rafmacalaba/data-use-mentions-extended huggingface dataset:
). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low macro‐ financial risks, low climate‐high macro‐financial risks, high climate and macro‐financial risks) by region. Thresholds are country median values of EIU indicator or climate variable as of 2017 (historical country median for the case of fuel exports). Numbers in columns are the number of countries in each category. EIU data for Saudi Arabia as of 2016.

Source: EIU Credit Risk Model, GermanWatch, Notre Dame GAIN, WDI. Staff calculations.

36
for age- and gender-specific labor force participation rates for 1960-2020 for up to 38 advanced economies and 142 EMDEs.<sup>20</sup> Completion rates of secondary and tertiary education are from Barro and Lee (2013) and the World Bank’s World Development Indicators; age-specific fertility rate and life expectancy are from the UN’s World Population Projections database; gender-specific secondary and tertiary school enrollment rates are from the World Development Indicators. The regression sample includes up to 35 advanced economies and 133 EMDEs for 1987-2020.<sup>21</sup> The regression results are broadly in line with findings in the previous literature (table 7).

First, among teenage and younger women, fertility rates are associated with higher labor force participation as mothers are more likely to discontinue their education and 19 This approach combines those by Fallick and Pingle (2007) and Goldin (1994). For the United States, Fallick and Pingle (2007) estimate labor force participation by age group and gender as a function of cohort and age fixed effects as well as business cycle fluctuations. Goldin (1994) models aggregate labor force participation rates as a function of country-level variables such as female schooling. The regression used here incorporates both cohort effects and country-level variables modelling human capital and other factors driving labor force participation.

> 20 This is an unbalanced sample because some of the exogenous variables are not available for the full period for all countries. However, the regression results are robust to restricting the sample to the balanced panel with fully available data.

> 21 Since UN data for life expectancy is only available for five-year periods, historical life expectancy data from the World Developing Indicators database is used. For projection years or missing data, UN World Population Statistics are spliced with data from World Development Indicators database.

34
0.57|
|School has a foodprogram for students|0.81|0.71|0.86|0.71|1.00|
|Observations|21|7|14|7|7|

_Notes:_ This table shows descriptive statistics of the available variables at baseline for the sample of participant schools. All information was obtained at the school level. Data were obtained from the Educational Censuses for El Salvador and Guatemala (2018) and self-collected by the authors for Honduras. Column (1) presents the mean characteristics for the full sample of schools and column (2) for the schools assigned to the _Clubs_ group. The average characteristics for schools assigned to either of the psychology-based interventions are presented in column (3). Characteristics for schools in each of the interventions ( _Virtue_ or _Mindful_ ) are presented in columns (4) and (5), respectively. Total school enrollment refers to the enrollment in the school, not to the ASP or study. “Observations” indicates the number of schools within each group. The symbols representing unadjusted _p_ -values in the balance tests for all available variables are as follows: Clubs vs (Mindful + Virtue): *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Clubs vs Mindful: _† † †_ p _<_ 0.01, _††_ p _<_ 0.05, _†_ p _<_ 0.1. Clubs vs Virtue: _§§§_ p _<_ 0.01, _§§_ p _<_ 0.05, _§_ p _<_ 0.1. Mindful vs Virtue: +++ p _<_ 0.01, ++ p _<_ 0.05, + p _<_ 0.1. For more information on balance tests, exact _p_ -
their model to a sample of 300 user accounts from our own database. The out of sample accuracy in that case is 91%.

For the second step, which is concerned with gender classification, we use the Twitter Demographer package, which offers a user-friendly API. The underlying prediction method is built on the M3 classifier introduced by (Wang et al., 2019). The predictors in this case include the user’s name, screen name, bio description as well as the user’s profile image (which involves image recognition analysis). Three distinct datasets are used, each serving a specific purpose, for training and out of sample performance testing of the M3 classifier:

1. A Twitter dataset for the period 2014 to 2017 for which gender and age of users have been identified.

2. An images dataset, comprising a total of 523,051 images. This dataset is derived from head-shots of actors sourced from IMDB and profile pictures extracted from Wikipedia. These images bolster the classifier’s ability to incorporate visual cues in its predictions.

3. A dataset derived from crowd-sourcing efforts where user gender and age are collected across 32 different languages.

The reported out of sample accuracy of the gender classifier is 91.8% (Wang et al., 2019). When we apply the model to a sample of 200 individual users from our database, we obtain an out of sample accuracy of 88%.

The gender classification is computationally expensive, in large part stemming from the image recognition component that is utilized to infer the gender of the user. For this reason, we first draw a simple random sample of 1000 user accounts for each hashtagyear-month and group these into individuals and organizations (the first step). Among the users that are identified as individuals, the gender classifier is applied to a simple 10
example, although we use the 1999 share of total banking assets held by foreign-owned banks from Claessens and Van Horen (2008a), we derive similar results when we use the foreign claims on the countries in the ECA region (as a share of GDP) as reported by the Bank for International Settlements.

