rafmacalaba/data-use-mentions-extended huggingface dataset:
)|
|Male unemployment rate|-0.006|0.002|
|_Average real hourly wages_|(0.147)|(0.702)|
|Female-services|0.011|**0.186*****|
||(0.769)|**(0.003)**|
|Male - industrial production|**0.078****|-0.014|
||**(0.016)**|(0.795)|
|**N**||179362|

Notes: The sample is restricted to females between 18 and 65 years old. P-values in parentheses, *** p<.01, ** p<.05, * p<.10. Source: Own calculations using data from Census, ENOE and SEDESOL.

31
(Larson et al., 2014.) This underscores the importance of empirical evidence from a range of countries, and especially evidence that can illuminate pathways underlying these relationships.

The central findings of the representative phone survey in PNG illustrate the importance of trust in the COVID-19 vaccine and social norms in driving behavior. Less than one in five adults were planning to get the COVID-19 vaccine and the main reason people provided for not wanting to get vaccinated was concerns about side effects. Trusting the COVID-19 vaccine and believing it has important health benefits were positively correlated with people’s willingness to get vaccinated. Similarly, believing others will get vaccinated and encouraging others to get the vaccine were also positively associated with people wanting to get the COVID-19 vaccine. However, there was no clear relationship between other potential explanators of vaccine hesitancy, such as being concerned about COVID-19, people’s most trusted source of information about the COVID-19 vaccine, their history of receiving vaccines for other diseases (for example, measles or polio), and their willingness to be vaccinated. This survey also showed that overwhelmingly the most common way people say they would become more likely to get the COVID-19 vaccine is by hearing from health experts, particularly in the form of face-to-face communication.

The online randomized survey experiment builds on the results of the phone survey by showing that providing a short message about the relative safety of the COVID-19 vaccine (designed with the aim of building trust in the vaccine) substantially increased people’s willingness to get vaccinated. Only 19.3 percent of respondents in the control group (that is, where no intervention was provided) stated they would be willing to get the vaccine, whereas 30.6 percent of respondents that received the relative safety treatment said they would get vaccinated, representing an over 50 percent increase. The two other intervention arms that were provided (referring to expert advice and social norms) did not have a consistent impact on people’s willingness to get vaccinated. The main findings of the online randomized survey experiment
Pol icy Research Wor king Paper 10796

# **Abstract**

The World Bank recently introduced a new key indicator to guide its work: the number of countries with high inequality, defined as a Gini index above 40. The new indicator was introduced as part of the new World Bank vision of ending poverty on a livable planet. This paper reviews why reducing inequality matters for ending poverty on a livable planet, summarizes the advantages and disadvantages of using the Gini index to track inequality, outlines challenges in measuring inequality, and discusses what a Gini threshold of 40 implies. Using the most recent data for every country, 52 countries of a total of 169 countries are classified as high inequality countries, which represents a decline from 77 countries at the beginning of the millennium.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at dmahler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
characteristics, income and demographic information. The large number of observations combined with substantial rate changes under the price reform help identify both price and nonprice segments of the demand function.

A common issue of using survey data for statistical analysis is that we only selectively observe those who choose to report. In the Turkish household budget survey, about 28 percent of households with access to electricity did not report electricity expenditure. To address potential sample selection bias, I apply Heckman’s selection model (Heckman, 1979) but do not find evidence of selection on unobservables, which suggests that sample selection bias is not a concern in this study. I then use the model to estimate the distributional impact of the 2008 tariff increase in Turkey. To lend credence to the specification and results, I also conduct out-ofsample tests of the model that show how well it predicts consumption responses to new price changes in 2009.

Overall, I find that a 10 percent increase in electricity price will cause an average household in the bottom income quintile to reduce electricity consumption by 1.8 percent, and an average household in the top income quintile to reduce electricity consumption by 5 percent, _ceteris paribus._ The poor are less flexible in adjusting electricity consumption most likely because they are close to their minimum electricity consumption level and have fewer options for switching to other types of fuel.

