rafmacalaba/datause-encoder-data huggingface dataset:
# Sovereign Bonds since Waterloo

Josefin Meyer (DIW Berlin, Kiel Institute and CEPR) Carmen M. Reinhart (Harvard University, CEPR and NBER) Christoph Trebesch (Kiel Institute and CEPR)<sup>1</sup> _JEL codes_ : E4, F3, F4, G1, N0 1 We received very helpful comments from Laura Alfaro, Darrell Duffie, Rui Esteves, Gita Gopinath, Şebnem Kalemli-Özcan, Sam Langfield, Matteo Maggiori, Vincent Reinhart, Moritz Schularick, Frank Westermann and from conference participants at the NBER IFM Summer Institute 2018, the ASSA Meetings 2015, the Macrohistory Workshop in Bonn, the Sovereign Debt Conference in Zurich, DebtCon2 in Geneva, the Financial Crises conference at the LSE, as well as at seminars at UC Berkeley, Harvard, LUISS, EIEF, and at the Universities of Cologne, Frankfurt, Humboldt, Melbourne, Munich and Oxford. We also thank the editors and four anonymous referees for many helpful suggestions. Melanie Baade, Angelica Dominguez, Carl Hallmann, Moritz Müller-Freitag, Clemens Graf von Luckner, Khanh Phuong Ho, Tim Hofstetter, Torge Marxsen, Philipp Nickol, Maximilian Rupps, Sebastian Rieger, Paul Röttger, Christopher Schang and Julian Wichert provided excellent research assistance. We thank Julian Schumacher for sharing data on missed payments in recent bond defaults. Josefin Meyer gratefully acknowledges support by the European Commission’s Marie Curie Fellowship Programme under REA grant agreement no. 608129. Christoph Trebesch gratefully acknowledges financial support from the DFG Priority Programme “Experience and Expectation: Historical Foundations of Economic Behaviour” (SPP 1859) and from the Junior Researcher Fund of LMU München. All remaining errors are our own. Contact: jmeyer@diw.de; <u>carmen_reinhart@harvard.edu; christoph.trebesch@ifw-kiel.de.</u>
# **1. Introduction**

Small and medium-sized enterprises (SMEs) tend to face greater financial constraints than large firms, in part because they are subject to information asymmetries that are less salient for large firms. SMEs often lack audited financial statements and other information about their operations, and as a result, financial institutions have difficulties assessing the risk of lending to them (de la Torre, Martínez <mark>Pería,</mark> an <mark>d Schmukler</mark> 2009).

Several studies have documented that information sharing, credit bureaus, and credit scoring can increase credit to SMEs (Berger, Frame, and Miller, 2005; Brown, Jappelli, and Pagano, 2009; Love and Mylenko, 2003; Martinez Pería and Singh, 2014). However, not all countries have credit bureaus and where bureaus exist, the information they provide may be limited, for legal and institutional reasons. For example, the average credit bureau in Latin America and the Caribbean complies with only half of best practices and covers only 41.2 percent of the adult population (Doing Business Report 2017).

Thus, even though credit scoring can improve SMEs’ access to credit, it may take years to pass legislation that will lead to improvements in the quality and depth of the information recorded by credit bureaus. Even after credit bureaus are set up and working well, building an accurate credit-scoring model often requires several years of credit history. Additionally, loan applicants are subject to a chicken-and-egg problem. Bureau information is most useful for making credit decisions regarding loan applicants with a detailed credit history, but applicants can only build that history by getting credit, for which they need a good credit history. Therefore, credit markets in many countries may have to rely on alternative lending technologies to screen potential clients.

One such alternative lending technology relies on psychometric testing to screen loan applicants. This paper studies the effectiveness of a psychometric tool for lending to SMEs in the context of a pilot exercise conducted by EFL, a fintech company founded in 2010, and a financial institution in Peru. EFL has developed an alternative credit information tool that can potentially be used by lenders to better screen loan applicants. This tool uses a psychometric application to assess the SME owner’s creditworthiness. According to EFL’s website, the tool has been used to screen close to 1 million loan applications in 15 countries across four continents as financial institutions are exploring how to incorporate the tool into their lending process.<sup>6</sup>

> 6 https://www.eflglobal.com/ accessed on March 27, 2017. In November 2017, EFL merged with Lenddo, which uses mobile & digital footprint data for credit scoring. For more information on LenddoEFL, see include1billion.com.

