rafmacalaba/data-use-mentions-extended huggingface dataset:
_Table 3_ - Years of life lost related to dietary risks in Brazil

|Diseases related to dietaryrisks|Years of life lost|
|---|---|
|Ischemic heart disease|1,700,337|
|Intracerebral hemorrhage|371,371|
|Diabetes mellitus type 2|358,077|
|Total cancers|335,480|
|Ischemic stroke|281,207|
|Colon and rectum cancer|204,317|
|Subarachnoid hemorrhage|148,322|
|Hypertensive heart disease|60,017|
|Stomach cancer|39,746|
|Esophageal cancer|38,375|
|Breast cancer|32,387|
|Tracheal, bronchus, and lungcancer|20,656|
|Other cardiomyopathy|19,691|
|Chronic kidneydisease due to hypertension|11,773|
|Chronic kidneydisease due to diabetes mellitus type 2|11,361|
|Aortic aneurysm|11,041|
|Chronic kidneydisease due to other and unspecified causes|11,022|
|Chronic kidneydisease due toglomerulonephritis|10,726|
|Other cardiovascular and circulatorydiseases|9,911|
|Atrial fibrillation and flutter|6,022|
|Non-rheumatic calcific aortic valve disease|3,035|
|Endocarditis|2,909|
|Chronic kidneydisease due to diabetes mellitus type 1|2,733|
|Rheumatic heart disease|2,209|
|Peripheral arterydisease|1,389|
|Total|3,694,114|

Source: Global Burden of Disease 2019

# 4.3. Hospital Information System - SIH/SUS

The average expenditures for the treatment of each disease are derived from the Hospital Information System of the Brazilian Unified National Health System (SIH/SUS), which adheres to the International Classification of Diseases, 10th revision (ICD-10). It is important to note that these expenditures primarily reflect the public sector (based on reimbursement rates).

To establish the medical expenditures associated with each risk factor-related disease, we link them to the
can adapt to climate-driven changes in labor availability by adjusting firm locations. For instance, Jin et al. (2021) document higher establishment closures in U.S. counties exposed to abnormally high temperatures due to lower local demand. It is not clear

> 2Boustan et al. (2020) detect increases in out-migration from U.S. counties affected by severe natural disasters, but we did not find empirical evidence on effects on migration from natural disasters in LMICs.

6
|||✓|✓||
|Observations|1,194|1,188|1,194|7,363|3,399|2,752|
|Countries|180|179|180|180|180|178|
|Years|14-20|14-20|14-20|92-20|05-20|05-20|
|Within R-Squared|0.032|0.075|0.125|0.151|0.079|0.068|
|R|obust standa<br>*** p_<_0.01|rd errors in<br>, ** p_<_0.05|parenthes<br>, * p_<_0.1|es|||

_Notes._ All specifications include country and year fixed effects. Robust standard errors, clustered by country, are in parentheses. _p_ -value for the null hypothesis that the estimated effect differs across night lights data sets in brackets. “Within R-Sq.” is adjusted and within all fixed effects. In comparison with Table S1 Column 6, which presented results using DMSP-OLS alone, Column 6 uses the same OLS specification but extends the sample through 2020 using VIIRS. This column becomes Table S3 Column – 1 in the text. Note, two countries are dropped in comparison with Column 5 Bermuda and the British – Virgin Islands because they are smaller than the resolution of OMI, and hence not included in the NO2 sample. In Column 6, the satellite average is computed across OLS sensors only.

DMSP-OLS specification from Table S1 Column 6, which starts in 2005 where OMI NO2 data begins, with Column 3. Column 6 now uses satellite averages, for comparability with Henderson et al. (2012). This is equivalent to Table S3 Column 2, below. Including country-specific time trends finally yields Table 2, Column 2, the baseline night lights specification used in the text.

