rafmacalaba/data-use-mentions-extended huggingface dataset:
children are stunted under risk model 1 (Table 2). According to risk model 2, an estimated 475,340 (extremely high risk) to 921,537 (high risk) children are underweight, and 540,627 (extremely high risk) to 1,062,574 (high risk) children are stunted (Table 2).

# **4. Discussion**

Bangladesh has made significant progress in food security and public health in the last few decades. The contribution of groundwater resources to economic development, social wellbeing and public health has been paramount. Despite the importance of groundwater and its critical role in all sectors including health, no multi-hazard risk maps for Bangladesh currently exist. The links between adverse human health and long-term exposure to elevated arsenic (As) and salinity concentrations in drinking water is well-established in the country. Access to drinking and irrigation water supplies is also affected due to widespread depletion of shallow groundwater storage in various parts of Bangladesh threating food security. Here, for the first time, we develop multi-hazard groundwater risk maps for Bangladesh at the national scale by applying geospatial techniques and formulating a set of criteria involving both groundwater quality and storage indicators. We generate two groundwater risk maps combining multiple hydrological hazards such as elevated groundwater As, salinity and depleted dry-season groundwater levels – all of which pose a threat to public health through unsafe and unsustainable drinking water supply from shallow groundwaters.

Furthermore, we test the validity of our national-scale groundwater risk maps using an exposure data set of As-affected population in Bangladesh. The arsenic-affected population data set provided by the Directorate General of Health Services in Bangladesh (DPHE/JICA, 2010) shows that some 18,672 (51%) of the total of 37,039 patients (i.e. primarily skin lesions) are located in areas where >80% wells are found to be As contaminated. Overlaying on our groundwater risk maps, we find that some 1,000 (3% of total patients) to 22,450 (61%
finds excess female child mortality in 20 of the 32 surveys. Using Svedberg's normalization on the other hand (modemr day Sweden as the normalization, which comes close to no normalization at all for child mortality), the number of samples with excess female mortality is roughly equal to that with excess male mortality.

Beyond these disagreements though, the authors concur that anthropometric indicators do not show anti-female bias in the Sub-Saharan African samples. Perhaps more importantly for our paper, the authors both state that the differentials considered in the Sub-Saharan African context are much smaller than those found in South Asia.

For the cross-country analysis, our data source is most similar to Hill and Upchurch (1995) as they also use DHR data to construct their index of gender differentials. They find a female disadvantage in under-five mortality in 31 out of 38 surveys _relative_ to the female disadvantage in a set of Northern European countries from 1820-1963 with matched average mortality. As mentioned above, such an interpretation must be made with caution. Consistent with our findings, however., they find that the region with the highest gender disparity in child mortality is the "Middle East Crescent" (which includes Egypt, Jordan, Morocco, Pakistan, and Tunisia).

As in our findings, Hill and Upchurch find that expanding the set of indicators does not necessarily reinforce the gender disadvantage result. For example, they find a disadvantage in the percent of females who are immunized in 58 percent of the samples, who are stunted and wasted in only 17 and 24 percent of the samples, who have had diarrhea or ARI in 9 and 26 percent of the samples, and who receive treatment of fever or ARI in 43 and 30 percent of the samples.

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mortality for theUN Inter-<br>agency Group for Child Mortality Estimation. Third on accuracy of debt reporting as<br>classified by the World Bank (Source: World Bank WDI metadata). Fourth, on<br>availability of safely managed drinking water data for use byWHO/UNICEF Joint<br>Monitoring Programme.Fifth, on labor force participation data for use by ILO. While<br>these data sources provide only a partial coverage of data used by international<br>organizations, they do provide an indication of the performance of the national<br>statistical system.|
|Dimension 2.1: Data<br>releases|SDDS/e-GDDS subscription. This indicator is based on whether the country subscribes<br>to IMF SDDS+, SDDS, or e-GDDS standards. The source is the IMF Dissemination<br>Standards Bulletin Board. This is a reliable data source but we recognize that it is a<br>proxy for the concept we are seeking to capture rather than a direct measurement.|
|Dimension 2.2: Online<br>access|ODIN Open Data Openness score (Jamison Crowell et al. n.d.). This is a well-<br>established data source with good country coverage, which scores countries based on<br>whether indicators are available online in a format that is machine readable, in a non-<br>proprietary format, downloadable, with metadata available and terms of use. Scores<br>range from 0-1. For more details, consult theODIN technical documentation|
|Dimension 2.3:<br>Advisory/ Analytical<br>Services|Not included because of lack of established methodology. This could be a new<br>indicator of the number of non-recurring products on NSO website (ad<br>hoc/experimental rather than regular releases). The indicator is the number of products<br>found. No established source exists for this indicator.|
|
because time-use specialists suggest it is more appropriate for analyzing time diary data (Stewart 2009). The reported zeroes in any activity might be just measurement error rather than non-participation. Foster and Kalenkoski (2013) find that Tobit estimations are sensitive to censoring window. Nevertheless, we run Tobit regressions on time spent on secondary childcare with full control variables to check the consistency of the results with OLS estimates and confirm that they are consistent with the findings presented in this paper.

