rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5821

# **Abstract**

The political economy of health care is complex, as stakeholders have conflicting preferences over efficiency and equity. This paper formally models the preferences of consumer and producer groups involved in priority setting and judicialization in public health care. It uses a unique dataset of stakeholder perceptions, from Uruguay, to test whether these hypotheses are consistent with empirical evidence. The results suggest that the expectations of the political economy literature are supported: 1) regulators of public healthcare are less concerned with efficiency considerations than consumers; and 2) less organized groups are more concerned about equity than more organized interest groups. With respect to the consequences of health litigation, the findings are only partially consistent with the health care governance literature. Consumers perceive litigation as more beneficial than health care providers and regulators do. Counter-intuitively, powerful interest groups seem less willing to use litigation to shape policy outcomes.

This paper is a product of the Leadership Unit, World Bank Institute. 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://econ.worldbank.org. The author may be contacted atahamilton3@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 the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
time lights data is obtained from Beyer, Jain and Sinha (2021). The authors obtained raw night-time lights data from VIIRS-DNB Cloud Free Monthly Composites (version 1) and corrected the raw data for outlier observations (averaging cells overtime and clustering areas based on the intensity of night-time lights). These corrections follow the approach advocated by Elvidge, et al. (2017). Values of night-time lights are reported in nanowatts per square kilometer. We collapse the monthly nighttime-lights aggregates from Beyer, Jain and Sinha (2021) to yearly levels before evaluating growth rates.

Nielsen’s surveys track sales of consumer goods through retail store level surveys, covering a network of mom-and-pop stores as well as modern retail stores in 52 cities and 2700 villages across India. The instrument collects quantities, prices and sale values of both branded and non-branded items. We use estimates of quarterly growth in store-level sale values from publicly available sources<sup>32</sup> . The quarterly growth values are aggregated at the yearly level by taking simple averages, see Figure 25.

Both night-time lights and Nielsen’s store-level surveys indicate welfare indicators peaked in 2017 and 2018. This period coincides with rapid rate of poverty reduction in our analysis. The sources also suggest a slowdown in 2016 and 2019 which further supports our finding that the rate of poverty reduction peaked in 2017-2018 and moderated in 2019.

# **6.3 A rise in urban poverty in 2016 followed by a rapid rise in consumption in 2017**

**Consumption growth trends from the IHDS-3 can help validate a break in poverty trends around 2016** . The break in poverty reduction around 2016 coincided with a rise in urban poverty in that year. Household consumption strongly rebounded thereafter. Households interviewed by the IHDS in February to April 2017 reported a negligible rise in consumption since 2011-12. In contrast, household consumption for interviews conducted between May to July 2017 is 5 percent higher than 2011 on an annualized basis. Consumption of the first cohort of households was plausibly affected by
for these. Data on fiscal spending by program type was derived from an inventory of social protection programs, prepared by Espinosa (2011) as well as Nuñez (2009, 2011).

58. **On the basis of the caveats stated above, the following package of policy measures appears attractive and merit further analysis.** The proposal includes a reduction of tax incentives and benefits (tax expenditures) in the personal income and the value added taxes which would yield substantial additional tax revenues (2.4 percent of GDP). When put to their most effective use, the additional revenue could be used to finance the introduction of a tax declaration for independent workers (0.5 percent of GDP) and an expansion of the conditional cash transfer program (1.9 percent of GDP). This proposal would be fiscally neutral and would reduce Colombia‟s Gini coefficient by about 4.6 percentage points, bringing it lower than in Chile and close to that of Costa Rica. Alternative options are considered below.

59. **A reform of personal income tax exemptions could reduce the Gini coefficient by up to 3.1 percentage points, if additional tax revenues are spent effectively.** The reform proposal contains the following elements: (1) elimination of the exemption of tax of the first 25 percent of income; (2) elimination of deduction for voluntary savings and pensions accounts; (3) introduction of a tax on pension income.<sup>26</sup> These measures would initially lower the Gini coefficient by 0.7 points and increase tax revenues by 0.9 percent of GDP, as illustrated in example A.1 of Table 8. The most effective identified use of the additional tax revenue would be through a combination of two measures. First, the introduction of an income declaration for independent workers, which would cost 0.5 percent of GDP in lost tax revenues and reduce the Gini coefficient by a further 1.9 points. Second, by
b Estimation by using the census data as benchmarks and link them with the adjusted unemployment data. See Xue & Zhong (2003) for details.

