rafmacalaba/data-use-mentions-extended huggingface dataset:
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# Table 9: DEBT INDICATORS FOR AND ECONOMIC PERFORMANCE OF HIGHLY INDEBTED COUNTRIES

||1980|<br>1981|<br>1982|<br>1983|<br>1984|1985|1986|
|---|---|---|---|---|---|---|---|
|Debt Service<br>Ratio(percent) 1/|18.1|<br>20.0|<br>24.9|<br>24.7|<br>23.1|<br>26.1|<br>29.0|
|Interest<br>toExports<br>Ratio (percent)|8.8|10.3|<br>14.3|<br>15.0|<br>15.0|<br>17.1|<br>18.7|
|Net Transfer<br>(US$ billion)|8.8|18.4|3.8|-7.4|-17.5|-26.1|-24.9|
|_GNP_<br>(US$ billion)|858.7|941.0|841.3|726.5|746.3|750.3|776.5|
|_Exports_<br>(US$ billion)|163.0|167.1|145.2|138.1|153.7|149.1|132.3|
|Imports<br>(US$ billion)|196.1|222.8|203.0|157.4|158.4|155.8|150.4|

_1/_ Debt Service relative to exporl:s of goods and services. Source: World Debt Tables, 1987-83 edition, World Bank 1988.
To provide a better sense of the overall and direct effect of agricultural subsidies on water resources, analysis is undertaken for the sample of countries for which subsidy data is available. A major challenge with quantifying support in agriculture is the difficulty in obtaining consistent measurements of such support across all developed and developing countries. Agricultural support estimates are obtained from a combined database following Gautam et al. (2022). It is important to distinguish between agricultural supports that are coupled to input use and output levels versus those that are decoupled from production and provided as direct payments to farmers. Coupled subsidies incentivize production and provide direct subsidies on output or input that create incentives to increase output. In contrast, decoupled supports are not linked to production and avoid altering incentives to change input or output levels. Instead, they provide direct income support to producers, thus acting as lump sum subsidies and are less distortionary.

To separate producer support into coupled and decoupled payments, country-level estimates of annual support to agricultural producers from the OECD's Producer Support Estimates (PSE) database data are obtained from the OECD's Composition of Producer Support Estimate tables. These estimates are used to construct coupled and decoupled support as shares of the total value of production. PSE are defined as the annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm gate level, arising from policy measures that support agriculture. They are available for 24 countries and the European Union.

In certain parts of the world, like South Asia and Sub-Saharan Africa, aggregate coupled support can be negative due to the inclusion of market price support, a variable in the database that accounts for trade measures and policies such as export bans which can lead to a net tax on producers, when global (free trade) prices exceed the domestic price. For this reason, three measures of coupled subsidies are used in the analysis. In one variant, market price support is removed to focus only on the portion of the subsidy that amounts to direct producer support
urban extent is a challenge across the world, and results that are internationally comparable is the goal. This requires focusing on a set of key indicators available for the vast majority of countries.

At the same time, the set of indicators chosen is broader than is generally the case when measuring the urban extent. Official estimates of urbanization rates by national governments and international organizations rely almost exclusively on data from the ground. The key indicators considered are population size, population density and administrative category. Conversely, products recently developed by the remote‐sensing literature focus almost exclusively on data from outer space. Built‐up cover and nighttime lights are the preferred indicators in this respect. But rather than choosing one set of indicators or the other, our analysis combines data from the ground with data from outer space.

Data from the ground are taken from the Spatial Database for South Asia, which georeferenced both the 2001 and 2011 Census of India to the digitized official boundaries of administrative units (Li and others 2015). A range of georeferenced indicators from the ground are available for 2001 and 2011 across all units in the country. The indicators are population size, population density, employment structure, access to services, dependency ratio, and literacy rate. The administrative type of each unit is also available, as described earlier.

Following the practice of national governments and international organizations, we use _population size_ and _population density_ at the town and village level as our explanatory variables from the ground. This choice is consistent with that of studies for more advanced economies that rely on high‐quality microdata from the ground (Michaels, Rauch, and Redding 2012; Rozenfeld and others 2011). Population density is computed as the ratio between the population of the administrative unit and the size of the area within the corresponding polygon boundary. In some specifications, we also consider the units’ _administrative type_ as defined by the 2011 Census.