10
g. UNESCO 2018, OECD 2003). For instance, for the relative employment rate, a relative employment rate at or near one reflects a similar situation of persons with and without disabilities in terms of the share who are employed.

The indicators under study are listed in Table 2.2. To compile this list of indicators, we started from the list of SDG indicators of the Inter-secretariat Working Group on Household Surveys (2018) in a Report to the 49th Session of the UN Statistical Commission (doc CN.3/2018/7) which showed that approximately one third of the SDG indicators are or can be compiled from household surveys. From this list of indicators, we selected those indicators that might be found in general household surveys or censuses, since health or other topic specific surveys are beyond the scope of this study. We selected and were able to produce estimates for those in Table 2.2 (e.g. unemployment rate, out of school rates for children in the household).

We then added indicators that are known to be particularly suited to assess the situation of persons with disabilities and could be estimated with some of the data sets above: employment population ratio, economic insecurity (food insecurity, exposure to shocks) and indicators that may reflect the extra costs of living with disabilities for households (poverty status once health expenditures are removed from total consumption expenditures), as well as material well-being 9
government. The unit cost is calculated as total public spending divided by the total number of beneficiaries and allocated to households after taking into account the number of beneficiaries in the household. This constitutes the value of in-kind transfers.

For education transfers, we identify the per beneficiary public spending using total education spending at primary, secondary and tertiary levels for two types of schools—boarding and day school—divided by the number of enrollees for each level and type of education. The beneficiaries are identified using survey questions on school enrollment. For health transfers, we employ an insurance value approach by assigning the same per capita spending to everybody sharing the same characteristic such as age, state, type of care, gender, rural/urban etc., with the assumption that the probability a person gets access to these services is the same as that prevailing for others with the same characteristics.

# **_Education_**

Beneficiaries are identified based on information on the household member’s level of school (from primary school to college), ownership of school (public or private), location of school (in Bhutan or overseas) and the type of school (boarding school or day school). Only students attending public schools in Bhutan are

> 22 This average excise tax rate is consistent with the tax rate calculated for the overall liquor revenues and taxes in NSB (2014), “Alcohol Use and Abuse in Bhutan”.

> 23 The Bhutan Input-Output matrix is obtained from Asian Development Bank (2018), “Economic Indicators for South and Central Asia—Input-Output Tables”.

15
**Table 2:** Effects of Turnovers on Bureaucratic Organization

||**Villag**|**e heads**|**Village gov**|**ernment**|
|---|---|---|---|---|
||New leader|Tenure (yrs)|% New appts|% Reshuffing|
||(1)|(2)|(3)|(4)|
|New village head|0.835<sup>_∗∗∗_</sup>|-4.908<sup>_∗∗∗_</sup>|0.182<sup>_∗∗_</sup>|0.089<sup>_∗_</sup>|
||(0.084)|(1.236)|(0.085)|(0.045)|
||[0.101]|[1.527]|[0.100]|[0.052]|
|Observations|442|443|510|510|
|Control mean|0.035|7.96|0.33|0.036|
|Robust p-value|0.000|0.001|0.042|0.073|
|<br>MSE-opt. bandwidth|15.8|31.2|22.0|18.9|
|Effective obs.|172|285|256|235|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observations are village heads in columns 1-2 and villages in columns 3-4. The dependent variable is: in column 1, a dummy equal to 1 if the village head in our survey data is a different individual from the incumbent competing in the most recent village election;
formal sector employees the estimated wage premium is not statistically significant in the majority of countries.

There are plausible arguments both for and against limiting the comparison group to private formal employees or private sector workers in the same occupation. Unfortunately, the data used in this study are too coarse to determine which comparison group generates more credible estimates. The results, therefore, highlight the importance of further research that utilizes richer data from specific contexts to better understand the pros and cons of using different comparison groups when estimating public sector wage premia.

The paper is structured as follows. The next section describes the data sources and variables. Section 3 outlines the empirical strategy. Section 4 discusses the results, and Section 5 concludes.

