rafmacalaba/data-use-mentions-extended huggingface dataset:
the m</mark> acro and micro economic <mark>literature tends to find overall positive effects of subsidized childcare on female and maternal employment in higher income countries, particularly in countries with relatively higher costs of childcare at baseline (Olivetti and Petrongolo 2017). Family-oriented policies (including family subsidies that could be used for childcare) along with labor market institutions (such as employment protection) explain almost 25 percent of the actual increase in LFP for young women in 15 European Union countries (Cipollone, Patacchini, and Vallanti 2014).</mark> <mark>The relationship between maternal labor market outcomes and access to childcare may be different in developing countries. First, the nature of jobs available for women in low- and middle- income countries may be compatible with simultaneously minding children: agricultural work or selling goods at a local market can be done with children alongside. Indeed, Aaronson et. al. (2021) find that fertility, which is proportional to maternal years devoted to childcare, is not significantly related to female labor supply in developing countries, only in developed countries. Examining data from 103 countries (both developed and developing) over 200 years, they conclude that there is greater compatibility of childcare with agriculture. FLFP may be already high in poorer nations due to the rural nature of their economies, and less dependent on availability of childcare services. Aligned with this hypothesis, in a country-level analysis examining differences in preschool enrollment between 1965 and 1980, O’Connor (1988) argues that increases in preschool enrollment are likely driven by increases in women’s participation in industrial and service sectors.</mark> <mark>Second, women’s labor market response to childcare supply or costs depends on the current level of female participation in the labor force (Akgunduz and Plantenga 2018). Thus, in comparison to findings from high income countries, we may expect greater heterogeneity in impacts of childcare across different low- and middle- income countries, given a wider range of
|South<br>Asia|World|
|Social Assistance/Safetynets|51.9|51.1|63.3|13.2|15.6|33.2|41.6|
|Cash Transferprograms|5.9|9.2|16.4|2.9|3.8|0.9|5.7|
|Social Pensions|2.6|4.4|9.5|0.1|4.4|0.4|2.9|
|Other cash transfers||||||||
|programs: family, child or<br>disability allowances|1.9|32.0|1.2|0.1|5.6|0.1|5.0|
|Conditional Cash Transfer<br>program|29.6|0.0|0.0|0.3|0.0|0.4|5.1|
|In-kind foodprograms|7.1|8.8|7.0|5.1|0.5|27.1|15.4|
|Other social assistance<br>programs|18.5|11.7|35.6|7.3|2.8|9.1|15.0|

Source: World Bank (2008) Note: *Country-specific wellbeing distribution: Quintile distribution estimated for each country.

> 4 Safety nets include cash transfers (conditional or unconditional), social pensions, other cash transfers such as family or disability allowances, in-kind food programs and other social assistance programs such as school feeding, cash-for-work programs, for example. See World Bank (2008).

4
like Zimbabwe. Spatial analysis methods of this nature can be taken as a useful model for similar contexts where vaccine prioritization may be important in public health planning. It is also a data scarce environment when compared to some of the countries studied more intensively during the pandemic, and thus can provide a useful example of how similar contexts can be studied under such circumstances.

## Data

The main data sets used in this analysis are: the Demographic and Health Survey (DHS) from 2015 in Zimbabwe; population density, night-time light intensity for 2015 and distance to nearest Open Street Map (OSM) road obtained via WorldPop (www.worldpop.org); and poverty Small Area Estimations (SMEs) from the World Bank at ward level (Masaki _et al._ , 2020).

The 2015 DHS was used to inform the creation of a hand-washing risk index drawing from data on access to hand-washing facilities with soap and water at the household level. Respiratory illness data was taken from the same DHS survey but only available for children under five years of age. Studies have indicated that children can often be the source of respiratory illness transmission to
-|---|
||Total|Agriculture|Industry|Services|Agriculture|Industry|Services|
|2009|54.6|13.0|13.8|27.7|5.9|2.5|12.4|
|2010|57.5|13.4|15.0|29.1|5.8|5.2|12.9|
|2011|59.9|14.3|15.4|30.2|2.9|8.4|4.9|
|2012|63.2|14.8|15.7|32.8|6.7|2.4|4.0|
|2013|67.2|15.4|16.7|35.0|2.9|2.2|8.4|
|2014|69.0|16.0|16.4|36.7|4.3|6.8|6.8|
|2015|67.9|16.6|14.9|36.4|3.7|-2.2|4.8|
|2016|68.5|17.2|15.2|36.1|4.1|-8.9|-0.8|
|2017|69.8|17.5|15.5|36.7|3.4|2.1|-0.9|
|2018|71.4|18.0|15.9|37.5|2.1|1.9|1.8|
|2019|70.0|18.3|15.0|36.7|2.4|2.3|2.2|