Not surprisingly, lower income households experienced a greater welfare loss as a percentage of income. The welfare loss from the 2008 price increases, approximated by consumer surplus change, is about 164 Turkish Lira (TL) for a bottom income quintile household, or 2.16 percent of household disposable income, compared with 330 TL, or 0.75 percent of income for the top quintile. However, because expenditures on electricity have been a moderate component of household budget in Turkey - they represented 3.5 and 4 percent of the household’s disposable income in 2008 and 2009 - the impact of the 2008 tariff increase on consumer welfare has been limited. These results are
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survey waves that inform our analysis are the baseline for the respective panel survey programs; thus, their unit costs are applicable in the context of the cross-sectional household surveys that will be supported by the World Bank over

> 13 As recommended by the World Bank Household Survey Strategy, the questionnaire should cover relevant Classification of Individual Consumption According to Purpose (COICOP) categories with sufficient specificity and should elicit information on quantities of food items that are consumed (in addition to expenditures). In doing so, the surveys will continue to fulfill their traditional roles of providing vital inputs into constructing national accounts and the consumer price indexes.  Beyond the collection of consumption/income data, the multi-topic questionnaire should also cover an integrated range of topics including demographics; education; health; housing; wage, non-farm self-, and on-farm self-employment activities and income; non-labor income; asset ownership; food security; shocks and coping strategies; social protection; and child anthropometry (World Bank 2015b). For instance, in line with the data priorities articulated in the World Bank Gender Strategy, the recommendation is to gradually expand the scope of individual-level data collection and to collect data not only on education, health and employment, but also on ownership of and rights to physical and financial assets (World Bank 2015d). To the extent possible, the core modules for the collection of key indicators for the SDGs are recommended to be harmonized across countries, taking into account countries’ different starting points and capacity.

> 14 The LSMS-ISA is a household survey program that was established with a grant from the Bill and Melinda Gates Foundation to provide financial and technical support to governments in sub-Saharan Africa in the design and implementation of multi-topic, national, panel household surveys with a strong focus on agriculture. The program is implemented by the Living Standards Measurement Study (LSMS) housed in the Survey Unit of the World Bank Development Data Group (www.worldbank.org/lsms).

7
human capital, the effect of adult height on worker productivity and wages, standard estimated values for production factor shares and the effect of schooling and height on earnings. Appendix 1 describes the parameter values that were used to calibrate the model and the sources from which these values were obtained. Several additional parameter values were used in the expanded version of CKW 2017 model.

Firstly, we parametrize the sectoral gender-specific friction and the gender-wage gap. This parametrization is drawn from a secondary-data analysis of the HIES 2014 from LISGIS (2015). The secondary source facilitates the estimation of earnings per unit worked (month, week and hour) for each employed person. We recognize that earnings measurement at the micro-level has a noisy and volatile nature<sup>15</sup> but it has the advantage of being detailed to the intended population of interest for the adoption at the macro-level in this study. We find that the sectoral friction is noticeable in the higher wages of the modern sector across gender. The wage of female workers in the modern sector is 72.2 percent higher than the wage of female workers in the traditional sector. The average wage of a male worker in the modern sector is 46.2 percent higher than the average wage of a male worker in the traditional sector. Likewise, we find that women have lower wages than men in both the modern and the traditional sector. The average wage of a male worker in the modern sector is 6.5 percent higher than the average wage of a female worker in the same sector. And the average wage of a male worker in the traditional sector is 25.5 percent higher than the average wage of a female worker in the traditional sector.<sup>16</sup> The modern-traditional divergence is substantially accentuated, particularly for women, and the gender-pay gap is observed in both the modern sector and the traditional sector to differing degrees.

Secondly, we use the adapted CKW 2017 model to estimate gender-specific contributions to output when we incorporate a series of
# Figure 21. SSA: Economic growth and drivers of potential growth

A. GDP Growth <!-- Start of picture text --> A. GDP Growth B. Contribution to potential GDP growth<br>Percent SSA Median Percent TFP Capital Labor Potential growth<br>10 8<br>6<br>8<br>4<br>6<br>2<br>4 0<br>2<br>0<br>-2 EMDEs SSA SSA excl. South<br>2000-21 2000-10 2011-21 2022-23 Africa<br>C. Investment growth D. Potential TFP growth<br>Percent Percent<br>SSA Median SSA Median<br>14<br>4<br>12<br>10 3<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent<br>SSA Median 3 SSA Median<br>100<br>80<br>2<br>60<br>40<br>1<br>20<br>0 0<br>2000-20 2011-20 2000-2021 2000-2010 2011-2021<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables; UN Population Prospects; World Bank; World Bank, World Development Indicators database.

