rafmacalaba/data-use-mentions-extended huggingface dataset:
Gu and Stangebye, 2023). We contribute to this literature by compiling rich new data and by integrating a simple information acquisition problem with a quantitative sovereign debt and default model that features asymmetric information about the sovereign’s true debt level.

Our findings have important implications for empirical research more broadly, as debt statistics are essential input variables for a large variety of research designs. Researchers should be wary of the fact that data downloads from different vintages of debt statistics can lead to significantly different results and can therefore complicate the replication and updating of empirical work (also see Aruoba, 2008; Christensen and Miguel, 2018; Goes, 2024). Empirical applications in macroeconomic forecasting or asset pricing, in particular, need to take into account that debt figures tend to get upward revised after their initial publication (Frankel, 2011; Beaudry and Willems, 2022; Akey et al., 2023; Estefania-Flores et al., 2023).

The rest of this paper is structured as follows: Section 2 motivates our approach by presenting two recent case studies. Section 3 introduces our new database of debt data revisions and our measure of hidden debt revelations. We explain in detail how our measure of hidden debt should be interpreted and point out important caveats. Section 4 presents the main statistical properties of debt data revisions, and it documents new stylized facts about the size, characteristics, and timing of hidden debt and its revelation. Section 5 presents our quantitative model of sovereign debt and default with hidden debt and an endogenous monitoring decision. Section 6 uses our new data to numerically discipline the model: we measure the impact of hidden debt on equilibrium default incentives, sovereign spreads, and welfare. Section 7 concludes.

# **2 Case studies**

In this section, we present two recent case studies to motivate our approach and to illustrate the main idea of using debt data revisions as a measure of hidden debt. First, Mozambique where the country’s Constitutional Council ruled that hidden debts were illegally contracted. Second,
Figure B.8: Number of Electronic Payment Transactions in Argentina (Placebo) <!-- Start of picture text --> A. Raw Data<br>2009m12 2011m6 2012m12 2014m6 2015m12 2017m6<br>Year-Month<br>B. Deseasonalized Data<br>2009m12 2011m6 2012m12 2014m6 2015m12 2017m6<br>Year-Month<br>13<br>12<br>Log Number of Transactions<br>11<br>13<br>12<br>Deseasonalized<br>Log Number of Transactions<br>11<br><!-- End of picture text --> Notes: This figure plots the log number of transactions with electronic payment technology in Argentina between 2009 and 2017. The data is obtained from the Central Bank of Argentina. Panel A plots the raw monthly aggregate values. Panel B plots the the de-seasonalized series after taking out month-of-year fixed effects, as per equation 1 (linear specification). The vertical line marks August 2014, when the VAT rebates in Uruguay entered into effect. This figure is discussed in Section 4.3.

56
are also highly volatile, and nearly 90 percent of the territory receives less than 350 millimeters (mm) of rain every year, which results in regular droughts (World Bank 2008a). This has a significant impact on the economy, since 45 percent of Niger’s GDP comes from the agricultural sector, which is predominantly rain fed.

Niger remains one of the poorest countries of the world, though it has ample natural resources. The country ranked last among 182 countries in the Human Development Index (HDI).<sup>2</sup> With an annual per capita income of $300—compared against the Sub-Saharan African average of $500—in 2008, 60 percent of the Nigerien population lived below the poverty line of $1 per day. Poverty incidence is highest in rural areas, where 85 percent of population lives. In the states of Tillaberi and Dosso, more than 70 percent of the population lives under the poverty line (figure 3b). The country’s precious mineral resources

> 2 The HDI provides a composite measure of three dimensions of human development: living a long and healthy life (measured by life expectancy), being educated (measured by adult literacy and gross enrollment in school), and having a decent standard of living, as measured by purchasing power parity (PPP) and income.

4
increases the likelihood of chronic poverty due to disruption of income-generating activities and depletion of infrastructure and basic services (Bratti, Mariapia, and Alfonso 2009; Bozzoli and Brück 2009; Bozzoli, Brueck, and Muhumuza 2016).