Note: the table shows sectoral GDP data. Data in the last three columns on sectoral
population size and area within EAs is much more easily handled algorithmically. By taking into account recent data on boundaries, roads and other features the algorithmic process also creates EAs that are more practical for fieldwork and can be updated as new data become available. Finally, because each EA is already associated with an estimation of population size, these EAs can be easily turned into a nationally representative sampling frame.

One limitation of the method we have presented is that it relies on good quality data. Modeling population density is not the object of this work (Lloyd et al. 2017; Wardrop et al. 2018), however, reliable population estimates are crucial to use our method to obtain operational EAs. In addition, in Somalia, the method works well where OSM data sets and other ancillary data were available and of good quality - mostly in the highly populated areas or where volunteer OSM mapping effort has been highest. For instance, the boundaries of the first category (Figure 4a, b, c, g, h and i) are aligned with visible demarcations on the ground. This is since the used geospatial layers to automatize the EA delineation were derived from high-resolution satellite imagery. This pattern can be seen in the second category, but there is often a visible discrepancy to the actual natural boundaries (Figure 4 d and f) where natural boundary data appear not to exist. Finally, EA boundaries in the third category (Figure 4e) are devoid of boundary data and rely entirely on the quadtree algorithm to generate EAs. The lack of data or vast featureless areas is particularly problematic in rural areas because large stretches of land in Somalia are not or only sparsely populated. In addition, OSM road coverage does not exist in these areas. However, we note that the algorithm could easily be updated and re-run when new data become available. With the increasing prevalence of open-source data in development contexts (e.g. AidData.org, GRID3), these data gaps are likely to be filled before the next
scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 1*** 0.98***<br>H<br>Kendall tau-b 0.82*** 0.99*** 0.9***<br>Spearman 0.98*** 0.86*** 0.82***<br>A<br>Kendall tau-b 0.9*** 0.67*** 0.7***<br>Spearman 0.97*** 0.99*** 0.97***<br>M0<br>Kendall tau-b 0.89*** 0.96*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6<br>-.05<br>CCDF<br>.4 Difference<br>.
<!-- Start of picture text --> Low income 30.5<br>Average across all<br>countries: 30.7%<br>Lower middle income ed<br>Upper middle income 34.3<br>incomeaveragelevel A<br>High income 28:2!<br>0 25 50 75 100<br>SDG Gender Indicator Availability<br>The 25th percentile of the SDG gender availability is 26%. The median is 32% and the 75th percentile is 38%. Means are<br>unweighted. Circles are sized according to population<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>y = 45.76 + -3.20 x, R2 = 0.03<br>& (5.58) (1.33)<br>n<br>5 75<br>o<br>2<br>To<br>£<br>[U}<br>B<br>aa@ °° °@ @ e re)@ % @<br>B © °8 8, & ee @¢ * e<br>2 Pf © ° © ° % Fog 8 66 Pog<br>><br>ns ee oe Eas — oath ng og<br>Fa o ° °° %e © oo f°, ee © ©<br>@= 2 @ orn)@ @ Q @©e@ e @ ° @ @<br>S ° eo ° @<br>< -e°©© eto) ©<br>@@<br>()<br>1,000 10,000 100,000<br>GDP per capita, PPP (current international $), logged<br>region9 . @@ EastEuropeAsia & CentralAsia& Pacific @@ LatinMiddleAmerica East
and ethnicities.<sup>12</sup> Immigrant networks are an important factor in the location choices of prospective migrants as networks can facilitate settling down, adaptation to a new geographical environment, job search process. If destination district � has historically hosted a large share of immigrants from origin district �, we expect individuals from district � who decided to migrate to move to district � because of the large share of immigrants coming from the same origin district already residing therein. Therefore, the historical immigration rates gives variation in the number of immigrants in destination districts that is presumably exogenous to current consumption levels. The identifying assumption is that the historical distribution of immigrants across districts is likely to be uncorrelated with consumption patterns of households in the same districts about 6 to 20 years later, depending on the time restrictions we impose on the stock of migrants that defines our immigration variable.<sup>13</sup> Since the identification of the causal effect of migration and consumption relies on this assumption, we consider potential threats to its validity.

