rafmacalaba/data-use-mentions-extended huggingface dataset:
2021b). Consumption data is available for 148 countries from 2000 to 2019, which restricts the time dimension of the combined dataset for this study.

Overall, the unbalanced panel data we use contains 133 countries over a 20-year period (2000–2019), totaling 2,465 observations. Table 1 shows the summary statistics of the variables for 2017.

**Table 1. Summary statistics for 2017 of the variables used in this study**

||**Mean**|**Standard deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|**PM2.5**|16.84|15.54|1.27|90.45|
|**Gasoline price**|0.99|0.38|0.11|1.92|
|**Diesel price**|0.83|0.45|0.00001|2.32|
|**Coal price**|3.68|1.55|0.21|22.64|
|**Gasoline consumption per capita**|192.20|234.01|2.81|1,245.44|
|**Diesel consumption per capita**|344.19|478.16|2.37|4,619.94|
|**Coal consumption per capita**|256.20|448.54|-2.41|2,513.82|
|**GDPper capita**|22,099.75|22,366.75|773.57|126,183.70|

# 3.2. Estimation methods

We model the relationship of PM2.5 concentrations, fuel prices, fuel consumption, and GDP per capita as follows: PM2.5i,t = αi + β1
the labor force) in Lebanon reported having worked in the last week; while in our out-of-camp Jordan sample, employment rates were the highest as a share of labor force participation at 69 percent, but still accounted for only about one-fifth of the 11 Overcrowding and the quality of housing emerged frequently in focus group discussions of how refugees are living in the host countries. For instance, in all four focus groups conducted in Kouachra, a rural site in the North governorate of Lebanon, most of the respondents said that they lived with their families in “storage rooms.” Each storage room is about the size of a one-car garage, and there are several such structures constructed one beside the other, usually on the side of the road, that were originally built for purposes other than human habitation. Some examples of statements were: “Ten of us live in one room.” [ _Female, Beirut_ ]; “Rentals start from $200 for a one-room house. My whole house in narrower than this room. I live in it with my 3 children.” [ _Male, rural site, Mount Lebanon_ ]; “Some of us had to live in a hen coop. We really need houses. We have no electricity and no water. The room is composed of four walls and a roof; nothing more nothing less.” [ _Female, Rural area, North governorate_ ]. “I live in a one-room basement. The bathroom and kitchen are inside the room. There are bad odors, high humidity, and mold. The doctor told me while treating my son for asthma that I should move to another home. But I don’t have the money.” [ _Female, rural site, Beqaa governorate, Lebanon_ ] 12 A 2014 UNHCR survey finds that rents are among the largest expenses for refugees, representing 50 percent of monthly refugee household expenditure (UNHCR, 2014).

13 Apart from camp sites, respondents across all qualitative study sites in both Lebanon and Jordan complained about being exploited by
set of countries being measured. To ensure availability and comparability, it is best to develop a set of indicators among the countries that are the most resource deprived first and expand from there. To maintain consistency with the literature, this paper selects indicators that are widely cited for each of the dimensions and listed in table 1.

# **d. Data limitations and dimensions considered in measuring job quality**

Given the tension described above between the completeness of data for one country versus producing comparable measures of job quality across the developing world, the approach as laid out in the methodology section follows Brummund, Mann, and Rodríguez-Castelán (2018) and develops a practical measure based on information available for most countries instead of limiting this study to a few countries with rich data. Data availability on all four dimensions of job quality varies across countries because labor force surveys or labor modules in household surveys reflect the labor market and regulatory environment of a country. For example, economies with a mainly casual labor force use more questions to understand the hazards and working conditions of employees, while countries with strong regulatory frameworks and a mostly formal labor force provide detailed information on benefits and income, but less on working conditions.

Coverage of the four dimensions also varies across employment types within countries. Surveys usually distinguish among four main employment types—wage employment, self-employment, unpaid employment, and other, such as apprenticeship—and divide the questionnaire into a comprehensive set of questions for wage employees and a short questionnaire for workers in all other employment types. In most countries, the coverage is therefore much more limited on the other employment types than on wage employees. Household surveys may provide income information for self-employed individuals in the earnings or household enterprise modules, but at the expense of a shorter labor module for wage employees. If both household and labor force surveys are available, the labor force surveys provide better coverage of the four dimensions even if only for wage employees.

