rafmacalaba/datause-encoder-data huggingface dataset:
# Eurobonds: A Quantitative Analysis of Joint-Liability Debt

Vasilis Tsiropoulos<sup>_∗_</sup> World Bank

## **1 Introduction**

This paper introduces bonds with joint liability in a model where two small open economies borrow from risk neutral international lenders. Under joint liability a default in one country makes the other country liable for its debt. This feature introduces the potential for contagion of default decisions, while introducing further repayment guarantees for lenders. Hence, the introduction of this instrument generates _contagion_ risk, in the sense that this instrument may push some countries to default even though they are individually solvent. On the other hand, it may generate _cheaper credit_ , thereby helping enhance financial stability. In this paper I quantify the effects and generate predictions about the welfare implications of introducing joint liability bonds under different underlying fundamental conditions.

The recent Eurozone crisis has highlighted the necessity for the development of financial instruments that mitigate the effects of the financial crisis and stabilize the yields of sovereign bonds. One of the mechanisms that was proposed by the European Commission (2011) as a potential shield of future financial crises is the implementation of bonds with joint liability (Eurobonds).<sup>1</sup> On the one hand, some member states may increase their debt accumulation with this mechanism, since they will have easier access to financial markets. This would be

> _∗_ Email: vtsiropoulos@worldbank.org

> **Acknowledgment:** The views expressed in this article are mine and do not necessarily reflect the views of the World Bank. I am greatly indebted to Juan Carlos Conesa and Eva Carceles-Poveda for their encouragment and guidance. I would also like to thank Alexis Anagnostopoulos, Marina Azzimonti, Gabriel Mihalache, Pau Pujolas, Pavlos Karadeloglou, Doerte Doemeland, Tito Cordella, and participants of Stony Brook University workshop, The Economic Seminar of Central Bank of Ireland, Irish Economic Association Annual Conference, The 11th Annual Economics Graduate Students Conference, The 69th Annual New York State Economics Association, The 27th International Conference on Game Theory, and the seminar of University of Piraeus.

> 1Not to be confused with Eurobond, which are bonds denominated in a currency other than the local currency of the issuing country.
decreasing with improvements in the fiscal position since 2004, interest rates and nominal spreads still remain very high compared to peers.

**Figure 24: Real lending and deposit interest rates**

<!-- Start of picture text --> Ex-ante real interest rates Real lending interest rate (%)<br>50<br>40 Malawi<br>30 Uganda<br>20 Mozambique<br>10<br>Burundi<br>0<br>Botswana<br>-10<br>-20 Tanzania<br>-30 Zambia<br>-40 South Africa<br>-50 Ghana<br>Lesotho<br>Kenya<br>Lending Deposit<br>0 4 8 12 16<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source: International Financial Statistics, IMF_ Since reform, private investment has shown some elasticity to the real lending rate (although as already discussed the data is patchy): private investment collapsed during the high inflation and high interest rates of 1995-2003, and NSO data suggests a strong recovery associated with lower interest rates from 2004.

For the informal sector which includes rural smallholders, access to finance is reported as the most important constraint to growth<sup>12</sup> . In 2004, 14% of rural households had access to a loan but only 4% from a formal financial institution (IHS, 2005). Rural households have an exceptionally low asset base – only 26% of own a bed, 2% own an oxcart (ibid) – which greatly impedes self-financed investment. The PVA (WB, 2006) argues that illiquidity prevents investment, since farmers revert to traditional crop varieties using minimal inputs. The authors perform a regression into the determinants of yield efficiency and conclude that liquidity considerations through credit or assets have the strongest effect of all variables on the yields of both maize and tobacco<sup>13</sup> . Similarly a regression performed on the determinants of commercial cropping, found that liquidity factors were also strongly associated with the decision to grow cash crops<sup>14</sup> . The significance of the smallholder liquidity constraint is illustrated by the substantial supply response created by the Input Subsidy Program since 2006.

# **Savings Supply**

Do high interest rates result from a shortage of savings? Real deposit rates have hovered around zero since 2004 indicating that banks have made little effort to attract savings.

