rafmacalaba/data-use-mentions-extended huggingface dataset:
characteristics, such as urban or rural residence, household size, education level, gender, and race of household head.

# 3.2. Change in medical expenditure

The long-term change in spending on health care that would result from a reduction in the consumption of each product is estimated using the static model described by equation 4. Estimates of the cost of treating diseases related to the consumption of each product are distributed proportionally to the number of households in decile jj that consume product . The list of diseases that are considered related to consumption comes from the Global Burden of Diseases dataset and will be explored in section 4.2. pp jj To analyze the long-term impact on health care spending resulting from a reduction in the consumption of each product , we employ a static model as outlined in equation 4. In this model, estimates are generated by attributing the public expenditures from administrative data on treating diseases associated with the consumption of each product to the number of households in decile jj that engage in the consumption of said product . The data source for the associated medical expenditure estimates is described in section 4.3. pp jj T T <u>j</u> p j j �−1�∗ T T E Eq. 4 CCTTCCTT TToo TTE ttE TT TToo E TTEEjj jjEECCEETTCCEECC ∆MMp iippu EEeep IIppiiu = ��1 + εε ∗∆PPjj TT E EEjjEETTE EEjj

# 3.3. Change in years of productive life

The impact of a reduction in the consumption of processed and ultra-processed foods on long-term income is derived from the reduction in years of life lost (YLL) and the associated loss of income in those years. The use of both processed and ultra-processed foods is due to a data limitation that will be explored in section 4.2. Equation 5 provides an estimation of the average number of working years lost within decile and is derived from the years of life lost due to diseases associated with product . This estimation takes into account the proportional distribution based on the number of households
**TUR**|**Turkey**|20.10|61.6%|38.4%|22.07|95.0%|5.0%|
|**TZA**|**Tanzania**|0.26|3.4%|96.6%|0.77|82.0%|18.0%|
|**UGA**|**Uganda**||||-0.09|316.5%|-216.5%|
|**YEM**|**Yemen**|0.06|124.3%|-24.3%||||
|**ZAF**|**South Africa**|9.62|96.2%|3.8%|12.26|73.6%|26.4%|
|**Medi**|**an - all countries**||90.5%|9.5%||84.5%|15.5%|
|**Medi**|**an - countries in both**|||||||
|**sub-p**|**eriods**||90.5%|9.5%||82.5%|17.5%|

Notes: the terms of the decomposition shown in the table are based on measures in the country-year level dataset (file CY.dta).

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the macro balances and the household data in construction of the Social Accounting Matrix implies that household factor income must be larger than household consumption for most households to allow for the transfer of capital to foreigners as well as to pay for investment. It follows that the change in factor income as a percent of consumption will be larger than the change in factor income as a percent of household income.

> 15 An individual is classified as skilled if he or she has any education post-high school. We defined skills at the individual level. We define labor and capital shares individually, and then aggregated factor shares within the household.

19
In addition, we use information on past shocks as reported in the MxFLS household and community questionnaires to check the robustness of our results to locality-specific events.

A related, though different, concern has to do with a possible bias arising from location-specific time trends. The regression equation controls for state-time fixed effects, and all effects are estimated relative to control communities. However, if there is permanent divergence or convergence in growth rates between our treatment community groups and the control community group, time effects are not completely accounted for. Also, one might worry that the relatively small number of control communities in our sample might affect our results. To address this concern, we also report results following an alternative identification strategy that does not use children in control communities. We estimate equation (1) for each locality group separately, which means that now impact effects are directly identified out of differences across the four cohort-time intercepts.<sup>22</sup> The cost to following this alternative is that now we need somewhat stronger assumption to control for time trends within locality groups. For community groups 1 and 3, we need the difference between a given cohort and the younger (older) group to be a good counterfactual for the difference between that cohort and the older (younger) group. Furthermore, there is no way to control for time effects when we evaluate the second group of communities. One additional feature of this alternative is that now the analysis can be restricted to the sample of households who report being program participants.<sup>23</sup> In addition, Mexico experienced a financial crisis in 1994, known as the Tequila crises, which generated a widespread fall in income, consumption and wages. This is important for us because the group of children we labeled “older cohort” includes those children born between 1994 and

> 22 Due to sample size constraints, covariates are no longer cohort specific. 23 This is not possible under the main identification strategy, because by construction, there are no program
grandfathering.<sup>5</sup> Third, any socioeconomic classification based on a distribution of labor earnings may be endogenous to the very transition that it is intended to capture, which can be reasonably assumed to be strongly (even if not exclusively) motivated by wanting to improve individual or household earnings. So can other categorizations based on consumer goods and assets that are collected in the Colombian survey and whose acquisition may also depend on increased disposable incomes after transitions take place. Of course, a relative poverty measure will not eliminate endogenous concerns, but to the extent that it limits the number of categories to analyze (only two categories, below or above the median, instead of five or ten as in income quintiles and deciles), biases should be less critical.