Note: EMDEs = emerging market and developing economies; SSA = Sub-Saharan Africa. Shaded areas indicate forecast. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21.

A.C.D.F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of six EMDE regions; vertical lines denote range
DiD coefficient is 0.6 for the total NPLs, and 3.71 and 1.04 for nonfinancial corporation and household NPLs, respectively.

To learn more about the time dynamics of NPLs and as a further robustness test for our analysis, we implement a panel event study. The visual representation of our results is in Figure 6. Before the disaster took place, there were no statistically significant differences between the trends of the two sets of provinces. Post disaster NPLs in the affected provinces start to diverge from their counterfactuals at the fourth quarter after the event, reaching a maximum of 1.43 percentage points for the total credit portfolio and 2.52 for the nonfinancial corporate credit portfolio. In the fifth quarter, NPLs start to decline, fading the differences between the two groups. In the seventh quarter, the effect on NPLs is statistically equal to zero. This progressive reaction is expected as NPLs are a slow-moving indicator only recorded when payments are overdue by (usually) more than 90 days.

The higher proportion of borrowers not able to meet their payment obligations suggest insufficient protection against natural disasters in the LAC region. For instance, following the 2018 drought, the production of soy and corn in Argentina dropped by 31.3 percent and 12.5 percent, respectively (Central Bank of Argentina 2018). In the absence of a developed insurance market that can assist in smoothing such negative shocks, firms' profit margins are expected to be substantially hit, hindering their ability to honor the loan payments. In that regard, whereas the depth of the insurance market in LAC (insurance premiums as percentage of the GDP) is higher than in most regions, it is significantly lagging behind high income countries.<sup>32</sup> For example, in France, one of the high-income countries with higher insurance penetration, a recent survey on the preparedness for climate change shows that physical risks appear to be modest in the short term as these risks are covered by insurance companies (ACPR 2019)
variables to proxy four of these channels, but the district data on land and consumer prices are not available separately for rural and urban areas. We add these variables to both the baseline (as defined in (2)) and the IV specifications to obtain a sense of the relative importance of these channels.

To capture the consumption linkage effect of urbanization, we use cropspecific shares of cultivated land. We interact this share at the beginning of the period of our analysis with the urban population variable to test the impact of urbanization across districts on the basis of their production dependence on specific crops. This measure relies on the assumption that a district’s supply is a good proxy for urban demand. To identify the relevant crops, we first test for the relationship between urbanization and the main crops across Indian districts.<sup>29</sup> Among the crops that display a significant relationship, we then select the relatively large crops, those with a share of total cultivated land above 5 percent throughout the period. These cultivations are most likely to affect rural economic livelihood.

These crops are rice (which covers, on average, 15 percent of the total cultivated land) and pulses (8 percent). These are important expenditure items for Indian households (Subramanian and Deaton, 1996). India is the world’s largest consumer and producer of pulses and is one of the largest consumers and producers of rice. This choice is reinforced by the results of Jha et al. (2005), who show that the rice market is not integrated within India. As shown below, our analysis suggests that urbanization is associated with an increase in the cultivation of rice and a decrease in the cultivation of pulses. Although the latter result is expected (because pulses are considered a relatively poor crop), the former result is surprising because rice is usually substituted with wheat and other food products as income rises. One reason that this substitution might not occur in India is that during the period of our analysis,
status, whether individual consider themselves a member of an ethnic or racial minority group, socioeconomic status (upper, upper middle, lower middle, or lower), place where individual lives (rural, small, or large city), whether individual has citizenship or a valid residence permit for the country in which they live, whether individual lives in a place with outdoor spaces during COVID-19, log of pre-pandemic GDP per capita of 2019, Purchasing Power Parity (PPP) (constant 2017 international $), and regions (South Asia, East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa; North America; and Sub-Saharan Africa)<sup>7</sup> ; and is a vector of errors. Moreover, to understand the mental health of the respondents during the COVID-19 pandemic, εε Moreover, to understand the mental health of the respondents during the COVID-19 pandemic, an ordered probit model is estimated based on the anxiety and depression measure constructed using the Patient Health Questionnaire (PHQ-4) scale. For the ease of administering the survey over an app, the questionnaire includes standardized questions for constructing the PHQ-4 scale. PHQ-4 scale is used extensively in research and clinical settings as a general indicator for anxiety and depression. The advantage of the PHQ-4 over the other mental distress measurements is that it is an ultra-brief screener, which can be used to indicate whether individuals may benefit from a further in-depth assessment (Stanhope 2016).