One potential threat is that historical inequality and poverty levels that contributed to determine historical immigration rates also affect current levels of consumption due to the inter-generational transmission of poverty. In order to rule out this possibility, we control for a set of historical variables from the 1984 Census that are meant to proxy the historical inequality and poverty levels. The controls include the share of the population aged 6 and above with no schooling and the share of the employed population aged 10 and above engaged in agriculture.

Another threat to the validity of our instrument is that the historically high immigration rates triggered a more rapid development process in high-immigration districts, and this in turn caused changes in the distribution of income and ultimately consumption that are still observed today. Given the lack of information about historical inequality indicators, we include the Gini index of years of schooling among the population aged 6 and above and the average number of years of schooling of the population aged 6 and above, where education aims at capturing
# **Acknowledgments**

We thank Lina Cardona, Carlos Da Maia, Francis Mulangu, Mario Negre, Soudiki Soubeiga, and Michael Weber for their help obtaining data; Haishan Fu, Olivier Dupriez, Craig Hammer, and Jed Friedman for their support; and Brian Amaro, Nahum Maru, and Rohan Sikand for help with initial analysis. This project was partially funded by the Knowledge for Change Program’s Phase IV-funded programmatic research project “Understanding Trends in Sub-National Differences in Economic Well-Being in Low- and Middle-Income Countries" and by the Keck Foundation.

## **Author Contributions**

DN, TK, MB, SE, and DL conceived of the project and designed analysis; RL processed data; ZZ and TW led the analysis; ZZ, DL, DN, and MB wrote the paper.

## **Code and Data Availability**

Code to conduct analysis and generate figures is available at <u>https://github.com/Z-Zheng/dynamic_highres_poverty. We do not</u> currently have permission from country national statistics offices to share the household level data or image-level labels.

Page 10 of 11
no growth in the survey cost.

# **IV. Total survey implementation and technical assistance costs**

Building on our discussion in the previous section, a baseline total survey implementation cost estimate is calculated assuming that 1) each country implements a multi-topic household survey every three years, allowing for the estimation of official poverty and inequality statistics; 2) a sample of 7,000 households is interviewed per survey; 3) each country’s estimated survey implementation cost per household is identical to the regional average derived for that country from our database of survey implementation costs; and 4) the survey implementation costs per household grow at an annual rate of 2.5 percent. We will refer to these as our _preferred implementation cost assumptions._ Under these assumptions, an estimated **USD 692 million** in 2014 prices would be needed for conducting multi-topic household surveys every three years in the 78 countries of interest over the 15-year period between 2016 and 2030.

13
propensity score matching methods. They report earnings losses during the first five years after displacement that range from 18 to 35 percent for workers whose firm closes, and from 14 to 25 percent for workers who exit a firm that suffers a mass layoff.

The literature has poorly studied the impacts on labor market outcomes of job displacement in developing countries. Eslava, Haltiwanger, Kugler and Kugler (2010) study the Colombian labor market and look at how trade liberalization affected workers displaced due to plant closings. The authors document the job reallocation that followed trade liberalization in the country and analyze workers ́ outcomes after they reentered the workforce. They find that displaced workers experience a reduction in wages and tenure (time in a job) and a lower probability of working in the formal sector within the first two years after their displacement. Yet they find that Colombian workers’ wages fully recover three years after a plant closing.

For the case of Mexico, Kaplan, Martinez, and Robertson (2005) look at changes in formal wages following job displacement. They use administrative data from Social Security records to study the effect of job displacement on wages under different economic conditions. They find large wage losses when displacement occurs in periods of high unemployment and in regions with less economic activity. Their study is subject to two limitations. First, the Social Security records only collect information on formal employment and do not differentiate between informal employment, unemployment, and individuals who have left the labor force. Second, the Social Security records do not make it possible to identify the source of job separation, and as the authors acknowledge, “the cause of separation is important because workers who leave voluntarily are more likely to have more positive economic prospects after they leave their current firm. Including voluntary separation would, therefore, bias the estimated effects of displaced workers upwards.” (Kaplan _et al_ ., 2005).