# **B.2 Results without Country Time Trend**

Table S3 conducts the same estimation
Policy Research Working Paper 10469

# **Abstract**

Urbanization combined with climate change are exacerbating water scarcity for an increasing number of the world’s emerging cities. Water and sanitation infrastructure, which in the first place was largely built to cater only to a small subsector of developing city populations during colonial times, are increasingly coming under excessive strain. In the rapidly growing cities of the developing world, expansion does not always keep pace with population demand, leading to waterborne diseases, such as cholera (Vibrio cholerae) and typhoid (Salmonella serotype Typhi). Funding gaps therefore make targeting for efficient spending on infrastructure upgrades essential for reducing the burden of disease. This paper applies geospatial analysis in Lusaka, Zambia, in the context of the cholera outbreak of October 2017 to May 2018, to identify different water and sanitation infrastructure investment scenarios and their relative impact on reducing the risk of cholera in the city. The analysis presented uses cholera case location data and geospatial covariates, including the location of and access to networked and non-networked Water and sanitation infrastructure, groundwater vulnerability, and drainage, to generate a high-resolution map of cholera risk across the city. The analysis presents scenarios of standalone or combined investments across sewerage coverage and maintenance, on-site sanitation improvements, piped water network coverage and quality, and ensuring the safety of point source water. It identifies the investment most strongly correlated with the largest reduction in cholera risk as the provision of flush to sewer infrastructure citywide. However, it also considers the trade-offs in terms of financial cost versus health benefits and takes note of where the next highest health benefits could be achieved for a much lower cost. Finally, the analysis was done in the context of a considered restructuring of an existing World Bank investment, the Lusaka Sanitation Program. It identifies what appears to be the most efficient combined initiative as partial sanitation investment scale-up and investment in piped water in 10 priority wards where the cholera risk was highest.

This paper is a product of the Water Global Practice. It is part of a
the Humanities and Social Sciences (Area I) category who would receive a 30% credit, could receive a 40% loan when she chooses an Economics and administration (Area II) subject, and a 50% loan when she chooses an Engineering and Natural Sciences (Area III) discipline. If a student is credit constrained, she might opt for a study field for which larger credits are granted (for a given level of tuition fees). In this sub-section we investigate the hypothesis that access to loans has an effect on career choice. To that end, define the following dummy variables: DAREA_I=1 when the student chooses a study in Area I and 0 otherwise. DAREA_II=1 when the student chooses a study in Area II and 0 otherwise. DAREA_III=1 when the student chooses a study in Area III and 0 otherwise.

The proposed econometric model is a PROBIT model of the type where _j_ is an index denoting the student’s career choice.

Appendix Table 3 shows the results using the SOFES database. The main findings are as follows: The treatment effect for Bachelor-students in Humanities and Social Sciences (Area I) is negative around DS=0.08, but positive around DS=0.15 (Appendix Table 3A). The latter effects are quantitatively more important than the former, but not robust to increases in the subsample and inclusion of other controls. A more convincing positive treatment effect is found for Master-students in Area I (Appendix Table 3B). Around DS=0.08, the causal impact of the loan on the career choice of Humanities and Social Sciences (Area I) is about 15%-point. With regard to Bachelor-students in Economics and Administration (Area II) careers, mostly positive impacts are found around DS=0.08, but negative effects occur at DS=0.15. As no statistically significant treatment effects are found for Bachelor-students in Area III, this points at movements mainly between Area I and II.

Appendix Table 4 shows
.7|6.02|
|Housing (thousand NIS)|12,411.3|372|346.6|
|Fixed transport to work(thousand NIS)|79.8|329.3*|306.8|
|Pension contributions**|368860.5|34|31.8|

Source: World Bank and Ministry of Finance staff.

* Admin number from 2017

** Based on the employee contributions component only.

**Israeli tax policy allows deductions for disabilities, 52 percent of contributions to national health insurance, rental income, disabilities, and two tax credits: a tax credit**<sup>**41**</sup> **that varies by gender, and a tax credit of 35 percent of total contributions to pensions.** The size of the disability exemption varies based on duration; however the PECS 2016/17 does not indicate duration and so the full exemption of 72,720 NIS is applied to all individuals that report having a complete disability. A tax credit of 2.25 credit points (NIS 483 per month) was allocated to men and 2.75 credit points (NIS 591 per month) to women (a tax credit was worth NIS 215 per month in 2017). An additional 0.25 credit points were applied for travel to place of work for all men, and an additional 0.75 credit points for women. Due to data limitations, the following additional credits are not applied: credits for individuals who are supporting a spouse, have a working spouse, are a single parent supporting a child, have completed a degree within the last 2-3 years (depending on the degree), have applied for credit for individuals supporting a parent within an

> 41 Note that a tax credit differs from an exemption in that it is subtracted from an individual’s PIT liability, rather than from their taxable income.

28
status, and marital status. For women, the surveys include age at cohabitation, which we use as the age at marriage, and birth history, which includes the timing of the first birth. Importantly, these surveys contain complete household rosters with data on employment status for _both_ married and unmarried women, which will be essential to the pseudo-panel approach below.

> 1Other countries in South Asia are excluded because the DHS data lack sufficient information on employment status for both married and unmarried women.

6
e. percentage of the population that lives below the official national upper poverty line, which is based on household's poverty status assessed using per capita consumption) are shown in Figure S1. According to the 2011 National Population Census, conducted by the Bangladesh Bureau of Statistics, the total population of the country was 144 million with 76% of the total population living in rural areas in Bangladesh (see Supplementary Texts for further details).

