rafmacalaba/datause-encoder-data huggingface dataset:
In what follows, section two discusses the far-reaching impacts of climate shocks and their link to resource degradation, the importance of these phenomena to Ethiopia, and the scope for analysis created by free access to archived medium-resolution imagery that now covers several decades. Section three describes the study area, the nature of the interventions, the data, and outcome variables of interest based on both remotely sensed imagery and ground-based measurements. Section four discusses the econometric approach and estimated impacts on indices derived from satellite imagery and ground measurements, and section five considers the policy implications of the findings.

# **2. Conceptual Framework**

The increased frequency and severity of climate-related shocks are often thought to imply a need to enhance the resilience of farmers in developing countries in order to facilitate sustainable development. Yet unless the effectiveness of such interventions can be assessed, choosing an optimal level or mix of instruments may be difficult. Our analysis demonstrates how remote sensing can help address this issue; it looks at the advantages and disadvantages of commonly used indices in terms of the particular aspects of initiating sustainable land management interventions in Ethiopia.

# **_2.1 The Far-Reaching Impacts of Climate Change_**

Global models nearly unanimously predicted that climate change would result in more frequent and severe weather shocks and ultimately to consequences from longer-term shifts in temperature, rainfall patterns, and water availability. History suggests that rising temperatures substantially reduce growth rates, agricultural and industrial output, and political stability, so that their negative impacts on poor countries are likely to be substantial (Dell et al _._ 2012).

While these qualitative effects of climate change have been known for some time, recent studies have increasingly quantified the size and incidence of their consequences. Although trade with countries less negatively affected by, or even benefitting from, climate change can mitigate adverse effects (Lybbert et al. 2014), agricultural losses are still estimated to reach one-sixth of total crop value (Costinot et al _._ 2016). Beyond effects on output, climate shocks affect human capital accumulation (Dinkelman 2015) and contribute to intra- and inter-country migration. This may give rise to urbanization without growth (Gollin et al _._ 2013), where, partly as a result of the difficulty of providing services and enforcing planning standards, costs are high and agglomeration benefits are low (Venables 2017), a pattern already observed in some parts of Africa (Henderson et al _._ 2017).

Three factors make it difficult to discern the most effective and sustainable types of interventions. First, soil erosion and changes in water content, the main effects of certain interventions, are difficult to observe; traditional methods of information collection, such as household surveys, are ineffective. Both density of 4
and recalled that she answered many questions incorrectly. Researchers explained this result by suggesting that the university students formed initial hypotheses about students’ abilities upon watching the “class video” but were able to ‘overcome’ them when no performance information was available. In contrast, even objectively ambiguous performance data about Hannah’s ability caused the subjects to falsely confirm their initial hypotheses, resulting in biased assessments of Hannah’s ability. The study shows how subconscious processes (using automatic thinking— principle 1) may play into unequal schooling experiences for student from different social backgrounds.

A modern means of measuring bias is the Implicit Association Test (IAT), which assesses the speeds at which subjects match attributes (such as “good” and “bad”) to concepts or identities (such as “female” and “male”). Response times are faster for pairings between concepts and attributes that are closely associated in subjects’ minds. Differences in matching speeds are thought to reveal “implicit biases” that underlie automatic thinking. Researchers used IATs to examine the bias of Dutch teachers. They found that although teachers’ self-reported measures of prejudiced attitudes were unrelated to their students’ achievement, the implicit measures of — — teacher prejudice thought to predict behavior arising from automatic thinking (principle 1) explained differing ethnic achievement gaps across classrooms (van den Bergh and others 2010). This finding supports the idea that teacher expectations can enlarge social inequalities in educational attainment.<sup>2</sup> Two distinct identity dynamics may impair the educational outcomes of marginalized groups. In the first, students aware of a stigmatized identity exhibit lower performance, possibly as a result of (1) anxiety that taxes their cognitive functioning, (2) disengagement stemming from a lack of belonging, or both. The resulting poor performance reinforces the perception of inadequacy, and this in turn further increases anxiety and withdrawal, which in turn further reduces performance, creating a vicious downward cycle of decreasing performance and disengagement (Cohen et al. 2009). In the second identity dynamic, teachers respond to students’ negative social identities with low expectations and prejudice that may lead them to supply the students with fewer resources – both material (such as textbooks) and nonmaterial (such as encouragement). If both identity dynamics are operating, stigmatized identities may be

