rafmacalaba/data-use-mentions-extended huggingface dataset:
have environmental impacts if green technology is used. Some of these jobs benefit from the green revolution, such as miners of lithium for electric car batteries. Others are negatively affected, such as coal miners and those working in coal-fired plants. We include both in our analysis.

While many studies in various countries use tasks information from the US Occupational Information Network (O*NET) inventory and its associated Green Economy Program (GEP) (Consoli et al. 2016; Vona et al. 2019, 2018) to measure green jobs, we find these data are not appropriate for our analysis for several reasons. The O*NET is a US cross-sectional database with detailed information about work context, tasks, activities and skills at the occupational level.<sup>16</sup> The O*NET Green Economy program (GEP) identifies 12 industries that should be impacted by the greening of the economy and an associated list of occupations and 14 International Standard Classification of Occupations 1988 (ISCO-88) is a four-level hierarchically structured system that allows all jobs in the world to be classified into unit groups based on their similarity in terms of the skill level and skill specialization required for the jobs. The most aggregated level is the one-digit code (10 major groups), followed by the two-digit code (28 sub-major groups), the three-digit code (116 minor groups) and the most detailed level of the classification is the four-digit code (390 groups). The SASCO includes an additional 39 occupations, reflecting uniquely South African labor markets.

15 To ensure there is no double counting of individuals, we divide the survey weights by the number of quarters.

16 The O*NET is the primary source of occupational information in the United States. The data are collected through industry expert interviews and surveys of representative samples of workers. https://www.onetonline.org/ 5
.3<br>0.2<br>0.1<br>0<br>Direct taxes Direct transfers Indirect taxes Subsidies Education Health<br>Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10<br>Source: World Bank Group staff estimates based on ENPH 2017<br><!-- End of picture text --> Figure V-3 presents the general results of the tax system and spending in Colombia. As can be seen, except for indirect taxes, all other policies in Colombia are progressive and contribute to reducing inequality in the country. Annex A shows both the Kakwani coefficient for each of the taxes and transfers considered in this paper, as well as the marginal contributions for the reduction of inequality and poverty when pensions are considered a transfer. The results are presented in Annex B when pensions are considered deferred income. What follows from this section seeks to discuss the main results.

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Bulgaria, for example, total subsidies are high and similar every year. In other countries, such as Argentina, losses are more volatile and extremely high as a percent of GDP.

**Figure 3 Main expense items of infrastructure SOEs, by sector**

<!-- Start of picture text --> a. Employment costs b. Fuel costs<br>250 150<br>200<br>100<br>150<br>100<br>50<br>50<br>0 0<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>c. Maintenance costs d. Government take<br>250<br>20<br>200<br>15<br>150<br>10<br>100<br>5<br>50<br>0 0<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Percent of operating income Percent of operating income<br>Percent of operating income Percent of operating income<br><!-- End of picture text --> _Source_ : World Bank Infrastructure SOE Database.

_Note_ : Data are for 2009–18. Boxes show interquartile variation (25–75 percent) of subsidies to assets. Dark lines across the boxes show the median per year. Whiskers show the maximum and minimum of this variable per year, capped at 1.5 times the interquartile range. Dots represent outliers. Government take is defined as corporate taxes paid plus dividends paid to the government.

The main expense of SOEs in the roads sector is maintenance, with a mean of 132 percent of operating expenses and a median of 35 percent. Maintenance costs are not only the most important component of expenses, they are also highly volatile (the interquartile range is close to 120 percent of revenues), largely because most of the road SOEs in our sample do not have 15
2019 according to the list of all schools in the country with students in grade 5. The same schools were surveyed in 2021. The sample frame was not renewed due to no major changes in the sampling frame over the two years. This identification strategy allows us to compare the performance of grade five cohorts in 2019 and 2021 and assess the trends in system-level student outcomes. The caveat with this identification strategy is that it ignores the changes in student composition or other factors that change from one year to the next and thereby may affect cohort-level learning outcomes.

Our second identification strategy allows us to control for the student population and use data from students who appeared for mathematics assessment both in 2019 when they were in grade five and in 2021 when they were in grade seven. This allows us to control for time-invariant student-level characteristics and analyze the change in student-level learning outcomes. Though the attrition rate was high, we compare the learning outcome of students in 2019 who appeared for the 2021 assessment and those who did not and find that the difference between the student populations is 3
increases in life and non-life IP. This may not be surprising since the S-curve argument states that small increases in GDP per capita in low and lower-middle income countries are not strongly associated with big increases in IP.