> 22 We also included the timing of the 2008-2009 BEEPS survey in a simple cross-sectional regression of the 20082009 financial constraints, although it was not significant. This could be because perceptions of financial constraints are forward looking and thus all respondents had a reasonable sense of the coming crisis, regardless of the month in which they were interviewed.

18
2

# **1. Introduction**

In a recent paper (Wagstaff et al. 2018), we provided an overview of an international database on Health Equity and Financial Protection Indicators (HEFPI). The data set provides data on the delivery of health service interventions, health outcomes, and ‘financial protection’ in health, at both the population level, and for subpopulations (defined by household living standards) along with a summary measure of inequality known as the concentration index (Wagstaff et al. 1991; Kakwani et al. 1997). The data are computed from well-known household surveys that have been conducted by, or in partnership with, national governments, such as the Demographic and Health Survey (DHS) and the Living Standards Measurement Study (LSMS).

This paper outlines changes that have been made in the 2019 version of the HEFPI database. On the financial protection side, the number of indicators has been expanded in the 2019 database from five to 14. Moreover, several estimates have changed from the 2018 database, reflecting in part our analysis of new surveys (or adaptations thereof) since releasing the 2018 database, but also refinements we have made to out-of-pocket expenditure estimates for some surveys included in the 2018 database. On the health equity side, the 2019 HEFPI database includes 197 more datapoints than the 9,733 in the 2018 database. This increase is the result of two major counteracting enhancements of the database: the addition of 535 new datapoints, of which 493 come from recent Multiple Indicator Cluster Surveys (MICS) and several new DHS; and the removal of 338 previously included datapoints for eight health equity indicators which, after a thorough quality review, were concluded to be substandard. These changes are detailed in the sections below.

# **2. Financial protection indicators**

On the financial protection side, for the 2019 HEFPI database, 1,846 surveys were analyzed and 650 were retained covering 149 countries. The number of indicators has also been expanded from 5 to 14.
presents suggestive evidence for women‘s empowerment and greater role in household decision making in villages with electricity, compared to villages without electricity, in Bangladesh.

One study that has examined these issues using a sampling and estimation approach designed to identify causal effects is ADB (2010). Their survey of rural Bhutan elicited information on women‘s involvement in decision-making. Based on propensity score matching, they found that electricity significantly increased women‘s participation in decisions related to health and education, but not finance. Focus group participants explained ―that access to electricity empowers women through health, education, gender equality, and domestic violence awareness programs on television. Thus, women are becoming more assertive and confident. Further, on average, women save 1.5 hours every day in cooking, which enables them to attend village meetings and voice their concerns.‖ ADB (2010) argues that ―the impact of television as a medium of news and information cannot be overstated. An overwhelming 86.8% of electrified households, and nearly 70% of unelectrified households, agree that television is a good source of news and information.‖ And ESMAP (2004) argues that ―reading and watching TV are significant factors in raising awareness, broadening horizons, and educating rural women and thus should be seen as status-enhancing activities.‖ Thus, the primary story here appears to be about television. It is clear that across cultures, one consistent, immediate impact of electrification is that people spend more time watching television, whether because households buy televisions or because of the decreased cost of watching television previously powered by battery.<sup>50</sup> For example, IEG (2008) found that across countries, electrification explains 20% of increases in TV ownership over time (coefficient = 0.47, statistically significant at 5% level). IEG (2008) further argues that having a television in the home (as opposed to a public place) is particularly important for women‘s access. Many studies report
improves as more sightings occur in non-advantaged million new reports per day. The following section tests alphahull boundary mapping from the current provided by existing expert maps.

GBIF species and the smaller inventory of expert range maps, with two primary objectives. The first is incorporating more species into an expanded view of global biodiversity. The second is assessing the relationship between GBIF occurrence maps and expert range maps that may “undershoot” if their This final condition may seem redundant, since the initial condition is exclusion of species with fewer than five unique occurrences since 1970. However, a species can pass the initial condition but fail the final one because the alphahull algorithm may exclude an outlier point or two from its computation of the bounded area.
the Central Statistical Office (CSO) and Kurdistan Region Statistics Office (KRSO) have collected a continuous Income Household and Expenditure Survey (C‐IHSES) during 2014.

The objective of this survey was to provide a more frequent estimate of poverty and welfare in Iraq in the years in between the full‐blown IHSES surveys (2007, 2012, and planned 2017/18). It was designed to be representative at the governorate level, with the intention that each wave would be nationally representative, with an intended sample size of 13,834 households, drawn equally from each of the 18 governorates.