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**E. CHOICE EXPERIMENT**

# **ENUMERATOR READS:**

**In this section of the interview we want to try and understand what type of jobs and or employment opportunities you most prefer.  I will be doing this by presenting you choice options for two different jobs/employments and then asking you to tell us which one you prefer.**

**Not all jobs/employments come with ideal benefits and hence people choose from the alternative jobs/ employment opportunities they find, whereby each job comes with different levels of certain benefits such as written contract, duration of employment, notice of termination, leave facility, provident fund facility, working hours and of course monthly salary.**

**I will present you with two alternative jobs/employment opportunities that differ in the levels of benefit types that I just mentioned [ENUMERATOR: REPEAT THE BENEFIT TYPES]. Here we only list some main types /characteristics of an job/employment for you to consider and ask you to assume that any other characteristics that are not listed are similar for both alternative jobs that we present.**

**I will present you with 6 choice occasions and each time you will choose your preferred job.  Your choice and opinion are very important and can inform government policy makers to help them design appropriate benefit types in line with workers’ preferences.**

**When choosing a job, please compare all benefit types and differences in their levels for the two alternatives and choose carefully.**

**Before showing the actual 6 choice sets, I will now go through with you one example choice and help you to understand better and provide us your reasoned/thoughtful choice in the actual questions [ENUMERATOR: READ OUT EACH JOB ATTRIBUTE AND INDICATE WHAT LEVEL OF ATTRIBUTE EACH JOB HAS]**

_[EXAMPLE CHOICE]_

||JOB A|JOB B|
|---|---|---|
|Written Contract|6 months|1 year|
|TerminationNotice|15 days|15 days|
|Working hours|30-45 hours per week|45-60 hours per week|
|Amount of paid leave (not<br>including major government<br>holidays /festival leave)|10 days|10 days|
|Provident Fund|No|No|
|Monthly salary|20% higher than your current monthly<br>incomefrom maineconomicactivity|10% higher than your current monthly<br>incomefrom maineconomicactivity|

E0_choicetest

**If you are given the opportunity to choose from these two different jobs that differ in the levels of some or all benefit types, which job would you choose?**

1. Job A

2. Job B
headcount indices obtained from the EIBEP by region, and compares them with the estimates obtained by region, prefecture and commune in the Census. The idea is to use the precision of the EIBEP-based estimates at the regional level as a benchmark for assessing the precision of Census-based estimates. The stepped curve in the Figure represents the coefficients of variation associated with the different strata in the EIBEP. The curves in Figure 1 clearly show that our prefecture and commune-level estimated headcounts are not as precise as the strata-level estimates from the EIBEP, but since almost all of our Census-based estimates have a coefficient of variation below 0.2 (a common benchmark when constructing poverty maps) we feel confident that these estimates are precise enough to guide policy-makers.

A visualization of the headcount indices obtained for each commune in the Census is given in the Figure 2. Since the estimates are based on consumption aggregates derived in large part from the 2002/03 EIBEP, the map can be (loosely) considered as representing the geography of poverty in Guinea in that year (even though the data from the Census dates back to 1996, as already mentioned there were relatively few changes in key household characteristics between the two years). We have thus estimated poverty measures for a total of 341 communes.

# **3. Impact of the Food Price Crisis**

# **_3.1. Methodology_**

Mistiaen (2003) suggested that the poverty mapping methodology could be used to estimate the impact of a change in the price of rice in Madagascar for example due to a change in taxation on that product. The idea consists in estimating a new poverty maps using a revised consumption aggregate in the survey with the consumption data. This revised consumption aggregate takes into account the impact of the shock. By comparing the initial 10
# **1. Introduction**

Putnam (1993, p.167) conceptualizes social capital as "...features of social organisation, such as trust, norms, and networks that can improve the efficiency of society by facilitating co-ordinated actions.” Although the concept of social capital is frequently used in rather vague ways by a large part of the social sciences literature, the key element is that ‘relationships matter’ (Field, 2003). Social interaction enables members to share values and build societies. Therefore, social networks (and the relations of trust, tolerance and cooperation arising from them) allow members to resolve their collective problems more easily, facilitate community progress, and achieve goals.

The World Bank claims that _“Increasing evidence shows that social cohesion — social capital — is critical for poverty alleviation and sustainable human and economic development_<sup>1</sup> ”. In fact, there have been a growing number of efforts attempting to quantify the influence of social capital on economic development, as we discuss in the main text. Furthermore, several authors have linked the HIV/AIDS epidemic to social capital (see for instance Gaffeo, 2003), usually pointing out how factors related to the disease such as stigma, discrimination and the costs posed by care for the sick as well as orphans erode and put pressure on social capital.