> 2 More generally, biased perceptions can change behavior in ways that sustain the biases as “equilibrium fictions” (Hoff and Stiglitz 2010).

16
The Beta density distribution is a continuous function taking values between 0 and 1 and has as formula:<sup>10</sup> where B(β1, β2) is the beta function with the formula: The parameters β1 and β2 are positive. To normalize a given variable, say, income yi, to values between 0 and 1, we impose the transformation where mn and mx are the minimum and maximum values, respectively, of the distribution of yi.

The parameters β1 and β2 have the following moments estimators:<sup>11</sup> where x ̄ stands for the sample mean and s<sup>2</sup> represents the sample variance. The FGT poverty measures expressed in terms of the Beta density distribution function given in equation (7) become: where ztj<sup>0is the group and period-specific normalized poverty line, defined as</sup> ztj<sup>0=(ztj−mnj)/(mxj−mnj).Thus,equation(11)allowsustocompare</sup> model within-group distributions. They concluded that no single form is more appropriate in all cases or groups of households. However, the authors advocate, especially when detailed disaggregation is required, rather flexible forms as the Beta function, which allows, for instance, distributions to be very negatively skewed.

> 10See the description in “A thesaurus of Mathematics”, University of Cambridge, at http://www.thesaurus.maths.org.

> 11See the internet website “Scientific Resources: Statistics—Econometrics—Forecasting”, at http://www.xycoon.com/index.htm. Alternatively, one can estimate the parameters with Maximum Likelihood techniques.

13
# **V. CONCLUSION AND DISCUSSION**

63. This study contributes to the empirical evidence of the effectiveness of tax incentives in developing countries. Most of the literature on incentives is cautious about their ability to induce additional investment and export growth, highlighting instead the importance of fundamentals affecting the firms‟ decisions to invest, namely, expectations of future demand, the cost of capital, economic and political certainty, and the existence of strong legal institutions, a good business environment and good infrastructure. However, the practice of giving export incentives to promote industrialization and economic diversification remains a popular tool for many countries around the world.

64. In this study we provide a first quantitative analysis of the _South Africa Motor Industry Development program_ (MIDP) using the difference-in-difference methodology to assess to what extend the MIDP has affected South Africa‟s automotive export performance. To do this we take a two-tier approach. In a _first analysis_ , we apply this methodology to different manufacturing sectors within South Africa. In a _second analysis_ , we perform a comparative analysis between South Africa and a number of other African and middle income countries that are automotive producers and exporters.

65. From our analysis we find that the impact of the MIDP on automotive exports in South Africa is positive and significant. In particular, we find that (i) the largest response to the MIDP in terms of improved manufacturing exports occurs with a delay after the adoption of the law, suggesting that exports need time to fully react to the incentives; and (ii) in turn, the effectiveness of the tax incentives fades in time, reaffirming the common belief that tax incentives may affect some business decisions particularly in the short run, but they are not a primary consideration for investors in the long run.

25
indicates a unimodal hazard profile for a PPP project (figure 6). The risk of early termination for a project increases rapidly until around 20 percent of the project’s contract period elapses. It plateaus at this level and presents a slight decline until it reaches 50 percent. Beyond 50 percent of the contract period, except for a small increase around 80 percent, the risk of early termination decreases until the project approaches the end of the contract period.

**Figure 6 Smoothed hazard function estimate over the percentage of contract period elapsed**

<!-- Start of picture text --> .0015<br>.001<br>.0005<br>0<br>0 20 40 60 80 100<br>% of Contract Period<br>Hazard<br><!-- End of picture text --> _Source:_ Authors’ calculations using PPI database.

