rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Results of the adjusted delta test**

||**G**<br>**Delta**|**asoline**<br>**p-value**|**C**<br>**Delta**|**oal**<br>**p-value**|**D**<br>**Delta**|**iesel**<br>**p-value**|
|---|---|---|---|---|---|---|
|**Unadjusted**|14.099|0.000|13.676|0.000|14.933|0.000|
|**Adjusted**|17.145|0.000|16.431|0.000|18.174|0.000|

_Source:_ Based on the adjusted delta test of Pesaran and Yamagata 2008 Next, we proceed by determining the order of integration for each variable. Variables are said to be integrated of order _d_ when they become covariance-stationary after taking _d_ differences of former observations. In simple terms, _covariance stationarity_ implies that the time series has finite moments over time; for example, the mean does not eventually diverge to infinity. Without this condition, it is likely that we need to account for regression coefficients becoming biased due to spurious correlation; for example, we may find a positive correlation due to two variables experiencing growth at the same time when there is no underlying relationship. Depending on the cross-sectional dependency (CD) structure within a panel data, we can use either first- or second-generation unit root tests for this. First-generation tests are appropriate under cross-sectional independence; otherwise, secondgeneration tests are appropriate. We determine which generation to use via Pesaran’s (2004) CD test. As one might expect with macroeconomic variables, cross-sectional independence is rejected across the board for all variables, as is clear in table 3. So, we test the order of integration of the variables using the Cross-sectional Augmented Dickey Fuller (CADF) test developed in Pesaran (2003), and Newey and West’s (1994) rule-of-thumb for the number of lags included (4(T/100)^(2/9)≈3). For the CADF test, the results reject the null hypothesis of stationarity for all variables with the smallest p- value being 0.473, associated with PM2.5 readings (table 4). Therefore, none of the variables can be made stationary using up to 3 lags of the variable, showing that the variables are nonstationary. Given the results of the preliminary tests, we decide to use Pesaran’s (2006) CCE estimator, which is robust to CD and nonstationary data while allowing for slope heterogeneity, to estimate the model coefficients for all three fuels.

**Table 3. CD test results Table 4. CADF test results**

||**p-value**||**p-value**|
|---|---|---|---|
|**PM2.5**|0.000|**PM2.5**|0.473|
|**Gasoline****_p_**|0.000|**Gasoline****_p_ **|0.631|
|**Gasoline****_q_**|0.000|**Gasoline****_q_ **|1.000|
|**Coal****_p_**|0.000|**Coal****_p_ **|1.000|
|**Coal****_q_**|0.000|**Coal****_q_ **|1.000|
|**Diesel****_p_**|0.000|**Diesel****_p_**|1.000|
|**Diesel****_q_**|0.000|**Diesel****_q_ **|1.000|
|**GDP/cap**|0.000|**GDP/cap**|1.000|

_Source:_ Pesaran 2015 _Source:_ Pesaran 2003 _Notes: p_ = fuel price (U _S_ $ PPP) ; _q_ = quantity of fuel consumed per capita (toe) ; GDP/cap = GDP per capita (2017 US$ PPP).

9
# **GHANA: GROWTH PROJECTIONS**

## 1988 -1991

<!-- Start of picture text --> 6-<br>5<br>4<br>3<br>CL<br>0<br>Differ.<br>-<br>Target<br>-<br>Sustainable<br>1988 1989 1990 1991<br>Fiscal Years<br><!-- End of picture text -->
However, white-collar workers account for no more than 15% of all manufacturing workers. Whereas the female share of blue-collar workers varies from 11 percent in motor vehicles to 72 percent in tobacco, there is much less variation in the share of women in white collar jobs across industries.

White-collar workers are typically better educated than blue-collar workers (see also Lipsey and Sjoholm, 2004 and Harrison and Scorse, 2009). Gender differences in education vary within occupation: Male blue-collar workers are on average slightly better educated than female ones, whereas female white-collar workers tend to be somewhat better educated than male white-collar workers.

# **3.3 A Bird’s Eye View of Aggregate Adjustment Patterns**

## **3.3.1 Employment Adjustment**

Following Dunne et al. (1989), figures 2 present descriptive statistics on job flows separately by gender, with figure 2A documenting patterns for blue-collar workers and figure 2B documenting job flows of white collar workers. For both groups of workers, all measures of job flows become smaller in magnitude over time; both excess reallocation and net job creation diminished over time, consistent with declining labor market flexibility over time due to more stringent labor regulation.<sup>17</sup> The crisis is associated with a large drop in manufacturing employment opportunities with whitecollar workers suffering more severe job losses than blue-collar workers. Women seem to have fared slightly better in both occupational categories. While the difference in employment adjustment between men and women is small relative to total men and women employed, it is a substantial proportion of the total adjustment undertaken. With women representing 46% of the workers, they only suffered 34% of the net employment losses.<sup>18</sup> Thus the small gender differences in employment impact are in part attributable to men being more likely to be having white-collar jobs (sorting by occupation) and such jobs being more likely to be destroyed, and in part due to proportionately smaller reductions in the number of female jobs lost relative to men in both occupations.<sup>19</sup>

> 17 After the fall of Suharto, policy priorities shifted, with greater labor protections put into place (see World Bank, 2010), Hallward-Driemeier et al. 2010b.

