rafmacalaba/data-use-mentions-extended huggingface dataset:
In order to understand and map stakeholders’ positions on the consequences of judicialization of health, we collected two sets of data: first, with the help of a team of local consultants with an established national reputation in the fields of law and public health,<sup>37</sup> we identified a representative sample of key actors with influence and stakes on the specific issue of healthcare judicialization (representatives of several agencies within executive, legislative branch, political party leaders, members of the judiciary, pharmaceutical companies, patient/consumers groups, civil society representatives, doctors’ associations, insurers groups etc.). Subsequently, we conducted around 45 indepth face-to-face interviews in Montevideo (March-April 2011). The qualitative (semi-structured) component of the study allowed us to gather more fine-tuned theoretical insights and take into account additional stakeholders indicated to us by our respondents during the semi-structured interviews.

Second, incorporating these insights, we designed, pre-tested and implemented a quantitative stakeholder survey in order to systematically capture differences in evaluation of stakes across groups of actors within the policy domain. To our knowledge, this is one of the first attempts to test statistically stakeholders’ perceptions of the _efficiency_ and _equity_ of the public benefit plan in general, as well as the implications of rising healthcare litigation on these two issue dimensions in particular.

The survey design attempts to trace the entire process chain of health judicialization from origins to perceived consequences, and contains three main parts: art I taps into stakeholders’ general perceptions of the current Benefit Plan and priority setting; Part II shifts the focus to the origins of litigation and the perceived effectiveness of the judicial process, and Part III analyzes two major types of litigation cases (malpractice and access to high technology medication and treatments – _recursos de amparo_ ) as well as perceptions of the consequences of judicialization.

The political economy of healthcare in general has a high level of complexity, and has been conventionally characterized as a “fuzzy” domain (Brugha and Varvaszowsky : 44)<sup>38</
through easy comparison with cause-specific proportionate mortality rates of the most common causes of disease within a particular country.

Why compare with pre-pandemic mortality patterns? The expression of mortality in relative terms speaks to the fact that countries may have adapted to their specific patterns of mortality. Deviations from this pattern may create pressure points, such as for example on the health system. Comparisons with previous patterns give a country-specific flavor of the severity of the COVID19 pandemic, which could be used to corroborate realities on the ground. This relative expression complements other approaches – it does not substitute for them. A key advantage, particularly compared to the excess death ratio, is that the relative severity ratio is more readily available in countries where high-frequency data on mortality are not easily obtained in an accurate manner.

As for the COVID-19 data, we rely on the numbers reported daily by the Coronavirus Resource Center at Johns Hopkins University. We treat the reported data as it is presented. This sets the stage for the discussion in the next sections, which will touch upon data quality and other factors. It should be noted here, however, that the indicator of relative severity can be adjusted to take into account data quality issues. For example, if the estimate of COVID-19 deaths in the numerator were refined on the basis of excess death calculations, one would obtain a close relative of the excess death ratio.

The data on past mortality patterns is based on the WHO’s 2016 Global Health Estimates, which give us comprehensive information about all-cause mortality as well as the distribution of mortality across specific causes of death (WHO 2018a; 2018b; 2020a). We calculate the top causes of death for each country from the 123 disease families that can be derived from the latest International Classification of Diseases (ICD-10; WHO, 2016a). The main attraction of this data set is that it provides globally comprehensive and comparable data: we have data for 160 countries.

# **Comparisons across Countries**

Figure 2 puts a dimension on the unequal distribution of
and analyze a new global dataset of effective tax rate, extending prior work which focused on developed countries (Mendoza, Razin, and Tesar, 1994; Carey and Rabesona, 2004; McDaniel, 2007). This allows us to uncover new trends (most importantly, the rise in capital taxation in

> 2Rodrik (1997) finds that trade openness is associated with a decline in effective capital tax rates and an increase in effective labor tax for 14 OECD countries with high levels of capital mobility, between 1965 and 1991. In a sample of 14 OECD countries between 1981 and 1995, Swank and Steinmo (2002) find that trade is not associated with changes to effective tax rates, neither on capital nor on labor. Over the same period Slemrod (2004) finds that trade is not associated with changes to the statutory corporate income tax rate.

