rafmacalaba/data-use-mentions-extended huggingface dataset:
increased educational attainment. We have used several data sets to explore how these two factors influence support for addressing a global externality.

By estimating international Engel curves for energy intensive durables, we document that the growth of the urban middle class will increase electricity demand and that the grid around the world continues to be carbon intensive. Using data from California, we have documented that even in this rich, progressive state the vast majority of vehicles are gasoline fueled. Even in richer zip codes, the electric vehicle percentages are quite low. Unlike for local pollutants such as PM2.5, there is little evidence of an inverted “U” shape between carbon dioxide emissions and income. Richer people produce more greenhouse gas emissions in the absence of a global carbon treaty. Within Europe, we document that there are many cities featuring a high carbon average footprint. Such area’s middle class is likely to oppose carbon pricing. Those high carbon areas will feature elected officials who will be unlikely to embrace the green economy agenda. In this sense, our paper pinpoints pockets of “green resistance”.

In the final section of the paper, we use global micro data from the WVS to explore the correlates of environmental concern. Younger, more educated people are more likely to prioritize environmental protection. Such politically involved demographic groups help to overcome the free-rider problem that no one voter feels that her own activism matters.

Future research should explore how a low-carbon coalition engages in persuading the emerging middle class to join their coalition. <mark>Stavins (2022) posits that more social education on the risks of climate change can increase demand for carbon mitigation policies. The role of social media and salient events such as natural disaster shocks in stimulating short run demand for public safety investment merits more research (DellaVigna and La Ferrara 2015, Deryugina 2013).</mark> 15
the census contains information on the year individuals arrived in each location to account for insample migration in 1980 and 1985 in Columns 3–4 and 5–6. Results do not change. In addition, rates of migration across the boundaries are very low (less than one percent).

Figure 5. Effects of Guerrilla Control on Education Outcomes of the Nonmoving Population Only <!-- Start of picture text --> (a) Wealth Index (b) Average Years of Education<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km)<br>(c) Literacy Rate<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2
over the course of the 3-year project. Among other things, An Thinh shared their customer billing records, which include monthly water usage (in m<sup>3</sup> ) and payment, from the month of connection onward, including throughout the price experiment.

**Household listing** An exhaustive door-to-door listing in the three villages was completed in mid-2015, yielding a total of 1660 households, which includes multi-family living arrangements

5
# Networks, ...rms, and trade<sup>�</sup>

Paulo Bastos<sup>y</sup> Joana Silva<sup>z</sup> Keywords: Networks; gravity; export participation and intensity. JEL classi...cation: F1; F2; L1; L2

> �We thank two anonymous referees, Jonathan Eaton, Ana Margarida Fernandes, Caroline Freund, Bernard Hoekman, William Maloney, Caglar Ozden and participants at the Midwest International Economics Group Spring Meetings in Iowa, and the LACEA annual meetings in Buenos Aires for valuable comments and discussions. Joana Silva thanks Instituto Nacional de Estatística for providing access to the micro data employed in this paper.The views expressed in this paper are those of the authors and should not be attributed to the World Bank.

> yWorld Bank.

> zWorld Bank.

1
# **The Utilization-adjusted Human Capital Index (UHCI)**

Steven Pennings (spennings@worldbank.org)<sup>1</sup>

**JEL Codes:** J24, I1, I2, O4

**Keywords** :  human capital, labor market utilization, employment, health, education, development accounting

1 Any views expressed here are the author’s and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent. This work is a background paper for the 2020 HCI update. UHCI and utilization data can be downloaded from <u>http://worldbank.org/humancapital I thank Aart Kraay, Roberta Gatti, Michael Weber, Kathleen Beegle,</u> David Weil and a number of other reviewers for helpful comments. I am also indebted to Michael Weber for providing updated data from the JOIN database which splits employment by both status and economic activity.
0.08|
|Buy livestock|0.35|0.17|0.56|0.08|0.10|0.05|
|Start off-farm business|0.04|0.01|0.10|0.08|0.08|0.05|
|Buy food and other consumption items|0.08|0.01|0.16|0.17|0.18|0.16|
|Buy construction materials|0.03|0.01|0.06|0.07|0.02|0.14|
|Pay for health and education expenses|0.01|0.01|0.01|0.16|0.09|0.30|
|Other: travel,wedding,funeral,etc.|0.03|0.01|0.04|0.17|0.15|0.22|
|Number of households|352|189|163|105|68|37|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07. **Note:** None of the sampled households applied for a loan in the formal sector during the time to the survey.

