rafmacalaba/data-use-mentions-extended huggingface dataset:
R-squared|0.275|0.261|0.255|0.240|0.291|0.290|0.281|0.298|

_Notes:_ Dependent variable: annual growth rates averaged across 2000-2010. Human Capital - Schooling refers to estimates in 2000, the beginning of the time period. Human Capital – Penn World Tables refers to the measure of human capital in the Penn World Tables. Human Capital – Learning refers to the measure of human capital in this database from 2000 onwards. Human Capital - HDI Education Index refers to the measure of education included in the HDI in the year 2000. Results exclude countries in civil war, inflation crises and with rents from natural resources above 15 percent. All independent variables are transformed to log units to derive comparable elasticities. We control for initial GDP per capita refers to levels at the beginning of the period in the year 2000 in all specifications following standard practice in the growth literature.

_Source:_ Schooling data are from Barro-Lee (2013). HDI data are from the Human Development Index Education Index. GDP and human capital data are from PWT 9.0. Learning estimates are from our database.

22
. (2018) focuses on income mobility in the village of Palanpur in western Uttar Pradesh over a period of seven decades, extending from the late 1950s to 2015.

> 7 Some smaller panel surveys have been fielded for India, but none of these provides nationally representative data. For earlier studies that analyze these panel surveys, see e.g., Munshi and Rosenzweig (2009), Krishna and Shariff (2011), and Dercon et al. (2013) respectively for analyses of the Rural Economic and Demographic Survey panel between 1982 and 1999, the NCAER panel between 1993/94 and 2004/05, and the International Crops Research Institute for the Semi-Arid Tropics panel between 1975 and 2006.

10
# **Does Migration Reshape Expenditures in Rural Households?**

# **Evidence from Mexico**

The impact of migration on expenditure patterns in rural migrant-sending households has received considerable attention in the literature because of its ramifications for economic growth and demand linkages in rural economies. A key question that researchers have addressed is what impact, if any, households’ receipt of remittance income has on productive investments, which are considered to be a driver of growth in rural areas and a potential creator of local economic alternatives to migration. Empirical research on expenditures in migrant-sending households often has contributed to a pessimistic view of the impact of migration on development in migrant-sending areas. Most studies conclude that remittances are consumed instead of invested and thus are not put to productive uses in migrant-sending areas (for reviews, see Chami, Fullenkamp and Jahjah, 2003; Taylor et al., 1996; Durand and Massey, 1992; and Papademetrious and Martin, 1991).<sup>1</sup> However, other researchers find the opposite (e.g., Massey, et al., 1987; for a detailed review see Taylor, et al., 1996).

Two approaches dominate empirical research on migration and expenditures. The first is based on remittance use surveys, which ask remittance-receiving households what

> 1 Rempel and Lobdell (1978), reporting on a survey of 50 remittance-use studies for the International Labour Office, concluded that “most of the money remitted is used for increased consumption, education and better housing”.  Lipton (1980) likewise concluded that investment is a low-priority use of remittances in migrant-sending villages and that “everyday [consumption] needs often absorb 90 percent or more of a village’s remittances”.  One study cited in Chandavarkar (1980:39) concluded that remittances are “frittered away in personal consumption, social ceremonies, real estate, and priceescalating trading”.
port, airport, or more generally UN/LOCODE.

- Time of the event

- Type of the event (structured categories, specific to the dataset): e.g., container loaded on ship, parcels arriving at the bureau of destination, port arrival etc.

The individual voyage statistics are sliced to create lead time between two consecutive events either at the same location (e.g., container stay or dwell time at a port) or at two different locations. The data is then combined across voyages going through this location (if the consecutive events are at the same place), or through a pair of locations (if the consecutive locations are different). The data is processed according to a growing level of aggregation. First the information is aggregated by trade lane: Statistics of lead time of a shipment or cargo unit from origin to destination or key logistics steps, for instance dwell time at gateways (ports, airports, postal bureau) at origin or destination. The indicators in the LPI 2023 have been decided with the data partners depending on the ease of interpretation for final users.