_Note:_ Figure shows smoothed hazard rate estimate using Gaussian kernel and optimal bandwidth.

The cumulative hazard curve shows that the accumulated probability of distress increases steadily but its pace decreases after reaching around 50 percent of the contract period (figure 7, panel a). The Kaplan-Meier survival curve estimate mirrors the profile and implies that the probability of an average project to survive until the end of its contract period is 0.92 (figure 7, panel b).<sup>15</sup> Table 2 presents the results from estimating the flexible parametric proportional hazards model in equation (3). Column (1) presents the results when controlling only for the type of private participation and sector that the project belongs to. Column (2) presents the results when also controlling for the type of government support that the project receives and the contracting authority. Column (3) adds the investment commitment of the project as a control variable. Column (4) presents the results when institutional variables are included as controls. Column (5) introduces the macroeconomic shocks to the model and is the preferred regression for inference. Positive coefficients indicate factors that increase the cumulative hazard, and ultimately the probability of distress, and negative coefficients indicate factors that decrease the cumulative hazard, and ultimately the probability of distress.

> 15 Equations (A3) and (A4) in appendix A present the relationships among hazard, cumulative hazard and cumulative survival probability presented in figures 5, 6 and 7.

14
aggregate measure of exposure.

# **4 Results**

Table 3: Effects of exposure to COVID-19, rural versus urban

||Wage<br>aspiration<br>(1)|Wage<br>expectation<br>(2)|Aspiration<br>gap<br>(3)|Unrealistic<br>high aspiration<br>(4)|Realistic<br>aspiration<br>(5)|Unrealistic<br>low aspiration<br>(6)|Willing to<br>migrate<br>(7)|
|---|---|---|---|---|---|---|---|
|**Panel A: Rural**||||||||
|COVID shock<br>|-4,868.791**|-2,218.601|-2,650.191***|-0.312***|0.243|0.068|-0.325***|
||[1,692.240]|[1,591.703]|[690.605]|[0.082]|[0.176]|[0.135]|[0.089]|
|Outcome mean|19,491.57|16,550.58|2,940.99|0.53|0.25|0.22|0.50|
|Observations|1,898|1,898|1,898|1,898|1,898|1,898|1,875|
|**Panel B: Urban**||||||||
|COVID shock|-2,252.945|-890.597|-1,362.348|-0.159|0.034|0.125|-0.022|
||[2,368.673]|[1,679.992]|[2,504.449]|[0.173]|[0.197]|[0.207]|[0.198]|
|Outcome mean|20,109.20|17,679.41|2,429.80|0.50|0.26|0.25|0.45|
|Observations|1,282|1,282|1,282|1,282|1,282|1,282|1,268|

_Notes:_ Rural and urban refer to a respondent’s home location. All columns include the full set of controls mentioned in specification (1) except for a respondent’s location. Robust standard errors clustered by ITI district are in brackets. Variable definitions are in the online Appendix.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Table 3 presents results from estimating equation (1) separately for the rural and urban sub-samples. Panel A shows that exposure to the COVID-19 pandemic significantly decreased the wage aspiration (i.e., the reservation wage) of young women living in rural areas by 25 percent. The corresponding effect on their wage expectation (13 percent) was smaller and statistically insignificant. Together, these effects translate into a 90 percent decline in the aspiration gap among young rural women. The negative effect of the pandemic on the aspiration gap is driven by a decline in the share of respondents who have unrealistically high wage aspirations (column 4). Instead, the pandemic made the aspirations of young rural women more realistic (column 5). Urban women exhibit a similar pattern of results; however, the coefficients in Panel B are statistically insignificant.