3
ninth deciles. In terms of standard deviations, the disadvantage faced by Natives is greater in the US than in Argentina in all outcomes but precarious housing. And relative to the difference across counties, the magnitude of the disadvantage of Natives is greater in the US than in Argentina for all outcomes in which Natives face a disadvantage.

While a comparison of the disadvantages faced by Natives across Latin American countries falls beyond the scope of this paper, we can compare the disadvantages we document in Argentina with the disadvantages regarding labor force participation, unemployment and use of computers documented for other countries by Freire et al. (2015). Freire et al. (2015) document small unemployment gaps between Natives and non-Natives based on census data from urban areas in seven Latin American countries. In some of these countries, Natives have a lower unemployment rate, while in others they have a higher unemployment rate (the differences are 2 percentage points or less). As shown in Table 3, Natives experience 1.2 percentage points higher unemployment in Argentina (without distinguishing between rural and urban areas). For urban areas this gap is 1.3 9
.

- “Dartmouth Atlas Project.” 2019. Dartmouth Atlas of Health Care. 2019. https://www.dartmouthatlas.org/.

- Duflo, Esther. 2000. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” Working Paper 8061. National Bureau of Economic Research. https://doi.org/10.3386/w8061.

- Eurostat. 2019. “Total General Government Expenditure.” 2019.

- https://ec.europa.eu/eurostat/web/products-datasets/product?code=tec00023.

- Ganimian, Alejandro J, and Richard J Murnane. 2014. “Improving Educational Outcomes in Developing Countries: Lessons from Rigorous Impact Evaluations.” Working Paper 20284. National Bureau of Economic Research. https://doi.org/10.3386/w20284.

- IMF. 2019. “A Strategy for IMF Engagement on Social Spending.” IMF Policy Paper. https://www.imf.org/en/Publications/Policy-Papers/Issues/2019/06/10/A-Strategy-for-IMFEngagement-on-Social-Spending-46975.

- Jackson, C. Kirabo, Rucker C Johnson, and Claudia Persico. 2015. “The Effects of School Spending on Educational and Economic Outcomes: Evidence from School Finance Reforms.” Working Paper 20847. National Bureau of Economic Research. https://doi.org/10.3386/w20847.

- Ke, Xu, Agnes Soucat, Joe Kutzin, Callum Brindley, Elina Dale, Nathalie Van der Maele, Tomas Roubal, Chandika Indikadahena, Hapsa Toure, and Veneta Cherilova. 2018. “New Perspectives on Global Health Spending for Universal Health Coverage.” World Health Organization. http://www.who.int/health_financing/documents/health-expenditure-report-2017/en/.

- Kraay, Aart C. 2018. “Methodology for a World Bank Human Capital Index.” WPS8593. The World Bank. http://documents.worldbank.org/curated/en/300071537907028892
||LMI market share|0.00044|0.00102**|-0.00089**|-0.00029|
|---|---|---|---|---|---|
|||[0.00045]|[0.00051]|[0.00043]|[0.00081]|
|LIC|Export share|0.00074|-0.00083|-0.00136**|0.00038|
|||[0.00068]|[0.00072]|[0.00058]|[0.00068]|
||LI market share|-0.00057|-0.00088|0.00080+|0.00098|
|||[0.00128]|[0.00142]|[0.00056]|[0.00104]|
|Number of firm-year observations||8,876|7,900|8,443|7,848|
|Number of firms||3,385|2,786|3,031|2,752|

Source: GEOSTAT Statistics Survey of Enterprises for 2006-2017.

Notes: Robust standard errors clustered at firm level in brackets. Includes turnover, firm fixed effects, and year and industry effects. *** p<0.01, ** p<0.05, * p<0.10, + p<0.20.

14
_Progress in International Reading Literacy Study_ (PIRLS), which is applied to younger students, shares a number of common features.<sup>2</sup> As anyone who has been to school may recall, performance in a test, while probably preferable to a simple indicator of enrollment or attendance, is not a perfect measure of learning either. For one thing, tests and test items (i.e. questions) vary in difficulty. The final result is known to measure scholastic ability or learning achievement only imperfectly. For this reason, all of the aforementioned surveys present scores constructed from the raw results by means of Item Response Theory (IRT) models, which attempt to account for “test parameters”, so as to better infer true learning. This process generates an arbitrary metric for test scores, which are then typically standardized to some arbitrary mean and standard deviation.