# **6. Conclusion**

Following the insights of Greenwood and Jovanovic (1990) and subsequent empirical work on the link between intermediation and growth, this paper elicits relevant correlates of insurance market development. Based on 20 years of insurance premium data from 180 countries and a similar wealth of data on income, political and economic institutions and financial market development measures, our cross-country analysis shows that while the correlation between income per capita and insurance premium is undeniable, there are other strong correlates of insurance market development, often as strong as economic growth.

We find that the development of a country’s economic institutions and financial sector (measured by indicators such as credit to the private sector, bank account ownership rates, or the financial reform index) are important to insurance market development. Institutional quality and financial market development measures are more strongly correlated with life insurance penetration, compared to nonlife insurance penetration, controlling for GDP per capita. A plausible explanation for this is that purchase of life insurance is a voluntary decision more often than purchase of non-life insurance.

Finally, the strength of the relevant correlations depends on the country’s income level. In particular, our measures of institutional quality are more strongly correlated with insurance penetration than income per capita in low income countries, which corroborates the S-curve theory commonly explored in the literature.

Although only capable of establishing correlations, in this paper we hope to help expand the scope of the search for determinants of financial market development, especially insurance markets, by showing that income growth alone probably will not close the protection gap.

12
lower incidence of default on external debt among high-income countries). Accordingly, their relevance to advanced economies may be limited, in view of the greater degree of financial development and other structural differences. Moreover, the phenomenon of significant foreign equity liabilities is relatively recent (only growing rapidly since the early 2000s), such that studies concerning external debt do not necessarily carry over to total external liabilities. Finally, these studies consider gross external debt. While foreign debt assets for these types of countries might have been relatively small in earlier periods, the growth in reserve positions over the last decade means that there is a large divergence between gross external debt liabilities and net external debt liabilities for many countries.

As noted above, the debt overhang concept can also apply at the sectoral level and at the level of individual banks, firms and households in addition to the external dimension. There is a growing literature that shows that high public debts are associated with lower output growth (Checherita and Rother 2010, Kumar and Woo 2010, Reinhart and Rogoff 2010).

Kumar and Woo (2010) study a panel of 38 advanced and emerging economies over 1970 to 2007. These authors find that public debt ratios above 90 percent are associated with lower subsequent output growth, with the growth penalty substantially larger for emerging markets than for advanced economies. In particular, these authors estimate that a 10 percentage point increase in the debt ratio is associated with a growth slowdown of 0.15-0.2 percent in advanced economies but 0.3-0.4 percent in emerging economies. Moreover, they find that reduced investment is a key driver of the deterioration in growth performance.

In a study of euro area member countries over 1970-2009, Checherita and Rother (2010) also find a negative growth threshold of around 90-100 percent for the debt ratio. However, these authors caution that the lower-bound confidence interval of 70-80 percent calls for greater prudential policies at lower debt levels. In terms of mechanics, these authors find that high public debt adversely affects growth through several
the household surveys database: (1) agriculture worker; (2) manufacturing worker; (3) services worker; (4) adult not in the labor force; (5) elderly (above 65 years old); (6) child (under 15 years old).

These categories are of course adaptable depending on the information available in the survey and the objective of the study. Here the main objective is to differentiate farmers from the rest of the workers because some disasters will affect their income differently.

The database only gives an aggregated estimate of income at the household level, and does not provide individual income data. Since droughts affect differently the income of farmers and of consumers, we estimate the income of each category of people in the database. We calculate the � number of people in household _j_ who belong to each category ( ���� ) and we estimate current households’ revenue using these categorical predictors (with weighted linear least squares). Note that

> 1 Afghanistan, Angola, Albania, Argentina, Azerbaijan, Burundi, Burkina Faso, Bangladesh, Bosnia and Herzegovina, Belize, Bolivia, Brazil, Bhutan, Botswana, China, Côte d’Ivoire, Cameroon, Cabo Verde, Costa Rica, Dominican Republic, Ecuador, Arab Republic of Egypt, Ethiopia, Micronesia, Gabon, Georgia, Ghana, Guinea, Gambia, Guatemala, Guyana, Honduras, Haiti, Hungary, Indonesia, India, Jamaica, Kenya, Kyrgyz Republic, Cambodia, Lao People’s Democratic Republic, Lebanon, Liberia, Sri Lanka, Morocco, Moldova, Madagascar, Mexico, Macedonia, Mongolia, Mozambique, Mauritania, Mauritius, Malawi, Namibia, Niger, Nigeria, Nicaragua, Nepal, Pakistan, Panama, Peru, Philippines, Papua New Guinea, Paraguay, Rwanda, Senegal, Solomon Islands, Sierra Leone, El Salvador, Serbia, São Tomé and Príncipe, Suriname, Swaziland, Syrian Arab Republic, Chad, Togo, Thailand, Tajikistan, Turkmenistan, Tunisia, Tanzania, Uganda
33

# **Endnotes**

1 Recent empirical studies provide numerous cases of where trade restrictions have added to or would add to poverty. See, for example, Hertel and Winters (2006), Anderson, Cockburn and Martin (2010) and Aksoy and Hoekman (2010).

