rafmacalaba/datause-encoder-data huggingface dataset:
2 agricultural imports into tariffs (import taxes) which are inherently more transparent and simpler to negotiate (Winters 1987). With that came minimum market access requirements, which manifest themselves in the form of lower or zero tariffs on agreed quantities of imports (called tariff rate quotas, or TRQs), but higher (sometimes prohibitive) out-of-quota most-favored-nation (MFN) tariffs. The Uruguay Round agreements also explicitly recognized domestic producer supports and export subsidies for farm products, and quantitative restrictions or prohibitions on imports for sanitary or phytosanitary reasons. So while in principle that set of multilateral agreements was expected to add to transparency, in practice it remains very difficult to quickly identify the extent of distortions to agricultural prices and their changes over time.

An identification difficulty exists even for tariffs themselves. In tariffying, not only did countries choose to set specific (e.g., $x/kg) rather than or in addition to ad valorem (percent) tariffs,<sup>2</sup> but most bound their out-of-quota tariffs in the World Trade Organization (WTO) at rates well above those actually being applied. The URAA also provides a special safeguard mechanism for farm products to allow import surges to be curtailed. Thus almost all WTO members retain the freedom to raise applied rates at will – in some cases by several orders of magnitude. Furthermore, most countries offer preferential tariff rates to a subset of supplying countries. Some provide that formally as TRQs, while many others do it via bilateral and regional trading agreements. Still others offer non-reciprocal preferential market access to select developing countries such as under the European Union’s programs for so-called African, Caribbean and Pacific Island (ACP) countries and for all least-developed countries (under the ‘everything but arms’ program). Typically these discriminatory agreements have complex rules of origin that raise the cost of accessing the preferences, sometimes to prohibitive levels. Clearly, with differing in-quota and out-of-quota tariff rates for agricultural products, many of them specific or compound rather than just ad valorem, and with a wide array of preferential bilateral tariffs in place in most countries, there is no obvious single tariff rate that can

> 2 Specific tariffs offer (a) more protection against lower-priced substitutes, (b) growing protection against imports whose price is trending downwards over time, and (c) opaqueness because its ad valorem equivalent (AVE) is not evident from the tariff schedule without recourse to cif unit import value data. If ad valorem tariff cutting formulae are to be used in negotiating trade agreements, as under the WTO’s Doha Development Agenda (DDA), agreement first has to be reached on what border price to use to calculate the specific tariffs’ AVEs. In the DDA that took several months and many meetings during 2005.
**Table 4: Regression results: using dated explanatory variables for latest ratings as of December 2006**

|Dependent variable: Sovereign rating|**S&P**|**Moody’s**|**Fitch**|
|---|---|---|---|
|GDP growth|-0.47***|-0.03|-0.19*|
||(0.08)|(0.09)|(0.10)|
|Log of GNI per capita|-1.41***|-0.69*|-1.34***|
||(0.29)|(0.35)|(0.26)|
|Ratio of reserves to import and ST debt|-3.41***|-2.21***|-4.16***|
||(0.87)|(0.65)|(0.92)|
|Ratio of ext. debt to exports|0.68***|1.68***|0.77***|
||(0.24)|(0.25)|(0.23)|
|GDP Volatility (5-yr std. dev.)|0.37***|0.06|0.38***|
||(0.10)|(0.12)|(0.09)|
|Rule of law|-2.60***|-2.68***|-2.20***|
||(0.43)|(0.60)|(0.41)|
|Inflation||0.17***||
|||(0.04)||
|Observations|54|45|60|
|Adjusted R-squared|0.78|0.82|0.80|

_* significant at 10%; ** significant at 5%; *** significant at 1%. White robust standard errors are reported below coefficient estimates._

# Model validation using within-sample predictions

Our next step is to use the fitted model to predict the value of the dependent variable. Note that we have three models, one for each agency. Before proceeding to prediction, we check the validity of the model using a variety of methods. Model validation involves using the available sample to verify that the model would give reasonable predictions. Using a model for the latest rating as of end-2005, we predict ratings for 2006 and compare these with the actual ratings assigned in 2006. This within-sample forecasting allows us to compare the actual ratings with the ratings predicted by the model. The regression results for ratings as of end-2005 are qualitatively very similar to the results reported in table 4 above, confirming that the model is indeed very stable.<sup>15</sup>

> 15 These results are available from the authors upon request. We do not report these results here as they may make the article too long.