# **2. Data and Descriptive Statistics**

The analysis draws on data from the World Bank’s Worldwide Bureaucracy Indicators, a country‐level data set containing public sector labor market indicators produced by the World Bank.<sup>1</sup> The WWBI was in turn derived from the International Income Distribution Database (I2D2), which is a set of harmonized nationally representative household surveys—both welfare and labor force surveys— from approximately 130 countries. The I2D2 data set was supplemented with the Luxembourg Income Study (LIS), which similarly harmonizes household surveys from several mostly high‐income countries.<sup>2</sup> The indicators on public employment in the data set include the share of public employment relative to total, wage, and formal sector employment; and distributions of public and private sector workers by age, gender, and academic qualifications. The wage variables capture public sector earnings premiums by gender, age, area of residence, and occupation; and the distribution of public and private sector earnings, and the public sector earnings premium across the earnings distribution. We use the country‐level regional and income classifications from the World Development Indicators (WDI) database. A selected list of variables and their description is given in Annex 2.

We applied a variety
the 1997 experimental evaluation of the _Oportunidades_ program, a similar social transfer program to PAL, reveal that 0.7 percent of control group households migrated to the US within one year (Angelucci, 2015). Each household in our sample has on average one child between the ages of 0 and 6 and if we apply this migration rate in each of the 10 years between 2004 and 2013, we would expect as a rough approximation to not observe ENLACE tests for around 6.8 percent of children in our initial sample because they migrated abroad.

Third, a child could have never enrolled in school or could have dropped out of school before reaching the end of third grade. We can quantify this potential source of attrition using the PAL follow-up survey in 2005: 7 percent of the children aged 8 to 12 (the typical age of children in grades 3 through 6) report not attending school (and there is no difference across treatment groups).<sup>14</sup> Fourth, a student may have not been present on the day of the exam, or the exam was not offered at the school. We do not have data on absence rates in schools, but there is evidence that entire schools did not take the exam in certain years in areas where there is a strong representation of the National Educational Workers Syndicate ( _Sindicato Nacional de Trabajadores de la Educación_ ), a trade union representing 1.4 million Mexican teachers. In fact, the union scheduled strikes and disruptions specifically on the days of the ENLACE test as a form of protest and state-level variation in ENLACE take-up rates demonstrates their influence. For example, in Yucatan and Veracruz, where union membership is relatively low, take-up rates were constantly above 94 percent in all years between 2007 and 2013. In Chiapas, Guerrero and Oaxaca, however, where union membership is high, take-up varied considerably by year, with certain years having take-up rates of 60, 70, or 80 percent. Importantly, as these protests and disruptions are orthogonal
<!-- Start of picture text --> TM<br>ay<br>uo<br>fm)<br>1995 1996 1997 1998 1999 2000 aut bar2002 2003 2004 2005 2006 2007 2006<br>Data source: Pesquisa Nacional por Amostra de Domicilios (PNAD), 1995-2008<br><!-- End of picture text --> <!-- Start of picture text --> oo es<br>ae<br>/<br>/<br>-<br>oO weae<br>aulaewoon<br>nal<br>antall<br>wn —<br>weer”<br>1995 1996 1997 1998 1999 2000 aut bar2002 2003 2004 2005 2006 2007 2006<br>Data source: Pesquisa Nacional por Amostra de Domicilios (PNAD), 1995-2008<br><!-- End of picture text -->
when using multiple surveys (though simulation methods are sometimes used for that purpose). When a survey for a specific country does span multiple dimensions there can be much interest in exploring their joint distribution, though a MIP is not the only tool available for that purpose.<sup>7</sup> Another data constraint also points to the need for multiple measures in practice, namely that the data we typically use in measuring poverty do not tell us much about consumption within the household. To use such data we need to make assumptions about intra-household

> 6 Unique identifiers can in principle link households across two or more surveys, but this is relatively rare in practice, especially in developing countries.

> 7 See, for example, the study by Lokshin and Ravallion (2005) of the joint distribution of wealth and power. Lokshin and Ravallion use standard statistical tools for this purpose, including correlation and regression methods and contingency tables and related statistics for ordinal categorical data.

6
analyzing the demandside factors affecting mobile internet adoption. For instance, Srinuan, Srinuan, and Bohlin (2012) use data from Thailand to find that price, the availability of fixed telephony, and individuals’ age and location are strong determinants of mobile internet adoption. Consumers’ perspectives on service applications are also found to affect mobile internet adoption in Taiwan, China (Hsu, Lu, and Hsu 2007). Hasbi and Dubus (2020) provide evidence of the positive impact of being part of an online social community on mobile broadband use in Sub-Saharan Africa. Meanwhile, a cross-country/region analysis that presents commonalities and differences in how specific demand-side factors affect mobile internet adoption across developing countries is missing.

