rafmacalaba/data-use-mentions-extended huggingface dataset:
predicting poverty. The correlation between the predicted probability of being poor and the actual poverty status are significant compared to other studies. In addition, when we examined the headcount of households with a predicted probability of being poor larger than 0.50 and the headcount of poverty, we found that the ratio is close to 1 for most countries (for more details, see Appendix A).

We also test for confounding of coping variables dropping hazard variables. We find that for example some coping variables are related to hazards but as we expect hazards are not related to coping. Even though we are not trying to estimate the causal effects of harm or coping variables, the results align with the fact that harm variables can affect the present household's characteristics/copings but not the other way around. The coping and characteristics of the household seem not to affect the variables of harm as is expected. We also estimate the model excluding ThinkHazard! indexes and including the number of occurrences from EM-DAT which is a proxy of frequency of hazards based on historical data. The estimations of EMDAT coefficients do not show significant differences between both models (Table A.2 in the Appendix).

Following Chaudhuri (2003), we model the household income per capita considering idiosyncratic shocks. These shocks enter directly into the vulnerability estimation through the standard deviation of the income model's error term making distributional assumptions in the error variance to draw the poverty rate. However, identifying which type of shocks and risks are operating in the disturbance term is puzzling, making this a handicap. The incidence under this model works as an upper bound as it considers other shocks and risks that fall in the disturbance term, which are not captured through the observables. The results highly correlate with the original vulnerability measures from the Probit model. The details and results of these robustness checks are in Appendix B.

We also performed one more robust check constraining the hazard coefficients to positive values. Vulnerability to poverty using our approach might be higher or less than the
for which overweight/obesity is a major risk factor. These costs include treatment costs, disability-adjusted life-year (DALY) losses, and productivity losses. Table 4 summarizes the major costs included in previous studies, with brief descriptions of how they are calculated. It is proposed to use similar standard methods in the methodology proposed in the next section.

# 5. A Proposed Methodology for Estimating Economic Costs of Unhealthy Diets at the Country Level

Malnutrition in all its forms (as measured by undernutrition/overweight/obesity) is not synonymous with unhealthy diets. There are individuals who have impaired growth despite nutrient-adequate diets because of other factors. Infection (itself related to vaccine status and the water and sanitation environment) is a key factor (Figure 2). Similarly, there are individuals who are overweight/obese despite their diet because of other factors, such as exposure to an obesogenic environment (Figure 2). Finally, having a healthy weight does not imply having a healthy diet. Individuals may maintain a healthy weight while consuming a diet that is inadequate in micronutrients or not balanced or not moderated. One example of individuals with healthy weight but unhealthy diets is those consuming excess salt which can increase hypertension and risks of cardiovascular disease.

Hence, although it is useful to employ cost methodologies similar to those of existing studies of undernutrition and overweight/obesity, adjustments are required since healthy weight does not imply the diet is healthy, nor is the converse true.

Figure 3 illustrates this. The figure illustrates a hypothetical distribution of nutritional status in the population (in this case the population mean weight is in the healthy range, but that is not necessarily the case). The vertical lines delineate the population fractions who are with underweight, healthy weight, and overweight/obese. The dotted horizontal lines separate those within each nutritional status who have healthy diets and those who do not. We will use these fractions to estimate the associated economic costs, using data on stunting in children under 5 years of age to project productivity costs across
,143.

shift to online classes), and 47 percent reported that their households were economically affected by COVID-19 through loss of jobs, business, or income, or a household member’s illness.

# **3 Empirical Strategy**

To estimate the effect of the COVID-19 pandemic, following Bau et al. (2022), we create a “leave-one-out” exposure measure for each respondent by calculating the share of other respondents from her ITI who reported being negatively affected by COVID-19.<sup>4</sup> One concern with this shock measure is that it relies upon self-reported data and might be biased due to measurement error. Thus, we leverage administrative data to construct an additional districtlevel “aggregate COVID-19 shock” measure defined as the population-adjusted cumulative number of COVID-19 deaths between January 30, 2020 and October 31, 2021. We obtained

> 4More details on variable construction are presented at the end of the paper.