The objective of this paper is to attempt to quantify the impact of the HIV/AIDS epidemic on social capital using cross-country data. For this purpose, we will estimate reduced form regressions of the main determinants, as identified in the literature, of social capital, using national levels of trust from the World Values Survey (WVS) as a proxy for social capital. To our knowledge, there have been no previous efforts to evaluate this empirical question. Establishing an empirical link between the HIV/AIDS epidemic and “trust” helps uncover another channel through which social capital affects development.

With this objective in mind, in section 2 we briefly examine the links between social capital, development and HIV/AIDS previously identified in the literature. In section 3 we present and discuss the data used in our cross-country regressions. Section 4

> 1 <u>http://go.worldbank.org/C0QTRW4QF0</u>

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**Table 8: Simulated low work intensity rates**

||2008|2020<br>[assuming<br>national<br>goals are<br>met]|2020<br>[assuming<br>2000-11<br>growth<br>rate]|2020<br>[assuming<br>2000-08<br>growth<br>rate]|2020<br>(assuming<br>CEDEFOP's<br>forecast)|2020<br>[assuming<br>2008-11<br>growth rate]|
|---|---|---|---|---|---|---|
|Bulgaria|8.1|5.1|6.2|0.6|4.0|7.9|
|Czech Rep|7.3|4.4|7.0|6.1|3.2|7.0|
|Estonia|5.7|5.3|5.8|2.4|5.3|5.8|
|Hungary|16.2|8.8|15.8|14.9|13.0|15.8|
|Latvia|5.3|5.5|5.5|2.2|5.3|5.5|
|Lithuania|6.0|6.4|6.5|5.0|6.3|6.5|
|Poland|10.4|7.3|7.4|6.3|8.1|10.1|
|Romania|8.7|6.7|8.4|8.4|7.7|8.4|
|Slovakia|5.4|5.4|5.6|3.2|5.6|5.6|
|Slovenia|7.6|2.3|8.1|7.5|5.1|8.1|

_Source:_ World Bank staff calculations using EU-SILC data. _Note:_ We assume no deterioration in employment and education indicators by 2020 relative to their rates in 2008.

Anchored poverty rates are somewhat more stable across employment scenarios except in a few instances. The key exceptions are Bulgaria and Slovenia. In Slovenia, anchored poverty rates in 2020 are expected to be considerably lower under the Europe 2020 national target scenario than under any other scenario; while in Bulgaria, anchored poverty rates in 2020 are considerably lower under the 2000–08 employment growth rate scenario than any other employment scenario (Table 9). Achieving the Europe 2020 national targets is expected to lead the Czech Republic, Hungary, Romania, and Slovenia to enjoy the lowest anchored poverty rates across the different employment scenarios. In Bulgaria, the Czech Republic, Latvia, and Lithuania the CEDEFOP’s employment scenario for 2020 yields a lower anchored poverty rate than achieving the Europe 2020 employment targets.

In most NMSs, anchored poverty rates can be expected to decrease significantly if countries achieve their national targets for employment and education. Poland can expect a sharp reduction in anchored poverty from 16.9 to 3.5 percent; Bulgaria from 20.6 to 9.0 percent; Latvia from 25.8 to 12.5 percent; and Romania from 23.4 to 13.6 percent. The smallest reductions in the anchored poverty rates are expected in the countries with the lowest anchored poverty rates, namely, the Czech Republic, Slovakia, and Slovenia.
(2001) point out, “Homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people. The pervasive fact of homophily means that cultural, behavioral, genetic, or material information that flows through networks will tend to be localized” (pg 416). Rivera (2008) makes the important point that homophily is not only an implicit cognitive bias that is an inevitable by-product of social interaction, but also an explicit criterion that firms use to screen candidates, but not necessarily for the purposes of resource control or opportunity hoarding. “Rather, in many instances, homophily appears to be more of a _sense-making device_ in which individuals consciously and unconsciously seek points of commonality and make generalizations based on the presence or absence of common experience. Yet, regardless of the level of intentionality or “usefulness” in evaluation, selecting on similarity results in the reproduction of culturally homogenous organizations, even when not demographically so” (Rivera 2008, emphasis mine). Such “homophily” or gravitation towards candidates that most closely resemble the dominant workforce in an organization can be realized through the use of filters or screens used to make such selections; some of these screens are measurable and others are not. However, it is also important to add here that with increasing evidence on the value of heterogeneous work environments and the increasing evidence on discrimination, a large number of organizations are making efforts to seek out candidates that come from social strata different from the established culture of the firm.