2 Variable trade restrictions can also affect long-term investments and hence economic growth rates. Drawing on a broad range of developing country case studies, Bevan Collier and Gunning (1990) and Collier, Gunning and Associates (1999) suggest that faster economic growth would result from allowing producers access to high prices in those rare occasions when they spike, rather than taxing it away. According to the evidence in their case studies, this is because governments are more prone than farm households to squander the windfall either in poor investments or in extra consumption.

3 The new developing country estimates are less reliable than the high-income country ones, and the earlier estimates for developing countries in Anderson and Valenzuela (2008), for several reasons. One is that, to do the update promptly, producer prices reported to FAO had to be used for developing countries rather than more-nuanced prices available only in national statistical agencies. To minimize the errors this might introduce, the FAO producer prices in US current dollars were converted into an index set at 100 for 2004, and the 2004 prices in Anderson and Valenzuela (2008) were updated using the changes in that index for each country through to 2008. Likewise, to overcome delays in obtaining export and import volumes and values, from which border prices could be derived, the authors simply used the Thailand 5% broken rice and Canadian wheat prices (from World Bank 2010) to create indexes set at 100 for 2004 for those international reference prices, and the 2004 border prices in Anderson and Valenzuela (2008) were updated using the changes in each of those indexes through to 2008. The coefficients of correlation between those international reference
. Specifically, suppliers extend less trade credit when their relationship with the customer is longer, they are in closer geographic proximity to the customer, or they have a listing on the New York Stock Exchange (and thus information about them is readily available). Moreover, those authors also control for the market power of suppliers (using price-to-cost margins as reflected in the Lerner Index and measures of industry concentration) in their regressions and find that those with greater market power extend less trade credit to downstream customers (in line with the findings, e.g., in Fabbri and Klapper, 2016). Thus, Dass et al. (2015) provides evidence in support of both the equity stake and the market power hypotheses of suppliers’ trade credit behavior within the same theoretical and empirical models.

In sum, Chod et al. (2019) and Dass et al. (2015) illustrate the importance of data that capture more details of the relationships between suppliers and their customers, which have 14
., 2021), and as such, high volatility due to local shifts in supply and demand can be expected. With 87% of the population in rural areas, and highly localized price dynamics, a local-area CPI would be needed to more adequately describe price trends in different rural or poverty-stricken areas, where the majority of the population resides in fragile situations.

The paper uses end-of-July data available from IFPRI, downloaded from the Papua New Guinea Fresh Food Price Monitoring Tool on August 17, 2022. The data reports monthly prices in Kina (local currency) as measured in 8 different market locations throughout the country (Banz, Goroka, Kokopo, Kundiawa, Lae, Madang, Mt. Hagen and Port Moresby). Raw price data dates back to mid-2009, but there are periods of substantial data gaps, particularly between 2016 and 2018. In total, the data contains 79 unique food items, but most do not have a sufficient number of observations to create a time series.

Since the focus of the paper is on monitoring price inflation, a basket of food items with data coverage over a long time period is essential. The application also aims to understand what the right design may look like for future co-deployment of rapid survey systems and machine learning imputations, and so different data selections are used to investigate the trade-offs in data availability and survey coverage. Three baskets are selected. A 25-item version since 2009 and 2014 that
so we do not have information on their preferences.

> 10 Male and female focus group respondents were asked to identify one project from a list of 15 possible projects, while individual male household and female respondents were asked to select and prioritize three projects from the same group of 15. To ensure comparability, we focus on the project that was named as the most important in the household and individual surveys. Female respondents were asked the question twice in both the group setting and individually, but we use only information from the female individual questionnaire and check robustness with the female focus group. To increase statistical power in the analysis, we group the 15 possible project types into five categories: (i) roads and bridges; (ii) irrigation; (iii) drinking water; (iv) electricity; and (v) other (which include men's courses, health courses, schools, health facilities, seeds, agricultural equipment, livestock, microfinance programs, communal toilet facilities, and community centers). The four types of projects not included in the “other” category are the four most popular types of selected projects.