This study aims to (1) examine how socioeconomic variables and perception factors affect mobile internet adoption; and (2) compare differences in mobile internet adoption across different countries, regions, and demographic profiles from the demand side. The study adds to the literature in the following ways: (1) it is one of the first studies to explore determinants of mobile internet adoption in developing countries, (2) it uses nationally representative household-level data that follow a consistent methodology across countries allowing for cross-country comparisons, which is a rarity in the literature, and (3) it establishes a significant relationship between a few demand side variables and mobile internet adoption, which present significant policy implications. The study finds that demand-side factors are of critical importance for mobile internet adoption. Across the developing world, females, the elderly, those who live in rural areas, and those who have a relatively low level of income or education are less likely to adopt mobile internet. Social network effects are found to have a significant positive impact on the usage of mobile internet. In addition, across regions, although the factors affecting the adoption of mobile internet remain largely the same, the magnitude of their impacts vary.

The paper is organized as follows: section 2 outlines the study’s conceptual framework. Section 3 describes the data used.
exchange rates or floating exchange rate regimes during the sample period, according to the classification in Ilzetski _et al_ (2021). The difference between the two averages is not significant (p-value is 0.17). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on Ilzetzki _et al._ (2021) and WEO (IMF) data.
CIAT), 2011. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Urban Extents Grid. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY.

- Davis, B., Winters, P., Carletto, G., Covarrubias, K., Quiñones, E.J., Zezza, A., Stamoulis, K., Azzarri, C., DiGiuseppe, S., 2010. A Cross-Country Comparison of Rural Income Generating Activities. World Development 38, 48-63.

- de Foresta, H., Somarriba, E., Temu, A., Boulanger, D., Feuilly, H., Gauthier, M., 2013. Towards the Assessment of Trees Outside Forests, Resources Assessment Working Paper. FAO, Rome.

- Degrande, A., Schreckenberg, K., Mbosso, C., Anegbeh, P., Okafor, V., Kanmegne, J., 2006. Farmers’ Fruit Tree-growing Strategies in the Humid Forest Zone of Cameroon and Nigeria. Agroforest Syst 67, 159-175.

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exceeds 30<sup>_◦_</sup> C. We find that heat has larger effects on test scores for students in schools located in colder regions (Column 1 of Table 8). The impact is smaller and/or statistically insignificant for the upper (hotter) quartiles.

Since the locations in the first three quartiles experience relatively low mean number of days where the temperature exceeds 30<sup>_◦_</sup> C, we divide the sample into two groups: the first three quartiles, and the fourth quartile. We present the results for this break up in Table 9. We find that the negative association between high temperatures and test scores is driven by the first three quartiles (schools in colder locations). In hotter regions, though the relationship between hot days and test scores is negative, it is statistically insignificant. These results of hotter regions being able to cope with heat better are consistent with the findings of Zhang et al. (in press).

It is not obvious what kinds of adaptation are leading to these reduced effects in hotter regions. Based on data from World Bank Group (2018), only 8% (11 out of 137 schools) of Ethiopian schools have any adoption of heat adaptation technologies including fans or evaporative air cooling, and air conditioning<sup>20</sup> . This suggests that mechanical cooling is

> 20Notably, this survey sampled the largest school in each location, implying that the true number of Ethiopian schools with cooling technologies might be even lower than 8%.

13
and subsidy rates. The (regionspecific) parameters that govern land distributions are estimated using survey data on landholding sizes. Unobserved variables (e.g. productivities and taste shifters) are backed out via model “inversion” by solving the equilibrium system of equations while taking as given the values of observed variables (e.g. populations, wages, land endowments, crop outputs/prices) in 2019, our pre-ban baseline year.

In the fourth and final step, we use the estimated model to quantify the aggregate and distributional impacts of the ban, separate general- from partial-equilibrium mechanisms, examine how trade policies (e.g., import restrictions) interact with domestic policies (e.g., subsidies), and highlight how features of developing economies (e.g., land inequality) shape the welfare costs of trade 9As they link household income and agricultural expenditure share, non-homothetic preferences shape agriculture’s _aggregate_ share of expenditure and employment in equilibrium, and the aggregate size of the agricultural sector (relative to manufacturing) matters for welfare because manufacturing serves as a “buffer” for workers, providing them with an employment alternative during the agricultural downturn induced by the ban. Land inequality also influences the relative size of the agricultural sector: high land inequality leads to high income inequality, which lowers aggregate agricultural consumption because (given non-homothetic preferences) richer households have lower marginal propensity to spend on food. QRs restrict fertilizer imports while driving a wedge between international and domestic fertilizer prices, potentially creating rents that are captured by importers. Subsidies act as a transfer from mobile workers to farmers, so fertilizer import restrictions reduce this transfer, benefiting mobile workers.