12
benefit from interventions. It can also inform product design and policies to ensure that available financial services reflect the abilities and needs of the target audience.

Second, we document and examine the reasons the unbanked give for not having an account. In addition, for a subsample of 25 Sub-Saharan African countries, we examine why unbanked adults do not have a mobile money account. Understanding why people do not have an account could help in designing targeted financial education policies focused on expanding access to formal financial services.

Third, we document the reasons adults who have an account give for not using their account. As with the unbanked, understanding why people _with_ an account choose not to use their account can help target financial education on the benefits of formal financial services.

Our findings can be summarized as follows. The Global Findex 2021 data on who the unbanked and underbanked account holders are, combined with insights into the barriers they face, point to the role financial education can play in building financial aptitude as a key strategy for increasing financial inclusion. In particular, we document that 1.4 billion adults—or 24 percent of adults—worldwide remain without a financial account, making them _unbanked_ . Seven countries are home to more than half of the world’s unbanked population. We also show that unbanked adults are significantly more likely to belong to typically underserved groups, such as women, poor adults, less educated adults, and young adults. The data on self-reported reasons for not having an account show that a lack of financial abilities seems to constitute an important barrier.

Furthermore, in two case studies we examine how adults with an account use it. We consider India, which, at 35 percent, has the world’s highest rate of inactive accounts—seven-times the 3
heat wave of May 2010 (Azhar et al 2014). The action plan – which combines short-run actions to prevent deaths during an active heatwave with long-run investments in cooling city spaces through vegetation and building design – was credited with averting some 1,200 deaths per year in the city in the years immediately after its introduction (Hess et al, 2018). Its model of a ‘threshold-based’ heat action plan, in which localized temperature thresholds based on all-cause mortality data are used to trigger sector-specific actions in the health system, labor practices and public communications (among other domains), was recognized by the National Disaster Management Agency (NDMA) as a model for the development of city-wide heat action plans across the country.

A key feature of city Heat Action Plans as implemented in India and internationally is the large number of stakeholders that need to be involved for their successful implementation. Given the diverse physical and social settings in which heat stress affects humans – including homes, workplaces, streetscapes, transport settings, and public amenities such as hospitals and schools – agencies responsible for city planning, transportation and buildings management may have capacity to reduce heat within their sphere of influence. During a heat emergency, stakeholders that have points of contact with vulnerable individuals include city health departments, health care providers, employers, labor unions, community associations, NGOs and media (Knowlton et al. 2014). Adding complexity to the development of Heat Action Plans is the range of policy instruments that vary from direct governmental interventions (e.g., publicly funded heat wave alert system, provision of emergency medical care, green spaces) to mandatory regulations (e.g., building code amendments) as well as incentive-based and information-based interventions (e.g., subsidies for cool roofs and public awareness campaigns on staying healthy during extreme heat).

Studies have established heat mitigation benefits of (i) ‘place-based’ interventions such as urban greening and cool building materials, (ii) ‘people-based’ interventions such as heat health
# **Figure 1: Stochastic Dominance and Food**<sup>**Stamp Program**</sup>

<!-- Start of picture text --> Poverty Incidence Curves (year 1)<br>Cumulative<br>Percent of<br>Population<br>k=0.25z k=z k=2zIncome<br>(a) Poverty Incidence Curves, with and without FSP transfers --FOD<br>Cumulative<br>Area under<br>Poverty<br>Incidence Poverty Gap Curve (year 1)<br>with FSP<br>Shaded area in<br>panel (a) above -<br>Income<br>k=0.25z k=z k=2z<br><!-- End of picture text -->

- (b) Poverty Gap Curve with FSP transfer--SOD

Source: Adapted from Ravallion (1992).

13
.

- Berlinski, Samuel, Matias Busso, Taryn Dinkelman, and Claudia Martinez. 2016. 'Reducing parent‐school information gaps and improving education outcomes: Evidence from high frequency text messaging in Chile', _Unpublished Manuscript_ .