Employers may often set up overt or covert filters through which candidates may have to pass. Those who expect to be discriminated may not reach the screening stage, largely because they “opt out of the game”<sup>4</sup> . When discrimination is overt, employers openly express their preference for a certain type of applicant. Kuhn and Shen’s (2011) study of gender discrimination in a large sample of job advertisements taken from an internet job board in China, found that explicit, advertised gender discrimination is commonplace and over one-third of firms that advertised on the board during the authors’ twenty-week sample period placed at least one advertisement stipulating a preferred gender. Thus, when it is legal to express such preferences, a large share of employers does so. The authors also found that large firms, state-owned firms, and firms seeking workers with high levels of

> 4 For a discussion on “opting out” see World Bank, 2013

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Table A.6: Productivity Decompositions and Policy Changes: Alternative Measure of Input Tariffs

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|.010|.035|-.025|
||(.016)|(.024)|(.019)|
|Input Tariff, 1 year lag|-.900<br>(.110)<sup>_∗∗∗_</sup>|-.991<br>(.108)<sup>_∗∗∗_</sup>|.091<br>(.061)|
|FDI Reform|.019|.043|-.025|
||(.013)|(.014)<sup>_∗∗∗_</sup>|(.009)<sup>_∗∗∗_</sup>|
|Delicensed|-.015|-.003|-.012|
||(.016)|(.015)|(.011)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.105|.104|.014|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. Input tariffs are constructed based on the manufacturing sector only.

58
|P088656|2006|2009|3/24/2009|6 Closed|Senegal|
|---|---|---|---|---|---|
|P088656|2006|2009|6/30/2009|7 Closed|Senegal|
|P088656|2006|2010|12/30/2009|8 Closed|Senegal|
|P088656|2006|2010|4/30/2010|9 Closed|Senegal|
|P088656|2006|2011|11/16/2010|10 Closed|Senegal|
|P088656|2006|2011|6/22/2011|11 Closed|Senegal|
|P088656|2006|2012|10/25/2011|12 Closed|Senegal|
|P088656|2006|2012|5/21/2012|13 Closed|Senegal|
|P088657|2004|2008||0 Closed|World|
|P088657|2004|2009||0 Closed|World|
|P088657|2004|2010||0 Closed|World|
|P088657|2004|2011||0 Closed|World|
|P088657|2004|2012||0 Closed|World|
|P088657|2004|2013||0 Closed|World|
|P088663|2005|2005|5/5/2005|1 Closed|Bosnia and Herzegovin|
|P088663|2005|2006|11/11/2005|2 Closed|Bosnia and Herzegovin|
|P088663|2005|2007|12/21/2006|3 Closed|Bosnia and Herzegovin|
|P088663|2005|2007|3/14/2007|4 Closed|Bosnia and Herzegovin|
|P088663|2005|2008|12/9/2007|5 Closed|Bosnia and Herzegovin|
|P088663|2005|2008|5/28/2008|6 Closed|Bosnia and Herzegovin|
|P088663|2005|2009|6/11/2009|7 Closed|Bosnia and Herzegovin|
|P088663|2005|2010|12/29/2009<br>|8 Closed|Bosnia and Herzegovin|
|P088663|2005|2010|6/29/2010<br>|9 Closed|Bosnia and Herzegovin|
|P088663|2005|2011|2/28/2011|10 Closed|Bosnia and Herzegovin|
|P088663|2005|2012|10/30/2011|11 Closed|Bosnia and Herzegovin|
|P088663|2005|2012|1/16/2012|12 Closed|Bosnia and Herzegovin|
|P088663|2005|2012|4/24/2012|13 Closed|Bosnia and Herzegovin|
|P088663|2005|2013|12/21/2012|14 Closed|Bosnia and Herzegovin|
|P088663|2005|2013|4/27/2013|15 Closed|Bosnia and Herzegovin|
|P088663|2005|2014|7/13/2013|16 Closed|Bosnia and Herzegovin|
|P088663|2005|2014|12/1/2013<br>|17 Closed|Bosnia and Herzegovin|
|P088663|2005|2014|3/30/2014|18 Closed|Bosnia and Herzegovin|
|P088663|2005|2015|10/18/2014|19 Closed|Bosnia and Herzegovin|
|P088663|2005|2015|12/15/2014|20 Closed|Bosnia and Herzegovin|
|P088671|2006|2006||0 Closed|Kyrgyz Republic|
|P088671|2006|2007|8/29/2006|1 Closed|Kyrgyz Republic|
|P088671|2006|2007|2/7/2007|2 Closed|Kyrgyz Republic|
|P088671|2006|2008|9/17/2007|3 Closed|Kyrgyz Republic|
|P088671|2006|2008|1/25/2008|4 Closed|Kyrgyz Republic|
|P088671|2006|2009|7/28/2008|5 Closed|Kyrgyz Republic|
|P088671|2006|2009|6/9/2009|6 Closed|Kyrgyz Republic|
|P088671|2006|2010|2/20/2010|7 Closed|Kyrgyz Republic|
|P088671|2006|2011|10/8/2010|8 Closed|Kyrgyz Republic|
|P088671|2006|2011|6/27/2011|9 Closed|Kyrgyz Republic|
|P088671|2006|2012|4/3/2012|10 Closed|Kyrgyz Republic|
|P088671|2006|2013|12/7/2012|11 Closed|Kyrgyz Republic|
|P088671|2006|2013|5/27/2013|12 Closed|Kyrgyz Republic|
**Figure 4: Comparing Access Scores to Health and Education Outcomes**