Table 4 shows that the results for the overall sample are consistent with what we observe for the rural sub-sample. Exposure to the pandemic significantly decreased the wage aspiration gap by 77 percent, stemming from a larger decrease in wage aspirations relative to wage expectations. An average respondent’s likelihood of having unrealistically high wage aspirations declined by half.

Next, we investigate potential mechanisms underlying the effects described above. Recent 7
> <sup>- 4 -</sup>

# **II.   STRUCTURE OF THE DANISH PENSION SYSTEM**

# **A.   Public Pension Schemes**

These cover two schemes that are administered by public sector institutions and aim to provide universal or near-universal benefits. As already noted, the main scheme is unfunded and financed from general tax revenues, but the main supplementary scheme is financed from employer and employee contributions and is fully funded.

# **Social Pension**

Denmark was one of the first countries to introduce a public retirement pension system more than 100 years ago. It originally targeted the “worthy needy”, but has over time expanded to become a universal scheme. The social pension is paid to citizens and noncitizens, who are over 65 years old, subject to a residency test and proportionality rule. It is also subject to an earnings-from-employment test and is reduced according to specified rules. In addition to the flat pension, a supplement is paid to low-income pensioners subject to an income test.

Over 700,000 people were recipients of the social pension in 2003 (Table 1). This corresponded to 13 percent of the total population or 25 percent of the labor force. The average benefit amounted to 30 percent of the average wage or 33 percent of per capita income. The total cost of the social pension absorbed 4.4 percent of GDP in 2003, corresponding to 7.5 percent of the wage bill. However, these figures include pension supplements paid to low-income pensioners. The basic social pension, without any supplements, amounted to only 20 percent of the average wage.

**<u>Table 1  Social Pension:  Demographic and Economic Data, 1990-2003</u>**

||**1990**|**1995**|**2000**|**2003**|
|---|---|---|---|---|
|Pensioners (000s)|706|709|706|717|
|Pensioners/Total Population(%)|13.7|13.5|13.2|13.3|
|Pensioners/Labor Force (%)|24.3|24.7|24.4|25.1|
|TotalSocial Pensions (DKKbillion)|35.51|51.20|55.45|61.57|
|TotalSocial Pensions/GDP(%)|4.2|5.0|4.3|4.4|
|TotalSocial Pensions/WageBill(%)|7.0|8.5|7.5|7.5|
|TotalSocial Pensions/PublicExpend. (%)|7.6|8.4|7.9|8.0|
|Social Pension/Average Wage (%)|28.7|34.6|30.8|29.8|
|Social Pension/PerCapitaIncome (%)|30.8|37.2|32.5|33.1|

Source: Statistics Denmark The social pension represents by design a much higher proportion of income for low income groups that also have shorter periods of education. Available evidence shows that it represents more than 60 percent of the income of unskilled retired workers, about 40 percent for skilled retired workers, and less than 20 percent for more highly educated workers.
the Baier and Bergstrand (2009) model, but present results from OLS and a variety of other estimators to show that our conclusions are robust.

# **4 Estimation Results and Interpretation**

## **4.1 Baseline Results**

Estimates for the baseline model appear in Table 3. Taking Poisson (column 1) as the benchmark results, we find that standard gravity model variables have coefficients that are correctly signed, of appropriate magnitude, and at least 10% statistically significant. The only exception is the colony dummy, which carries an unexpected negative and 5% significant coefficient. The model’s overall explanatory power is relatively good, with an R-square of over 50%.

Of course, our primary interest is in the coefficients on the logistics variables. The data show that logistics clearly matters for trade performance in general: the exporter and importer LPIs both have coefficients that are positive and 1% statistically significant. This result is in line with other findings in the trade literature using the LPI as an explanatory variable, such as Hoekman and Nicita (2010). In quantitative terms, half-point increases in the exporter and importer LPIs are associated with trade increases of around 35% and 16% respectively.

The estimated coefficients on the two interaction terms between logistics performance and parts and components trade tell a mixed story in terms of our hypothesis. As noted above, we expect both coefficients to be positive and statistically significant. The importer interaction term is indeed signed as we expect, and is 1% significant. Its magnitude suggests that the semi-elasticity of trade with respect to importer logistics performance is about 45% stronger for parts and components than for final goods.