The four standardized indicators used for constructing the PHQ-4 scales are: (1) feeling of depression or hopelessness over the last 2 weeks, (2) feeling of disinterest or displeasure over the last 2 weeks, (3) feeling of nervousness, anxiety, or on edge over the last 2 weeks, and (4) feeling of inability to stop or control worrying over the last 2 weeks. For each of the four indicators, the respondents were asked to score how often they were bothered by the problem: not at all (
# **Who uses bottled gas? Evidence from households in developing countries**

Masami Kojima _World Bank_ Robert Bacon _World Bank_ Xin Zhou _George Mason University_

# **JEL codes:** D12

**Keywords** : LPG, liquefied petroleum gas, household surveys, biomass, fuel, cooking, energy poverty, Heckman, education, gender

The authors are, respectively, Lead Energy Specialist in the Sustainable Energy Department, consultant, and Ph.D. student. The authors are grateful to the Norwegian Agency for Development Cooperation for funding this study through its Petroleum Governance Initiative under the Oil for Development program. The paper benefited from helpful comments provided by John Newman, Nistha Sinha, Roy Van der Weide, and Ariel Yepez-Garcia, all of the World Bank. The findings, interpretations, and conclusions are the authors’ own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries.
public schools and in urban areas, the latter to reduce logistical complexity of parcel delivery— in the Lima region population is predominantly urban, whereas in Junín and Ancash approximately 2 out of 3 households are located in urban areas. Three types of schools were excluded from the sampling frame: (a) those located in districts in the top quintile of wealth; (b) those that operated night-shifts only; and, (c) those with fewer than 15 pupils in total. These restrictions resulted in a universe of 946 schools in the three regions. To maximize statistical power, it was decided to sample 800 from these 946 schools.

In total, 400 of the 800 schools were randomly selected to receive the ‘GYM package’. The study contains two equally powered samples: the _metropolitan_ samples with 398 schools from the Lima region, a densely populated area with over 12 million inhabitants, and the _regional_ sample which includes 402 urban and peri-urban schools in the regional towns of Ancash and Junín. To ensure balance in the experimental sample and maximize statistical power, treatment status was stratified across poverty quintiles and regional strata (Bruhn and McKenzie, 2008), making 12 strata in total.<sup>15</sup> **Table 1** provides an overview of treatment and control schools, across the pooled and individual experiments.

Treatment schools were targeted to receive the ‘GYM package’ and to implement the Grow Your Mind session during regular tutorial hours in 2015. In each institution that received the package, tutor teachers were asked to implement the session in every classroom for grades seven and eight. Control schools were not approached, and, thus, continued using the national curriculum in their tutorial sessions. The experiment was designed to assess the impact of the ‘GYM package’ using information from the students’ census evaluation ( _Evaluación Censal de Estudiantes_ , ECE), administered a few months after the intervention for those in the eighth grade, and more than a year later for those in the seventh grade (
, as opposed to being own account workers without any employees (Table 2). The difference in the share of workers who are entrepreneurs among return migrants and nonmigrants is quite large and statistically significant in Bangladesh and Pakistan. The association between return migrant status and entrepreneurship is robust to controlling for observable characteristics in a multinomial logit regression setting (Table 8). Self-employed individuals hiring paid employees in developing economies have been categorized as ”successful” entrepreneurs by the literature (Piracha and Vadean 2010; Gindling and Newhouse 2014). This suggests that return migrants also contribute to job creation in the home economy, in addition to being able to start their own account enterprise.

In all three countries, higher levels of education are associated with an increased probability of being in non-farm self-employment, except at the tertiary level. This finding is consistent with the work by Ilahi (1999) using earlier data on return migrants from Pak15