAM (Cubasch et al 1997), GFDL (Manabe et al. 1991), and MIROC (Hasumi and Emori 2004) in order to capture the wide range of plausible climate changes that this emission scenario might cause. Each climate model predicts both the current climate and the climate in 2100. Because of differences in the models, they generate a wide range of climate change predictions. The range of temperature outcomes across these four models

> 2 The Gulf states include Texas, Louisiana, Mississippi, and Alabama. The Southeast includes Georgia, South Carolina, North Carolina, and Virginia. The Mid Atlantic includes Delaware, Pennsylvania, New Jersey, and New York. New England includes Connecticut
the Vietnamese government, there were over three million victims (Heuveline 1998).

An independent Finnish Inquiry Commission estimated the death toll at one million using demographic accounting (Kiljunen 1984). Probably the most careful exercise of demographic reconstruction, comparing the population structure before and after the mortality crisis, has been accomplished by Heuveline (1998 (a)) using 1993 electoral lists.<sup>2</sup> He proposes, for the entire 1970-80 period, a central estimate between 2.2 million and 2.8 million, although he is not excluding values as low as 1.2 million or as high as 3.4 million.

> 1 See also on Mozambique, Brück (2000 and 2001) and on India, Justino (2001).

> 2 Earlier estimates based on the reconstruction approach include Ea (1981), Banister and Paige Johnston (1993) and Vickery (1984).

2
the ICT sector, first, by estimating the welfare impacts on current consumers and, second, by identifying potential new consumers brought into the market by a reduction in market prices (due to greater competition) and then estimating the welfare impacts. This approach is applied to Ethiopia, where, until 2019, the ICT market was dominated by a monopoly.

The analysis relies on data from the 2015/16 Household Consumption Expenditure Survey (HCES) in Ethiopia (CSA 2018). The simulation follows a reform scenario whereby the mobile services market share of Ethio Telecom, the state-owned telecommunication enterprise, declines from 100 percent to 45 percent, virtually ending its monopoly. The paper then models and predicts the extent of the adoption of ICT services and the expected expenditures on mobile services among both current and new users and, thereafter, the total welfare effects among all consumers.<sup>3</sup> Overall, the introduction of competition in mobile services in Ethiopia would reduce poverty by 0.31 percentage point. This effect can be disaggregated into poverty reduction among current consumers and among new users. Competition in the mobile market would reduce poverty among current users by 0.22 percentage

> 1 According to data of the International Telecommunication Union (ITU 2020), the region hosts 21 of the 25 most expensive countries in mobile voice, 19 of the 25 most expensive in mobile data, and 22 of the 25 most expensive in fixed broadband.

> 2 See Inclusive Internet Index 2020 (database), Economist Intelligence Unit, London, https://theinclusiveinternet.eiu.com/. 3 This part of the analysis introduces WELCOM’s Market Competition and the Extensive Margin Analysis (mcema) module (RodríguezCastelán et al. 2021). For WELCOM, see WELCOM Stata Tool, Global Solutions Group on Markets and Institutions for Poverty Reduction and Shared Prosperity, Poverty and Equity Global Practice, World Bank, Washington, DC, <u>http://dasp.ecn.ulaval.ca/webwel/
(2) where<sup>_s_</sup> _j_<sup>is the share of seats allocated to the district</sup><sup>_j_and</sup><sup>_v_</sup> _j_<sup>is district</sup><sup>_j_’s share of the</sup> population. Values greater than one denote overrepresentation of district _j_ , and the opposite is true for values smaller than one. The data needed to compute (2) come from Samuels and Snyder (2001) and Snyder and Samuels (2004), as well as from national sources (the Appendix lists these data sources).

# **3.2 Cross-country Panel Data**

We use data on democracy for a panel of eleven Latin America countries<sup>15</sup> , covering the period from 1870 to 2000. Our measure of democracy is the variable _polity2_ from the 2007 Polity IV Project dataset. This indicator is coded taking into account several features of a country’s political institutions, such as the openness and competitiveness of executive recruitment, the constraints placed on the chief of the executive, and the competitiveness and regulation of political participation. It ranges from –10 to +10 with higher values corresponding to better democratic institutions<sup>16</sup> . We normalize the measure so that all its values fall between zero and one.

Some of our cross-country regressions control for per-capita GDP, which we take from Maddison (2005) for all countries but Chile. For Chile, we use data from Díaz et al. (2008) since they provide data for more years than Maddison (2005).

# **3.3 Within-country Data**

Our source for Latin American within country data is Bruhn and Gallego (2010). This source provides data on income per capita, the Gini index, temperature, rainfall, and altitude, as well as a landlocked dummy, for different regions within fourteen Latin American
for 38 agricultural commodities. Their approach builds on the Deaton formulation through the additional inclusion of demand substitution, i.e., the second term in equation (2), while the other terms in the CV expression are ignored. They use country-level actual changes in domestic prices wherever available in their simulations. Except for Vietnam, they find an increase in poverty in all countries in their sample. Overall, they estimate a net increase in poverty of 44 million people.