Between 1996 and 2001, the number of urban and township workers fell from 149 million to 108 million, a net reduction of 41 million or 28%.8 This includes a main drop of 52.5 million in state-owned and urban collectives and an increase of 12 million in the emerging formal private sector (joint stock and shareholding companies and foreignfunded enterprises). Meanwhile, at the same time period, the employment in the urban informal private and self-employed sectors enjoyed a 13.3 million, or 57% increase. The number of rural emigrants is also rapidly increasing. Current estimates of the number of migrant workers fluctuate between 40 and 80 million, which is about 6-10 percent of the total labor force.9 According to the survey data from the National Statistics Bureau, from 1997-2000, the share of rural migrant labor as a percentage of the total rural labor force increased from 18.1% to 23.6%, i.e., from 83.2 million to 113.4 million, increasing 10.07 million or 10.8% annually on average.

With the “revitalizing China with science and education” policy, China has achieved great success in several new industries in the past five to seven years, particularly high technology and ICTs. China has also been successful in modernizing and restructuring the economic base of coastal cities through the “special economic zone” approach. However, these new industries are limited in terms of employment-generating capability. In the late 1990s employment in the 53 designated high-technology parks (mostly in the coastal areas) had reached only 2.2 million people, even though several parks have enjoyed rapid and sustained growth (annual growth in sales of 50%). 10 Electronics and telecommunications industries, despite considerable efforts toward their development, do not generate many new jobs. 11 Altogether these industries’ share reached 8.7
tives. The data on violent events is used as a control variable in our econometric model. We also use population data, based on the 2008 census, in order to scale the number of ex-combatants in each colline.

Official Demobilization Registers. The National Commission for Demobilization, Reinsertion, and Reintegration in Burundi provided us with registers of ex-combatants by colline and faction, along with their sex, age, military rank, colline of origin and of return, as well as the date of their demobilization. The registers contain precise information about each demobilized ex-combatant as well as the exact number of demobilized soldiers in each colline. The large variation in the number of demobilized ex-combatants per colline allow us to identify the spillovers of the DDR program in Burundi.

# _3.2. Identification Strategy_

Our identification strategy is based on a lagged dependent variable model with province fixed effects. The lagged dependent variable model should be preferred to the difference-indifferences model when the assumption “that the most important omitted variables are timeinvariant doesn’t seem plausible” (Angrist and Pischke, 2008). The particular histories of civilian and ex-combatants households motivate an estimation strategy that controls for lagged dependent variables directly and dispenses with households fixed effects. Furthermore, when autocorrelation of outcomes is low or when data is imprecisely measured, as is the case for many economic variables such as household incomes and expenditures (McKenzie, 2012), controlling for the lagged dependent variable is more powerful than either employing the differencein-differences estimator or the single difference estimator using only the follow-up data. Intuitively, “when the baseline data have little predictive power for future outcomes, it is inefficient to fully correct for baseline imbalances between treatment and control groups” (McKenzie, 2012). Because the coefficients of correlation correlation between our outcomes of interest in 2006 and in 2010 are low,<sup>14</sup> we include the lagged dependent variable as a control variable in the regressions.

According to this in accordance with this approach, we propose to estimate the following equation:

>
Bursztyn and Yang (2022), we establish an individual level measure of misperception based on how individuals’ perceptions compare to actual beliefs. The “true value” is determined by the average belief among respondents in a country who agree with a given norm. We assess the perceived community norm against this true value to calculate the proportion of “correct beliefs” among respondents. At the individual level, we can discern the fraction of the population that accurately assesses, overestimates, or underestimates the prevalence of a norm. Consequently, we report the proportion of respondents with accurate perceptions (those within 0.5 standard deviations of the truth); those who overestimate (respondents who hold beliefs that are at least 0.5 standard deviation greater than the truth); and those who underestimate (respondents who hold beliefs that are at least 0.5 standard deviation less than the truth).

We define _OverestimateNormij_ as a binary indicator that equals 1 for individuals whose perceptions exceed the true average belief by at least 0.5 standard deviations. Similarly, _UnderestimatesNormij_ equals 1 for individuals whose perceptions fall at least 0.5 standard deviations below the true average belief. The baseline category, _AroundNormij_ , includes individuals whose perceptions are within 0.5 standard deviations of the actual beliefs.

Since the reference group in our norm constructs is deliberately broad (i.e. neighbors), this may not directly correlate with individuals’ most influential social circles. The extent to which our behaviors are shaped by close personal connections versus the broader community remains an empirical question, hence our decision to keep a general reference group. Also since our norm constructs refer to “neighbors”, a group not synonymous with the Facebook user base itself. Therefore, while our analysis is useful for establishing broad patterns, we caution that any observed misperceptions might be attributable to sample selection rather than pluralistic ignorance.