16
approximately 72,000 children were unable to write their examinations in the North (UNICEF, 2005).

A recent study by UNESCO (2010) used 2006 Multiple Indicator Cluster Survey (MICS) to conduct a quantitative study on the relationship between education and war in Côte d‟Ivoire. This study finds an increase in the uneducated proportion of male cohorts in war-affected areas. Looking separately at the educational attainments for males and females, it concludes that for both genders the average educational attainment has dropped since the conflict broke out. To our knowledge this is the only quantitative study so far that examined the impact of war on education in Côte d‟Ivoire. However, this study does not draw any causal inference on the potential impact of war on education. In addition, the MICS survey was undertaken in 2006 just after conflict had reached its peak, and as a result it might not have demonstrated the full impact of war.

This study aims to bridge this knowledge gap. We calculate the average causal effect of civil war on education in Côte d‟Ivoire using the Household Living Standards Survey (HLSS) data collected in 2008 and the data on local incidences of conflict taken from the Armed Conflict Location and Event Database (ACLED). We use a number of empirical strategies to identify the causal effect of war on education for the school-going age-cohort. First, we use year and department of birth to determine an individual‟s exposure to war. The difference-in-difference outcomes indicate that the average years of education for individuals aged 10 to 22 is .94 years fewer compared to the individuals aged 23 to 32 in war-affected regions. However, the underlying assumptions for difference-in-difference estimation can possibly be violated in the present study by several factors - internal migration, heterogeneous selection into victimization within regions and varying intensity of conflict across regions. These could be potential sources of bias in the estimated causal effect.

The causal inferences on the impact of war can also be affected if education itself worked as a catalyzer of war. Moreover, there
from the GQ network experienced substantial increases in entry levels and higher productivity. Dynamic specifications and comparisons to the NS-EW highway system mostly confirm these conclusions, with the most substantial caveat being that the productivity gains may be upwardly biased by a pre-period dip. The GQ upgrades also appear to have facilitated a more natural sorting of industries that are land and building intensive from the nodal districts into the periphery locations; the upgrades also appear to be encouraging decentralization by making intermediate cities more attractive for manufacturing entrants.

There are several points of future research that we hope to undertake. First, we are continuing to examine the extent to which the GQ project improved the allocative efficiency of the manufacturing sector in India. Given the high levels of misallocation with which India is starting, improvements in allocation are most important. Second, we intend to study next the impact of the GQ upgrades on the unorganized sector. Ghani et al. (2012) highlight the extent to which the organized and unorganized sectors are moving in different directions within India, with the unorganized sector becoming more urbanized, and we need to better understand the role that infrastructure connections across districts play in this process. This work will also examine issues like the gender of business owners to understand how improved highways affect sub-groups of the population differently. Finally, looking beyond the manufacturing sector, it will be very interesting to use satellite-based data to examine the aggregate economic outcome associated with these upgrades.

21
a consultation.<sup>16</sup> For these, we set the value for OOP healthcare expenditures outside the context of a consultation to zero to avoid double counting these expenditures.

All the raw variables in GLSS 2016/17 have some very large positive outliers. For each of the raw variables and again for our calculated total OOP healthcare expenditures, we replace any value that is larger than the respondent’s household consumption (not per capita) or five

> 12 These are questions 9 through 12 and question 13 in Section 3A of the GLSS 2016/17 questionnaire. We have excluded question 14 (“any other services”) and question 15 (“travel”) from our total out-of-pocket healthcare expenditures as these are not healthcare expenditures per se.

> 13 Question 20 in Section 3A of the GLSS 2016/17 questionnaire.

> 14 Question 22 in Section 3A of the GLSS 2016/17 questionnaire.

> 15 Question 23 in Section 3A of the GLSS 2016/17 questionnaire.

> 16 Questions 12 and 22 in Section 3A of the GLSS 2016/17 questionnaire, respectively.

8
% for areas.

**C3. City per capita income measure** To study how city stocks vary with respect to income, we use the 2015 city GDP data from Chen et al. (2022). More precisely, they provide high-resolution “real GDP” data (in 2017 dollars) based on (not top-coded) night lights data as reported by the NPP/VIIRS satellite series. While night lights data could capture infrastructure (including electrification) and past economic development in addition to current economic development as infrastructure and urban structures are durable, we use this data in the absence of better data on city incomes for the whole world. To obtain a measure of city GDP, we rely on the total sum of lights within each city’s boundary as defined by GHS-UCDB (Florczyk et al., 2019). Finally, we use log(city GDP) / log (city pop.) as our city per capita income measure instead of log (city GDP / city pop.).