Other researchers have attempted a more complete welfare estimation by including the higher order effects of equation (2). Minot & Dewina (2015) use household survey data from Ghana to examine the sensitivity to various assumptions. Specifically, they consider the following four scenarios: (i) no demand or supply response and identical price rise for consumer and produce prices (the Deaton first-order approximation); (ii) similar to (i) but with producer prices rising twice as much as consumer prices; (iii) assuming household supply and demand response (with demand and supply elasticity of 0.3 assumed) and identical consumer and producer price rise; (iv) combination of (ii) and (iii). In the base scenario, they find that households are negatively affected in the short-term. When testing the sensitivity of their results to different assumptions the authors find that the results do not change significantly when supply and demand responses are included (i.e., scenario (iii)). This suggests that short-term estimates resulting from the first-order approximations are likely to be reasonably close to the impact in the medium and long term, particularly for price changes in staple commodities, which are often relatively inelastic in supply and demand. However, the change in results is more substantial in scenarios (ii) and (iv) when the assumption of identical price rises for producer and consumers is dropped. They find that the standard assumption predicts a 1 percent increase in poverty, while the alternative assumption in scenario (ii) predicts a 2 percent
attending school, to engage in temporary or seasonal work or to engage in informal activities. Atypical work is likely to be underreported in household surveys. Second, proxy reporting is common in household surveys and as the proxy respondent is often the male household head, data on youth and women may disproportionally suffer from ‘proxy response’ bias. The respondent, in fact, may accurately report his own activities, but under‐ or overreport activities of other household members, especially of household members who primarily engage in atypical work (Bardasi et al., 2011; Dillon et al, 2012). Third, many household surveys rely on the notion of the main and second activity and do not use appropriate screening questions to define ‘activity’ (Benes & Walsh, 2018c; Fox & Pimhidzai, 2013), which might again be problematic for household members engaging in atypical work such as contributing family members. Fourth, the sampling framework is more likely to miss individuals who (temporarily) migrate to other regions to find a job (Mueller & Chan, 2015).

Few empirical studies have examined the sensitivity of women’s employment to survey design and, as far as we know, not a single study has focused on young people (Koolwal, 2019). One robust finding in this literature is nevertheless that women, when responding to simple keyword question such as _‘Did you work in the last seven days?’,_ tend to report domestic work as employment, which upwards biases the female employment rate (Bardasi et al., 2011; Benes & Walsh, 2018d). Studies evaluating the sensitivity of women’s employment to other survey design features provide mixed results. Comparing self‐reporting versus proxy reporting in Tanzania, Bardasi et al. (2011) conclude that proxy reporting has no effect on the female employment rate, but substantially lowers the male employment rate. Exploiting variation within surveys, Comblon and Robilliard (2015) compare the effect of several survey design issues, including the wording of
found a <mark>moderate to high sensitivity, and a m</mark> oderate validity for self-reported coverage of RMNCH-N service utilization and quality of care in household surveys.<sup>39,40</sup> For quality of care, DHS uses objective questions on standardized medical procedures and thus, the typical subjectivity concerns for psychometric assessments may not be applicable here as shown by validating studies.<sup>39,40</sup> Also, while assessing the determinants of RMNCH-N service utilization, the service seekers’ experience is more meaningful than a facility-based empirical assessment of quality.<sup>40</sup> Third, this study did not have a primary data component, and, in particular, a qualitative component to triangulate why and how certain determinants influenced each outcome in a particular way. Therefore, drawing conclusions from the findings requires a cautionary approach. Fourth, the machine learning analysis provides the top determinants that influenced the study outcomes. But a careful approach (substantiated by literature) is needed while concluding the direction of association between a specific determinant and an outcome. This study used a vast contextual literature including the DHS reports for triangulation.

Fifth, this study exclusively focused on the pre-pandemic (COVID-19) phase and used one of the most recent household and facility surveys data. Findings may not be appropriate for any decision making during the pandemic but would certainly be relevant for the post-COVID phase. The existing evidence indicates that the determinants of RMNCH-N service utilization would remain largely the same in the post-pandemic phase. Nonetheless, low service provision, poor quality and financial constraints of service seekers perhaps would be multiplied and remain as the top determinants in the post-pandemic phase.<sup>41</sup> This study, however, also shows that service availability, quality and household wealth status are the top determinants even in the pre-pandemic phase. Lastly, this study did not assess the impact of any ongoing policy interventions. Thus, findings are not robust enough to make a conclusion on
8<br>-10<br>High incomeHigh income Upper middle incomeUpper middle income Lower mLower m idd le incomele income Low Low incom ee<br>Spain Mauritius United States Panama Chile Uruguay Romania Argentina Venezuela Iraq Ecuador Jordan Thailand Paraguay Russia Iran Malaysia Colombia Russia Indonesia South Africa Costa Rica Dominican Republic China Botswana Mexico Peru Namibia Guatemala Brazil Turkey West Bank and Gaza West Bank and Gaza Albania Armenia Ukraine Bolivia Sri Lanka Vietnam Nicaragua Moldova Comoros India Tunisia El Salvador Honduras Zambia eSwatini Lesotho Ghana Ivory Coast Kenya Tajikistan Ethiopia Uganda Guinea Burkina Faso Togo Tanzania<br><!-- End of picture text --> Source: World Bank estimations for the West Bank and Gaza based on PECS 2016-17. CEQ Data Center for the rest of the countries.