5
Policy Research Working Paper 10309

# **Abstract**

This paper assesses the reliability and validity of cognitive and socioemotional skills measures and investigates the correlation between schooling, skills acquisition, and labor earnings. The primary data from Pakistan incorporates two innovations related to measurement and sampling. On measurement, the paper develops and implements a battery of instruments intended to capture cognitive and socioemotional skills among young adults. On sampling, the paper uses a panel that follows respondents from their original rural locations in 2003 to their residences in 2018, a period over which 38 percent of the respondents left their native villages. In terms of their validity and reliability, our skills measures compare favorably to previous measurement attempts in low- and middle-income countries. The following are documented in the data: (a) more years of schooling are correlated with higher cognitive and socioemotional skills; (b) labor earnings are correlated with cognitive and socioemotional skills as well as years of schooling; and (c) the earnings-skills correlations depend on respondents’ migration status. The magnitudes of the correlations between schooling and skills on the one hand and earnings and skills on the other are consistent with a widespread concern that such skills are underproduced in the schooling system.

This paper is a product of the Development Research Group, Development Economics. 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/prwp. The authors may be contacted at dfilmer@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 of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of
.052_|0.594***|_0.065_|0.555***|_0.056_|0.649***|_0.067_|0.583***|_0.024_|
|Casual|1.105|_0.099_|1.062|_0.090_|0.715***|_0.077_|0.988|_0.122_|0.704***|_0.076_|0.824*|_0.095_|0.856***|_0.039_|
|_cons|0.062***|_0.037_|0.015***|_0.008_|0.054***|_0.041_|0.092***|_0.081_|0.031***|_0.025_|0.047***|_0.037_|0.046***|_0.015_|
|_N_|25,00|1|28,68|4|28,3|36|26,9|28|26,3|40|26,24|7|161,5|36|

Notes: *** p<0.01, ** p<0.05, * p<0.1 In the NSS, data for OBCs were not available before 2004.

The model also includes controls for the same woman’s characteristics as in the previous models.

24
works only for one subdelegacia and reports to the subdelegado.<sup>48</sup> There is an effort to apply homogeneous criteria in the enforcement of the law throughout the country. This is achieved giving continuous training of the labor inspectors, by having common implementation manual and a similar software.<sup>49</sup> The inspectors visit the plants with the objective of evaluating the compliance with several dimensions of the labor laws and regulations. For example, they inspect the compliance with regulations related with registration, wages, FGTS, working hours or leave bene ts.

We have administrative data collected by the Ministry of Labor in 2002 at the city level for different variables: number of inspected fi rms in the city, number of fi nes issued in the city. In particular we have information on the number of fi nes for different labor violations: informal worker fi nes, are fi nes related with the fi rm not registering the worker for a work permit, work load fi nes are fi nes related with the fi rm not complying with the official work load, wage fi nes are fi nes related with the fi rm not paying the minimum established by the law, hours of work fi nes are fi nes related with the fi rm not complying with the number of hours of work and the mandatory pauses, FGTS fi nes are fi nes related with the fi rm not making the mandatory discounts to the FGTS, transport subsidy fi nes are fi nes related with fi rm not paying the mandatory transport subsidy, and other fi nes are fi nes related with other mandatory obligations of the fi rm to their workers. The total labor fi nes is simply the sum of all these fi nes. This information was aggregated from the city level to the subregion level using information provided by the Ministry of Labor on all the cities that belong to each subdelegacia. Finally, we also have information on the number of regional offices in each subregion.

Data for population in 2002 and for the total number
period from 1990 to 2015 for 82 countries. In this dataset, each grid cell is recorded at 5 arc-min resolution. We then apply a similar exercise as in the dataset of Kalkuhl and Wenz (2020) and measure the incidence of poverty at different thresholds. We present the list of countries in our datasets in Table B2.