4
|---|---|---|
|Table 2|Means of Central Budget Infrastructure Expenditure Data ....<br>.......|15|
|Table 3|Means of Consolidated Budget Infrastructure Expenditure Data ...<br>.....|16|
|Table 4|Central Budget Regressions: Number of Observations by Country ......|18|
|Table 5|Consolidated Budget Regressions: Number of Observations by Country . .|19|
|Table 6|Central Budget Base Infrastructure Regressions .......<br>..........|21|
|Table 7|Central Budget Infrastructure Regressions With Time Dummies .<br>.23||
|Table 8|Central Budget Infrastructure Expenditures Summary Overall Results<br>....|34|
|Table 9|Consolidated Budget Base Infrastructure Regressions<br>.<br>.40||
|Table 10|Consolidated Buget Base Infrastructure Regressions With Time Dummies .|43|
|Table 11|Consolidated Budget Infrastructure Expenditures Summary Overall Results .|54|
Using these results, Tables 9 and 10 report the simulated values of expression (15) for alternative values of the Gini coefficient and the mean income / poverty line ratio, using the variance and covariance patterns of growth and inequality changes shown in the preceding table. Table 9 uses the values from the Dollar and Kraay sample, while Table 10 uses the values from Povmonitor. In both tables, a number close to 1 means that in the scenario in question changes in the poverty measure of interest are mainly driven by growth, whereas a number close to zero means that they are mainly driven by changes in inequality.<sup>24</sup> The simulations in Tables 9 and 10 suggest that, consistent with the previous discussion, in poorer and more equal countries growth accounts for a larger share of the variance of poverty changes. Conversely, inequality changes tend to play a more prominent role in richer and/or more unequal countries. This again suggests that in the former countries growth should be expected to be the main driver of poverty reduction, while the opposite would happen in the latter countries.

Notice also that, for any given configuration of Gini coefficient and per capita income / poverty line ratio, the relative contribution of growth to the overall variance of poverty changes declines as the poverty measure of interest varies from headcount poverty to the poverty gap and then its square. As noted by Kraay (2005), this is a natural consequence of the fact that more bottom-sensitive poverty measures place more weight on changes in the distribution of income than on changes in average income.

So far we have implicitly viewed alternative values of (ν _/z_ ) as reflecting different levels of average per capita income with a given poverty line. But they could also be interpreted the other way around, namely reflecting alternative poverty lines with a given level of average per capita income. In this view, the numerical results above imply that as the relevant poverty line _z_ becomes more generous -- i.e., as
**6.3 Summary of results**

Details of all the beta values calculated for this study can be found in Appendix A2. For each company, where possible, a five-year beta calculated over the period January 1990 to August 1995 is given in addition to a series of annual betas for the years 1990 to 1994. All are calculated using daily data and the index used in the calculation is given by the heading for each country. All estimates are asset betas (ie, corrected for the gearing level of the individual company) except where stated otherwise.6 Although individual companies are of interest, these results need to be summarised in some way so that overall trends may be identified. First, average beta values for the utility sectors in each country were calculated. These results are given in Table 6.2, while Table 6.3 summarises the corresponding regulatory regimes for each sector.

**Table 6.2: Average beta values by country and sector**

||**Electricity**|**Gas**|**Energy**|**Water**|**Telecoms**|
|---|---|---|---|---|---|
|UK|0.60|0.84|-|0.67|0.87|
|United States|0.30|0.20|0.25|0.29'|0.72 (AT&T)|
||||||0.52 (others)|
|Canada|-|-|0.25|-|0.31|
|Japan|0.43|-|-|-|0.62|
|Argentina|0.81**e**|_|_|-|0.86'|
|Chile|0.95e|_-||-|1.20e|
|
We emphasize that our results also hold when assuming that utility is _strictly_ increasing in relative income, instead of _weakly_ increasing. Indeed, Theorem 1 would be unchanged if the subset _U_<sup>_σ_ ̄</sup> excludes _u_<sup>0</sup> .<sup>41</sup>

# **6 Empirical Application**

In this section we apply our proposed index, the hierarchical headcount ratio ( _HHS_ ), to measure global poverty. The goal of this exercise is to illustrate how the switch of index can affect the assessment of poverty. We compare the evaluation of poverty according to the hierarchical headcount ratio both with the societal headcount ratio ( _HS_ ) using the same global poverty line, and with the absolute headcount ratio ( _HA_ ), arguably the most well-known poverty measure. We compare poverty at the country level as well as the evolution and distribution of global poverty. Besides the theoretical arguments for moving away from the societal headcount ratio, ultimately, such a change of index would be justified only if it significantly affects the measurement of global poverty.

## **6.1 Data and Parameters**

Our source of data is PovcalNet,<sup>42</sup> the on-line tool for poverty measurement developed by the World Bank, which offers income or consumption data from more than 1500 household surveys across more than 160 countries. In order to allow for cross-country comparisons, the World Bank converts income or consumption using the 2011 PPP exchange rates for household consumption from the International Comparison Program. Moreover, for the purpose of performing multi-country aggregations the World Bank defines reference years aligning survey estimates from different years. To analyze poverty at the global or regional level, we only use reference years.<sup>43</sup> We use data from 1999 up to 2015, the most recent reference year available. We take 1999 as our base year because before that, we have _HHS_ = _HS_ in several populous countries that still had median income below
viewed 320 caregivers, 139 facilitators and 40 members of the Community Coordination Committee (CCC). We also collected classroom observation data from the 40 escolinhas located in these communities. A detailed description of this qualitative study is presented A. in Appendix

# **4.2 Survey Instruments and Outcomes**

In this section, we summarize the survey instruments used to study the first stage (compliance), primary, and secondary outcomes of the study. Table A2 summarizes the survey modules and the corresponding data collection stages.