Note: Pensions treated as government transfers in China, Georgia, Guinea, Mali, Mongolia, Niger, and West Bank and Gaza.

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live and where they go. It is noteworthy that transport infrastructure forms a network. Thus, transport connectivity should be measured in a continuous manner and at a more granular level.

7. To overcome these data challenges, the paper proposes to construct a spatial connectivity dataset using detailed shipment data. Our spatial data allows for estimating transport costs or travel time at each individual road level. Dorosh et al. (2009) use similar geospatial data to identify the impact of market accessibility on crop production in Africa. Their cross-sectional approach remains subject to the potential risk of omitted variable bias and uncontrolled infrastructure endogeneity. This paper takes advantage of stop-by-stop freight shipment data collected over the period 2010-2020.<sup>1</sup> Detailed historical changes in transport connectivity in the Caucasus and Central Asia region are measured. Then, they are regressed on firm location data.

8. The remaining sections are organized as follows: Section II provides a brief regional and country context and elaborates on how our transport measurements are constructed for the sample region. Section III discusses our empirical strategy and summary statistics. Section IV presents the main estimation results. Section V discusses robustness and heterogeneity of the results. Then, Section VI concludes.

# **II. Regional and Country Contexts and Transport Connectivity Measurements**

9. Recent global crises, such as the Coronavirus Disease 2019 (COVID-19) and the war in Ukraine, have reminded us of the importance of maintaining efficient and reliable transport and logistics networks to ensure economic growth.<sup>2</sup> Because of the stagnation and faster-thanexpected recovery in merchandise trade and global outputs, the world shipping capacity remains constrained by shortages in logistical equipment, containers, service operators, such as truck drivers and port operators, and cargo vessels (UNCTAD 2021). According to the Drewry database, the average global maritime freight rate reached over US$10,000 per 40-foot container

> 1 See ADB (2020) for more details. We would like to express our
if the scarring effects of sustained employment and income losses as well as distress asset sales and learning disruptions for children in poorer households limit the recovery of poorer households relative to better off households over the medium-term (Kim et al. 2021; World Bank 2022b; Neidh ̈ofer et al. 2021).

In this regard, the dynamics of income losses examined in this paper would reflect employment changes in the intensive as well as extensive margin and indicate the extent of unrecovered welfare losses throughout the pandemic. This paper goes on to argue that the wider welfare losses beyond job losses, if left unrecovered, could amplify inequality in the longer term. The pandemic has led to significant losses of productive assets and human capital, particularly in poorer countries. Households disposed of productive assets as a way of coping with the income shock since the social protection response was largely inadequate, often relied on inefficient means, and did not last long enough in many lower-income countries (World Bank 2022b). The loss of assets could hurt the productive capacity of those households that need it the most during the recovery.

# **3 Data and methods**

The main data source for this paper is the November 2022 vintage of the World Bank’s harmonized HFPS data which was collected to monitor the impact of the pandemic on households. The first surveys were conducted in April 2020, with data collection continuing into 2022 in some economies. Since questionnaires differed by economy and across survey waves, responses were harmonized globally to construct an internationally comparable dataset with up to half a million observations for a given indicator. Data used for analysis in this paper were collected from 80 countries in five of the six World Bank regions over 343 survey waves between April 2020 and December 2021. The breakdown of countries with harmonized survey data by income level, region and timing is shown in Table 1. More than half are LICs and LMICs, including 19 Fragile and Conflict-Affected Situations (FCS). Of the 80 countries with surveys, 53 are located in Latin America
(the summary statistics can be found in Table A.1).

# **2.2 Environmental risk data**

Based on geo‐spatial data sets, variables are constructed to measure environmental risk at district and commune levels representing 8 environmental risks. These variables are based on historical risk profiles measuring the area’s exposure to fragile and severe conditions and not the actual environmental conditions at the time of the VHLSS surveys. The following variables are calculated to measure environmental risks at the district and commune levels (the summary statistics can be found in Table A.1):

**1) Air pollution** is measured by the area‐weighted mean of concentration (measured as micrograms per cubic meter) of particulate matter with a diameter of 2.5 micrometers or less (PM2.5) taking the 10 years‐ average value for 2000‐2010. The data is based on satellite imaginary using the total column aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer satellite instruments, which is combined with chemical transport model simulations, and ground measurements from 79 countries to produce a global spatial data set with 0.1° × 0.1° resolution (Brauer et al., 2015). PM2.5 includes dust, dirt, soot, smoke, and liquid droplets, which can lodge deeply into the lungs due to their small size. PM2.5 air pollution has been identified as a leading risk factor for global diseases (Forouzanfar et al., 2015).

**2) Tree cover loss** is calculated as the share of the area under tree cover in 2000 that suffered from a tree cover loss between 2000 and 2010. Tree cover is defined as canopy closure for all vegetation taller than 5m in height and is calculated from imagery from the Landsat 4, 5, 7, and 8 satellite data used to produce a global

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