# **B2. Inequality data**

The GSAP dataset is unique in terms of country-time coverage and in the breadth of the measures of inequality covered. As the main outcomes, we use the most widely accepted measures of income distribution including the Gini index and Theil index. These indices are computed on the income available to households after government taxes and transfers, excluding indirect and value-added taxes, public services, and indirect government transfers. The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus, a Gini index of zero represents perfect equality, while an index of 100 implies perfect inequality. Similarly, the Theil index measures an entropic ‘distance’ the population is away from the ‘ideal’ egalitarian state of everyone having the same income. The Theil index ranges between zero and infinity, with zero representing an equal distribution and higher values representing a higher level of inequality. As alternative measures of income inequality, we use the distribution of income share held by each decile and calculate different percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the Palma ratio (90/40 ratio). All income measures are converted to real terms using 2011 Purchasing Power Parity (PPP) dollars for comparison across survey years.

We
3 special attention to agriculture. They also include a number of methodological innovations such as data gathering at the individual and plot level, enabling more gender disaggregated analysis. The data are made publicly available one year after their collection.<sup>4</sup> An international consortium of researchers under the Agriculture in Africa Telling Myths from Facts project led by the World Bank, with complementary financing from the African Development Bank<sup>5</sup> , exploited the first rounds of these surveys to revisit common wisdom on African agriculture and its farmers’ livelihoods in the areas of agricultural technology, market engagement and structural transformation. Studies were each time framed around a cross‐country investigation of the conventional wisdom in these areas. A total of 12 broad stylized facts and sub‐facts on African agriculture and rural livelihoods were thus reviewed, the results of which are brought together in a special issue of Food Policy _,_ entitled _“Agriculture in Africa ‐ Telling Myths from Facts”._ All articles are available on line and open access.

This synthesis summarizes the key findings and reflects on their implications, including through a number of easily accessible and replicable tables and figures. This facilitates the policy dialogue and further research efforts including further updating as new information from LSMS‐ISA or related surveys becomes available. The findings at times confirm conventional knowledge, as one would hope, and put it on more solid empirical footing. More often they fine‐tune our understanding. But they also reveal some myths and raise new issues. Overall, the findings underscore the high academic and policy return from investing in regular, nationally representative data collection and continuous examination of conventional wisdoms.

The synthesis proceeds as follows. The next section expands on the underlying data base and the methodological approach taken. This precedes a synoptic overview of the 12 wisdoms revisited and the core findings obtained when submitting them to the data. Section three expands on each of them, including their implications for agricultural and rural development policies. Section four
We also observe disparities in home learning environments (consistent with H2) and in preprimary quality (consistent with H3). There are socio-economic differences in children’s home stimulation and components of their pre-primary education experience (including environment and teacher attitudes). Children who experience lower-quality home learning environments also experience lower-quality pre-primary education in some regards, but not all. Inequities are largest for structural quality (the pre-primary physical environment), whereas there is less inequity in process quality (teacher practices, children’s experience of quality materials, and adherence to the curriculum).

# **_5.2 Limitations_**

Our results indicate important disparities in ECD, home stimulation, and some aspects of preprimary quality and inputs that are critical to address. However, there are a number of limitations to our results that must be kept in mind and point to important areas for future work and research. First, we were only able to estimate correlations between SES, outcomes, and inputs. The causal effects of inputs, particularly pre-primary inputs, in LMICs are under-researched and an important area for future work. Second, we were comparing one measure of home stimulation to multiple dimensions of pre-primary quality. There may be other aspects of the home environment that we were not able to observe that are more or less unequal. Measuring quality of home stimulation or pre-primary learning environments is quite challenging, as is measuring the learning and development of young children (Burchinal, 2018). Ongoing efforts to improve measurement of ECD and early environments may reveal additional variation in inequality. Additionally, we do not know if one type of input (home or pre-primary, or a particular aspect of pre-primary quality) is more important than another in determining ECD.

Our analyses are based on a sample of pre-primary students. Not all children in Egypt attend preprimary; indeed, there is substantial socio-economic inequality in access to pre-primary (El-Kogali & Krafft, 2015). In the general population of pre-primary aged children (including those not attending pre-primary), there may
exposed to women in leadership roles, providing substantial evidence of the framework regarding attitudes and bias implicit in the Hoff and Stiglitz (2010).

Duflo (2005) provides an assessment of the case for political reservations for women and other historically underrepresented groups. Using evidence from India, Duflo (2005) concludes that reservations have been shown to incur a significant reallocation of public goods toward the preferred allocation of the group in power. Pande and Ford (2011) provide a more recent comprehensive review of the literature on gender quotas. They conclude that the political reservations led to a significant increase in women’s leadership and influence on policy outcomes, while noting at times more mixed evidence on some outcomes. This review also highlights the need for greater study of economic consequences.