# 3. Empirical analysis

The World Bank implemented Wave 1 (2016) and Wave 2 (2017-18) of the Somali High Frequency Survey to better understand livelihoods, vulnerabilities and poverty across 6
grid cell; and its sub-grid variations are captured statistically. Distinguishing characteristics of the VIC model include: subgrid variability in land surface vegetation classes; subgrid variability in the soil moisture storage capacity; drainage from the lower soil moisture zone (base flow) as a nonlinear recession; and the inclusion of topography that allows for orographic precipitation and temperature lapse rates resulting in more realistic hydrology in mountainous regions. VIC uses a separate routing model based on a linear transfer function to simulate the streamflow. Adaptations to the routing model are implemented in VIC to allow representation of water management effects including reservoir operation and irrigation diversions and return flows. Since its existence, VIC has been well calibrated and validated in a number of large river basins over the continental US and the globe. Applications using the VIC model cover a variety of research areas.

> 7Future climate data was provided by the WB Climate Change Knowledge Portal (http://climateknowledgeportal.worldbank.org). All climate models are credited to the Coupled Model Inter-comparison Project Phase 3 (CMIP3) of the World Climate Research Programme (WCRP) produced a multi-model dataset, referenced in the Intergovernmental Panel on Climate Change’s (IPCC) Third and the

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# **2.  Mangrove Species in the Sundarbans**

The Sundarbans region in Bangladesh is populated predominantly by seven mangrove species (see Table 1).<sup>6</sup>

# **Table 1: Bangladesh Sundarbans mangrove species**

|Scientific Name|Vernacular Name|
|---|---|
|_Avicennia alba, marina, officinalis_|Baen|
|_Excoecaria agallocha_|Gewa|
|_Ceriops decandra_|Goran|
|_Bruguiera gymnorrhiza_|Kankra|
|_Sonneratia apetala_|Keora|
|_Xylocarpus mekongensis_|Passur|
|_Heritiera fomes_|Sundri|

Source: Aziz and Paul (2015) In order to understand the geographic distribution of these various species, we started with a geographic overlay of Bangladesh Sundarbans forest with _upazila_ maps constructed from an administrative shapefile provided by the Government of Bangladesh. The Sundarbans mangrove region includes five _upazila_ s in Khulna Division: Dacope and Koyra in Khulna District; Mongla and Sarankhola in Bagerhat, and Shyamnagar in Satkhira. Figure 1 displays a high-resolution map of the distribution of Sundarbans mangrove species in 2013.<sup>7</sup> The map reveals concentrations of _Ceriops decandra_ (Goran) in southern and western Shyamnagar; _Excoecaria agallocha_ (Gewa) in eastern Shyamnagar, western Koyra and southern Mongla; and _Heritiera fomes_ (Sundri) in Koyra, Dacope and northern Mongla. Between these areas of concentration lie mixed patterns dominated by the first species entries in the legend of Figure 1.

> 6 About 99 percent of the forest area is accounted for by these seven mangrove species.  The region also includes scattered habitat for 20 other tree species.  Forest inventories conducted at different times have concluded that a significant area of Sundarbans has mixed varieties of mangrove species, with species proportions varying with site conditions.   Forest types are classified by dominant species (GoB 2010; Hussain and Acharya 1994).

> 7  This map has been provided by Department of Forest, Bangladesh.

6
_H_</sup> and _wj_<sup>_C_arethepercentageofchildrenwithahighschoolorlessandthepercentageofchildrenwith</sup> a university degree, respectively. _T_ is the number of working years and _β_ is the discount rate (0.95). For this calculation, I focus on the primary working ages (30-54).<sup>46</sup> I do not observe _Income_<sup>_H_</sup> and _Income_<sup>_C_</sup> (real annual income at different ages for high school graduates and college graduates) because children who are affected by the sanctions are not yet old enough to directly measure their earnings. Children’s future annual income may be imputed from the information on children’s levels of education, using the relationship between earnings and education in observed data. I consider different scenarios for changes in their education and income.<sup>47</sup> Similarly, I calculate the present value of lifetime earnings of the current generation using the annual income before and after the implementation of the sanctions to find parents’ earnings loss.