## **Compliance**

_Preschool enrollment._ To measure preschool enrollment, we ask caregivers in the baseline and endline surveys if the target child formally enrolled in an _escolinha_ . This outcome is measured as a dummy indicator that takes the value 1 if the caregiver reported that the child is enrolled in a preschool center at the time of the survey.

## **Academic Outcomes**

_Primary School Enrollment and Academic Achievement._ We use our endline survey data to measure if children are currently enrolled at primary school, whether they repeated a grade, and if they were at an appropriate grade for their age. Primary school enrollment is an indicator equal to 1 if the child is formally enrolled in school. “Repeated grade” is an indicator equal to 1 if the child had repeated a grade at least once during the past three years, i.e., since the project started. The variable “appropriate grade for age” is an indicator that takes the value 1 if the child is at a grade that corresponds to their age, while allowing children at the age limit to be in a lower grade.

_Child Cognitive and Social-Emotional Skills._ At baseline, we measured pre-intervention child development in domains that are considered to play a key role in early development (Sabol and Pianta, 2012; Piek et al., 2008), can stimulate future learning abilities (Cunha and Heckman, 2007), and are potential predictors for cognitive outcomes later in life.

istrative and
(or low) value. If those sectors represent only a small fraction of total trade flows, it would be misleading to impose an extreme value of time costs to all HS4 categories within the given GTAP sector.

We take a different approach to deal with the missing values. Specifically, we follow Minor and Hummels (2013) and replace missing values at the HS4 level by the average of non‐missing ones within any given GTAP sector. Doing so enables us to use trade flows of all HS4 / HS2 categories in the construction of weights. We clarify our procedure with the numerical example developed above. Consider the case where Hummels and Schaur (2013) were not able to estimate τ<sup>���</sup> , for example because the U.S. data do not feature any import of this category. In such a case, we create an average value τ<sup>���</sup> computed as the average “value of time” of all non‐missing HS categories within the GTAP sector. If the world consists of only our two countries 1 and 2, the formula for τ<sup>���</sup> would be simply: We then replace missing values in our data set with those averages and obtain: _Table 9: Disaggregated trade flows between country 1 and country2 in sector GTAP_1_

|**Origin**|**Destination**|**GTAP Sector**|**HS Category**|**Τ**|**Trade Flow**|
|---|---|---|---|---|---|
|Country 1|Country 2|GTAP_1|HS_1|τ<sup>HS1</sup>|5|
|Country 1|Country 2|GTAP_1|HS_2|τ<sup>HS2</sup>|20|
|Country 1|Country 2|GTAP_1|HS_3
# Introduction

In 2022, natural disasters led to over $220 billion in economic losses, affecting 185 million people.<sup>1</sup> Losses in 2023 are on track to exceed the previous year’s<sup>2</sup> and large-scale disasters, such as record extreme heatwaves, the violent monsoon in India, and a prolonged severe drought in the Horn of Africa have received widespread media and public attention.<sup>3–6</sup> The frequency and intensity of disasters and their impacts has increased over the last decades, a trend that is set to continue, and likely accelerate, due to climate change and global warming.<sup>7–12</sup> Reporting on disaster impacts relies predominantly on macro statistics. A key data source is the Emergency Events Database (EM-DAT), which is a publicly available global inventory of disaster impacts that is widely used in media,<sup>13</sup> research,<sup>14</sup> and policy reports, including recently the World Bank’s 2023 ‘Atlas of the Sustainable Development Goals’ and the Food and Agriculture Organization’s (FAO) 2021 report on ‘The impact of disasters and crises on agriculture and food security.’<sup>15,16</sup> Here, we offer a different approach to studying disaster impacts, based on survey micro-data. We quantify the value of crop production losses due to adverse climatic events on more than 120,000 fields across six African countries and study the impacts of these events on African agriculture, rural populations, and the national economies. Agriculture is a key sector, on which many households in the region depend for their livelihoods, especially the poor and rural households.<sup>17</sup> Agriculture dependent households are thought to be particularly at risk of suffering the impacts of climate change and adverse shocks. Climate change and natural disasters are expected to be especially severe in rural
population is weakly negatively correlated with PEFA scores, but can identify no clear pattern. He notes, however, that “binary associations are not necessarily significant from a statistical point of view and could therefore be potentially misleading”. Perhaps more usefully, De Renzio (2009) also presents a basic multivariate regression analysis, including assessment of the impact of population size. Results from the multivariate, in contrast to findings from the bivariate analysis, show that population is positively correlated with PEFA scores. No causal explanation for the observed relationship is suggested, other than that “there could be economies of scale in investing in budget systems in larger countries” (De Renzio 2009: 10).