However, the fieldwork was affected by the security situation in some governorates as a consequence of the influence of the Islamic State (IS) into Iraq’s northern and western provinces beginning June 2014. As a result, there are two groups of governorates in the final sample of the C‐IHSES: those where data collection was successfully completed and those where it could not be completed. Moreover, due to the evolving security situation, even in the first group of governorates where the intended sample was visited, the distribution of visits to certain primary sampling units was delayed or preponed.

In the first group, 13 out of 18 governorates were continuously surveyed over the entire period and the regions covered are the following: Kurdistan (i.e Duhok, Sulaimaniya and Erbil); part of Centre (Diyala, Babylon, Kerbela, Wasit and Najaf) and South (i. e. Qadisiya, Muthanna, Thi‐Qar, Maysan and Basrah).

21
**18** **<u>│</u>**

# Skills

FDI tends to flow increasingly into higher-skilled activities<sup>2</sup> over lower-skilled activities. In the manufacturing sector, the global inflow of investment to higher-skilled activities from 2009 to 2011 was nearly 10 times larger per year than that of lower-skilled activities. Moreover, the skill bias of FDI seems to be accelerating over the years. The annual average between 1990 and 1992 was about five times larger, and between 2005 and 2007 about 14 times larger (Moran, Gorg and Seric, 2016).

Overall, while there exist stark differences in skill utilization across countries as revealed by the shares of skilled workers in the sample of Enterprise Surveys, the majority of foreign multinationals in most countries offer training in much greater numbers than domestic firms (see Fig. 3 and 4). In countries where services and high-tech manufacturing FDI take place, foreign multinationals rely much more on skilled workers for their activities. This would be the case of Poland, Indonesia or the Philippines, which have already significantly upgraded their industrial capabilities and expanded services FDI over the last decade. On the other hand, in Mexico, Ethiopia, Romania, or Vietnam, foreign multinationals employ significantly higher shares of unskilled employees in low-technology manufacturing, while in Nigeria and the Democratic Republic of Congo low skill intensity reflects concentration of foreign MNEs on natural resource sectors. All these are examples of markets where efficiency-seeking FDI or resource-seeking investment is taking place on a larger scale.

Training offers across firms also vary substantially across countries, although there seems to be a consistent premium in shares of foreign multinationals that offer it relative to their domestic competitors.

Whether these differences in skill utilization and transfer translate into substantial effects on the aggregate level of skills in the workforce depends not only on the volume of investment activity, but also on initial skills endowments. In fact, there may be a cyclical relationship between FDI types and skill endowments in the economy: certain skill endowments
high in SSA and in Latin America and the Caribbean (LAC). Trade costs declined less in agriculture than in manufacturing over 1995-2019, falling from 194 percent to 170 percent, in part because of slower progress in reducing tariffs and the narrower coverage of trade agreements.

Goods and services trade are complementary. Tradable services are key links between stages of value chains and “enablers” of trade in goods, particularly communications, finance, business and logistics services. As a result, services account for almost one-third of the value added of manufacturing exports (Ariu et al. 2019; OECD 2022). Comparable cross-country data on services trade costs and on policies affecting trade in services are scant. The few attempts in the literature to quantify trade costs in services either rely on observed trade and value-added flows, akin to the methodology embedded in the UNESCAP/World Bank database for goods trade costs (Miroudot, Sauvage, and Shepherd 2010), or rely on an inventory of services trade restrictions (Benz 2017). Both types of studies suggest that trade costs for services are considerably higher than trade costs for goods, and that, unlike trade costs for goods, they have not fallen since the 1990s.

# V. Correlates of trade costs

Trade costs include the full range of costs associated with trading across borders. These include transportation and distribution costs (Marti and Puertas 2019; Staboulis et al. 2020), trade policy barriers (Bergstrand, Larch, and Yotov 2015), the costs of information and contract enforcement (Hou, Wang, and Xue 2021), legal and regulatory costs, as well as the cost of doing business across cultures, languages, and economic systems (Anderson and van Wincoop 2003).

## V.1 Candidate correlates

A number of correlates of trade costs have been identified. They include trade policies, shipping and logistics, regulations, uncertainty, and other factors.

## Trade policies: tariffs and trade agreements

Import tariffs raise trade costs. The contribution of tariffs to total trade
foreign ownership a¤ected establishments’resilience to the negative economic shocks using a worldwide establishment panel dataset that reports detailed operation, location, and industry information of over 12 million establishments in 2005-2008. We exploit how multinational corporation (MNC) subsidiaries around the world responded to the crisis relative to local establishments and the underlying mechanisms that led to the di¤erential impact. This question is central to ongoing policy debates over the role of foreign direct investment (FDI) in economic growth and volatility. For many countries, such as Ireland, Slovakia, Singapore, and Malaysia, which have heavily relied on FDI as an engine of economic growth, there are increasing concerns that FDI is more volatile than domestic investments and renders countries greater economic vulnerabilities especially during economic crises.