Results disaggregating transitions by category and poverty status confirm that poor and non-poor workers transition in different fashions (Table 6). For each category of informality and occupation, only the informal salaried, the informal self-employed and individuals out of the labor force have comparable diagonal results. In contrast, there are substantive differences in the relative proportions of those who did not transition among formal salaried, formal self-employed and unemployed. Among the former two, the non-poor in 2008 seem to have transitioned more than the poor, while the unemployed poor transitioned more than the non-poor. Results confirm a high volume of transitions when considering socioeconomic status. Again, the out-of-labor-force category is the most stable of all, followed by salaried workers (with non-poor being more stable than the poor). Very few of the remaining categories changed their labor condition. The magnitude of these transitions is not negligible and, again, transitions are asymmetric: a transitioning worker is more likely to remain within the same socioeconomic category in which the individual started in 2008 than to change socioeconomic status. A quarter of individuals moved into 5 As seen above (Table 1), classifications of workers based on SISBEN and poverty lines differ substantially (even though trends move in similar directions) given that the official
. As a measure of parental education, we rely on the completed years of schooling of the father of a child in all our analysis, in keeping with a large literature on intergenerational educational mobility (see the surveys by Torche (2019), Iversen et al. (2019), and Emran and Shilpi (2021)). All reported standard errors are clustered at the district level, following Duo (2001).

# (5.1) The Eects of Inpres Schools on Years of Schooling of Children

Table 2 reports the estimates of the mobility equation when children's educational attainment is measured by their completed years of schooling which is standard in many studies

> 33The placebo eects are not signicant at the 10 percent level also.

> 34The corresponding event study graphs for years of schooling also support the plausibility of the DiD design. We also checked the event study graphs including earlier birth cohorts in the pre-Inpres period (1950-1956) and the evidence is consistent with the graphs reported in the online appendix. The details are available from the authors.

16
/or credit card in 2014. The GDP data is from the World Bank World Development Indicators Database. The account ownership data is from the World Bank Global Findex Database. The credit/debit card data is from the Global Payments System Survey. Panel B plots the cross-country relationship between the size of the informal economy (measured as a share of GDP) and GDP per capita for 158 countries in 2012. The measure for the size of the informal economy is from Medina and Schneider (2018). The GDP data is from the World Bank World Development Indicators Database.

35
These findings are consistent at the country level, but with some exceptions. For example, in Ethiopia and Nigeria, male NEETs and male students aged 15-18 exhibit greater educational aspirations than their female peers ( **Figure 1** ). Conversely, in other countries, female youths aged 19-25 display greater educational aspirations than male counterparts. Moreover, in Malawi, female NEETs and female students aged 19-25 express higher educational aspirations than their male peers ( **Figure 2** ).

**Figure 2. Share of youths aged 15-18 who would like to complete university or post-graduate studies, by sex and current activity.**

**Figure 1. Share of youths aged 19-25 who would like to complete university or post-graduate studies, by sex and current activity** _._

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100% 95%<br>91%<br>88%<br>90% 85% 90%<br>82%<br>79% 79%<br>80% 80%<br>69%<br>70% 70% 65%<br>62%<br>59% 59%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity.

Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi -
20 relationship observed in the World Fertility Survey (WFS) data i. fairly robust to controls for household income, husband's occupation and urban/rural residence (Rodriguez and Cleland (1981)). On the other hand, there are complications in the relationship. For some surveys, the negative relationship is observed only over part of the educational spectrum, usually at higher levels. Indeed, for certain populations there is an "inverted-U" shape relationship , fertility ir.creasing with education at low educational levels (Cochrane (1978), Graaf (1979), Ashurst, Balkaran and Casterline (1984)). The most recently available and representative WFS data enable some further investigation of the relationship, subject to the qualification that certain weaknesses in data quality, especially age reporting, must reduce the ability of the WIS data to bear conclusions (Goldman, Rutstein and Singh (1985)). Examination of fertility differentials across educational subgroups for different countries with different overall fertility levels shows how specific patterns of differentials are associated with particular stages in demographic transition to low fertility (Singh and Casterline (1985), Cochrane (1983), Timur (1977)). For countries with high overall fertility, fertility remains constant or even increases with higher levels of female education, until the threshold of secondary education, where it falls rapidly. This pattern is often to be observed in countries in sub-Saharan Africa, and some in Asia. For those countries (e.g. in the Middle East and South America) which have reached the beginning of a decline in fertility, we find that the education/fertility
capture the direct impacts of poverty and inequality on growth, as well as potential indirect effects due to other variables excluded from the model (Galor, 2009). Our second model (M2) is taken from the empirical literature on inequality and growth.<sup>21</sup> It comprises a measure of market distortions, given by the domestic price of investment goods relative to that of the U.S., and a measure of human capital, which includes the average years of secondary education of the male

> 18  However, the simulations in Figure 1 suggest that the correlation between poverty and inequality may depend on the prevailing level of poverty: it may turn negative at very high levels of poverty, and vanish at very low levels of poverty. We return to the role of the prevailing level of poverty in Section V.

> 19 Using alternative poverty lines (US$1.25 and US$ 4.00) or definitions ( _P1_ and _P2_ , as defined in the Appendix), estimation results are qualitatively similar: positive and significant coefficients for per capita income, negative and significant for the Gini coefficient. Quantitatively, the parameter estimates differ as expected: for per capita income, they range from -0.021 for _P2_ (US$ 1.25) to -0.211 for _P0_ (US$ 4); for the Gini coefficient, they range from 0.1929 for _P2_ (US$ 1.25) to 0.390 for _P0_ (US$ 1.25). The regression _R_<sup>_2_</sup> ranges from 0.255 for _P2_ (US$ 1.25) to 0.575 for _P0_ (US$ 4).

> 20 This of course follows from the construction of the poverty data. See footnote 38 in the Appendix for details.

> 21 See Perotti (1996), Forbes (2000), Knowles (2005), López and Servén (2009), and Berg et al.
existing definitions of social sustainability and propose a pragmatic definition—instead of providing yet another highly complex characterization—that allows us to measure social sustainability across countries. Second, we construct a global database capturing the multiple aspects of social sustainability emphasized by the previous literature. Third, we provide empirical evidence on the relationship between countries’ poverty and income levels and their social sustainability indices. Based on such correlations, we conclude that, as expected, more socially sustainable societies are less likely to be poor—a result that is consistent across choices of social indicators and functional forms and remains the same when evaluating developmental outcomes other than poverty and inequality (such as human capital or human development). Perhaps less expectedly, these associations are not uniform across components of social sustainability, nor across regions worldwide. The evidence ultimately supports the notion that social sustainability matters not only in its own right, but also instrumentally, because it is associated with reduced poverty and increasing income.

This paper is organized as follows. Section 2 reviews the existing literature defining social sustainability and discusses the challenges in arriving at a consensus definition and measurement. Section 3 proposes an alternative, pragmatic approach based on an inclusive and utilitarian definition—away from rigid characterizations—that leads to a global database of social sustainability indicators. Using this database, we can measure each of the social sustainability dimensions we judge to be most meaningful. In section 4, we use those indicators to diagnose how social sustainability is associated with levels of monetary poverty, per capita GDP, and income inequality. Section 5 concludes with several policy implications associated with our key findings.

# **2. Existing definitions of social sustainability: Why is it so difficult to define and measure?**

Social sustainability comprises the multiple social aspects of sustainable development. The concept of sustainable development was defined by the World Commission on Environment and Development (1987:43) as “ _development that meets the needs of the present without compromising the ability of future generations to meet their own needs_ ”.
Establishment census data collected by national registries, statistical offices, and ministries are the gold standard for information on establishments and firms and serve as a sample frame for firm surveys. They provide information on the number of firms by size, age, sector, and often, formality status. Most African countries carry out such censuses with considerable time gaps between rounds, because of the time and effort required to survey the entire universe of establishments.