> 18 These differences are also substantial in terms of the number of people affected; A one percentage adjustment in the number of jobs for men (women) amounts to roughly fifteen thousand jobs.

> 19 Also note that the crisis led to a spike in excess churning driven by a simultaneous increase in job destruction and a decrease in job creation. Nonetheless, the fact that, even during the crisis, overall job creation rates were as high as 13.8% in 1997 and 16.3% in 1998 is indicative of high heterogeneity in firm performance.

11
(illustrated by the change in the height of the marginal benefit curves shaded by diagonal lines), and only a modest share of the increased lifetime income will come from the induced increase in years of schooling (illustrated by the cross-hatched area between _S*_ and _S**_ ).<sup>6</sup> Consequently, any response of years of schooling to increased life expectancy at birth will understate the induced increase in human capital resulting from the improved health.

The case we illustrate in Figure 1 shows expected time in school increasing as life expectancy rose, but that requires that the induced increase in marginal benefit from schooling is greater than the induced increase in the marginal opportunity cost from schooling. The opposite could have happened, in which case the child would spend less time in school in the good health state. Ultimately, which effect dominates is an empirical question we plan to resolve with evidence from 147 economies.

# **4. Empirical Framework**

Our empirical specification will focus on the schooling response to life expectancy at birth. To show why, note that equating the marginal benefit and marginal cost of schooling yields the relationship but as suggested by our previous discussion, ���, ���, ��_� , ��_� and ��� will all be endogenously determined by information obtained as the child ages. Some of the reduced form effect of life expectancy at birth will be found through these other factors whose values will depend in part on life expectancy at birth and in part on new information revealed over time.

To make this point more precise, consider the projection of the cost of schooling at time _t_ on information available at the time of birth.

> 6 This point was made by Bleakley (2007, 2010b).

10
7 <!-- Start of picture text --> Ituri<br>North Kivu<br>South Kivu<br>15°E 20°E 25°E 30°E<br>N5°<br>0°<br>S5°<br>S10°<br><!-- End of picture text -->

- (a) DRC, with North Kivu, South Kivu, and Ituri Shaded

<!-- Start of picture text --> G G<br>G<br>G<br>GGG<br>24°E 26°E 28°E 30°E 32°E 34°E<br>N4°<br>N2°<br>0°<br>S2°<br>S4°<br><!-- End of picture text -->

- (b) Kivu Provinces, _Territoires_ , and Focus Group Locations

Figure 1: Area of Interest: Kivu Provinces
3

# Recent Experience with Commercial Debt Reduction

# INTRODUCTION

Over the years, many proposals have been made to resolve<sup>the commercial debt problems</sup> of middle-income developing countries (see Carmichael<sup>(1989) for an overview). During a</sup> period of time, international policy makers considered market-base **d**<sup>ebt reduction an attractive</sup> means to solve the debt problem. Market-based debt<sup>reduction transactions involve the</sup> repurchase (or conversion into other claims) by the debtor of<sup>its commercial debt at a discount</sup> through an open-market operation. Market-base **d** ebt reduction<sup>transactions increased sharply</sup> after 1985, from about $1 billion in face value of debt retired<sup>to over $17 billion in 1988 (see</sup> the Quarterly Review, World Bank, various issues).<sup>The results were mixed, however, as debt</sup> burdens remained high and economic performance in many of<sup>the indebted countries remained</sup> poor.

Over this period, many proposals involving government intervention were<sup>made, often</sup> centered around an institution involved in buying up commercial<sup>debt at a discount and forgiving</sup> part of it. Most of these proposals involved a concerted<sup>approach to debt reduction, i.e., all</sup> creditors would have to sell their claims to the institution or<sup>be forced to participate in debt</sup> forgiveness in a uniform way. None of these concerted proposals<sup>were implemented to solve</sup> the commercial debt problems.