# **2.4 Comparison of the Gender Equality at Home Survey with Other Data Sources**

The Gender Equality at Home Survey represents a
is higher during the rabi season after MNREGA than prior to MNREGA.

It is difficult to reconcile these results - which point directly to an increase in demand for water despite potential exits from agricultural labor - with the hypothesis that higher groundwater levels are due to decreased demand for water due to exit from agriculture. Any explanation that relies on the other effects of MNREGA (employment, general economic improvements, etc) must reconcile both of these results: groundwater levels rise in areas that see strong MNREGA implementation and water use in agriculture increases, specifically water use from wells, in those areas.<sup>21</sup> Such an explanation must also reconcile the timing of these effects. The majority of MNREGA projects occurred during the rabi season (Imbert and Papp, 2014) – after rabi groundwater measurements are taken, not the monsoon or kharif season. We observe increases in groundwater availability at the start of the rabi season. That implies that the increase in groundwater we document primarily occurs prior to any exit from agriculture due to MNREGA would occur. We document increased demand for irrigation water at precisely the time of year that MNREGA driven agricultural exit should have led to less demand, while we observe increased recharge during the season when MNREGA driven exit should have had the smallest effects.

> 21The alternative explanations can generally explain one of the effects we document - for example, increased economic activity might have led to increased demand for water - but not both. To continue the example, increased economic activity should have led to increased demand for water in all seasons, which does not explain the increased recharge that we observe.

13
recorder should have medical training: for example, in Togo the enumerators were medical students and in Niger, enumerators were medical workers with at least a university degree.

We use data from seven vignettes capturing conditions associated with the main causes of child and maternal mortality in Sub-Saharan Africa: malaria, tuberculosis, diarrhea, pneumonia, diabetes, neonatal asphyxia and post-partum hemorrhage. All seven of these conditions are major global health challenges, with three of them only – diarrhea, neonatal asphyxia, and malaria – together causing an estimated 180,000 thousand disability-adjusted life years (see Table S3).

Table S5 below shows the starting scenario for the vignettes. Beyond the starting scenario, the vignettes proceed with additional information depending on the questions asked by the health provider.

**Table S5. Initial scenarios for vignettes**

|**Vignette**|**Description **|
|---|---|
|Acute diarrhea with severe<br>dehydration|An adult presents a 1 year child with diarrhoea.|
|Pneumonia|An adultpresent a5-yearold child withcough.|
|Diabetes Mellitus|An adult presents at the facility feeling weak<br>and without energy despite feeling hungry often<br>and eating frequently. The man is 48yrs old and<br>worksasaclerk.|
|Pulmonary Tuberculosis|A pregnant woman presents at the facility after<br>being told in the community<br>that she shouldgo for antenatal care.|
|Maleria + anemia|An adult presents a child with fever for some<br>time who hasgotten worse.|
|Post partum hemorrhage|A young woman presents at the facility with<br>vaginal bleeding 24 hours after delivery in a<br>health facility|

47
|
|Number of schools in 1921 per 1000 children|7.335|3.184|-4.151***|
||(0.213)|(0.382)|(0.456)|
|Number of schools in 1927 per 1000 children|9.149|6.807|-2.342***|
||(0.284)|(0.533)|(0.602)|
|Number of schools built betw. 1921 and 19727|6.111|26.000|19.889***|
||(0.600)|(3.732)|(2.277)|
|Number of schools built betw. 1921-19727 per 1000 children|1.814|3.694|1.880***|
||(0.123)|(0.517)|(0.357)|
|N|108|31|139|

**Notes** The table presents the average number of schools by provinces. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on yearly statistical yearbook of Greece, 1926-1927.

34
differently exposed to drought, with North America facing a more favorable climate and the Middle East and North Africa being the most exposed to droughts. In the Middle East and North Africa, the median 2003 cropland cell was exposed to 56 months of droughts over the 2003-2018 period, compared to 11 months for the median cropland cell in North America. The distribution of cropland also varies a lot in terms of distance to coasts, with the mass of cropland in North America being far from coasts (the Great Plains) and the bulk of cropland in the Middle East and North Africa being close to coasts (as the rest of the landmass in the region is mostly barren).

As shown in Table 1 above, the total amount of cropland has been relatively stable in each region over the 15 years of the studied period with the exception of Latin America and the Caribbean which

> 5 This is slightly contradictory with aggregated data produced by FAO, which estimates the increase in global cropland at 4.6 percent over the same period (for a different definition of cropland and a measure that is not georeferenced).

5
but in three countries, establishments with up to ten employees are identified as micro-enterprises. Small firms are defined as anywhere from 1-4 employees to 10-100 employees (Morocco). Few countries follow the standardized WBES definitions of firm size: 5 to 19 (small), 20 to 99 (medium), and 100 or more (large). In the absence of available micro-data for censuses and surveys, this heterogeneity, unfortunately, cannot be resolved.

Further, the definition of informality (if included in the documentation) differs widely across countries. Some countries inherently include activities of households as employers as informal activities; others classify any micro-enterprise as informal, independent of firm registration. Some countries draw the line between formal and informal firms according to firm registration, firm licensing, or both. Other establishment censuses and surveysexplicitly exclude businesses without fixed premises from the stock of businesses, and others classify itinerant traders, taxis, or market stands as informal solely based on the lack of fixed premises. Where possible, we avoid relying on countries’ definition of formality, but instead extract information on the number of registered firms, and the number of licensed firms as a second-best measure of formal firms.

# **3 Methodology**

This section describes the methodology we followed to calculate the number of firms by country and the subsequent distinction by size group and formality status. Because harmonization across establishment censuses or registries is not currently feasible or available, based on ILO data, we 7
2021). Floods are identified through a combination of news reports, government data, instrumental observations, and remote sensing technologies, including satellite imagery. Floods of Severity Level 1 and higher are included in the analysis, where a Level 1 flood represents "significant damage to structure or agriculture, fatalities, and/or 5-15 year interval since the last similar event". Based on these data, RWI grids that have been flooded between 2000 and 2018 are identified. The flood data are also used to count how many times the grids have been flooded. A similar process is repeated to identify firms’ experience with flooding between 2000 and 2013 using SHRID-level firm data.

**Urbanicity** . Urbanicity is defined based on travel distances to cities. Places within 10 minutes from cities with more than 10,000 people are coded as urban and the rest are rural. Based on this

6
:00 to 21:00 (riders usually stop working after 21:00). In addition, they were asked to specifically ride through the defined routes on their way to/from the selected spots. In both cases, monitoring was done during active hours of the day to correspond with riders’ typical working hours i.e. (from around 06:00 to 21:00) and the stoppage times were chosen to correspond to the highs and lows of expected typical diurnal profile ( **figure 4** ).

Real-time PM data from active monitors (AirQo and PurpleAir tools) were automatically uploaded to the cloud platform at a frequency ranging 30-80 seconds, with variations depending on mode i.e. whether static or mobile and available power options. The resulting data sets informed the average pollutant concentrations within specific static deployment neighborhood along with spatiotemporal variations from mobile sampling.<sup>14</sup>

# **2.3.1 Meteorological Data**

Supplementary meteorological data (humidity, temperature and precipitation) were obtained from weather stations situated within the national climatic zones in line with sampled locations. These compilations included both historical data sets and measurements obtained during the pollution assessment period. Sampled precipitation compilations were superimposed on pollution plots to indicatively assess the meteorological influence on seasonal variations.

> 14 All active monitors were collocated (in a controlled environment) with a high-end factory calibrated - instrument Turnkey Osiris (https://turnkey <u>instruments.com/product/osiris/) prior to deployment to establish any</u> correction factors, whereas analysis for the passive monitors was handled by an ISO 17025 accredited laboratory.

9
# **I. Introduction**

“What gets measured gets managed; and what gets measured gets done” is one of those ubiquitous (even <mark>cliché</mark> d) management phrases that hardly requires explanation; it seems immediately obvious that the data generated by regular measurement and monitoring is what makes possible the improvement of results. Less well known than the phrase itself is the fact that although it is commonly attributed to the acclaimed management theorist Peter Drucker, Drucker himself never actually said it.<sup>2</sup> In fact, Drucker’s views on the subject were reportedly far more nuanced, along the lines of V. F. Ridgway, who argued over 65 years ago that not everything that matters can be measured and not everything that can be measured matters (Ridgway 1956). Simon Caulkin, a contemporary business management columnist, neatly summarized Ridgway’s argument, in the process expanding the truncated to-measure-is-to-manage phrase to “What gets measured gets managed — even when it’s pointless to measure and manage it, and even if it harms the purpose of the organisation to do so.”<sup>3</sup> Ridgway and Caulkin’s warnings – repeated in various guises by many since<sup>4</sup> – remind us that an indiscriminate usage of quantitative measures and an undue confidence in what they can tell us may turn out to be highly problematic in certain situations, sometimes derailing the very performance improvements that the data was intended to support (Merry et al 2015). We hasten to add, of course, that seeking more and better quantitative data is clearly a worthy aim in public administration (and elsewhere). Many important gains in human welfare (e.g., recognizing and responding to learning disabilities) can be directly attributed to interventions conceived and prioritized on the basis of the empirical documentation of the reality, scale, and consequences of the underlying problem. The wonders of modern insurance are possible because