<!-- Start of picture text --> Figure 4A: Literacy Rate Figure 4B: Hospital Births<br>85% 85%<br>80% 80%<br>75% 75%<br>70% 70%<br>65% 65%<br>60% 60%<br>55% 55%<br>50% 50%<br>45% 45%<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Measured Acess Score Measured Acess Score<br>Literacy Rate (Percentage) Hospital Births (Percentage)<br><!-- End of picture text --> _Source_ : Data on Literacy rates and Hospital births are from India Planning Commission (2014).

# **_<u>3.3 Barriers to Access</u>_**

In this section, we review the self-reported barriers to applying for government provided goods and services that we use to construct our “access” measure.

Figure 5 outlines how these barriers affect access to government delivered goods and services.

Affordability is the biggest barrier to access to goods and services administered by state governments. Thirty-three percent of those who needed to register land/property or needed a driver’s license could not afford the cost of filing an application. Twenty-five percent could not access public utilities and health care for the same reason, while 20 percent could not use government run schools. Some services are paid — such as electricity, gas or water — while some are supposed to be free, including government-provided health care or government run public schools.<sup>16</sup> Presumably, governments do not charge a “usage fee” or “tuition” for public hospitals

> 16 Paul et al. (2004, Page 925): _“Healthcare facilities provided by the government are expected to cater to the needs of the poor and underprivileged by being free or subsidized. Around 40 percent of inpatients and 18 percent of outpatients paid a fee for the healthcare service. About 16 percent of inpatients reported payment of bribes.”_

17
32 statistically significant point estimate of around 23% ($24). The point estimate for public spending suggests a smaller though statistically insignificant percentage rise of about 7% ($22) on this component due to PBP arrangements. Again, the extra spending could have been due to an increase in expenditure per admission, or to extra spending elsewhere in the health system. It is worth noting that, although the magnitudes of the point estimates for our three health spending variables are larger for FFS than PBP methods, Wald tests of the hypothesis of equality of the FFS and PBP coefficients cannot reject the null hypothesis, with p-values in excess of 0.4.

_Amenable mortality_ . In spite of the higher hospital activity rates and spending estimated for countries using FFS methods in preference to historical budgets, there is no evidence that the introduction of the former payment method has any effect— either beneficial or deleterious—on amenable mortality. The estimated FFS coefficients are very small in magnitude for the six death causes examined here<sup>31</sup> , with negative and positive point estimates evenly distributed (the mean impact is just 0.4%). The p-values are very large, and none of the impacts comes anywhere close to being statistically significant. A different story emerges in the PBP results. We find that PBP significantly reduces amenable mortality for two causes, namely deaths by ischemic heart disease (reduced by around 4% according to the preferred model) and cerebrovascular diseases (diminished by around 5%). Furthermore, all but one of the point estimates is negative, and the magnitudes are considerably larger than in the case of FFS (the mean impact in the PBP results is -7.2%).<sup>32</sup>

> 31 Recall that our specification tests indicate that the preferred model in the case of the standardized death rate by diabetes fails to account adequately for reverse causality. For this reason, we do not discuss the regression estimates obtained for this seventh amenable mortality indicator.

> 32 In general, no statistically significant or otherwise noteworthy effects on our health sector outcomes are found for the primary care payment methods and global budget dummies in the preferred specifications.