What did occur was official support for a menu-based approach.<sup>In 1989, officials of</sup> several creditor countries-former Finance Minister Miyazawa of<sup>Japan, President Mitterand of</sup> France, and Secretary Brady of the United States--proposed topromote official support for debt and debt reduction (DDSR). The Brady plan finally adopted<sup>in May of 1989 involves the IMF,</sup>
6 end of the spectrum, households in one-third of countries have largely switched to a cleaner alternative and fewer than 20 percent of households use solid fuels as their principal cooking energy source. At the other end of the spectrum, in half the countries, more than half of the  households rely on solid fuels for cooking. In a handful of countries Angola, Botswana,  Nicaragua, Paraguay, the Philippines, Senegal, and Tonga households are almost equally divided between using LPG and using solid fuels (Table A1.1 in appendix 1).

Figure 2: Country statistics for solid fuels as primary cooking fuel <!-- Start of picture text --> 40 40<br>32<br>30 30<br>18<br>20 14 15 20<br>11 11<br>10 10<br> 20 15 12 16 12 35<br>0 0<br>0–10% 10–20% 20–40% 40–60% 60–80% 80–100%<br>Percentage of households using solid fuels as primary cooking fuel<br>% of countries<br>Number of countries<br><!-- End of picture text --> _Source:_ Authors’ calculations.

The data on LPG use by wealth/income quintile show that there are three separate trends that may be part of an overall pattern (Table 1):

- <u>Type A</u> countries where the use of LPG increases steadily with rising quintile. Fifty one of the 63 countries in the table follow this pattern. The share of households using LPG as the primary cooking fuel is often very much greater in the top quintile than in the bottom quintile, suggesting that there is scope for a substantial increase in the uptake of LPG in the future as both incomes and the fuel’s availability increase.

- <u>Type B</u> countries where the use of LPG declines with rising quintile. There are four countries in this category. In Belarus and Ukraine, a large share of the households in the lowest quintile uses LPG, but the share declines to virtually nothing in the top quintile because of the availability of natural gas. In Uzbekistan where natural gas also dominates household fuel use, the share of households using LPG declines from a mere 3 percent to 2 percent with rising quintile. In Trinidad and Tobago, LPG use is nearly universal in the lower quintiles but is substituted by electricity in the top quintile, in which the share of households using LPG falls to 78 percent.

- <u>Type C</u> countries, which appear to bridge types A and B. The use rate initially increases with quintile level and then declines. Eight countries follow this pattern.

Table 1: Percentage of households using LPG as primary cooking fuel by quintile

|**Region**|**Country**|**Source**|**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Type**|
|---|---|---|---|---|---|---|---|---|
|**Asia**|Bangladesh+ (2006)|MICS3|0|0|0.3|4.3|48|A|
||Cambodia|HH surveys|0|0.2|0.5|1.7|20|A|
||India|HH surveys|0.5|2.3|7.2|21|65|A|
with its definition. Such a database will contain unexpected payments from government to Eskom in order to avoid triggering guarantees.

Additionally, NTSA uses market and third‐party information for credit risk assessment. Market information includes secondary market prices and credit spreads for domestic and foreign currency bonds of Eskom. As Eskom is fully government owned, no traded equity prices can be observed. From rating agencies, NTSA uses both rating reports on Eskom as well as rating reports of peer companies outside South Africa (i.e. monopolistic state‐owned electric utilities) and rating methodology papers (i.e. Moody’s methodology paper for regulated electric and gas utilities) (Moody’s Investor Service, 2013). NTSA uses RiskCalc and Credit Edge as additional tools to assess credit risk. RiskCalc is software aiming to estimate default probabilities of privately held companies using the financial ratios of the respective entities. Credit Edge is similar software for publicly traded companies. NTSA employs this information complementarily. Ratings of peer companies and rating methodology papers have been used to develop an internal credit rating methodology (section 2.b.). Market prices are monitored to detect changes in market participants’ perception of credit risk which may trigger a review by the Credit Risk Directorate. These are ultimately used to support the determination of the internal risk rating that is assigned to Eskom.

Compared to other SOCs within South Africa and SOCs in other countries, the degree of information available on Eskom is rich. A major challenge in credit risk assessment for SOCs in developing countries is depth and reliability of available information. The choice and design of risk assessment methodologies (discussed below) needs to factor in these constraints.

# **iv) Internal resources and capacity**

The Credit Risk Directorate at NTSA is mandated with assessing the government’s credit risk exposure from government guarantees to SOCs, IPPs, and cash balances at commercial banks. Additionally, the team is undertaking analyses of fiscal risks from Public Private Partnerships (PPPs) and various government funds.<sup>10</sup> The Credit Risk Directorate is part of the Chief Directorate for Strategy & Risk Management, one of five Chief Directorates of the ALM division.<sup>11</sup> The Credit Risk Directorate is headed by a director and includes four permanent staff, supported by one to two interns on a rotating basis. Staff have predominantly backgrounds in accounting, finance, and economics. The Credit Risk Directorate works closely with staff in the Governance and Financial Analysis and Sectoral Oversight Chief Directorates at the ALM division. These chief directorates are responsible

> 10 Including the Compensation Fund, Export Credit Insurance Corporation South Africa, Road Accident Fund, South African Special Risk Insurance Association, and Unemployment Insurance Fund.

