rafmacalaba/data-use-mentions-extended huggingface dataset:
cost of rent extraction (NEDDs). It is therefore possible to develop specific predictions about how variation in the EDD/NEDD ratio will incentivize policy-makers in anticipation of voters’ reactions.

Specifically, this game theoretic set-up enables the derivation of two distinct hypotheses. Firstly, EDD policy-makers will be more reluctant to engage in rent extraction versus insulated policy-makers (although this result will only hold in democratic contexts); secondly, perception of rent extraction among voters regarding the overall level of ‘political’ rent extraction, will vary with the EDD/NEDD ratio (as a higher EDD/NEDD ratio will incentivize more policy-makers to desist from rent extraction in order to secure re-election). This two-stage test is a logical extension of the original focus of the Duch-Stevenson (ibid) and economic voting literature in general, and of the impact of context on the magnitude of voter reaction to incumbent behavior.

Ideally, it would also have been good if the operationalization and testing of the ‘voter component’ of the hypotheses derived by the model occurred in the same manner as Duch and Stevenson’s (2008) formalization. That is, using individual level survey data of voter perceptions of rent extraction before and after an election. However, due to the limited reliability of over time variation in perceptions of rent extraction noted

> 7 See Besley, (2006) for an introduction to the use of P-A in political economy analysis.

4
# **Summary**

We examine the effects of the introduction of Vietnam’s health insurance (VHI) program on health outcomes, health care utilization, and nonmedical household consumption. The use of panel data collected before and after the insurance program’s introduction allows us to eliminate any confounding effects due to selection on time-invariant unobservables, and our coupling of propensity score matching with a double-difference estimator allows us to reduce the risk of biases due to inappropriate specification of the outcome regression model. Our results suggest that Vietnam’s health insurance program impacted favorably on height-for-age and weight-for-age of young school children, and on body mass index among adults. Our results suggest that among young children, VHI increases use of primary care facilities, and leads to a substitution away from the use of pharmacists as a source of advice and non-prescribed medicines toward the use of them as a supplier of medicines prescribed by a health professional. Among older children and adults, VHI results in a marked increase in the use of hospital inpatient and outpatient departments. Our results also suggest that VHI causes a reduction in annual out-of-pocket expenditures on health, and an increase in nonmedical household consumption, including food consumption but mostly non-food consumption. Our estimate of the VHI-induced reduction in out-of-pocket health spending is considerably smaller than our estimate of the VHI-induced increase in nonmedical consumption, which is consistent with the idea that households hold back their household consumption considerably if, through lack of health insurance, they are exposed to the risk of large out-of-pocket expenditures. This is especially plausible in a country where at the time (1993), a single visit to a public hospital cost on average the equivalent of 20% of a person’s annual non-food consumption.

**Keywords:** Health insurance; impact evaluation.

# **JEL codes:** I1, O1

ii
al. 2003) or the Philippines (Yang 2008), where specific subsets of households benefited.

xiii As migration can have wide-ranging influences on households, without prior information we are unable to comprehensively assess common trends for migrant compared to non-migrant households, including for child anthropometric outcomes. One piece of evidence that is available, however, comes from subjective poverty questions asked in 2008, in which households were asked to place themselves on a six-step poverty ladder in 2008 and in the prior year. Both migrant and non-migrant households reported slight improvements, with 3 percent fewer households on the lowest two steps in 2008 compared to 2007, but no difference (0.2 percentage points) between migrant and non-migrant households.

xiv Other nonexperimental research provides evidence that catch-up growth at later ages (particularly in adolescence) is also possible (Foster 1995; Mani 2012; Prentice et al. 2013). The findings in Bevis and Villa (2020) contrast here as well, suggesting impacts of maternal health extending into adolescence. Since we examine children under six years old in 2008 (and thus under 10 in 2012), patterns of potential later adolescent catch-up would not influence the findings but could lead to modified conclusions in the longer term.

xv Children three years old or older in 2008 would be seven years old or older in 2012. While we measure children seven years old and older in 2012, we do not observe that age group in 2008, so cannot carry out a similar triple difference specification for the cross-sectional cohort analyses. All children 4–7 years old in 2012 were exposed to the shock in early childhood.

xvi As the child anthropometric analyses are reduced form and the variables examined in this assessment are potentially directly influenced by the shock, we do not directly condition on them in the models.

xvii We categorized households as food secure based on the Household Food Insecurity Access Scale (HFIAS), consisting of nine items and four frequency responses, using a 30-day recall period with reference to children under age six in the household
E..Ei.<br>i :**.**.i.<br>--i -.-.<br>|12<br>f.^<br>.<br>|72.<br>.<br>......<br>.........<br>|20.'<br> <br>.**.**-.i-.....<br>1:<br>|
|<br>Do no a pas7A||_1._|64|7.|46|54:|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

Sources: Household Survey in Seven Cities, Moscow, Dec. 1993; The Moscow Longitudinal EHousehold Survey, January 1996; Vladimir and Gorodet Longtdinal Household Survey, Oct 1994 and Dec. 1996.

In Moscow, the aspirations of households that stated that they had housing plans divided almost equally: 9.8% of the Muscovites still hope to receive an apartment from the state, and 10.8% are ready to try using other methods of obtaining housing. In Vladimir, the proportion of those intending to use market methods rose to 15.7% of all households, and the share of those who count on the state dropped to 6.7%; in Gorodets, these figures are 11.7 and 2.90/e respectively. While in Vladimir pro-market moods have long prevailed and are merely getting stronger, in Gorodets, a small town, ideas about search methods have changed radically in favor of the housing market.'<sup>7</sup> 17 Irest in the housing mailet was also displayed in other cities nd towns (see the survey of the demand for housing in seven Russian cities; Urban Institute, 1995).
with the hypothesis that property owners who had been more exposed to prior enforcement activity by the LRA were more responsive to the enforcement message.

In the absence of direct information on the different enforcement activities carried out by area, the billing and payment records provide some suggestive evidence of the outcome of enforcement efforts across areas. We use the delinquency recovery rate from the prior fiscal year as a proxy for the extent of prior enforcement efforts. Table A6, column 4, shows that for properties similar to the experimental sample (tax liability below $1,000 and with tax arrears), the payment rate in area two is 9 percentage points higher than in area one ( _p_ = 0 _._ 0003), and the rate in area three is 5 percentage points higher than area one ( _p_ = 0 _._ 057). This pattern is consistent with a greater effect of the enforcement treatment in area two due to higher credibility of enforcement based on past experience.

Although the LRA did not systematically record follow up enforcement activities carried out against delinquent property owners in the treatment group that did not respond, there were no significant changes to their collection capacity around this time and the available evidence suggests that their actions, if any, were not substantial. We find no impact of the intervention beyond the first year (table 4 and figure 4). This result contrasts with the sustained compliance impact in experiment one, where there was a notable investment in identification capacity and indicates that shifting long-term beliefs about enforcement probability requires real changes in collection capacity.

# **6 Conclusion**

This paper offers a framework to study tax capacity of emerging economies as three different dimensions: identification, detection, and collection capacity. Given the well-established body of evidence on the importance of verifying the amount of tax liabilities, for example, through third-party information, this paper focuses instead on identification and collection. It highlights the importance of first of all identifying taxable entities and tax bases to make the tax base more legible to the
# **1 Introduction**

Household surveys that are designed for national or sub-national (i.e. regions or states) level parameter estimates often lack a sufficiently large sample size to generate accurate direct estimates for smaller domains or sub-populations.<sup>1</sup> Any area for which the sample is not sufficiently large to produce an estimate of adequate precision is referred to as a small area. Consequently small area methods attempt to address low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from supplementary sources. In the methodology presented by Elbers, Lanjouw, and Lanjouw (2003) the desired estimate is produced by a model done using survey data which is then linked to outside information such as a population census, administrative records, and/or geospatial information obtained through remote sensing. As Rao and Molina (2015) state, the availability of these auxiliary data as well as a valid model are essential for obtaining successful small area estimates (SAE).

In the case of poverty measures, which are nonlinear functions, small area estimation methods coupled with Monte Carlo simulations are a useful statistical technique for monitoring poverty along with its spatial distribution and evolution.<sup>2</sup> Poverty estimates are often produced by statistical agencies, and commonly are the product of a household survey. Household surveys usually are the main source of welfare (expenditure or income) used to produce poverty estimates, yet most are only reliable up to a certain geographical level. Therefore, a commonly adopted solution has been to borrow strength from a national census, administrative records, and/or geospatial information which allows for representativeness for small areas. Nevertheless, these outside data sources often lack detailed expenditure or income information required for producing estimates of poverty. Small area methods attempt to exploit each data’s attributes to obtain estimators that can be used at dis-aggregated levels.

Poverty at lower geographical levels can be used to identify areas that are in need
high. Cumulatively these expenses represent a significant part of the household budget. In relative terms, phone and electricity expenditures are high, while expenditures on water and garbage collection services are low. Non-poor households and those who live in formal settlements generally spend more for all services – possibly reflecting higher levels of consumption (rather than higher unit costs). Further investigation is recommended to understand the unit costs and relative affordability of each of these services.

Regressions analyses of rental data reveal that households place considerable value on access to infrastructure. In and of themselves, nominal infrastructure variables explain 40% of the overall variation in rents. By comparison, quality-adjusted infrastructure variables explain 55% of the variation in rents. Among the seven infrastructure services examined, the following four – electricity, water, toilets, and garbage collection – are associated with higher rents. Overall, rent paid by tenants is tied not only to the availability of basic infrastructure services (with higher rents paid for housing with access to services), but also to the quality of these services (that is, better quality services yielded even higher rents).

Our analysis also identifies key factors that contribute to a household’s likelihood of having access to infrastructure services. Household characteristics matter –infrastructure access is higher in households with higher income, male household heads, and more educated household heads. The location of the household and housing characteristics also matter. Larger and better-quality housing is associated with better access. Residents of certain types of housing are systematically more likely to have access. For example, residents of single-family housing are more likely to have access to private toilets and water connections than residents of compounds or dormitories. Residents of informal neighborhoods are systematically less likely to have access to service than those in formal neighborhoods. And there is also a city effect. Interestingly, access to any given infrastructure service is strongly correlated with the presence of other infrastructure services. Tenure status also influences infrastructure access, with renters more likely to have access to electricity, garbage collection and sewerage
conception of privacy as a fundamental human right reflects its own history and cultural trajectory which other countries might not share.

To ensure that the personal data of EU citizens is not abused abroad, data can be transferred out of the EU only under certain conditions. One is that the country meets the EU’s “national adequacy” requirement by enacting a national privacy law essentially equivalent to that of the EU. So far only five countries have received a positive national adequacy finding (Argentina, Uruguay, Israel, New Zealand, and Canada (commercial organizations)).

A national law imposes the same standard on all firms in the country, regardless of where they sell, at home or abroad. That could adversely affect poorer countries. Prematurely stringent privacy laws could hurt the development of markets by inhibiting the flow of information. For example, the reporting of personal credit histories is critical to consumer credit, and privacy laws could create significant asymmetries of information and affect the efficiency of markets (Kitchenman, 1999).

If a country’s national law fails the EU adequacy test, as happened in the case of India, their firms will be required to use either Binding Corporate Rules (BCRs), designed for multinational companies to move data globally, or Standard Contractual Clauses (SCCs) for each business deal. Both instruments require the levels of protection, oversight and access for individuals that would be offered in the EU. Both also require a data controller or processor, who can be held liable for breach, to be established in an EU Member State.

Both routes are costly and time-consuming. The requirement of a presence in the EU increases costs and limits the benefits of seamless cross-border digital trade, especially for smaller firms. A survey in India of the impact of the earlier, less-stringent EU Data Protection Directive revealed that the BCR process took over six months, and 90 percent of the respondents used SCCs which also involved a complex process and took on average more than 3 months (NASSCOM-DSCI, 2013). As
repository of online tools and resources** for e-readiness assessment and understanding the digital divide.

# _<u>NUA.com How Many Online?</u>_

Nua was founded in 1996. Nua Internet Surveys was acquired in June 2001 by the Scope Communications Group, Ireland's leading IT Media Company.

This online source for information on Internet demographics and trends maintains a dataset estimating how many people are online, based on extensive examination of surveys and reports from around the world. The data can be viewed by region and country.

Governance and corruption though not related directly to trade are important measures of the environment of investment and business. (See Annex I for indicators of investment climate, nontariff barriers and standards)

# **2.6 Development Indicators**

_<u>Development Data Platform (DDP)</u>_ DDP is a web resource for retrieving and reporting development-related data. It can prepare web reports, charts and maps which can be published and shared over the web. It provides powerful features to customize these reports and manage them. DDP includes both time series (macro) and survey (micro) data on a wide range of topics of interest to development practitioners. It provides basic analytical tools and secure access to time series databases and to surveys, including their 37 ____________________________________________________________________________________________________________
'PopDens' provides insights into how population density might influence or be influenced by specific flood events. The variables 'cf_cvgBuff05' and 'avg_radBuff05' described above, measure economic activity through cloud observations and nightlight radiance, respectively. Additionally, they are averaged over adjacent pixels to provide context for each location.

‘Treated' and 'Treated_after' distinguish areas affected by conflict before and after the flood in each country : March-April 2019 are months where the flood occurred in Mozambique while July 2022 was considered as the flooding month in this analysis for Nigeria. This is essential for determining the causal inference of conflict impact. 'Settlement' and 'Urban_Suburban' categorize urbanization levels to understand how different types of areas are affected by and respond to both floods and conflict events. Lastly, 'Fatalities' provides a direct measure of the human cost of conflicts.

These variables collectively enable a comprehensive analysis of the effects of floods and conflicts when they co-occur in the same location. They are used to analyze the impacts of floods and conflict on specific aspects of economic activity, as potentially inferred by nightlight changes. Table 1 describes these variables, their units of measurements, and how they will be used in the analysis.

Table 1. Description of variables

|**Variables**|**Description**|**Unit**|
|---|---|---|
|lat|latitude|Decimal coordinates|
|lon|longitude|Decimal coordinates|
|months_EE|Month since the event|Months (positive if after event, negative if before<br>event)|
|flood|Flood variable|=1 or 2 if medium reliability, =3 if high reliability|
|PopDens|HRSL population density|Person/km<sup>2</sup>|
|cf_cvgBuff05|Total<br>number<br>of<br>cloud-free<br>observations that went into each<br>pixel (averaged over the adjacent<br>pixels)
countries, the analysis in this essay will use household surveys. It is well known that household surveys do a poor job at capturing top incomes and there is no reason why this issue should be any less severe in developing countries. Furthermore, as I will discuss in more detail below, household surveys in poor countries tend to use consumption expenditures – not incomes – which understates living standards at the top and thus understates levels (and possibly also trends) of inequality. Finally, those emerging economies which use income surveys typically do not capture capital incomes, and information on wealth – the very topic of C21 – is practically non-existent. Paraphrasing Atkinson and Bourguignon (2015), it is important to be very clear about what the data can and cannot say, while at the same time not rejecting all evidence on the grounds of imperfect measurement.

The available evidence suggests that the Gini index of the global distribution of income has fallen for the first time since the Industrial Revolution, a development that is likely to continue. This decline was driven by falling inequality between countries, i.e. average incomes converged across countries. This is likely to continue, leading to further declines in global inequality. But this reduction was counteracted by an increase in (population-weighted) within-country inequality, such that the average person lived in a country where inequality was increasing. Looking at only developing countries, average national inequality increased during the 1980s and the 1990s while it declined in the 2000s. This recent fall was driven by Latin America, while China appears to have plateaued. The changes in global and within-country inequality coincided with rapid globalization since the late 1980s, enabled by technological change. It seems that globalization has had important distributional effects (within countries and globally), and between skilled and unskilled labor, as well as between capital and labor.

This essay is structured in six main sections. I begin with describing the global distribution and trends in global inequality, drawing mostly on Lakner and Milanovic (2016). Section II moves from global
Capital Index (S-HCI) (D’Souza, Gatti and Kraay, 2019), which captures the expected future human capital of children born today for children of different parts of the income distribution. Households with better outcomes from human capital development are considered to receive a higher-value public service than those with lower outcomes. We adjust the education benefits to account for the income quintile gradient in learning-adjusted years of school, which combines information the quality and quantity of education that a child can be expected to obtain.

# Allocating in-kind benefits: Health

To estimate the monetary value of health services we also follow a government cost approach and allocate benefits to those that _use_ public health services. The unit cost for inpatient and outpatient services is estimated with the sectoral budget from BOOST. To determine the number of users, we take inpatient and outpatient usage rates from the 2017 Demographic and Health Survey (DHS)<sup>39</sup> and apply these to the 2015 Census Jordanian population. Although the budget data may include the cost of providing health services to non-Jordanian households, including refugees, this share may be declining since 2014 when health subsidies for refugees were substantially reduced (Conway _et al_ ., forthcoming).<sup>40</sup> This means that our calculations are an upper-end estimate as costs per person where the total number of users includes non-Jordanians and refugees are lower. Because HEIS does not have information on the use of health care services, we randomly allocate the usage rates from the DHS to households in HEIS. Use rates vary by insurance status,<sup>41</sup> location (urban or rural), and socioeconomic status when possible,<sup>42</sup> and apply specific usage rates for these breakdowns. We also adjust for differences in household size between each household allocated to receive health benefits and the national average, as well as between all the households that are allocated to receive health services (inpatient or outpatient) and the national average. The estimate