The first exercise is to calculate what the expected magnitude of the children’s income would be if the sanctions had not been imposed. The more reasonable scenario is that after the sanctions were lifted children would not go back to school and the wage rates do not recover. The results of cohort analysis in section 7 (Table 6, Panel B) provide evidence for this assumption that affected children did not return to school. Also, this assumption that the wage rates do not recover after the sanctions are lifted, is reasonable considering the fact that average real income has increased by only 5% between 2013 and 2019. In this scenario, I assume without the sanctions, the next

> 46Since HIES is a cross-sectional survey, I observe single-year measures of the earnings. Such short-run measures of workers’ earnings may include both measurement error and transitory fluctuations in earnings. Thus, I select a period to observe the representativeworkers when their earnings are most likely
unobservable household and community characteristics that impact consumption are not included in the regression, and _are correlated with the level of the drought shock in a given year_ , we may be capturing biased estimates of the relationship between drought and consumption. We can include average rainfall at the community level to help control for this, or alternatively, a fixed‐effect at the zonal level (there are 68 zones in Ethiopia). The datasets used in the analysis do allow a rich set of controls in which we aim to capture as many characteristics as is feasible, without over‐fitting the model to the dataset at hand. With this in mind, we also use the statistical learning procedures to validate the model.

The question of internal validity may be more weakly stated as being a “stable” observable relationship between drought‐crop‐loss and consumption over several specifications (and external validity over several datasets), and answerable by looking at whether the point estimates and associated p‐values established through the regression work, and statistical learning through the bootstrapping method described later. By giving a level of significance around the relationship between consumption and crop loss (defined by the coefficients output from the regression, and their associated functional form), the derived p‐values give us information on the strength of fit of the modelled relationship. The bootstrapping method generates a mean estimate of the relationship and a related standard error (see below for more detailed methodology) which allows us to examine the stability of the coefficients that have been derived.

However, it must be noted that from an econometric perspective, the absence of random application of the shock to households (e.g. if at all there may be a bias for households more susceptible to the impacts of shocks locating themselves in hazard‐prone areas) limits the extent to which internal robustness/validity can be examined using cross‐sectional data. The purpose of the research sidesteps this issue somewhat, as the question of interest is not primarily about causality _per se_ but rather,
<br>Mining and quarrying 7%<br>5%<br>20% 5%<br>Agriculture, hunting, forestry, and 10% 16% 14% 12%<br>fishing<br>5% 5% 5%<br>0%<br>2017 2018 2019<br><!-- End of picture text -->

**Source** : Authors’ calculations with data from Jordan LFS and UNCOMTRADE

6 | P a g e
|Yes|Yes|
|Conflict dummy|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|22764|22864|18378|18378|21451|21547|17201|17201|
|R-squared|0.076|0.969|0.792|0.950|0.076|0.972|0.796|0.953|
|Dep. Variable [mean]|0.029|283.912|0.247|3.626|0.030|273.537|0.240|3.642|

Notes: (1) The table shows the regression results across districts in Africa during 2002–2007. (2) The results show the local economic impacts of China’s WTO accession based on the scale of mining operations. (3) The standard errors, clustered at the country level, are in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. (4) Population density is measured as population per square kilometer. (5) Growth, Gini, and Sen Index are measured using lights data as described in the data section. (6) Fixed effects include time-fixed effects, district-fixed effects, and country-year-fixed effects. (7) Climate is measured using the standard deviation of rainfall distribution at the district level. (8) Analysis based on World Bank GEM data has fewer observations because the GEM prices data covers fewer mineral products than in the USGS data.

Source: Authors’ estimations

||Table 7b:Het<br>_U_<br>|erogeneous e<br>_SGS Consta_<br>|ffects bym<br>_nt prices_<br>|inerals’val<br>|ues.<br>_World_<br>|_Bank (GE_<
. In this sense, cities lying above (below) the fitted line are thus predicted to have more (less) overreporting than those lying on the fitted line. Therefore, while negative values of our measure could indicate under-reporting in some cases (especially when the magnitude is large), we should not take the negative sign literally as under-reporting.