> 11 See the National Treasury website (http://www.treasury.gov.za/nt/Top%20Structure%20Organisation.pdf) for details of the organizational structure.

11
Mean effects eighteen months after completion of training are presented Table 4. Relative to short-term effects, a decay in the magnitude and statistical significance of treatment effects emerges for all outcome variables except self-employment. The effects on skills matching dissipate and become statistically not significant, while effects for the employment outcome become either negligible or negative. For monthly earnings, the point estimates from both ITT and TOT parameters are still in line with short-term findings because smaller but positive statistically significant effects emerge depending on the specification used. Self-employment, on the other hand, is the only outcome that shows steady positive and statistically relevant impacts over time with sizable point estimates ranging from 0.04 to 0.092.

By comparing the MVTP confidence intervals for earnings ITT treatment effects, which are measured in terms of percent gain relative to the control group and reported in footnote 13, against the corresponding findings that emerged from eleven RCT vocationaltraining programs that targeted youth worldwide (see Table 1), somewhat sizable effects can be observed. This is particularly interesting given the short duration and low cost of this training initiative. As a result, and although there is uncertainty associated with the gains in earnings, a straightforward cost-benefit calculation reveals that it requires 6 to 8 months of treatment effects to recoup the costs of the courses. This is plausible given the short-term impacts depicted in Table 4.<sup>15</sup> These findings are also in line with other studies that measured effects over a longer time span and found that treatment effects fell further over time. (e.g., Alzúa et al., 2016; Ibarrarán et al., 2019). Overall, results suggest that training helped trainees move to tailored

> 15 This calculation considers the explicit costs of training that range from USD $90 to USD $140 plus implicit (forgone) costs that are computed as one month of control group mean earnings times the probability of being employed. Monthly benefits are computed using the statistically significant ITT monthly earnings depicted in column 4 in Table 4 (Tughrik $53255, or equivalent to approximately USD $27). A conservative skills depreciation rate (20 percent per year) is assumed for this calculation.

21
# **1 Introduction**

Public debt statistics are a cornerstone of macroeconomic analysis. Investors, taxpayers, and academic researchers all have a keen interest in the level and composition of a country’s public debt. However, these statistics are subject to major limitations and incomplete reporting (World Bank, 2021; Horn et al., 2021). When Zambia and Chad sought debt restructurings in 2021, it took them more than six months to assemble comprehensive debt data and reconcile it with the records of their creditors (Estevão, 2021). Most famously perhaps, large revelations of previously unreported debts triggered major debt crises in Greece and Mozambique (Reinhart and Rogoff, 2011a,b; IMF, 2018). Despite the importance of hidden debt, there is little research that systematically measures its magnitude, characteristics, and effects.

We fill this gap by quantifying hidden debt by systematically tracking ex post revisions of debt figures across different editions (“vintages”) of the most widely used international debt statistics. The key idea is as follows: when previously unreported loans are reported, past debt statistics need to be revised. Tracking these revisions allows us to quantify the scale, characteristics, and timing of hidden debt accumulation and revelation. We apply this approach to a new and comprehensive dataset of the past 51 vintages of the World Bank’s International Debt Statistics that we digitize from archival sources all the way back to the 1970s. Our new approach and data allow us to systematically document the degree of under-reporting for more than 50 years of data and for up to 146 developing and emerging market countries.

Our empirical analysis makes three novel contributions to our understanding of hidden debt. First and foremost, we document that there is a pervasive under-reporting of public debts. We show that debt data revisions are systematically upward biased for almost all debtor regions and income groups. Across all countries and years, we identify USD 1 trillion in “hidden” sovereign borrowing that is added to debt statistics only in hindsight, more than twelve percent of total sovereign borrowing by all countries in our sample. This amount is a lower bound for the true magnitude of hidden debt since not all unreported debt is eventually revealed. In the cross-section of countries, hidden debt levels are highest in countries with weak institutions and low capacity, however, even the countries with the strongest institutions in our sample exhibit systematic downward bias in their debt reporting. Comparing across creditor groups, we document that non-bond private loans and bilateral loan instruments are the most prone to under-reporting.

Second, we show that hidden debt tends to accumulate when growth is strong and be revealed in economic downturns. For example, the COVID-19 recession was followed by the largest hidden debt revelation in 50 years.<sup>1</sup> We document that the procyclicality of hidden debt accumulation and revelation is mainly driven by time-varying monitoring efforts. In our

> 1Recent World Bank efforts to strengthen debt reporting are discussed in World Bank (2022a).

1