Indeed, the latter measure does not, in our view, properly capture city incomes due to nonlinearities in the relationship between lights and population. As shown for 324 U.S. metro areas in Appendix Figure C.5, log (city GDP / city pop.) decreases with city population size for larger cities, for example due to street lighting being a non-rivalrous good. Indeed, the use of street lighting by some individuals does not diminish its availability to other individuals. As such, the number of street lights per capita should increase less than proportionally with respect to population. There could be of course other reasons. In contrast and as can be seen in Appendix Figure C.5, log (city GDP) / log(city pop.) increases (slightly concavely) with city size.

# **C4. Relation between city per capita income and city population size**

As shown in Appendix Figure C.5 for 324 U.S. metro areas, city
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Tariff information is provided via <u>Taric code, and Taric description. Information on quotas in</u> force <u>via origin or order number.</u
# **1 Introduction**

“Zero Hunger” is the second of the 17 United Nations (UN) Sustainable Development Goals adopted by all Member States in 2015. Achieving this target by 2030 is a central challenge, as the UN Food and Agricultural Organization (FAO) reports more than 2 billion people are currently estimated to suffer from hunger or food insecurity (FAO, 2019). Globally, more than 820 million people are undernourished. About 20% of those undernourished are located in Africa, in particular the Sub-Saharan Africa region, which has also experienced the fastest rise of malnutrition (FAO, 2019). The topic has gained renewed attention due to famines in Somalia in 2011-12 and the “four famines” (Maxwell et al., 2020) that took place in the Republic of Yemen in 2016 and South Sudan, Somalia, and northeast Nigeria since 2017. In their recent report, the World Food Programme (WFP) stresses the additional burden the COVID-19 pandemic imposes on already acutely food-insecure populations (WFP, 2020). As of the time of writing, the FAO raised a food crisis warning as locusts swarms are ravaging crops and pasture in East Africa (FAO, 2020).

In the fight to eradicate hunger, some major recent developments were made in the measurement and classification of food insecurity. For instance, the Food Insecurity Experience Scale (FIES) used in the SDGs distinguishes between three levels of severity: food security or mild food insecurity, moderate food insecurity, and severe food insecurity. In the latter stage, people have typically run out of food and have not eaten for one or more days. Different from this survey-based approach, the Integrated Food Security Phase Classification (IPC) system was developed to support decisionmaking with a clear analytical focus. The Food Security and Nutrition Analysis Unit (FSNAU) of the FAO pioneered this system in Somalia in 2004. Since then, the IPC framework has been adopted by various international organization and humanitarian response agencies. In our study, we rely on
"spacers"); and those who wish to prevent additional pregnancies ("limiters"). Overall, there are more "limiters" than "spacers" in developing countries. WFS/CPS data demonstrate that about one quarter of contraceptive users in Asia we--e "spacers" versus about one-third in Latin America and the Middle East. In kfrica, however, three-quarters of the relatively small number of users were "spacers" (Population Reports, 1985).

Ag. Current use of contraception is highest among women aged 30-39 (Population Reports, 1985) and is lower among women in their forties and twenties. The differentials are relatively small, however. In 16 selected developing countries in the mid- to late-seventies, prevalence rates were 37, 47, 51, 48 and 48 for the 5-year age groups between 20 and 44 years of age (Population Encyclopedia, 1982). (No African country was included in this analysis). Use is least common among the youngest women, increases durinLg the middle to late childbearing years, and then declines among the oldest women (Population Reports, 1985). Women aged 40-44 are less likely to use contraception than younger women, especially in Latin America. On the positive side, family planning programs are reaching many women in their thirties, who generally speaking, are at greater risk than those in their
the construction of the cohorts for analysis, and addresses the assumptions and concerns underpinning the construction. Girls are included in the treatment cohorts if they live in the stipend districts and in the comparison cohorts if they live in non-stipend districts. In addition, both groups have to meet one of the following criteria to ensure that the analysis is restricted to the relevant girls, namely those who could enroll in middle school for at least one year during the period of study: (a) girls who were enrolled in grades 7-12 in 2007-08 and/or grades 6-11 in the previous year (2006-07) are assumed to have been exposed for at least one year to the program; (b) for the rest of the girls who were enrolled in neither 2007-08 nor 2006-07, only those whose highest grade completed is at least grade 6 and at most grade 10, and who are likely to have been enrolled in a middle school grade between 2003-04 and 2005-06 (based on the age-grade distribution of the reference cohort at baseline) are assumed to have been exposed for at least one year to the program.