- Botea, Ioana, Shubha Chakravarty, Sarah Haddock, and Quentin Wodon. 2017. "Ending Child Marriage: Reproductive Health Outcomes and Delaying Marriage and Childbearing for Adolescent Girls " In.

- Burde, Dana, and Leigh L Linden. 2013. 'Bringing education to Afghan girls: A randomized controlled trial of village‐based schools', _American Economic Journal: Applied Economics_ , 5: 27‐40.

- Cárdenas, Sergio, David K Evans, and Peter Holland. 2015. 'Early Childhood Benefits at Low Cost: Evidence from a Randomized Trial in Mexico ', _Unpublished Manuscript_ .

- Castro Martin, Teresa. 1995. 'Women’s Education and Fertility: Results from 26 Demographic and Health Surveys', _Studies in Family Planning_ , 26: 187‐202.

- Chaudhury, Nazmul, Jed Friedman, and Junko Onishi. 2013. 'Philippines conditional cash transfer program impact evaluation 2012', _Manila: World Bank Report_ .

- Conn, Katharine M. 2017. 'Identifying effective education interventions in sub‐Saharan Africa: A meta‐ analysis of impact evaluations', _Review of Educational Research_ , 87: 863‐98.

- Dhaliwal, Iqbal, Esther Duflo, Rachel Glennerster, and Caitlin Tulloch. 2013. 'Comparative cost‐ effectiveness analysis to inform policy in developing countries: a general framework with applications for education', _Education Policy in Developing Countries_ : 285‐338.

- Di Gropello, Emanuela, and Jeffery H Marshall. 2011. 'Decentralization and educational performance: evidence from the PROHECO Community School Program in rural Honduras', _Education Economics_ , 19: 161‐80.

- Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2017. 'The impact of free secondary education: Experimental evidence from Ghana', _Massachusetts Institute of Technology
# **2.3 Setting**

The 12 middle-income countries included in this study were selected based on three key criteria: the share of GDP that is spent on energy subsidies, the number of adults with internet access, and the availability of recent fiscal incidence analysis based on detailed household survey data. The starting point for the selection process was the complete set of low- and middle-countries that spent at least 1 percent of GDP on energy subsidies for consumers in the most recently available country-level data. Given that this is an online study with a large sample size, it was also necessary to restrict the set of countries to those with at least 5 million people aged 18 years or older with internet access (this threshold is based on previous experience collecting similar sample sizes online, such as Hoy [2022]). Finally, in order to include an information treatment about the degree of regressivity of energy subsidies, it was necessary to focus on countries where fiscal incidence analysis has been conducted using recent household survey data that broadly reflect the current distribution of energy subsidies in that country (this led to the elimination of several large countries, including India and Iran where such analysis was not available).<sup>7</sup> Collectively, the final 12 countries’ spending on fossil fuel subsidies was almost US$200 billion—around a quarter of all fossil fuel subsidies in middle-income countries in 2022.

The 12 countries fall into two groups based on the main energy source that is subsidized (see Table 1). In seven of the countries (Angola, Argentina, Bolivia, Ecuador, Indonesia, Kazakhstan, and Nigeria), fuel is the main energy source that is subsidized (IMF 2023; IEA 2023). In the remaining countries (Bangladesh, Egypt, Ghana, Pakistan, and Vietnam), electricity is the main energy source that is subsidized (IMF 2023; IEA 2023). While this distinction between groups of countries is an important one, there is notable variation within these groups
the individual started in 2008 than to change socioeconomic status. A quarter of individuals moved into 5 As seen above (Table 1), classifications of workers based on SISBEN and poverty lines differ substantially (even though trends move in similar directions) given that the official poverty lines are well below the median of per capita household incomes in the sample: COP 281,384 and COP 850,999 respectively for 2009. Also, the estimated poverty incidence from the FSLS sample, at 70.5 percent, doubles the official poverty incidence for the 13 metropolitan areas, estimated by MESEP (2010) as 30.6 percent.