> 11 In the event of two or more projects having the same number of respondents preferring them and these numbers exceeding the number of respondents preferring other projects, the respective projects were all marked as the most preferred.

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23 disasters whose effects regularly permeate the economy through product (food prices) and factor (labor) markets.

The econometric results suggest that households in Vietnam largely manage to cope with the immediate effects of drought events, through irrigation, though that the frequent occurrence of droughts erodes their capacity to cope with them, resulting in substantial welfare loss especially among households in drought prone areas closer to the urban centers, where disaster relief is less pronounced. Localized floods exert important welfare losses, on average an estimated 7 percent, though much of that loss appears to be recuperated through capital replacement in the subsequent year, at least among those who are only occasionally affected by such floods. Those who are frequently affected suffer more, though the long run effects from frequent exposure to localized floods are substantially less than those from frequent exposure to droughts, partly due to a responsive relief system in the more urban areas that are frequently affected. Riverine floods cause substantial welfare damage (23 percent) in the short run, though less so among households who are frequently exposed. Not only do they tend to be much better served by disaster relief, especially when close to the metropolitan centers, it is important to note that not all riverine floods recorded in DFO are experienced as negative events, as households built they livelihood systems around them, especially close to the metropolitan areas. They learned to live with floods. Finally, the largest damage is inflicted by hurricane force winds, with regular exposure and proximity to the urban centers, which both exacerbate the welfare losses substantially.

Overall, the study shows that there is promise in using meteorological data to construct spatially disaggregated weather event and weather hazard maps, the first objective of the study. Such data are becoming increasingly available and natural event and hazard maps have also important direct applications for policymakers beyond their use in estimating the welfare effects of disasters. The fine‐tuning and validation of such maps is an important area for further research. Second, there are important long run negative effects of being regularly
causes the aggregate level of food expenditures to be lower than expected. The consequences of this error are clear in terms of food/non-food shares but less so in terms of their impact of the food component of the food poverty line. This is because calculating the food PL relies on the pattern of consumption, not the level.<sup>26</sup> Baskets relying on underreported consumption may imply a very different dietary pattern than would more precise consumption data. It is uncertain, a priori, whether the resulting food PLs will be biased upward or downward: this depends on which types of items are underreported. If relatively inexpensive items are underreported, the resulting basket will be biased upward (as will the food PL); if more expensive foods are underreported, the opposite will be true.

> 24 As an additional check, we reproduced the test and procedures for 2002/2003, obtaining similar results. These findings are available upon request.

> 25 Food consumption is calculated by aggregating food expenditures. Non-food consumption expenditures, instead, are constructed by using information on education, health and some utilities fees/expenditures by calculating an implicit value, such as for housing or the use value of durable goods (see Deaton and Zaidi, 2002). In the specific situation discussed, we argue, the level of non-food consumption of urban households was correctly estimated. The problem arises when we merge food with non-food consumption and we look at their shares. Being food consumption dramatically under-estimated, non-food consumption share is relatively overestimated; hence it looks like these households spend a disproportionate amount in nonfood items -a typical consumption behavior of better-off households- while at the same time they cannot meet a minimal dietary requirement. Indeed, a clear contradiction.

> 26 This is because PLs are rescaled to meet the minimum caloric requirement (2150 calories per capita per day).

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)|11.4 (3.0)|
|**5**|103,736 (65)|496,231 (88)|599,967 (83)|10.7 (3.7)|13.2 (4.5)|12.8 (4.3)|
|**All**|289,935 (24)|1,052,765 (74)|1,342,700 (51)|10.9 (2.9)|12.2 (3.6)|11.9 (3.4)|

_Source:_ Authors’ calculations. _Notes_ : The number of purchasing households is followed by the percentage of households in the quintile that purchased LPG in parentheses. The percentages are based on the total number of households in each rural quintile, urban quintile, and national quintile. The average quantity purchased in kg per month by purchasing households in each quintile is followed by the quantity purchased per person in parentheses.

Table A2.4 shows the share of total expenditure allocated to LPG averaged across all households  that is, averaged across all users and non-users  as well as for user households only. The share for all households increased—except in the top quintile—and the share for user households decreased with quintile level. The coefficient of variation of the calculated prices of LPG was 0.11 (based on household weights), indicating that 95 percent of prices were within 80 percent to 120 percent of the national average price.