To gain intuition for whether our measure is an appropriate proxy of the extent of manipulation, we link it with cities’ annual growth targets, typically set at the beginning of the year. We expect that cities that have achieved the yearly target (i.e., reported growth more than the target growth) are more likely to exaggerate their growth figures compared to those that have failed to do so—the former group should have more manipulators than the latter group. Figure 4 plots the distribution of our manipulation measure for these two groups. Indeed, the distribution of the target-reaching group (solid line) lies to the right of the distribution of the target-failing group (dashed line). In fact, nearly 60% of cities in the

> 29 The eight economic regions are: Northeast, North Coast, Eastern Coast, Middle Yellow River, Middle Yangtze River, Southwest, Northwest, and South Coast.

> 30 Gibson et al. (2021) show that the performance of DMSP data in predicting GDP is worse for lower-level spatial units, for lower density areas, and for smaller areas. Given the size and population density of Chinese cities, their results suggest our use of DMSP data to predict city-level GDP growth is appropriate and relatively accurate. 31 In robustness checks, we also use more sophisticated specifications (e.g., province-specific slopes or a quadratic specification) and add more controls when predicting GDP growth in Eq. (1), such as electricity consumption, bank loans and freight volume. However, we do not incorporate these into the baseline regression Eq. (1) since the current specification already explains more than 99.5% of
Section 3 outlines our methodology, followed by the presentation and discussion of results in Section 4. Section 5 covers the limitations of our study, while Section 6 explores the policy implications. Section 7 concludes the paper.

# 2. Data

The datasets used in this study are described in Table 1. These input datasets have different spatial resolution. For the subsequent sections of the article, we use the spatial resolution of the global horizontal irradiance (GHI) map, i.e., 0.2° (~22 km). We apply this resolution to all input datasets by nearest neighbour interpolation. The shape file on the type of GDEs is rasterized at this same resolution. Besides, we use irradiance data from 2020. In Figure 1, we plot the annual average of GHI for 2020, static water level, aquifer transmissivity, groundwater storage, population density, renewable groundwater resources and type of GDE across subSaharan Africa.

|**Data**|**Description**|**Unit**|**Spatial**<br>**resolution**|**Temporal resolution and**<br>**coverage**|**Year of**<br>**release**|<br>**Provider**|**Type of**<br>**data**|
|---|---|---|---|---|---|---|---|
|Global<br>Horizontal<br>Irradiance<br>(GHI)|Radiation received by a horizontal<br>plane from all directions.|W/m<sup>2</sup>|0.2°<br>~22 km|One temporal vector for each<br>location.<br>Data from 2005 to 2020 with<br>a time step of 30 min (some<
# **Box 4: Financing Education Development in the Republic of Korea**

The Republic of Korea has taken a progressive approach to universalizing primary and secondary education. In 1954, the government launched its first plan for free compulsory primary education financed by the Education Tax Act of 1958 and foreign aid. Between 1954 and 1959, public education spending as a share of the overall government budget increased threefold from 4.2 percent to 14.9 percent. Nearly 80 percent of this increase was devoted to primary education, and by the late 1950s and early 1960s, primary school enrollment was nearly universal (Kim 2002).

After the expansion of primary schooling was complete, the government shifted the focus of its funding to secondary education (see figure below). Secondary education spending as a percentage of total public education spending surged from 21 to 39 percent between 1970 and 1987. This funding provided schools with enough resources to increase the secondary school gross enrollment rate from 39 percent in 1971 to 101 percent by 1996.<sup>a</sup> While primary public education spending as a proportion of GDP fell, absolute levels of public education spending at the primary level continued to rise in real terms over the same period. Since the universalization of secondary schooling in Korea in the late 1990s, the government’s focus has shifted, to expanding higher education.