Data from outer space are selected building on the remote sensing literature, as well as on recent developments in the
_et al._ (2020) show that statistical discrimination that is based on inaccurate beliefs about decision-relevant characteristics can

> 3A user-friendly introduction to those papers can be found in Persico and Todd (2008), which also discusses some caveats to the theory. Another related paper is Eeckhout _et al._ (2010), which shows that random crackdowns can be an optimal policing strategy. Lazear (2006)’s paper on high-stakes testing in education is also relevant for its potential applicability to crime.

> 4Armenter and Koren (2014) show that, even in U.S. data, trade data are extremely sparse. The most common number of shipments for a product-country-firm observation is one. Related facts have been observed in data from other countries. Our data from Serbia offer an example.

> 5Furthermore, while we focus on the planner’s problem (i.e., the inspecting agency), both Knowles _et al._ (2001) and Persico (2002) look at the optimal behavior of a single officer (among many) to determine if there is any discrimination in equilibrium. This distinction is important since when deciding on search intensity, the agency takes into account the effect of its decision on the overall compliance rate, whereas the individual does not.

4
# **D Dropping Flood Districts**

Figure D.1. Women’s Marriage Age Post the Gujarat Riots of 2002 <!-- Start of picture text --> .5<br>0<br>-.5<br>-1<br>Marriage Year<br>Data Source: NFHS-4<br>Diff-in-diff Coefficients<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV dropping all flood affected districts. The outcome variable is women’s age at marriage. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level.

39
**Recovering Income Distribution in the Presence of Interval-Censored Data**<sup>*****</sup>

Gustavo Canavire-Bacarreza<sup>†</sup> Fernando Rios-Avila<sup>‡</sup>

# Flavia Sacco-Capurro<sup>§</sup>

**Keywords:** interval-censored data, Monte Carlo simulation, heteroskedastic interval regression,

wages JEL Codes: C150, C340, J3

> * The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The authors would like to thank Ximena del Carpio, Leonardo Luchetti, Carlos Ospino, Daniel Mahler and the participants at the 5th IZA Labor Statistics Workshop: The Measurement of Incomes, Living Costs and Standards of Living and the 2022 Stata Conference for helpful comments and suggestions.

> † The World Bank, gcanavire@worldbank.org

> ‡ Levy Institute at Bard College, f.rios.a@gmail.com

> § The World Bank, fsaccocapurro@worldbank.org
that the citation data capture scholarly influence across a wide range of publication types, not just a narrow range of academic journals.

As a source of citation data, Google Scholar does, however, have its disadvantages. Titles are also sometimes incorrect, incomplete, ambiguous, or appear in multiple versions. The tool we used to ―harvest‖ citation data went through multiple versions before we could be sure it could cope with mispunctuation and misspellings. Even then we found mistakes, and had to correct manually or drop records that were flagged as outliers and/or which erroneously appeared highly ranked in the tables below. One defect of Google Scholar we have not corrected is the scattering of citations across multiple documents. Some publications appear with somewhat different titles in draft form or in working paper form, and will appear as different publications in Google 16 For details of Google Scholar coverage, see http://scholar.google.com/intl/en/scholar/inclusion.html.
64 exporters and importers for the year 2015, the latest year available.<sup>21</sup> Finally, both equation (1) and (2) also include the term C ∗intlo . This vector refers to several control variables included in the baseline model and as part of the robustness checks below. Most importantly, this term includes the data model each country follows by itself, regardless of its trading partner. The reason to control for this trait is that it may not be by coincidence that countries choose to apply a particular data model. Indeed, this choice may be driven by the extent to which a country trades digital services with partner countries to begin with. As such, by not including these controls, this relationship would suffer from reversed causality or would be influenced by the fact that precisely a partner country shares the same data model.

These control variables need to be interacted with a dummy indicator that reflects whether a country-pair are dissimilar or not, called “intl”. This aims to cover for trade costs affecting international flows and not domestic flows (i.e. internal trade). The use of this measure also follows the recent gravity literature such as the one developed in Yotov (2012). In particular, Yotov _et al._ (2016) states that the inclusion of this dummy leads to the theoretically consistent identification of the effects of bilateral trade policies as shown in Dai _et al_ . (2014). Also, this interaction term can be 21 See footnote 10 that explains the decision to select TiVA instead of other services trade sources. The choice is mainly driven by both data availability and consistency between the services trade databases. Compared to the ITPD-E database, which is extensive in the coverage of developing countries, the following points matter for this choice. First, the TiVA data separates the various digital services sectors of interest. The ITPD-E dataset instead takes the various sub-sectors of the digital economy together. Second, many developing countries included in the ITPD-E database show
the Census of that year.