15
Classification (ISIC) – of an individual’s sector of employment, with tariffs faced in India's major export markets at the Harmonized System subheading (6-digit) level.<sup>7</sup> The same concordance was used to determine the variability of NTMs imposed on imports from India by the same top importers for the same reference year across different sectors.<sup>8</sup> The number of NTMs applied to India in this analysis includes those applied by these partners on an MFN basis, e.g. those NTMs that are not targeted specifically at India but at all exporters to that country.

In the household surveys under consideration, an individual’s sector of employment is based on activities pursued during certain specified reference periods: one year, one week and each day of the reference week. The activity status determined on the basis of each reference period is referred to as ‘usual status’, ‘current weekly status’ and ‘current daily status’, respectively. The ‘usual status’ approach is generally preferred as it is based on a relatively longer time horizon. Nonetheless, the current weekly status approach is important in any analysis of wages because these are expressed in weekly terms. Each approach is guided by the ‘majority time spent’ criterion. For instance, under the ‘usual status’ approach, an individual is considered ‘employed’ in a particular industry if he or she spent the majority of his or her time in the last 365 days on that economic activity, rather than being unemployed or engaged in non-economic activities.

The paper uses two correlated data sets to identify the relevant sector of employment.

1. Household level data where the sector of employment is based on ‘usual status’ of the household head with the corresponding household weekly consumption, which was used as proxy variable for income.

2. Individual-level data based on members within the household reporting an economic activity and corresponding income from such activity during the reference week.

The advantage of using
the average annual fall in yields due to saline waters. Impacts on crop production are seen at relatively low levels of salinity and tend to rise at a near linear rate as water salinity rises. A satellite‐based measure of agricultural productivity, combined with estimated elasticities of agricultural yields to saline surface water, and globally modeled data on water salinity reveal significant annual losses in food production. A simulation of global food losses due to salinity is performed by training a predictive model of EC at a 0.5 degree grid between 1992 and 2013, and by combining these predictions with the elasticities we estimated. It is estimated that water salinity reduces global agricultural production by 124 trillion kilocalories per year, the equivalent annual food budget of 170 million people. These results suggest that saline surface and irrigation water are a global concern for food security that is underappreciated and deserves greater attention.

The relationship between saline water and agricultural production is well known and documented as far back as ancient Sumer (Thompson 2004). Salt can interfere with crop growth in a variety of ways; by displacing other important nutrients like nitrogen and phosphorus, interfering with photosynthesis and chlorophyll production, withdrawing water from surrounding soil, and increasing plant energy requirements for extracting water from soil (Warrence, Bauder, and Pearson 2002). Crops are not universally or uniformly impacted by salinity. Plants that have higher Na<sup>+</sup> and Cl<sup>−</sup> concentrations in their leaves tend to be more salt‐tolerant as it reduces the amount of salt ions that enter plant cells through osmosis (Munns and Gilliham 2015). Recently it has been estimated that approximately 1,125 million hectares of land are salt‐affected, of which approximately 76 million hectares have been salinized by human‐induced activities (Sanower 2019).

2
Breloff (2010). Gutierrez and Singh (2013) also provide evidence of a positive association between mobile money usage and aspects of the regulatory regime related to e-contracting, consumer protection, and interoperability, among other factors, for a sample of thirty-five countries. On the benefits of digital identification systems, and biometric identification in particular, in contexts where human capabilities (illiteracy and low levels of financial/digital literacy) constrain financial inclusion, see Muralidharan, Niehaus, and Sukhtankar (2016).

> 6 Individual education and income were also significantly associated with broader measures of financial inclusion in studies that used Findex data (see, e.g., Allen et al., 2016; Deléchat et al., 2018).

5
a particular age, than women born in the preceding years. The gap is however almost negligible.

Agency, and more generally women’s empowerment, is influenced by age and education differentials among spouses.<sup>21</sup> The two middle panels of Figure 4 show these differentials, which are calculated by subtracting the years of schooling and age of a woman from the years of schooling and age of their spouse. In most countries, a positive gender gap in education remains between men and women across time but for more recent cohorts in Bangladesh and Maldives, the gap is negative. The reversing of the gap in Bangladesh is encouraging and may be a consequence of targeted policies for girls’ education. The

> 19 Questions included here are those referring to who has a say on (i) large household purchases, (ii) woman’s health care, and (iii) women’s visits to relatives and friends. Possible answers are: (a) respondent alone; (b) respondent and husband/partner jointly; (c) husband/partner alone; (d) someone else; and (e) other. Having a say is defined as choosing answers (a) or (b).