Andrews (2010c) confines his analysis to Africa, investigating patterns in PFM performance and relationships between PFM performance and country characteristics using PEFA data. Through coding PEFA indicators according to different categories of process, Andrews reaches a series of powerful conclusions regarding the political economy of PFM reform in Africa and the tendency of PFM systems to take on the “form” of international good practice, without necessarily altering underlying and politically-influenced functions (see also Andrews 2009). For African countries, scores for budget preparation processes are comparatively stronger than those for budget execution and oversight processes, reflecting the fact that it is easier for developing, and especially fragile countries, to develop a budget than to ensure its effective and accountable implementation. Actual practices also tend to lag the implementation of new laws and processes, with stronger scores against indicators that can be improved by stroke-of-the-pen measures than those that impact on the way things are done and resources allocated on the ground. Finally, scores are generally higher against indicators for processes that can be implemented by a narrow, concentrated set of actors. Conversely, processes are generally weaker when they involve multiple players, especially outside of central agencies, such as Ministries of Finance. Andrews (2010c) also reaches some broader conclusions regarding country characteristics influencing PFM performance in Africa, showing correlations between improved PEFA performance and sustained economic growth,
, where ethnic networks help overcoming information problems linked to the very nature of the goods exchanged (Rauch and Casella, 2002, Rauch and Trindade, 2002). How the relationship of substitutability or complementarity between trade and migration is impacted by the skill composition of migration, however, remains unclear. In the same vein, whether FDI and migration are substitutes (as one would expect) or complements remains an unanswered question, although many case-studies suggest that migrants’ networks favor what sociologists have labelled ”brain circulation” or ”brain exchange” (e.g., Saxeenian, 2001, Arora and Gambardella, 2004).

In terms of our notations, and as in the case of repatriated savings, such migrant networks favoring knowledge di¤usion and foreign direct investment can be modeled as a negative shock on the risk premium, 1⁄4; with similar e¤ects on the capital/labor ratio and, thus, on domestic wages and incentives to invest in education.

# 4 Policy implications

It follows from the theoretical and empirical survey above that the consequences of the brain drain for source countries are ambiguous. Optimal policy responses are thus unclear. In this section, we discuss two important policy issues regarding the source countries, migration and education policies. Our discussion is based on the out28
to CPI bias. This assumption may not hold if there are additional, unobserved forces that shift the Engel curve - such as changes in preferences, or variations in the way consumption data are collected. Analysis in Gibson, Le and Kim (2016), for example, suggests that the method does not perform well in a spatial context. A similar concern is that even though the method controls for changes in household characteristics, biases can arise from assumptions about the nature of relationship between these covariates and the food share. For example, Logan (2009) argues that the effect of household size on demand varies over time and that this is not captured by the standard Engel curve estimation because of the way demographic effects are modeled. Using household survey data on the United States from 1888 and 1935, Logan (2009) finds that when changing household size effects are taken into account, estimates of CPI bias are reduced by 25 percent.<sup>5</sup> A related critique is that Engel curve estimates conflate various types of CPI bias and are hence difficult to interpret. As noted earlier, Engel curve estimations are used primarily to tackle biases arising from commodity and outlet substitution. However, as argued by Beatty and Crossley (2012), the method may conflate the above form of CPI bias with inflation inequality across the distribution (plutocratic bias). This may be problematic, in particular, if Engel curve estimations are used to make statements and recommendations referring to the quality of the CPI and its methodology. Plutocratic weights, for example, are preferred for some purposes of the CPI, such as the deflation of economic aggregates (Pollak 1998) and are hence (at least partly) an explicit component in the design of the index.<sup>6</sup>

# **III. Empirical methodology**

Our starting point is the Leser-Working form of the food Engel curve, which is defined as a linear function of the log of real total expenditure and a relative price term,
larger. In low-income countries, this is likely due to the pandemic not hitting rural areas (where the poor predominantly live) as forcefully, while in high-income countries this is likely due to the extensive social protection programs implemented in 2020.

All our results relate to household disposable income or consumption expenditure for 2020. It is likely that with other measures of welfare, or with the same measure of welfare beyond 2020, the results would be different. Our results, for example do not speak to what happened to wealth inequality or health inequality during 2020. Our results also do not speak to what happened in 2021 and beyond, where data is even more sparce. It is likely that inequality in vaccine access and take-up between countries allowed wealthier countries to recover faster, reinforcing the increase in between-country inequality experienced in 2020. It is also likely that some wealthy countries stopped their emergency social protection programs in 2021, leading to increases in withincountry inequality.

31