Table 2 presents cross tabulations along several dimensions of participation and shows that participation along one dimension need not imply participation along others. For example, '' Such a data set would have to identify exogenous variation in government policy that is independent of firms' decisions over formality. This identification is extremely difficult in practice because most policies are implemented nationwide, confounding the effect of policy changes with business cycle and macroeconomic forces that also influence fmn behavior. A differences-in-differences approach that utilized between state (or province or region) variation in policies would work in principle. But the existence of multi-establishment **firns** that cross state lines would make assignment into the proper treatment groups problematic.

12
Our findings also shed light on the importance of using task measures that vary at the individual level instead of at the occupation level. A simple decomposition shows that less than half of the variation in the WFH index is explained by variation between 4-digit ISCO occupations (see Table A 2 in the appendix). Most of the variation in the tasks related to WFH takes place within narrowly defined occupations.

The correlation between economic development and the amenability of jobs to working from home is positive within the PIAAC and STEP datasets. When we also consider the availability of internet access at home, the relative ranking of countries does not change significantly. That is, poorer countries have a lower share of jobs that are amenable to be done at home (Figure 5). For example, the Netherlands is 0.38 standard deviations above the average PIAAC country in terms of its jobs’ amenability to working from home, while Ecuador and Turkey are 0.65 and 0.5 standard deviations below the average, respectively. In the PIAAC sample, other countries whose jobs are also more amenable to WFH are Belgium and the Nordic countries. In contrast, Peru, Mexico, and Chile have jobs that are more vulnerable in this regard. The correlation between GDP per capita and the WFH measure is negative for the LMPS countries, as Jordan ranks higher in terms of WFH amenability despite having a lower level of GDP per capita. However, this may be explained by Jordan having higher internet penetration than Egypt and Tunisia.<sup>6</sup>

> 6 According to data from the World Development Indicators, the share of internet users in the corresponding survey year was 62.3 percent in Jordan, and 46.9 and 46.1 in Egypt and Tunisia, respectively.

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# **Impact of the West African Ebola Epidemic on Agricultural Production and Rural Welfare:  Evidence from Liberia**<sup>**1**</sup>

Alejandro de la Fuente<sup>2</sup> Hanan G. Jacoby<sup>3</sup> Kotchikpa Gabriel Lawin<sup>4</sup> _Keywords_ : Ebola, Liberia, Agriculture, Welfare . _JEL Codes:_ Q12, I31, N37

> 1 The authors are grateful to Andrew Dabalen and Kristen Himelein for their comments on an earlier version. We like to thank Siobhan Murray for her assistance on geo-referencing and mapping; as well as Min. Tolbert Nyenswah, Luke Bawo and Nelson Dunbar from the Ministry of Health in Liberia for providing the Ebola administrative data and graciously helping us understand how they were collected. The findings, interpretations, and conclusions of this paper are those of the authors and should not be attributed to the World Bank Group or to its member countries.

> 2 World Bank (adelafuente@worldbank.org)

> 3 World Bank (hjacoby@worldbank.org)

> 4 Consultant (kotchigab@gmail.com)
financed only around 5%-10%. The private sector contributed during the 1990s roughly between 20%-25% of investment in the sector.<sup>_xi_</sup> Currently, in developing countries, the public sector, the largest contributor to the financing of infrastructure, is spending roughly between around 2% (in high middle income countries) to around 4% (in low income countries) of GDP on infrastructure. For the low and low middle income countries, this is up to 3% lower than the estimates of their investment needs. During the 1990s, the public sector has tended to reduce its own participation in the sector as a result of: (i) an unmet hope for a major financing of infrastructure by the private sector, (ii) fiscal adjustment programs, and (iii) decentralization resulting in mismatches between resources and needs. There is indeed growing evidence that more often than not fiscal retrenchment has resulted in a disproportionate reduction in public investment expenditures, particularly in infrastructure, as a result of the use of the current budget deficit to GDP ratio as the single yardstick to assess fiscal performance. Easterly and Serven (2003) find that for Latin America, the contribution of the reduction in public infrastructure investment to the fiscal adjustment of the region varied between 31.5% in Mexico to 174.3% in Brazil. But the problem is more generalized. In India for instance, total investment in infrastructure was 5.4% in 1990, including 4% of public sector money. By 1998, total investment in infrastructure had dropped to 4.6% with a decline in public investment by 1%. Private investment had only increased by 0.2%, not nearly enough to offset the drop in public investment.