17
# **Assessing the Effect of Public Capital on Growth:**

**An Extension of the World Bank Long‐Term Growth Model**

Sharmila Devadas and Steven Pennings<sup>1</sup> Development Research Group, World Bank JEL Classification: O40, O57, H41 Keywords: Long‐term growth, infrastructure, public capital, public investment efficiency

> 1 The views expressed here are the authors’, and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent. The authors are grateful to Norman V. Loayza for guidance, Luis Serven for helpful comments and Jorge Luis Guzman for research assistance. The authors also thank the Global Facility for Growth for Development Trust Fund, supported by the Korean Government, for financial support. The companion LTGM‐PC spreadsheet and appendices are available at www.worldbank.org/LTGM..
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23
- 26 -

Finally, the third column in Table 2 presents coefficient estimates of the net return to factor inputs, along thi lines of<sup>equation</sup> (11). The estimated effects of the different tax instruments are similar to those reported in column (2). The coefficient on<sup>the weighted tax</sup> variable (i) is similarly significant, and close to one in value.

As noted previously, there are two paths by which taxes can affect income growth. The first is through the productivity of<sup>inputs, as the</sup> regressior- **a** bove have been attempting to measure. The second is<sup>through</sup> the supply of factors; higher tax rates may reduce labor supply and<sup>the</sup> supply of investment.

A regression which explains private investment for the sample is presented in column 1 of Table 3. In this first regression, tax variables are excluded; coups have a signficant negative effect on investment, while oil producing countries tended to have investment rates 7.5<sup>percentage</sup> points above non-oil producing countries. In addition, the impact<sup>of</sup> government consumption (e.g., government current expenditures)<sup>on</sup> investment appears to be positive and significant.

Column (2) includes the overall tax effort. Surprisingly, the coefficient is positive; countries with higher tax rates experience higher investment rates.<sup>13</sup> One explanation is that increased tax revenue scales back deficits and, by freeing private savings from government use, increases the supply of funds for private investment purposes. There are two problems with this explanation. The first is that the primary source of private investment in Africa is from foreign sources (or retained earnings of partially foreign-owned corporations); domestic savings in most

> 13/ This finding echoes Koester and Kormendi (1988), whc used a larger data set of 63 countries.
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**Commander, Simon, and Jan Svejnar.** 2007. “Do Institutions, Ownership, Exporting and Competition Explain Firm Performance? Evidence from 26 Transition Countries.” IPC Working Paper No. 28. International Policy Center, Gerald R. Ford School of Public Policy, University of Michigan.

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33
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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, Zambia which faced continued concerns related to incomplete debt reporting.

## **2.1 Mozambique**

In 2016, Mozambique made international headlines when news reports revealed USD 1.2 billion in unreported sovereign debt liabilities. During the early 2010s, Mozambique was considered a development success story. The discovery of large natural gas deposits off the country’s northern coastline was followed by a surge in foreign direct investment and years of high real GDP growth. In 2013, at the height of the boom, the country issued sovereign bonds worth USD 850 million in international capital markets to fund investments in a tuna boat fleet. In the same year and the year after, state-owned enterprises used government 4
# Data and methods

The experiment was embedded in the Listening to Central Asia series of surveys in the Kyrgyz Republic, Uzbekistan, and Kazakhstan, surveys that are designed to continuously monitor wellbeing and views on topical policy issues.<sup>6</sup> The surveys are collected from a single (“omniscient”) household member, and household selection was designed for national representativeness.

Fieldwork was conducted in two phases. In the first phase, a national in-person household survey was conducted in each country using regionally stratified two stage cluster sampling procedures. In the second stage, a randomly selected sub-sample from the first stage in each country was recruited in a monthly panel survey using a phone-based method conducted by private survey companies under the direction of World Bank staff. The sub-samples were each nationally representative and for the present study include a total of 6,783 individual responses (in Uzbekistan, the Kyrgyz Republic, and in Kazakhstan). Data collection for the survey experiment was conducted in May-June 2021.

For the core experiment, respondents were assigned a set of 8 separate vignettes, each with three randomly generated components of the form: “A <RANDOM AGE>-year-old <RANDOM GENDER> works as <OCCUPATION>. <HER/HIS based on RANDOM GENDER> monthly gross earnings total <RANDOM MONTHLY PAY> (before taxes and extra charges).” Following each vignette, the respondent was asked: “Is this person severely overpaid, overpaid, fairly paid, underpaid or severely underpaid.” The content of the randomly generated portions (including all components set off by “<>” in the example above, apart from occupation) followed the design described in Table 1. Every respondent was asked similar questions over a fixed set of eight occupations, including: store clerk, farm worker, receptionist, software engineer, manager, medical doctor, taxi driver, and schoolteacher.