Upazila-level, percentage of households with access to tubewell and tapwater supplies in Bangladesh are shown in Figure S2. The data on access to water supply come from the 2011 Census of Population and Housing. The national average of households with an access to tubewell and tapwater supply (town or municipal water supply via piped network) is 82% and 6
the living conditions were such that survival after the first year was also problematic. The analysis did not show significant differences in the infant and underfive mortality across genders.

Figure 7 compares the one year survival probability for children with rural and urban backgrounds<sup>12</sup> and figure 8 proceeds similarly for different schooling levels of the mother. In both cases, there was no statistically significant differences between the two groups for birth cohorts born in the 1970s, whereas, for subsequent birth cohorts, born after the mortality crisis, urban children are less likely to die (the difference is statistically

> 10 One year and five year survival probability could also be calculated from the siblings’ mortality data, but, in order to compare child mortality in 1975-79 with current levels, it is better to use information about the children than about the siblings of women aged 15-49 in 2000, as they are on average younger. Also, it is preferable to use the characteristics of the mother rather than those of the sister, as an explanatory variable.

11 Notice that, especially for the probability to survive the first five years, the 1970-74 birth cohort was at risk during part of the 1975-79 period.

> 12 Notice that, while, when analyzing adult mortality, I used the rural/urban background of the sister during her childhood (only if she was born before 1975), in the analysis of child mortality I am using the current rural/urban location of the mother.

8
## 5.3.4.   Regional heterogeneity

Next, we run models to test whether the effects of conflict events on our main outcome variables of interest—food insecurity and consumption—vary across the different geopolitical zones. We have reason to believe this could be the case because the nature and intensity of conflict varies across the three zones.

In table 9, the zone indicator is interacted with the conflict event variables. We can see that the effect of conflict on consumption is driven by the North East (column 5). We also find that the relationship between conflict events and food insecurity is strongest again in the North East (column 2), and it is also statistically significant at the 5 percent level in the North Central (column 1).

# 5.4.   Robustness checks

## 5.4.1.   Alternative CSI

In the main analysis, we have used the CSI with five items, a measure considered to be valid across different contexts. The GHS food insecurity module has a sixth item, “Limit the variety of foods,” that can be included in the index. We have therefore conducted the analysis with an index that includes this additional item (in the construction of the index, this item takes the lowest weight, 1). The results are presented in appendix C, table C.3. We can see that the results are statistically significant at the 5 percent or 1 percent level, depending on the specification, and they are similar to those in table 2. The results on the additional item separately (columns 5 and 6) are also statistically significant.

## 5.4.2.   Alternative samples on food insecurity

A second telephone survey round focusing on food insecurity was conducted between August 15 and September 8, 2017. During this second round, 581 of the 717 households in the conflict telephone survey were re-interviewed (only the 717 were attempted to be reached).<sup>27</sup> Due to the attrition, we
.40|O40|0.30|0.20|0.20|0.10|0.00|0.00|

<!-- Start of picture text --> Optimal value of 7 0.60 0.50 0.50 0.40 0.30 0.30 0.20 0.10 0.10 0.00 0.00<br>‘Overall primary balance to total output<br>Minimized Loss function 21.25 21.64 22.02 22.41 22.81 23.16 23.42 23.63 23.70 23.59 23.30<br>Optimal value of 7 0.50 0.40 0.40 o.40 040 0.30 0.30 0.20 0.20 0.10 0.00<br>General index of macroeconomic volatility 2<br>Minimized Loss function 12.95 13.90 14.93 16.01 17.09 18.19 19.28 20.33 21.35 22.36 23.30<br>Optimal value of 0.50 0.50 0.50 0.40 O40 0.30 0.20 0.20 0.10 0.00 0.00<br><!-- End of picture text -->
## SPLIT DECISIONS

Table 6 shows the impacts on household decision-making by applicants. Household survey respondents were asked who bears the principal responsibility for household decisions in five areas. They could answer themselves, another identified member, or shared decision-making by themselves and another identified member. The outcome variable in this table is an indicator for whether or not the job applicant is a principal or shared decision-maker in each area.

Migration by the applicant causes important changes in how household decisions are made. It causes decision-making by the applicant to decline in all areas, though these declines are only statistically significant in the case of “major purchases” and “weekend activities”. The lower half of the table considers only applicants who were married at baseline. For them, the magnitude of the declines is much larger, and they are statistically significant for “childcare”, “major purchases”, and “weekend activities”. A graphical representation of the “childcare” row is shown in the lower-right panel of Figure 3.

# 6.3. Effects on individual children

Table 7 shows the effects on children. The table considers household members under age 18 who are the children of the applicant or the applicant’s spouse.