While some form of economic censuses exists for Botswana, Burkina Faso, Cameroon, the Democratic Republic of Congo, the Arab Republic of Egypt, The Gambia, Ghana, Mauritius, Namibia, Rwanda, Sierra Leona. Tunisia, Senegal, and Uganda, it should be noted that access to most of this census information is constrained or aggregated in statistical reports.<sup>5</sup> Comprehensive micro-data for this exercise was available only for Kenya, Burkina Faso, Cameron, Rwanda, and Ghana.

Although census data are available for some countries, coverage on economic censuses or firm registries varies considerably, compromising harmonization of the collected data across countries. For example, among the available micro-datasets, Cameroon’s registry excludes the taxi industry, while Ghana’s registry includes modern agriculture, albeit it states that the agriculture sector is not covered. In general, most of these registries and censuses focus on the non-agriculture sector, but to varying degrees.

We extract information about firm characteristics from World Bank Enterprise Surveys (WBES). WBES are firm-level surveys aiming to provide data that are comparable across countries for a representative sample of firms with 5 or more workers. The surveys cover a broad range of business environment topics, including access to finance, corruption, infrastructure, competition, and performance measures. WBES provides a comprehensive cross-country database for 194,000 manufacturing and services firms in 155 countries. The surveys, however, include mostly formal establishments with five or more workers, and therefore exclude micro-enterprises and informal firms, with a few exceptions. This focus on small to large formal
16 Using household and individual panel data, the authors further explore the relationship between agricultural commercialization, welfare and nutritional outcomes. Conventional wisdom suggests that the more farmers commercialize their operations through increased product‐market orientation, the better off they can become. Greater market‐orientation of agriculture would therefore be expected raise incomes, improve consumption, and enhance nutritional outcomes in rural households (von Braun and Kennedy, 1994).

The authors find little evidence of this in the three countries studied, underscoring that many factors other than the degree of agricultural commercialization intervene in shaping nutritional outcomes, including other agriculture related factors. The articles in the 2015 Journal of Development Studies Special Issue Vol 51, Issue 8 guest‐edited by Carletto et al. (2015), explore some of them, such as the role of diversity in crop production and livestock ownership. Livestock seems to emerge as particularly important and positively linked to nutrition, with crop production and diversity of production positively associated in certain contexts. Bio‐fortification also pays off. Given the current emphasis on nutrition‐sensitive agriculture, a better understanding of the transmission channels between crop choice, agricultural market engagement and nutritional outcomes continues to be a research priority.

_There is substantial excess seasonality in food prices._ Although it is commonly accepted that seasonality permeates African livelihoods, surprisingly little attention is paid to it. Because of the seasonal nature of agricultural production, one area where seasonality manifests itself is in food prices. But there is also very little systematic evidence on the extent of food price seasonality, and what is available is largely dated. Better integration of domestic food markets today may explain part of the neglect. Gilbert, Christiaensen and Kaminski (2017) conclude that “while we all know about seasonality, it is very unclear precisely what it is we know.” The authors show that seasonality in staple crop prices can be substantial. This is achieved using trigonometric and sawtooth models to overcome some of the systematic upward bias in seasonal gap estimates from the common monthly dummy variable
> (2) ii,tt<sup>= αα</sup> cc,tt<sup>+ γγ</sup> ZZ<sup>DDLLIICCDDLLDDCC</sup> cc,tt<sup>+ ττ</sup> tt<sup>+ υυ</sup> ii<sup>+ εε</sup> ii,tt Where L LLIIL is the log of billionaire net worth in USD for individual<sup>DDIIDDLLDDPPD</sup> _i_ at time _t_ ; D is the log difference of the commodity price index in country _c_ , is a vector of country-level controls including structure and size of the economy. LLIIL is the year fixed effects and represents individual billionaire DDIIDDLLDDPPD fixed effects. As a robustness check, we estimate equation (2) using country fixed effects instead of DDLLIICCDDLLDDCC billionaire fixed effects. Alternatively, we also estimate equation (2) using resource rents in place of ττ υυ commodity price shocks.

Our identification strategy allows us to account for several endogeneity issues. The commodity price shock variables are plausibly exogenous considering most countries are price takers in most commodities they trade, hence limiting the simultaneity bias. We limit omitted variable bias in several ways. Billionaire fixed effects are used to account for time-invariant billionaire-specific and country-specific unobservables. This can include education, ability, as well as geographic location and main sector of activity if they do not vary over time. The year fixed effects capture common year shocks. We also include country-level covariates that capture the size and structure of the economy, that could be important predictors of billionaire net worth.

6