The above studies primarily use the cross-sectional nature of the randomized reservation system to identify treatment effects. However, variation in the timing of the implementation of the 73<sup>rd</sup> Amendment was also plausibly exogenous, as noted above and further tested later in this paper. Iyer et al (2012) use this state-level variation to investigate the effects of political representation on crime against women, finding significant evidence that political empowerment resulted in greater reporting of crimes against women.

# **3.  India Manufacturing Data**

Our primary data sources are repeated cross-sectional establishment-level data from surveys of the unorganized manufacturing sector carried out by the Government of India. The data are taken from surveys conducted in fiscal years 1994, 2000, and 2005.<sup>7</sup> This section describes some key features of these data for our study, and we refer readers to Ghani et al. (2013a,b), Nataraj (2011) and Kathuria et al. (2010) for greater details and general descriptive statistics on these manufacturing surveys.

Our work mainly considers the portion of the Indian economy surveyed by the National Sample Survey Organisation (NSSO). The NSSO periodically collects information on a

> 7 In all cases, the
and only about four percent for the purposes of studying.

There is imprecision associated with these population estimates, which is magnified by the lack of census data for Uzbekistan (no census has been conducted since independence, though one is planned for 2022). In particular, Tashkent’s population has clearly increased since independence, but incentives around registration are such that administrative records are likely inaccurate to some degree. However, there is broad consistency between the last Soviet Census and current official population estimates. The 1989 Census estimated the population of Tashkent at 2.1 million people. Current official statistics estimate that the population of Tashkent city included 2.14 million people in the year 2000, rising to 2.5 million in 2019. This means that as a share of the total population, Tashkent has in fact become smaller, despite remaining the most popular destinations of the small number of domestic migrants recorded over this period. The city represented 8.7 percent of the total population of Uzbekistan in 16
detect at least a $1.40 improvement, Comoros $2.10, Ethiopia $1.70, Sierra Leone $1.00, and Tunisia $1.90. These are computed using the country specific cluster sizes noted in Table 2 and assuming a standard deviation of $8, intra-cluster correlation of 0.10, 5 percent type I error rate and 80 percent power. All these calculations also assume 100 percent take-up. In practice, however most programs did not have 100 percent take-up. Particularly low take-up rates in Urban DRC (56 percent) and Egypt infrastructure (40 percent) will reduce the power to detect significant effects substantially.

11
15 local inequality may lead to increased property violence (Demombynes and Özler, 2005). But there is much less evidence that inequality is indeed so much greater in metropolitan areas than in smaller towns. Although this is a popular notion, it is one for which very limited statistical backing exists – in large part for previously mentioned reasons: household surveys are not representative at the level of smaller towns, and so we know very little about local inequality in them. It may be that the accumulation of anecdotal evidence of large inequalities in developing country metropolises is itself simply another reflection of metropolitan bias: Journalists and photographers, like most economists and policy analysts, prefer to visit Casablanca than Figuig<sup>16</sup> , and Rio de Janeiro than Bertolínia.<sup>17</sup> To shed some additional light on this matter, we now turn to some evidence from Morocco. Table 5 presents FGT(0) and three inequality measures (the Gini coefficient and the two Theil indices) for each of the five largest cities in the country, as well as the aggregate inequality for three city size categories. Overall intra-city inequality does not appear to be positively correlated with city size in this small sample, but this is not the main point. Taking advantage of the fine spatial disaggregation made possible by a poverty map, we calculated inequality measures for various individual neighborhoods within each of these cities. We further classified these neighborhoods into slums and nonslums<sup>18</sup> . We then decomposed the two Theil indices of inequality<sup>19</sup> for each of the five cities, into a component due to inequality _within_ each of the two groups of neighborhoods, and a component _between_ the two. For the argument that within-city inequality is egregiously large in metropolitan areas and large cities to hold (in Morocco), it would be necessary (but not sufficient) for the between-group shares reported in the last two columns of Table 5 to be substantial. In the