Based on the sample of 40 developing countries used in the analysis, the
mean price of wheat increased from 8.20/kg in wave 1 to 10.48/kg in wave 3; the mean price for maize increased from 5.33/kg in wave 1 to 6.40/kg in wave 3. For purposes of our analysis, we compute a yearly average price for each of the three crops at the zone level.<sup>1213</sup>

# **4. Empirical Approach**

To assess how food prices affect child undernutrition, we estimate the following equation: Where Y�� is the outcome indicator for child _i_ at time _t_ in terms of the z score for height for age and weight for height;<sup>14</sup> X�� is a vector of individual characteristics; X�� is a vector of household characteristics; X�� is a vector for community characteristics such as road access and access to markets. Our variable of particular interest, P��, is the price for each crop at the district/zone level within each region in Ethiopia while γ� are region fixed effects<sup>15</sup> and ε�� is the error term _._ For stunting, which measures persistent long-term child undernutrition, the impact of changes in prices is likely to take time to be realized. We therefore lag the crop price by one period before merging again with wave 2 and wave 3 of the LSMS data set and include P���� in the model. For wasting, which captures more rapid weight loss, we use the contemporaneous price.

The vector of control variables is drawn from previous studies that seek to explain child undernutrition rates (see Cintron, Seff, and Baird (2016), Edris (2007), Alemayehu _et al._ (2015), Liben, Abuhay and Haile (2016)). Specifically, we control for individual, household, and community factors that are associated with individual nutritional outcomes. Individual variables include a
real panel data is typically less easily available repeated cross-section datasets, researchers have determined the conditions under which the latter can produce unbiased estimates equivalent to the ones based on estimating genuine panels. The pseudo-panel methodology in which repeated cross-sections are treated as panels was introduced by Deaton (1985) and consists of estimating economic relationships based on cohort means rather than individual observations. This approach is based on the assumption that the cohort population means are genuine panels in that, at the population level, the groups contain the same individuals over time. Applying the pseudo-panel analysis to our data we find our results to be robust to the changes in the methodology.<sup>29</sup> The successful

> 27 Jasso and Rosenzweig (1988), Borjas (1989) , Borjas and Bratsberg (1996), Hu (2000), Lubotsky (2007) 28 All the detailed information available in the census databases comes from the answers to the long-form questionnaires, which are filled out by one in six persons residing in the US.  The sample changes from census to census, making it impossible to track the same individuals over time.

> 29 We first focus on the 1975 arrivals, and used Education Index 1 as a measure of occupational placement. We construct cohorts - in the sense implied by the pseudo-panel theory- by country, period of arrival (1970-1974; 1975-1979), and education level (unskilled, representing individuals with  less than five years

15
Sample only includes individuals surveyed in Nepal DHS 2001 and 2006, aged 10 to 18 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either, but these districts did not experience any casualty during the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

40
# **1 Introduction**

Gender norms are often emphasized as key to help explain remaining gender gaps in the labor market (Bertrand, 2020; Goldin, 2021).<sup>1</sup> For example, the stereotypical gender roles that men should be the main income earners or “breadwinners” and women should primarily be responsible for the care of children and household chores or “caregivers” in the household are widely acknowledged normative constraints to women’s economic empowerment (Jayachandran, 2021). Despite the recognition of the importance of social norms for gender equality among economists and policy makers, very little development-funding is currently devoted to programs that directly influence norms-related change.<sup>2</sup> In this paper we collect and analyze data from 111 countries to characterize how gender beliefs and norms relate to economic behavior across the world. We use the findings to help highlight some potential entry points for policy to address gender norms.

Women’s economic decisions may be constrained by a range of social norms that influence the types of roles and responsibilities that are acceptable for men and women and uphold widely shared conceptions of masculinity and femininity (Bicchieri, 2016; Marcus and Harper, 2014). Akerlof and Kranton (2000, 2002, 2010) translated theories of social identity into an economics framework and propose that social identity influences economic outcomes because deviating from the prescribed behavior is inherently costly. Norms (often subconsciously) encourage behaviors that are socially valued and discourage behaviors that elicit social sanctions and stigma (Bernhardt et al., 2018). Gender norms are likely to significantly constrain women’s choices about whether and which types of work to pursue, and equally may prevent men from engaging in care and domestic chore activities.

Our interest in this paper is to contribute to a growing literature that examines societal norms as a barrier to female labor market outcomes (e.g. Bernhardt et al. (2018); Bursztyn and Yang (2022); Giuliano (2021); Jayachandran
production. Improved roads could reduce violence against civilians and generate taxable traffic that can generate longer-term revenue, through roadblocks for example, rather than through more violent, ‘roving bandit’ tactics. More work and access to more granular data will be needed to fully disentangle these effects.

Overall, our results suggest that roads do not affect violence only through one channel but might increase violence through increasing opportunities for attacks and raids while at the same time increasing stability by increasing state capacity or opportunity costs for violence. Such a complex role of trade linkages is consistent with recent theoretical work (Couttenier et al. (2023)).