Considering only those of schooling age (⩾ 6), migration does not affect school enrollment. This is to be expected given that over 98% of children in this age group are already enrolled. Migration does cause a 41 percentage point increase in the probability that a child is in private school (from a base of 28 percent). Migration also causes a 30 percentage point increase in the fraction of children who are receiving awards at school. This could be due to better performance in school, or due to a different propensity for private schools to give awards.

Considering children of all ages, migration by the applicant does not cause changes in the probability that these children visited a health facility in the previous month. There is suggestive evidence that it causes more of those visits to be at private health facilities
p�p matrix capturing short-run effects; μ� is a vector of constant terms; ε� is a p�1 vector of error terms; and ∆ is the first difference operator. Given that all variables are in logs, their differences represent growth rates.

To ensure valid statistical inference, the analysis controls for three outliers (1985, 1992, 2009) using impulse dummies. The years 1985 and 2009 coincide with periods of severe famine and global recession respectively, while 1992 marks the advent of market-oriented reforms. Of the three outlying observations, the 1992 outlier was found to have significantly caused a mean shift in the growth rates. The sharp turn from a ‘state-led’ economy to a ‘market-oriented’ one appears to have placed some of the variables on a higher growth trajectory. An algorithm searching for breaks developed in Doornik _et al_ . (2013) was used to determine the existence, timing, and significance of breaks in mean growth rates. Accordingly, the above-mentioned event was modeled using a broken linear trend �t����� in the longrun relations, β<sup>�</sup> x� (to account for the change in trend slopes underlying the long-run equilibrium relationships) and a step dummy �D�92�� in the equations, ∆x� (to model the mean-shift in the growth rates of the macroeconomic variables). See Haile (2015a) for more details.

Note that the annual sample encompasses two starkly distinct political and economic regimes: the socialist Derg regime (1974-1991) and the incumbent EPRDF regime (post-1991). Therefore, to make sure that the estimated coefficients represent constant parameter regimes, the empirical model is reestimated using quarterly data for the period 1993-2013. The model for the quarterly data included a number of impulse dummies to account for extraordinary observations.

# **Results**

## **The long-run impact of a devaluation on the trade balance**

Table 3 reports the identified long-run structure of two long-run relations. The
11 In this paper total federal budget transfers to regions were defined as the sum of mutual settlements, subventions, short-term loans, and budget loans received by the regions (World Bank, 1995a). Additionally, in 1994 and 1995, grants received by the regions under the Federal Fund for the Support of the Regions (created in 1994) were included. At the very least, mutual settlements were received by all regions and thus, total transfer data exist for all regions in all years.'<sup>5</sup> All information on non-fiscal indicators derive from various Goskomstat publications. Many of them are the same used by Kitty Stewart in her study (Stewart, 1996), who kindly provided us with access to her data base. Data for the autonomous regions, which are the smallest administrative units in terms of population, are not available in the case of some variables, and for this reason sample sizes of 77 and 78 instead of 87 will be noted in some regressions. No significant differences in statistical results associated with the change in the sample size were found.

In the regressions for each year, variables from that year are used, unless otherwise indicated. In 1995, a number of social and demographic variables from 1994 were used in the absence of these data for 1995; but these would not have changed appreciably from one year to the next. The estimates for real household incomes for 1995 were calculated based on the nominal mid-year data, while for other years average annual nominal incomes were used.

# **Factors used in the analysis**

Our analysis was focused on the identification of determinants of overall national economy expenditures as well as the components of those expenditures. In all, a total of twentyfour unique (that is, not counting twice the same type of data for different years) independent variables were examined as potential subsidy determinants in the regression analysis. These variables represent both supply-side factors characterizing the availability of financial resources in regions which potentially might be spent on subsidization, and
Policy Research Working Paper 8736

# **Abstract**

In discussing the inordinately low employment of Indian women in urban areas, several studies have argued that culture and attitudes have created a labor market that is inherently discriminatory. The unsaid corollary is that culture is slow and hard to change and so, women will stay out of the labor market until social change occurs. The empirical evidence on the role of culture is slim at best. This paper fills the void in the policy literature, as it assesses the relative role of culture, as signified by attitudes of employers, and firm characteristics in hiring women. The paper is based on a unique survey of 618 firms in three of the largest cities in the state of Madhya Pradesh (India)—Bhopal, Indore, and Gwalior. Using detailed descriptive, bivariate and multivariate analysis at the firm level, the hiring process, and attitudes toward male and female workers, the paper addresses the issue of culture and firm characteristics, while noting that the two are not necessarily in binary opposition. The results reinforce the conventional wisdom in some ways and are surprising in others. The most salient result is that employer attitudes matter much less for the chance that women will be hired, than do firm and location characteristics. This has significant policy implications, the most important of which is that female employment in urban India is amenable to policy intervention, and that it is not necessary to wait for culture to change.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at mdas@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective