rafmacalaba/data-use-mentions-extended huggingface dataset:
To illustrate these challenges, Figure 1 shows that countries with higher vulnerability to climate change also face higher sovereign risks. The climate vulnerability index comes from the new IMF Climate Change Dashboard and comprises climate-driven hazard and exposure, vulnerability, and lack of coping capacity. The EIU Country Risk Index indicates sovereign debt risks, based on more than 50 institutional, macrofinancial and structural variables. Climate-fiscal nexus due to the simultaneous exposure to both risks suggests that emerging market and developing economies (EMDEs) are more sensitive to physical risks of climate change and feature tighter fiscal space. When natural disasters hit an economy, a fiscal response to address economic and distributional effects may lead to a debt increase which carries downside risks of its own. Potential debt distress and limited access to international financial markets could reduce the intended fiscal support and exacerbate growth and fiscal prospects.

This paper examines to what extent the impacts on economic growth after natural disasters are accompanied by widening fiscal deficits and corresponding pressures on public debt. It relies on the ifo GAME database (Felbermayr and Groeschl, 2014), which contains exogenous measures of physical disaster intensity, well suited for causal analysis. Regression results suggest that natural disasters lead not only to output losses, but also to further deterioration of countries’ fiscal positions. A disaster of one standard deviation above the disaster index mean leads to 1.32 percent lower output per capita growth, 1.58 percent higher government debt, and 0.88 percent increase in fiscal deficit. Local projection estimates indicate that the adverse effects persist in the years after the disaster, not only on impact. Finally, the effects are significant in the subsample of EMDEs, while the advanced economies remain stable after disaster episodes.

In addition, I provide a general equilibrium interpretation of the empirical results using a Dynamic stochastic general equilibrium (DSGE) model. The model explains the propagation mechanism of a natural disaster shock that affects agricultural productivity. Based on Gallic and Vermandel (2020), it features farmers endowed with land with timevarying productivity subject to
to<sup>measure</sup> consequences is a major task often beyond the efforts and time of evaluators. Instead recourse is often made to measures of intermediate output, such as the numbers of immunised children, or<sup>other</sup> utilisation and coverage data, for which information is more readily available and which are more clearly related to the intervention. Economic evaluation focused on such measures of health service output is perhaps more appropriate in the light of the difficulty of measuring health outcome.

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br>Family farm<br>0<br>1991 2005 2001 2005 2000 2005 1992 2005<br>Ghana Tanzania Rwanda Uganda<br>(%)<br>Households with source of Income<br><!-- End of picture text --> _Source: Annex table 1 and authors‟ calculations_

# **Livelihood strategies and household welfare**

The reason agriculture households add NFEs is that HE owners‟ earnings are usually higher than in agriculture. However, Figure 13 panel (i) using data from Rwanda shows that they also have a wider variance. Similar data from Uganda shows a very high variance in average daily earnings for HE owners and household members working in the business, compared with other income sources (Fox and Pimhidzai, 2011). In urban areas, the differences are not so large. Data from Dar es Salaam, (Figure 13, panel (ii) non-agricultural earnings only), shows that in this capital city where opportunities for both wage employment and HE earnings are highest, median HE earnings are slightly lower than median wage earnings. Not surprising given the higher education levels that wage and salary employees have, the upper tails of the wage earnings distributions are fatter than the HE ones. But still, given the substantially lower level of education in HE owners compared with wage earners in Dar es Salaam, (Kweka and Fox, 2011), it is hard to argue that HE owners do poorly with the education that they have - indeed, as noted 24
as a major destination for Costa Rica’s exports, from 26% in 1982 to 38% in 1992 and 46.5% in 2003 (Figure 1.2). This was in part the result of a significant increase in foreign investment inflows, with the establishment of companies such as Componentes Intel in 1998.

<!-- Start of picture text --> 53.16%<br>20.76%<br><!-- End of picture text --> <!-- Start of picture text --> 26.08%<br><!-- End of picture text --> <!-- Start of picture text --> 47.86%<br>13.91%<br><!-- End of picture text --> <!-- Start of picture text --> 38.23%<br><!-- End of picture text --> <!-- Start of picture text --> 42.12%<br>11.42%<br><!-- End of picture text --> <!-- Start of picture text --> 11.42%<br>46.46%<br><!-- End of picture text --> Source: Prepared by the authors based on figures from PROCOMER.

The good export performance and the higher foreign direct investment (FDI) inflows, together with an adequate foreign exchange policy, have resulted in a relatively satisfactory improvements in the balance of payments’ trade account, since although at present the current account deficit is higher in absolute terms, this deficit has tended to decrease relative to the size of the economy (Figure 1.3).

<!-- Start of picture text --> Figure 1.3 Costa Rica: Current Account Deficit in US Dollars and as a Percentage of GDP<br> 1983-2003<
fishery communities located downstream in contaminated zone were affected more heavily, compared to those located upstream and thus closer to safe waters. This, in consequence, seems to have induced different coping responses. Satellite data suggests a strong northward movement in fishing pattern of the affected upstream fishers. Being able to travel to safer fishing locations allowed these workers to maintain the number of work hours; however, their average monthly income still declined by as much as 45 percent in 2016. In contrast, fishers located downstream were more likely “trapped” inside the contaminated zone; their average income was cut by more than half. This adverse circumstance led to a significant reduction in fishery work hours and an increase in the likelihood of working secondary jobs.

Next, we study aspects of disaster recovery, examining the impacts of _Formosa_ on both fishing intensity and fishery income over time. We find a gradual reduction in disaster damage to both outcomes. We also show that, by the last quarter of 2016, while fishing activity in the affected region had largely recovered, the average monthly earnings of affected fishers were still only two-third of the base level in the first quarter (i.e. the pre-disaster period). Finally, we study potential spillover effects of _Formosa_ to other related industries in the affected provinces. Among the industries deemed eligible for _Formosa_ compensation by the government, we show that fishers in freshwater fisheries, especially those located downstream, actually benefited from the incident in terms of earnings.

We focus on examining the immediate impacts of _Formosa_ on fishery workers, and how the affected fishers adapted to this extreme environmental shock, for at least two important reasons. First, fishery is a major industry in Vietnam, accounting for 19.97 percent of the country’s total agricultural GDP in 2016, according to the Statistical Yearbook (General Statistics Office, 2016b). Fishery-related activities also make up a considerable share of the economics in the central coastal of Vietnam. At the four-digit Vietnam Standard Industrial Classification (VSIC) level, the single
to associate degrees and certifications from the institutions providing this level of schooling. Government expenditure in public education is considered an in-kind transfer because the service can be accessed by all school-age children regardless of socioeconomic background.

Expenditures on health also increased in the period 2017–21, accounting for 7 percent of annual government expenditure as of 2018 (figure 16, Appendix A3). In this paper, funds spent by the government in the health sector amount to in-kind transfers to the public because these services are available to all. Citizens, residents, and visitors can freely access the services in the public health system with no differentiation in fees<sup>6</sup> according to income level or socioeconomic class.

In addition to the in-kind transfers in education and health, the government implemented a social protection system within the SEED program. The program targeted school-age children, people with disabilities, the elderly, the chronically ill, pregnant women, lactating mothers, and the adult poor. Beneficiaries are selected via proxy means testing, which uses household characteristics such as age, household size, marital status, assets, disability, and housing conditions as indicators or “proxies” to determine household expenditure or consumption. Cash is transferred to households: in 2017, it was EC$300 per month or EC$3,600 per annum. In 2018, the amount given to elderly households was increased by EC$100 per month, and in 2022, the government raised the amount paid to all beneficiaries by EC$150 per month. Government annual expenditure on SEED increased from EC$14 million in 2017 to approximately EC$18 million in 2021 and is expected to have increased further in 2022. From 2017 to 2021, the government spent an average of EC$2.9 million per year on the school feeding program.

# **3. Data Sources**

The primary data source is the Survey of Living Conditions and Household Budgets (SLCHBS) conducted by the Central Statistical Office (CSO), the country
Some of these may seem self evident, but they also require empirical testing. For example, competition might not influence public health care providers unless their budgets or incomes are affected by the number of services they provide; and market wages might have limited influence on the ability to recruit qualified staff if non-financial motivation for joining the public health care service is strong.

As a first step to analyzing governance of public health care provision, it would be useful to control for:

- share of consultations by public and private health care providers

- ratios of public nurses‟ and doctors‟ average monthly income relative to high school and college graduates

- number of annual graduates from nursing and medical schools relative to public health care service vacancies

- World Bank measures of governance of public institutions

More detailed information might be necessary. For example:

- average number of private health care providers operating within 10 km of each public health care facility

- ratios of health workers incomes to specific alternative professions (e.g. teaching, law) or statistically controlled analyses of wages relative to the larger labor market

- country governance indicators that are directly relevant to the health sector (e.g. the CPIA health sector component).

Additional contextual factors that have been measured and used in other studies include sociodemographic data (e.g. female literacy rates, income distribution, ethnic and religious fragmentation), labor market indicators (e.g. unemployment rates among high school graduates), and quality of public institutions (e.g. the World Bank‟s CPIA).

# **5. Ways to Measure Governance in the Health Sector**

This section addresses practical issues for measuring governance in the health sector. It describes leading approaches to measuring the five governance performance measures identified above. It concludes with a discussion of strategies for measuring governance determinants. The appendix provides links to websites for a number of survey instruments and measurement initiatives.

# **_Governance performance_**

The strategy for measuring governance performance measures in ways that are comparable
_g_ = _k_<sup>2</sup> are the most used in this literature. With the hyper- _g_ we can only assign large prior mass to regions with high _g_ by taking _a_ very close to 2, which gives us a fat tail for _g_ , but does not really change the shape of the prior. In order to propose an alternative class of priors in the next subsection, we initially focus on the shrinkage factor, _δ_ .

# _3.3. A Benchmark Beta Prior_

If we start from a Beta( _b, c_ ) prior on the shrinkage factor _δ_ = _g/_ (1 + _g_ ), we can base our proposal on ensuring that prior moments are reasonable (they always exist, as we are dealing with a finite support). In particular, let us set the mean shrinkage factor equal to the one that corresponds to the Fern ́andez _et al._ (2001a) recommendation _g_ = max _{n, k_<sup>2</sup> _}_ . This fixes one parameter as a function of the other, as then _b/c_ = max _{n, k_<sup>2</sup> _}_ . The second parameter can then be chosen by considering the spread around this mean: where is the chosen prior mean of the shrinkage factor, and thus larger _c_ corresponds to a tighter prior around this mean.

For example, for the cross-country growth data used in Fern ́andez _et al._ (2001b) with _k_ = 41 and _n_ = 72, the value _c_ = 1 would correspond to a prior standard deviation of the shrinkage factor of 0.0006 (approximately equal to 1 _− d_ ) and the corresponding _b_ = 1681. For _c_ = 1 _/_ 1000 we obtain a prior standard deviation of 0.015 (approximately 25 times 1 _− d_ ) and this corresponds to _b_ = 1 _._ 681. For likely choices of _c_ (
# **1. Introduction**

What is a city? The most candid answer may be “I know one when I see it.” The subjectivity of the city concept is understandable because of the continuum between urban and rural spaces. The many terms used to describe this in‐betweensuburb, exurb, edge city, and urban sprawl, among otherstestifies to the absence of a clear divide. Just like beauty, a city is in the eyes of the beholder.

Official answers to this question are less straightforward, varying substantially across national governments and international organizations. A glimpse at this diversity is provided by World Urbanization Prospects, a comprehensive source of international urban data that builds primarily on official information. About 45 percent of the 232 countries reported in its 2014 revision used each country’s own administrative definition as the sole or primary criterion for distinguishing urban from rural. Some 27 percent used population size as the sole criterion, and another 25 percent adopted a combination of metrics. The remaining 3 percent of countries had no official definition (UN 2015). Moreover, many countries change their definitions of cities periodically.

Not surprisingly, this diversity of criteria results in a lack of consensus on which urban agglomerations should be considered cities. It is thus not clear how urban areas can be compared across countries and over time.

This seemingly trivial issue has important implications. The rural‐urban transformation drives economic development and is often intertwined with structural transformation. Because cities create prosperity by bringing together households and firms and making everyone more productive, the rural‐urban transformation is central to development economics, and cities are at the core of economic geography (Fujita, Krugman, and Venables 2001; Henderson 1974; Krugman 1991; Lewis 1954).

However, the difficulties in identifying urban areas have undermined empirical efforts to connect with the theoretical literature and even to establish the stylized facts with confidence. For example, it is widely accepted that a country’s urban population share is related to its income
specific top quantiles such as the top 1% or 10% (Burkhauser _et al._ , 2016), we follow the same approach as Czajka (2020): for each year, we replace the survey with tax data starting from the point where the quantile functions from both sources cross.<sup>12</sup> We first collapse the tax records in up to 37 quantiles - nine quantiles between the top 10% and top 1%; nine quantiles for the top 1% to top 0.1%; 9 quantiles between the top 0.1% and 0.01%; and the highest ten quantiles up to the top 0.001%.<sup>13</sup> As mentioned before, for the period 2003 - 2010 we have very limited information on third-party reporting, and are thus unable to capture a large share of income from individuals who do not file PIT. We adjust the level of income in top quantiles in those years to match the ratio between income declared in PIT forms and total income for the 2011-2019 period, where we observe all sources of income.<sup>14</sup> We then compute, for a range of income levels, the share of individuals with incomes above that level in tax and survey data – as a rule, for low income levels we observe more individuals with higher incomes in the survey data, so this ratio is below unit, and at very high income levels that ratio often far exceeds unit. We choose as the merging threshold the lowest point when that ratio equals one, and replace survey data with tax records above that income level.<sup>15</sup> We present a diagnostic assessment of our merging procedure in Table 2. In column (1) we illustrate the total reference adult population in each year, computed using the household survey. In 2019 there were approximately 5.4 million adults over 20 yearsold in Honduras out of a population of over 9 million individuals
to heightened entry rates and industry sorting, but the implied size of these effects is much smaller and rarely statistically significant. This is perhaps due to the greater incentive for larger plants that trade at a distance in the formal sector to pick their location more selectively. Another potential root cause, which we begin to explore in this paper, is differences in the initial level of financial development.

# 3 Data

Our platform combines financial loan data from RBI with evidence on GQ implementation.

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_d_<sup>_R_</sup> ( _j_ ) = 1) but do not know _all_ households that have it available. In principle, we could estimate the proportion of such households from the data because we model the choice of nonrelative care and observe the households using it. Since the proportion is poorly identified in the data, we calibrate it. Compton and Pollak (2015) use data from the second wave of the National Survey of Families and Households (NSFH) to estimate distances from the husband’s mother and the wife’s mother conditional on their education. Using their estimates and our sample data on household parental education, we arrive at 40% of households having relative care available.<sup>24</sup> Parents in the two low-income locations, in turn, are more likely to have relative care available than others (Table D.2).

# **4.3 Estimation Method**

We estimate the model by Simulated Method of Moments. This is particularly well-suited for our model, which lacks a closed-form solution. Let **_θ_** be a column vector of dimension _P_ , equal to the number of parameters to be estimated. Our estimator, **_θ_**<sup>ˆ</sup> SMM, is such that: where **z** is the column vector of _B > P_ sample moments to match, and **s** ( **_θ_** ) is the column vector of the corresponding moments evaluated at **_θ_** . Weighting matrix **W** is diagonal; weights are the bootstrapped standard deviations of the sample moments.<sup>25</sup> We perform full-solution estimation, which requires equilibrium computation for every value of **_θ_** .

We estimate the model in two steps. In the first step, we match 77 moments aggregated over the four locations and recover the structural parameters common to all of them. In the second step
|**0.028**|**0.520**|**0.050**|0.0<br>0.1<br>0.2|1 2 3 4 5 6 7 8 9 10|
|**Uganda (2012)**|**0.012**|**0.471**|**0.025**|0.0<br>0.1<br>0.2<br>0.4|1 2 3 4 5 6 7 8 9 10|
|**United States (2016)**|**0.065**|**0.136**|**0.325**|0.0<br>0.1<br>0.2<br>0.3|1 2 3 4 5 6 7 8 9 10|

Source: authors calculations based on CEQ Data Center on Fiscal Redistribution (2022) 13
The data from the mostly small and medium-sized enterprises included in the Investment Climate Surveys—the surveys used in the analysis in this paper—are broadly consistent with the macroeconomic data.<sup>5</sup> Manufacturing enterprises included in the surveys were less likely to export in most of the countries in Sub-Saharan Africa than similar enterprises in Asia. Furthermore, enterprises in Sub-Saharan Africa also exported less as a share of total output— only about 12 percent on average—compared to about 22 percent of output in Asia (see Table 2).

Although the average is relatively low for the eight countries in Sub-Saharan Africa, it is important to note that there are large differences between countries within Africa. For example, manufacturing enterprises export more in Senegal, Kenya and Zambia than in the other countries for which data were available. Whereas 58 percent of Kenya enterprises and 43 percent of Senegalese enterprises included in the samples were involved in exporting, only 7 percent of firms in Ethiopia and 12 percent of firms in Mozambique did the same.

In principle, the large pools of workers with relatively modest levels of education in most countries in Sub-Saharan Africa would appear to make these countries attractive platforms for exporting labor intensive goods to capital-rich industrialized economies. In practice, however, the small and medium-sized enterprises included in the Investment Climate Surveys mostly export to neighboring countries within Sub-Saharan Africa rather than to Western Europe or other industrialized economies. In all countries other than Ethiopia, enterprises were more likely to export to neighboring countries than they were to export to more distant European markets (see Table 3). This pattern appears to be true for both landlocked countries (e.g., Uganda and Zambia) and countries with access to the sea (e.g., Tanzania and Kenya).

Although Ethiopia appears to be an exception to this general rule—Italy, the United Kingdom and Germany are the three most important export destinations for Ethiopian enterprises—it is important to note that very few Ethiopian enterprises export at all (see
3.2)|-14.4<sup>∗∗∗</sup>|(3.0)|
|Distance to town (km)|0.0|(0.0)|-0.0|(0.0)|0.0|(0.1)|
|Year and season FE|Yes||Yes||Yes||
|District FE|Yes||Yes||Yes||
|Observations|35627||24643||24643||

Note: FE stands for fixed effects. The dependent variables are per capita consumption without aid (food and cash) in columns 1 and 2 and per capita food consumption (without transfers) in column 3. In Column 2, we restrict the sample to households included IHS 3 and 4 so that we could measure food consumption without transfers in a similar way. Due to lack of access to Stata dofile used to construct consumption aggregates in IHS2, we could not reconstruct food consumption without transfers. Standard errors are clustered at the district level. Standard errors in parentheses: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 In conclusion, the welfare analyses conducted in this study show that drought in the growing season reduces consumption per capita (without aid) in the months following harvest by 5 to 12 percentage points. The amount of welfare loss depends on the intensity of the drought. Excess rainfall in the first two months of the growing season (November– December) decreases food consumption per capita by 1.8 percent for each day of excess rainfall in that period, while excess rain in later months of the growing season has less impact on consumption.

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