**Table 1: Randomized elements of the experiment design**

|**Dimension**|**Values**|
|---|---|
|Sex|Male/Female|
|Age|25/35/45/55|
|Pay|KZT: (a) 200,000; (b) 300,000; (c) 400,000; (d) 500,000; (e) 600,000<br>KGS: (a)10,000; (b) 20,000; (c) 30,000; (d) 40,000; (e) 50,000.<br>UZS:(a)2.5 mln; (b)3.5 mln; (c)4.5 mln; (d)5.5 mln; (e)6.5 mln.|

As demonstrated in table 2, randomization was successful across all assigned treatments, as well as for each country individually. There were no statistically significant differences across any treatment arms for key respondent characteristics including gender, (log) income, subjective poverty status at the time of interview, and respondent age. In each country, women were over-represented among respondents (ranging from 59 percent of respondents in Uzbekistan to 70 percent in the Kyrgyz Republic). Average respondent age ranged from 43 in Kazakhstan, to 48 in Uzbekistan. The proposed pay levels were set such that category (b) was the average wage reported by the official statistical authority in each country, while

> 6 See https://www.worldbank.org/en/country/uzbekistan/brief/l2cu; and <u>https://www.worldbank.org/en/country/tajikistan/brief/listening2tajikistan.</u>

7
technology shocks just as long-run identifications can. We consider three alternative approaches to sharpen the identification of technology shocks in the presence of confounding shocks. The first is a modification to the Max-Share approach. This modification hinges on the choice of horizon used in estimation. We next propose a spectral identification approach whose rotation is in the frequency domain. Finally, we modify the spectral approach by substituting the long-run variance-covariance matrix with one that can be reasonably obtained in small samples.

When using Monte-Carlo simulations to validate our methodologies, we highlight that a key drawback of this exercise is that few DSGE calibrations contain nontechnology shocks with a material impact on the variance of productivity. That is, in order to generate non-trivial confounding shocks, we need to incorporate certain kinds of stochastic processes not usually assumed for these models, but which are present in the data. As an alternative, we employ a simple two-variable model that provides greater transparency around the data-generating process than a DSGE model.

In summary, this paper makes the following contributions to the literature. First, we demonstrate that the Max-Share identification is susceptible to bias from confounding low and high-frequency shocks. Second, we propose three alternative identification methodologies. The first, Non-Accumulated Max-Share (NAMS), shows a lower degree of bias in the presence of low-frequency confounding non-technology shocks. The second and third, labeled Spectral and Limited Spectral approaches, show less bias in the presence of non-technology high and medium frequency shocks other than technology. Finally, we show that both the Max-Share and our improved alternative identifications are more robust than long-run restrictions to the problems of both confounding non-technology shocks and lag-truncation bias identified by Chari et al. (2008). Therefore, SVAR techniques can still play an important role in testing the predictions of macroeconomic models.

The remainder of the paper is as follows. We first revisit existing technology identification methodologies, drawing attention to newly identified shortcomings of the Max-Share approach before outlining several proposed improvements. We then show the performance of the existing and new identifications in Monte Carlo simulations on DSGE-generated data and data generated by a simple 2-variable model. Finally, we take each specification to data for the United States and several major emerging and developing economies (EMDEs), finding that there are several sources of contamination in traditional VAR approaches that can be addressed with our new methodologies.

# **2 Empirical Approaches**

In this section, we briefly revisit the standard long-run and Max-Share identification of a technology shock. The potential for the Max-Share approach to be contaminated by other non-technology shocks is then explained, before we detail three approaches that can reduce contamination, depending on its source.

5
Policy Research Working Paper 8742

# **Abstract**

Students around the world are going to school but many of them are not learning—an emerging gap in human capital formation. To understand this gap, this paper introduces a new global data set measuring learning in 164 countries. The data cover 98 percent of the world’s population from 2000 to 2017. The data set will be publicly available and updated at regular intervals by the World Bank and is designed to serve as a public good to accelerate global policy and research agendas focused on quality education and human capital formation. The paper presents several motivating facts in a first application of the data: (a) although enrollment has increased worldwide, learning progress is more limited; (b) girls outperform boys on learning—a positive gender gap—in contrast to a negative gender gap observed for schooling; (c) human capital when measured by both schooling and learning accounts for between a fifth to half of cross-country income differences—a middle ground in the recent development accounting literature and (d) average estimates mask important underlying heterogeneity by country income status and region. These stylized facts demonstrate the potential of this new global dataset to reveal insights into the relationship between human capital and economic development.

This paper is a product of the Education Global Practice and the Office of the Chief Economist, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/research. The authors may be contacted at hpatrinos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team