Using these standardized test scores, a number of studies have attempted to provide international comparisons of educational inequality on the basis of _achievement_ , rather than _attainment_ . Micklewright and Schnepf (2007) and Brown et al. (2007) examine the robustness of measures of central tendency and dispersion in the distribution of student achievement obtained using different surveys, by comparing the measures and country rankings across them. They find broad agreement across surveys, but also some evidence that the specific statistical models used to estimate IRT adjustments do affect results, in particular for less developed countries. Marks (2005), Schultz, Ursprung and Wossmann (2008), and Macdonald et al. (2010) examine the question of intergenerational persistence in educational achievement, which is closely related to that of inequality of opportunity, and present cross-country comparisons of measures of the association between student achievement and certain family characteristics.

This paper seeks to contribute to that literature by proposing two simple and closely-related measures of inequality - one for educational achievement and another for opportunity to education – and reporting them for all countries that participated in the 2006 wave of PISA surveys. To measure inequality
# _2.2 Flood fatalities_

Unlike equation (1) and most previous papers on natural-disaster fatalities, the unit of analysis in our model of the number of people killed ( _D_ ) is not country-year but rather an individual disaster,<sup>3</sup> in our case a particular flood event _i_ in a given country and year. The benchmark specification parallels equation (1), with _vijt_ now used to denote the error term: _Dijt_  _g_ ( _Popijt_  1, _Yjt_  1, _G jt_  1, **Z** _jt_ , _c j_ ,  _t_ , _vijt_ ) (2) One difference is that _Popijt_ is now event-specific. As described later, we generated this variable by overlaying the area of a country affected by a particular flood event with a global population map. Income per capita and the governance indicators are only available at the country-year level, so they are matched more coarsely to the corresponding flood events. For the same reason as in equation (1), we lag all the explanatory variables except a country‘s natural characteristics.

In addition to event-specific deaths, the DFO provides a measure of the physical magnitude of each flood event ( _M_ ). This variable enables us to decompose equation (2) into two components. The first is a model of the effects of income and governance on deaths, conditional on flood magnitude: _wijt_ is the error term. This equation isolates the direct effects of the variables, as shown in the right-hand side of Table 2.<sup>4</sup> The second is a model of effects on flood magnitude:

> 3 Kahn (2005) analyzed death and destruction caused by individual earthquakes but not other individual disasters.

> 4 We cannot distinguish between the preemptive and reactive channels highlighted in Table 2, as we do not have data on the quality of the emergency services, early warning systems, or specific zoning and building regulations.

8
34 Table 1. Descriptive statistics: main variables

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**Panel **<br>|**A: Scho**|**ol Level**<br>||||
|Enrollment|119,061|470.661|651.445|1.000|10320.000|
|Promotion Rate|119,061|94.290|10.241|0.000|100.000|
|Dropout Rate|119,061|3.214|8.874|0.000|100.000|
|Saber 11 Math STD. Scores|60,348|0.097|1.003|-3.946|7.803|
|Saber 11 Language STD. Scores|60,348|0.099|0.997|-4.027|5.013|
|Public Schools|119,061|0.602|0.489|0.000|1.000|
|N. of Teachers|44,890|32.852|32.940|1.000|347.000|
|Pupil/Teachers Ratio|44,890|21.260|39.266|0.120|2332.000|
|Temporal Teachers (%)|44,890|21.787|25.812|0.000|100.000|
|Capacity Gap at 2016|16769|60.644|130.314|0.000|2875.000|
|**Panel B: **|**Munici**|**pality Le**|**vel**|||
|Predicted Cum. Venezuelans Infow|6,874|6.746|22.
specified, expenditure on food and non-alcoholic beverages is separated into two categories in this paper: food and non-alcoholic beverages purchased for home consumption, including takeaway food (“food”); and food and non-alcoholic beverages consumed in restaurants, canteens and cafeterias (“restaurant food”).

5 Food made up 18.5% of total household expenditure, on average, in 23 OECD countries examined by Thomas (2020) using household budget survey microdata. In contrast, the next most common expenditure categories – newspapers and books – together made up only around 1% of total household expenditure, on average, in the same 23 countries.

4