<!-- Start of picture text --> As Universal Primary Enrollment Was Achieved, Government Spending Shifted toward Secondary<br>Education<br>a. Education spending as a percentage of GDP b. Secondary school learning outcomes<br>(Harmonized Learning Outcomes (HLO) scale)<br>7<br>Total 600<br>6 Primary<br>Secondary 500<br>5<br>400<br>4<br>300<br>3<br>200<br>2<br>1 100 Mathematics<br>Science<br>0 0<br>1970 1975
The estimation of labor inputs on smallholder farms is complex and vulnerable to misreporting.<sup>6</sup> Smallholder farms typically mostly employ family labor, and, so, there is no wage income on which to anchor recall. Written records are rarely kept, and the respondent must rely on recall to report on past events. To arrive at the total amount of labor allocated by a household to farming, the household must accurately report the plots under cultivation, the specific household members who worked on each plot, the activities performed, and the timing and duration of these activities. Farming is a seasonal activity, and work patterns are irregular during the season. Reporting on the typical or average amount of time spent farming requires, after the completion of the season, remembering distant events and performing complicated mental calculations. Alternatively, reporting hours worked in the last seven days at any single point during the agricultural season will not necessarily be indicative of total labor during the season if labor inputs vary greatly during the season.

# **Insights from cognitive psychology**

The design of the survey instrument itself may also influence the quality of data on family farm labor. Considering common survey practices and the features of smallholder farm labor, alongside insights from the social and cognitive psychology literature, there is a particular need for caution in interpreting farm labor data taken from household surveys.

Perhaps the most important aspect in our context is the implications of the recall period. The effects can operate through faults in memory. Forgetting an event is considered more likely as time passes. Alternatively, telescoping, by which a respondent remembers a distant event as if it occurred more recently, can result in memory-driven distortions, particularly in longer recall periods (Sudman and Bradburn 1973). An example is a respondent who worked on the farm 35 days ago, but who reports that he worked on the farm in the past 30 days. Beegle, Carletto, and Himelein (2012) find little evidence that longer recall periods lead to less reliable
brought investments and increased productivity in lagging, predominantly rural areas and the slowdown in productivity growth in more affluent, urban areas, where employment shifted toward less dynamic, low-productivity nontradables as cities de-industrialized (Jedwab et al., 2022). Convergence also narrowed the income gaps with leading metropolitan areas, including the gaps exploitable through migration to the top locations, especially among the bottom 40 percent. Still, at the end of the 2010s, there were sizable differences in per capita household labor earnings with the leading areas in nearly all countries, mainly reflecting differences in educational attainment, in line with the sorting of skilled workers in leading areas, but in a few countries large differences in the returns to endowments indicate potentially significant reutrns to migration to the leading areas, especially for residents of the poorest, remote regions. Rather than a clear rural-urban-metropolitan divide, the location-premia distributions of different locality types overlapped to a great degree and differences in the average location productivity premia in urban and rural areas were relatively small in most countries. While the places with the highest location premia were not necessarily the leading metropolitian areas, in all cases, they were among the top 25 percent of most productive localities.

The remainder of the paper is organized as follows. Section 2 reviews the literature. Section 3 discusses the methodology and the data, while Section 4 presents the empirical results. Section 5 summarizes the findings and offers conluding remarks.

# **2. The literature**

There is a large literature on the size and evolution of territorial differences in income and welfare in Latin America. Most studies that belong to this literature focus on one country or a limited set of countries, use data up to the early 2000s, and rely on per capita GDP data at the first administrative level (i.e. states, provinces, or departments). Most studies find evidence of absolute convergence at the first administrative level, albeit at a speed below the one observed in developed countries, in Brazil (Azzoni, 2001; Serra et al
specifications. Third, we evaluate candidate models’ out-of-sample predictions across different PMT cutoff scores using several evaluation metrics. Finally, we explore the performance of hypothetical targeting methods and transfer schemes using the final PMT.

## _Data and Variables_

That data used to develop the Sudan PMT comes from the National Household Budget and Poverty Survey (NHBPS 2014/2015) conducted by the Sudan Central Bureau of Statistics.<sup>9</sup> The survey includes modules on consumption, household characteristics, education, employment, housing, and asset ownership. The survey, representative at the national level, as well as states and rural and urban areas, includes 11,953 households.

> 9 This is the most recent household survey data available at the time of this study.

7