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# **Hidden Debt Revelations**<sup>∗</sup>

Sebastian Horn David Mihalyi Philipp Nickol César Sosa-Padilla<sup>†</sup>

**Keywords:** _hidden debt, sovereign debt, default_ **JEL classification:** _F34, H63, G01_

> ∗We received valuable comments from Fernando Arce, Tamon Asonuma, Gadi Barlevy, Volker Clausen, Aitor Erce, Stelios Fourakis, Juan Carlos Hatchondo, Aart Kraay, Leonardo Martinez, Julian MartinezIriarte, Marti Mestieri, Ugo Panizza, Juan Passadore, Carmen Reinhart, Diego Rivetti, Juan Sanchez, Zachary Stangebye, and Christoph Trebesch as well as from seminar participants at the Kiel Institute, the Inter-American Development Bank, the University of Duisburg-Essen, the Ruhr Graduate School in Economics, the World Bank, the University of Rochester, the University of Michigan, Purdue University, the Chicago Fed, the Richmond Fed, the 2024 NBER IFM Spring Meeting, the 2023 SED Annual Meeting and the 2023 Annual Meeting of the Verein für Socialpolitik. We thank Evis Rucaj and the entire team of the World Bank Development Data Group for answering countless questions on the International Debt Statistics. Gregor Ilsinger and Robert Remy provided excellent research assistance. We thank the German Federal Ministry for Economic Affairs and Climate Action and the German Federal Ministry of Finance for their financial support. All views expressed in this paper are those of the authors. They do not necessarily represent the views of the World Bank.

> †Sebastian Horn: World Bank. Email: shorn1@worldbank.org; David Mihalyi: World Bank, Kiel Institute. Email: dmihalyi@worldbank.org; Philipp Nickol: UDE, RGS. Email: philipp.nickol@vwl.uni-due.de; César Sosa-Padilla: University of Notre Dame, NBER. Email: csosapad@nd.edu
Contrary to Akin and Hutchinson (1999), who found that bypassing behavior is not very different across income groups in Sri Lanka, we find evidence of two distinct types of bypassing behaviors in Chad: poor individuals tend to bypass higher quality facilities because they cannot afford them whereas richer individuals bypass lower quality facilities and go for better care. Furthermore, we find that bypassing is significantly more prevalent among higher income groups. In that respect, income inequalities in Chad create health service inequalities. There are significant differences in patients’ choice of facility across income groups as well as between rural and urban populations..

The paper is organized as follows. Section 2 describes the health sector in Chad. Section 3 presents the data from the health facility survey. Section 4 documents the available health infrastructure and services in Chad. Section 5 describes patients’ characteristics and facility choices. Section 6 presents evidence of bypassing activities and examines differences among income categories. Section 7 introduces a simple theoretical model of bypassing. Section 8 presents econometric estimates of bypassing activities and Section 9 concludes the paper.

# 2  The Health Sector in Chad

Chad, a Central African country with a population of 8.8 million, is one of the poorest countries in the world, and with a GDP per capita of US$304 ranks 100th out of 103 countries (UNDP, 2005). Since independence in 1960, the country has experienced ongoing political instability which culminated in the 1979 civil war. Its economy is based mainly on agriculture and cattle rearing. The primary sector accounts for 38% of GDP and employs about 80% of the labor force. The adult illiteracy rate is 74.5%. Only 34% of the population has access to safe water and 8% to sanitary facilities.

## 2.1  Health Indicators

Health indicators in Chad are low and are even below what would be expected at the equivalent level of GDP (World Bank, 2004). Life expectancy at birth is 43.6 years and the child mortality rate (under 5 years) is 200 per 1,000 live births. Mother mortality is among the highest in Africa, close to 1100 for 100,000 live births. The 2