We use the household data to estimate the influence of the program on such longer-term outcomes as educational attainment and labor market, marriage, and fertility decisions. The general specification of the base regression equations for the DD analysis is as follows: where _i_ indexes individuals, _t_ is the time subscript, _T_ is a dummy variable taking a value of 1 for post-program year and a value of 0 for the pre-program year, _FSSP_ identifies the location of the girl between stipend and non-stipend districts, and X is a vector of baseline household demographics, community-level characteristics, and measures of education market place and school quality. _P_ is a vector of dummy variables to capture other programs that were introduced by the GoP along with the FSSP and sought to build school facilities, provide free textbooks, recruit contract teachers, and revitalize school councils.<sup>21</sup> The outcomes of interest ( _Y_ ) measure educational outcomes (middle school
The multi-country studies that exist show relatively high levels of hesitancy to take the COVID-19 vaccine, but many of them focus on high-income countries. A systematic review and meta-analysis of 28 large (>1,000) nationally representative samples in 13 high-income countries collected between March and October 2020 found, across all 13 countries, that 72.9% of the sample intended to vaccinate (95% CI: 66.6% to 78.4%) (Robinson et al. 2021). The study also found that as the pandemic progressed (between March and October 2020), the percentage of people intending to vaccinate decreased and the percentage of people intending to refuse vaccination increased, although intentions varied between samples and countries. Another study of nationally representative surveys from mid-2020 in 19 countries, including 12 high-income countries (HICs), found an average across countries of vaccine acceptance rate across the 12 HICs of 66.6%, with wide variation ranging from 80% in the Republic of Korea to 55% in Russia (Lazarus et al. 2021). Both studies observed that being female, younger, of lower income or education level, and belonging to an ethnic minority group was consistently associated with being less likely to intend to vaccinate.

Less is understood about the levels and reasons of COVID-19 vaccine hesitancy in low- and middleincome countries. A few studies reported multi-country survey results from developing countries (de Figueiredo and Larson, 2021, Facebook 2021, Gallup 2021, Kanyanda et al. 2021, Lazarus et al. 2021, Solis Arce et al. 2021, and Wouters et al. 2021) or systematic reviews of individual country surveys (Sallam 2021), but few of the studies were based on nationally representative samples of respondents or included comparable results for many countries. Taken together, the available literature from low- and middleincome countries suggests that vaccine acceptance is higher in East and South Asia and Latin America and lower in the Middle East and Eastern and Central Asia. Results from Africa were mixed, as
We attempt to overcome some of the issues discussed above with the use of cross-sectional data at the school and household levels from 2003 through 2009 and quasi-experimental methods. More specifically, the analysis contrasts cohorts of girls in treatment and control districts in double-difference and RDD frameworks.<sup>16</sup> While double-differencing compares the changes in the outcomes of analysis between stipend and non-stipend districts overtime, RDD exploits the literacy rate cut-off for program eligibility across districts. The RDD models are also implemented within a DD framework as done by Chaudhury and Parajuli (2008). Therefore, both models compare the trends in stipend and non-stipend districts, which helps address possible selection bias introduced by time-fixed characteristics related to nonrandom program placement. In order to check the sensitivity of the findings to the issue of participation bias, additional models were estimated with samples that do not condition on enrollment in middle school.<sup>17</sup> The validity of the identifying assumptions of the DD models and the implications on the expected selection on returns to schooling are discussed in the robustness section.

We construct treatment and comparison groups of girls at both the school and household levels. At the school-level, the analysis draws upon administrative data from school censuses to estimate the annual average changes in total female enrollment at public schools in stipend districts (treatment schools) for the period between 2004 and 2010 relative to the pre-program level (2003-04 academic year). These changes are then compared with the corresponding annual changes in female enrollment observed for public schools in non-stipend districts (control schools) during the same period. This approach is limited, however, by the fact that schools are the units of analysis, and it is not possible to tease out other behavioral responses regarding the investments in the human capital of girls.

To overcome that, we also use household surveys to estimate changes at the individual level. The research design for this approach follows for the most part the same identification strategies and assumptions as the school-level analysis. However, the richness of information
all levels, distributing social assistance from the commune- or village level down to poor households would still be an improvement from the current provincial level.

# **8. Conclusions**

This paper combines the 2009 Vietnam Population Census and the 2010 Vietnam Household Living Standard Survey to estimate poverty and inequality indexes for all the

> 9 See for example the World Bank Policy Research Report, “Localizing Development: Does Participation Work?” (World Bank, 2012).

29