Evaluating the role of foreign ownership during economic crises poses several challenges. First, it is di¢ cult to disentangle the e¤ect of foreign ownership from other establishmentlevel characteristics such as size and productivity and macroeconomic factors such as market

> 1 Baldwin and Evenett (2009) compile some of the leading explanations.

> 2 One exception is Tong and Wei (2009), who examine whether the volume and composition of capital ‡ows a¤ected the degree of credit crunches faced by a sample of emerging-economy manufacturing ...rms in the recent crisis. See Section 2 for an overview of this study and other papers, such as Alvarez and Görg (2007), Desai, Foley and Forbes (2008), and Kalemli-Ozcan et al. (2011), that explore the role of foreign ownership in establishment responses to past regional economic downturns and currency depreciations.

2
sector dummy variable, except in the case of Berth6lemy and Bourguignon (1992), where it is based on the difference between coefficients for government and other formal sector jobs. For large values of the c coefficients, in absolute terms, the percent premia is approximated as 100[exp(c)-1]. Data sources and sample sizes are reported in brackets. CILSS is the Cote d'Ivoire Living Standards Survey, a household survey with national coverage. IIES is a series of labor force surveys carried out by the Institut International d'Etudes Sociales in the capital cities. ONFP is a plant-level survey covering large firms, but reporting data on individual workers. RPED is the Regional Program for Enterprise Development, a plant-level survey which includes data on up to 10 workers in each firm.

_45_
- Fernald, John G (1999). “Roads to Prosperity? Assessing the Link between Public Capital and Productivity”. _American Economic Review_ 89.3, pp. 619–638.

- Freedman, Seth M et al. (2023). _Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines_ . Working Paper 31842. National Bureau of Economic Research.

- Gaggl, Paul, Rowena Gray, Ioana Marinescu, and Miguel Morin (2021). “Does Electricity Drive Structural Transformation? Evidence from the United States”. _Labour Economics_ 68, p. 101944.

- Jaimovich, Esteban (2019). “Roadways, Input Sourcing, and Patterns of Specialization”. _European Economic Review_ 120, p. 103319.

- Jedwab, Remi and Adam Storeygard (2021). “The Average and Heterogeneous Effects of Transportation Investments: Evidence from sub-Saharan Africa 1960–2010”. _Journal of the European Economic Association_ jvab027.

- Kahn-Lang, Ariella and Kevin Lang (2020). “The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications”. _Journal of Business & Economic Statistics_ 38.3, pp. 613–620.

- Kassem, Dana (2021). _Does Electrification Cause Industrial Development_ . Working Paper.

- Klein Goldewijk, Kees, Arthur Beusen, Gerard Van Drecht, and Martine De Vos (2011). “The HYDE 3.1 Spatially Explicit Database of Human-Induced Global Land-Use Change over the Past 12,000 Years”. _Global Ecology and Biogeography_ 20.1, pp. 73–86.

- Lee, Kenneth, Edward Miguel, and Catherine Wolfram (2020). “Does Household Electrification Supercharge Economic Development?” _Journal of Economic Perspectives_ 34.1, pp. 122–44.

- Leturcq, Guillaume (2018). _Dams in Brazil: Social and Demographical Impacts_ . Springer.

- Lewis, Joshua and Edson Severnini (2020). “Short-and Long-Run Impacts of Rural Electrification: Evidence from the Historical Rollout of
may be related to some of the trends documented in Y. Feng et al. (2024). We also note the decreases in non-labor force participation, but the coefficients are too imprecise for firm conclusions.

Finally, we look at changes in the jobs ladder based on changes in sector of employment (agriculture, manufacturing and services). Table 8 shows that movements out of agricultural employment and into manufacturing employment are associated with increases in formal wage employment but that growth in services does not show the same relationship. We also explore the extent to which changes in trade orientation and capital deepening are correlated with better jobs outcomes. Table A16 looks at correlations between exports and capital stocks and movements on the jobs ladder. However, many of these variables are available for only a subset of country-year surveys and, as such, we do not see any significant within-country correlations.

Table 8: Sectors of employment and changes in the jobs ladder

||(1)<br>Upper-tier<br>self emp.|(2)<br>Formal<br>wage|(3)<br>Upper-<br>tier<br>informal<br>wage|(4)<br>Lower-tier<br>informal<br>wage|(5)<br>Lower-tier<br>self emp.|(6)<br>Unemployed.|(7)<br>Inactive|
|---|---|---|---|---|---|---|---|
|Emp. share in|-0.337|0.811|-0.077|-0.550**|-0.281|0.121|0.313|
|manufacturing|(0.234)|(0.531)|(0.064)|(0.225)|(0.560)|(0.126)