# **6 Conclusion**

Understanding the impacts of roads on violence is important to better support the efforts of stabilization and reconstruction in conflict-ridden countries. The present paper quantifies the impact of road construction and rehabilitation on violence in the DRC by using temporal variation in road project rehabilitation using a new database of large inter-city road projects

> 11 We use the R package for extraction of the yearly data from `https://github.com/worldbank/black marbler?tab=readme-ov-file#bearer-token-` .

> 12This goes against the findings from McGuirk and Burke (2020) about producing cells where higher crop prices lead to a lower probability of conflict in food-producing areas.

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_(ii_ ) the Short Grit Scale (Duckworth and Quinn, 2009), _(iii)_ a measure of frustration reaction, _(iv)_ the Motivational Frameworks Questionnaire (Gunderson et al., 2013), _(v)_ and locus of control (Skinner et al., 1990), and _(iv)_ present bias. All outcomes were measured using tested and validated self-report scales, which were adapted to children from North Macedonia and translated (back-and-forth) to Macedonian and Albanian languages.<sup>14</sup>

- (2) GPAs at different points in time as a measure of academic achievement: short-term, i.e. immediately after the intervention in the fourth quarter of the school year 2015/2016; medium-term, i.e. half a year later in the second quarter of the school year 2016/2017; and longer-term, i.e. one year later in the fourth quarter of the school year 2016/2017. In North Macedonia, grades are recorded on a one-to-five scale, whereby one denotes the lowest and five the highest grade.

Data on the different socio-emotional skills and students’ socio-economic characteristics (which are otherwise not available in official records) were collected through baseline and endline surveys. To analyze whether the intervention shifted socio-emotional skills generally, and to reduce issues around dimensionality and multiple hypotheses testing, we construct a socio-emotional skills index (S/E skills index) that summarizes the different measures of self-reported socio-emotional skills. It combines the measures of deliberate practice beliefs, grit, frustration reaction, motivational frameworks, locus of control, and present bias. Following Anderson (2008), we construct this index by _(i)_ switching the sign of the variables included in the index (if needed), so that a positive direction always indicates a "better" outcome; _(ii)_ standardizing each variable (to have mean zero and standard deviation one, i.e. z-scores); _
gives the coverage rate by region and for each reference year; for this purpose, a country is defined as being covered if there was a survey (in our data base) within two years of the reference date (a five-year window). The coverage rate in 2008 varies from 47% of the population of the Middle East and North Africa (MENA) to 98% of the population of South Asia. Naturally, the further back we go, the fewer the number of surveys—reflecting the expansion in household survey data collection for developing countries since the 1980s. And coverage deteriorates in the last year or two of the series, given the lags in survey processing. Most regions are quite well covered from the latter half of the 1980s (East and South Asia being well covered from 1981 onwards). Unsurprisingly, we have weak coverage in Eastern Europe and Central Asia (EECA) for the 1980s; many of these countries did not officially exist then. More worrying is the weak coverage for Sub-Saharan Africa (SSA) in the 1980s; indeed, our estimates for the early 1980s rely heavily on projections based on distributions around 1990. The weak coverage for EECA, MENA and SSA in the 1980s is evident in Table 1. Our estimates for these regions in the 1980s are heavily dependent on the extrapolations from NAS data.<sup>24</sup>

# **4. Measures of absolute poverty**

Table 2 gives the absolute poverty rates—the percentage of the population living below $1.25—at three yearly intervals during 1981-2008. Table 3 gives the corresponding results for $2.00 a day, which is the median poverty line amongst developing countries as a whole (RCS).<sup>25</sup> Over the 28 year period, we find that the percentage of the population of the developing world living below $1.25 per day was halved, falling from 52% to 22%. The number of poor fell by 600 million,
# **Figure 4: Mortality during the 1975-1979 period in Cambodia, by birth cohort and area of origin of the family. From sibling's mortality data, DHS Cambodia 2000.**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>30-34 25-29 20-24 15-19 10 to 14 5 to 9 0 to 4 Not yet born<br>Age in January 1975<br>Family with rural origin Family with urban origin<br>Probability to die between January 1975 and December 1979<br><!-- End of picture text --> 24
Where INDIVIDUAL refers to data provided only by a single institution on individual _i_ . For example, total debt with a given bank (and not total system wide debt) is employed, and initial rating with a given bank (and not the average or worse rating across the system)<sup>9</sup> .