For most countries, the trend in public sector financing has thus been downward as seen in Figure 2 representing data from the IMF Government Finance Statistics for a sample of 17
record the quantity of bird species they encounter. Further details, such as the number of observers participating in the trip, the hours spent birding, and a geotagged timestamp, enrich the dataset. This geolocation data enables us to identify birding trips within specific protected areas, and the trip duration and number of observers data allow us to control for survey effort.

Contrary to our other analyses, our unit of observation in this case is the individual birding trip. This choice is informed by our adoption of the main specification from a recent study that examined the relationship between air pollution regulation and bird abundances using eBird data [59]. Replicating this established specification limits our researcher degrees of freedom. In this specification, the dependent variable is the log total number of birds observed on a trip, and the control variables are duration of the birding trip in hours, the number of observers in the birding party, and fixed effects for hour of day, protected area, and calendar year. We also follow Ref. [59] in dropping the top 1% of bird abundance observations in order to remove outliers. We imputed missing values of birding trip duration and number of observers using their respective means calculated from non-missing values within our dataset, which encompasses eBird observations inside our protected areas of interest. We add as control variables three indicators for whether birding trip duration, number of observers, or hour of day were missing, which allows us to retain these observations in our regression. This regression employs 145,200 observations drawn from 20 treatment group protected areas and 106 control group areas between 1998 and 2022. Despite differences in the unit of observation and control variables, this regression applies the same dynamic difference-in-differences estimator as in our other regressions [49]. Table S7 displays cohort-specific ATTs. Four cohorts have negative ATTs; however, these cohorts contribute only 11% of treatment observations.

Figure S2 exhibits a decreasing trend in bird abundances in the years prior to transference. In the absence of transference to AP management, bird abundances
Our paper contributes to this literature by focusing on the impact on non-performing loans of climate-related natural disasters.

As mentioned above, extreme weather events may decrease a borrower’s likelihood to repay, increasing credit risk. By the same token of argument, natural disasters trigger a decrease in the value of real assets through physical destruction and loss of functionality (e.g., negative spillovers from destruction of infrastructure), decreasing the value of collateral in secured borrowing. Nonetheless, the overall increase in non-performing loans might be diluted by increases in lending.

An extreme weather event is usually followed by a sharp rise in new loan requests and a contemporary surge of deposit withdrawals, as affected individuals and firms need to make up for the economic damage and an increase in current expenses (Brei et al., 2019). This rise in the demand for credit may not be matched by an increase in supply, keeping low credit growth and raising the incidence of eventual non-performing loans related to the disaster. On the contrary, banks could mitigate the impact of shocks by cutting lending most where their comparative advantage is least (i.e., where competing banks have similar access to information). For instance, banks increase lending in affected areas while decreasing financing in those unaffected areas where they do not have branches (Cortés & Strahan, 2017) and in non-affected regions (Ivanov et al., 2020). In contrast, various papers suggest that banks decrease the supply of credit post-disaster. For example, BSP (2020) uses regional and bank-level data from the Philippines to show a deterioration of loan growth and loan quality in the country’s banking sector after extreme rainfall events between 2014 and 2018.

This paper contributes to this strand of the literature by providing the first systematic crosscountry study on the effect of climate and environmental related physical risks on banking sector stability. It provides generic estimates of the effect of such natural disasters on an entire set of financial sector soundness indicators based on the most comprehensive cross-country historical sample.
0.165|0.030|
|Observations|2,412,052|9,416,097|2,981,696|1,585,079|
|Controls|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|School|School|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each educational level using specification (4) in Table 3. Clustered standard errors by municipality are shown in parentheses. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

23