> 20 The age profiles of agency in Bangladesh and India show that women older than 30 years have higher agency within their households on average than women less than 30 (Figure A4 in the Annex).

> 21 See for example Carmichael, 2011 and Mogford, 2011, Nyyssola 2007 for different implications of the age and education gaps among spouses.

18
measures and controlling for firm characteristics, we evaluate the importance of access to finance for firms’ employment growth. We find that access to different forms of finance matters. These results from our cross-country firm-level analysis suggest that governments seeking to improve the business environment and promote firm growth should make financial sector reforms a priority.

Objective business conditions vary systematically across firms of different sizes and ages, and good business conditions favor smaller firms, especially micro firms. Micro and small firms gain the most from access to finance in forms ranging from simple to more sophisticated—from a loan or overdraft facility to sales credit to external finance for investment. This finding holds not only globally but also for different regions. While sales credit is important only for micro and small firms, having a loan or overdraft facility and receiving external finance for investment promote employment growth for firms of all sizes across regions. And sales credit and external finance matter for firms of all ages.

The firm size distribution is skewed toward smaller firms. The skewness declines with firm age—and is more present in Africa, among firms that are credit constrained, and among those that perceive access to finance as a serious obstacle. These findings call for policies favoring the development of the financial sector, which can help small firms grow into medium-size and large firms.

The findings have several implications for developing countries. First, because the constraints faced by firms differ across countries and, within countries, across sectors, policies to promote firm growth need to be tailored to each country and sector. Second, finance appears to be the most binding constraint across sectors and countries, suggesting that reforms in this sector could yield broad benefits—including by helping to address the problem of the “missing middle” in developing countries. Third, while access to finance is a binding constraint on firms’ growth in all developing countries, the fact that industrial development in some countries took off faster and sooner than others indicate that there are other constraints that are not captured by the Enterprise Survey.
Item**<br>Share Wave 2|**s**<br>Share Wave 2<br>Imputed|
|---|---|---|---|---|---|---|
|Core|38|94%|79%|29|79%|47%|
|Module 1|21|2%|8%|14|5%|14%|
|Module 2|18|2%|6%|15|6%|15%|
|Module 3|19|1%|5%|16|7%|18%|
|Module 4|18|1%|4%|15|5%|11%|

Source: Authors’ calculations based on SHFS Wave 2.

> 7 The list of consumption items used in wave 2 of the SHFS is discussed in section 3.1.

> 8 Generally, previous consumption surveys in the same country or consumption shares of neighboring or similar countries can be used to estimate food shares. In the worst case, a random assignment results in a larger standard error but does not introduce a bias. The assignment of items to modules is very robust and, thus, even rough estimates of consumption shares are sufficient to inform the assignment without requiring a baseline survey.

18
27

# <u>Figure 1. Types of household survey datasets used in analysis of out-of-pocket health expenditures</u>

<!-- Start of picture text --> Household Budget Surveys<br>Household Income &<br>Country‐specific surveys<br>Expenditure Surveys<br>Health Expenditure<br>Surveys<br>CWIQ (Core Welfare Indicators<br>Questionnaire)<br>E123 (Enquêtes 1‐2‐3)<br>LSMS (World Bank Living<br>Standardized ex ante Standards Measurement<br>Study)<br>MCSS (WHO Multi‐Country<br>Survey Study on Health and<br>Responsiveness)<br>WHS (WHO World Health<br>Survey)<br>Multi‐country collections<br>Eurostat HBS collection<br>Luxembourg Income Study<br>Harmonized ex post<br>World Bank regional<br>collections, e.g. ECAPOV<br>World Bank other collections,<br>e.g. SHES<br>Household surveys<br><!-- End of picture text -->