We use the scoring model to predict default probabilities for cases when banks do and do not share information. In Table 5 we construct tables using the same methodology as in Table 4, to demonstrate gains in access to credit and improved portfolio quality for a large and a small financial institution<sup>10</sup> . Information sharing is shown to significantly improve a bank’s ability to determine the likelihood of default for its customers. For a target approval rate of 40%, access to data from a PCR reduced the predicted default rate by 41% for a large bank, from 2.2% to 1.3% of the relevant portfolio. The results are even more impressive when repeated with a smaller financial institution – the expected default rate falls by 78% when PCR data are added to the institution’s own information for a targeted approval rate of 40%. Since larger institutions have a larger and potentially more varied portfolio to begin with, it is logical that smaller institutions would benefit more from sharing information. As the target approval rate is increased, the difference between the gains for a large versus a small bank is virtually eliminated: at a 60% target approval rate both institutions lower defaults by about 35% and at a target 80% approval rate by about 20%.

Data sharing can also enable both large and small lenders to increase their lending activities for a given level of risk or default. Panel B of Table 5 presents the results of this exercise with Argentine data where a default rate is targeted and then the number of borrowers who could be approved for that level of default is calculated. If a large bank was targeting a 5
submissions.aspx)

- US Energy and Information Administration (EIA). (released 7/5/2018). Data - Petroleum and other liquid ( https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm)

- World Bank (2020). The Pan-Arab Region Energy Trade Platform (PA-RETP): The Value of Electricity Trade, Unpublished Report, World Bank, Washington, DC.

World Bank. (2013). Integration of Electricity Networks in the Arab World: Regional Market Structure and Design. Report No: ACS7124. Midle East and North Africa (MENA) Regional Department, World Bank, Washington, DC.

World Bank. (2016a). Commodity Market Outlook. October 2016.

35
smoothing parameter for annual data. In the robustness section, we also present results for a different filter that avoids many of the shortcomings of the typical procedure (Hamilton, 2018).

When GDP growth accelerates, most governments in South Asia also tend to spend more. The cyclical components of public spending and GDP move strongly together in Nepal (0.40), Pakistan (0.37), Bhutan (0.36), and Bangladesh (0.33). The analysis of the co-movements between growth rates, rather than cyclical components, yields similar results. By this metric, however, Pakistan’s government spending is the most procyclical in the region (0.37). Except for Sri Lanka, the procyclicality of public spending increased after the Global Financial Crisis (see Table A3 in the Appendix).

The inflation tax seems acyclical in most South Asian countries, as no statistically significant relationship in either direction emerges. The two exceptions are Bhutan and Nepal, in which the inflation tax declines with stronger economic activity. In terms of deviations from trend, the elasticities are -0.33 and -0.44 respectively. In terms of growth rates, the relationship is only significant in Nepal.

**Table 1** <u>Correlation coefficients between fiscal variables and GDP</u>

|**Country**|<br>|**Government**<br>**expenditure**|Obs.|**Inflation**<br>**tax**|<br>Obs.|**Tax**<br>**revenue**|Obs.|
|---|---|---|---|---|---|---|---|
|_Bangladesh_|cyclical components|0.33*|28|-0.25|24|-0.07|28|
||growth rates|
paved roads, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access) to four (full access to all four infrastructure components).<sup>5</sup> India’s economy is undergoing dramatic structural changes (Desmet et al. 2011). From a starting point in the 1980s when the government used licensing to promote industrial location in regions that were not developing as quickly, the economic geography of India has been in flux as firms and new entrants shift spatially (e.g., Chari 2008, Fernandes and Sharma 2011). One feature for a district that is important in this transformation is its link to major cities. We thus include a measure from Lall et al. (2011) of the driving time from the central node of a district to

> 5In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are robust to excluding these major cities from the analysis sample.

9
household productive capacity. The analysis presented in this paper can thus be easily replicated in any country in the case of other industries affected by high market concentration.

As in most simulation methods, there are areas of improvement in this proposed approach. First, the simulation is based on partial equilibrium analysis. So, it focuses on the first-order effects of competition on consumer welfare through the price channel, potentially missing other impacts through alternative channels, such as labor markets, asset accumulation, or the diversity of alternatives available in purchasing. Second, by using household budget surveys, which typically do not include detailed information on the quality or brand of the goods and services consumed, the analysis may not capture the potential welfare effects brought about by changes in quality or the effects of alternative pricing schemes. This work may be the subject of future research to improve the simulation approach, which has the potential of becoming a powerful tool to motivate competition reforms around the world.

16
**The long‐term impact of international migration on economic decision‐making : evidence from a migration lottery and lab‐in‐the‐field experiments**

John Gibson, _University of Waikato_ David McKenzie, _World Bank_ Halahingano Rohorua, _University of Waikato_ Steven Stillman, _Free University of Bozen‐Bolzano_ _Keywords_ : Migration, economic beliefs, preferences, household efficiency, transnational household _JEL codes:_ O12, F22, D13, D81, P1.