> 12 84% of informal firms reported access to finance as a serious obstacle to growth (ICA, WB, 2006) 13 p179, Malawi PVA Volume II, World Bank, 2006

> 14 P209, ibid

- 28 -
22 counties. Statements that population growth is worse in poorer countries have yet to attract

# much, if any, empirical support.

Table 4: Partial **Correlation of Labor Force Growth with Growth Rate of** Output per Worker, 1960-1990. Sample divided **by** income dass.

|Control variables:|None||YO,BMP,LLY,XS||
|---|---|---|---|---|
|All|-.10<br>(.37)|-.12<br>(.28)|0.04<br>(.73)|79|
|OECD|_50_<br>(.02)|-.08<br>(.07)|-.04<br>(.89)|22|
|Low Income|0.05<br>(.83)|0.07<br>(.75)|_-.05_<br>(.82)|24|
|Middle Income|-.12<br>_(.51)_|-.12<br>(.53)|-.02<br>(.94)|32|

Notes: p-levels of test for zero correlation in parenthesis.

# B) Results by land endowment per rural individual

The previous results, dividing the sample by region, level of development, or income, have not yet solved our puzzle of discovering for which group of countdes poplation growth has a negative effect. One possible rationale is that the level of resources available for expansion of production.is more important than the level of income. While population expansion may have a negative effect in poor and land poor Bangladesh it may not have the same impact in poor, but land rich, Zambia. This resource depletion effect of population growth should be higher the larger the dependence on natual resources in fixed supply and the nearer the _25_ Kley and Schmidt (1994) table 3 shows negaxive linear inraction terms in the 1960's.and 1970's but a small positive interaction term in the 1980s. The results with interactioc terms using an LDC only sample are more ambivalent with a positive interaction term in the 1960s and 1980s and a negative term in the 1970s (appendix table C2): All of the estimated interaction terms (positive and negative) are stai.icaly insignificant.
# Source: World Bank 2003.

At the country level, natural disasters can have numerous adverse social, developmental, economic and fiscal impacts. Among those are: the worsening the fiscal position, as the spending needs rise; the weakening of trade balance, as the country’s export capacity falls and reconstruction needs increase demand for imported goods and materials. This in turn is likely to put downward pressure on the exchange rate for local currency, reflecting both the weakening trade balance and reversal in private capital flows due to the shaken confidence of foreign investors, and give rise to inflation (Freeman, Keen and Mani 2003).<sup>2</sup> As a consequence of limited insurance coverage for catastrophic risks provided by the local market and the lack of risk awareness or economic incentives to engage in ex ante risk management, governments in developing countries generally rely on domestic budget, including diversion of resources from other projects, and on extensive financing from international donors to absorb the losses caused by natural disasters.

Table 1 shows the gap between developed and developing countries in terms of insurance coverage for natural disasters. Only 5% of the US$22 billion losses caused by an earthquake were covered by insurance in Turkey in 1999, compared with 47% of losses insured in the case of Northridge quake in the US and 100% of losses insured in the case of winter storms in France.

**Table 1. Economic and insured losses from major natural disasters.**

|Event (year)|Country|Economic losses<br>(US$ billion)|Insured losses<br>(as a percent of<br>economic losses)|
|---|---|---|---|
|Earthquake (1999)|Izmit Turkey|22.0|5%|
|Hurricane (1999)|Honduras|3.0|6%|
|Floods (1997)|Poland|3.5|6%|
|Earthquake (2001)|Gujarat/Bhuj, India|0.6|2%|
|Earthquake (1992)|Northridge, USA|43.0|47%|
|Winter storm (1999)|France|6.2|100%|

Sources: Gurenko (2004), Swiss Re (2002), IIASA (1999).

The impact of natural disasters on the economic growth and the domestic budget is illustrated in Table 2. Although the US Northridge quake caused US$43 billion losses, its impact was almost negligible on the US economic growth (0.3%) and limited on the government revenues (2%). On the contrary, in countries where these infrequent shocks cannot be absorbed by the insurance industry and thus are borne by the country itself the

> 2 The above mentioned deterioration in the current account balance however can be somewhat offset by the inflow of foreign donor aid and payments by foreign reinsurers under reinsurance contracts with the local insurance market.

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Clearly the deficits associated with disability are large compared to other sources of inequality. In all these countries the gender gap in enrollment is small relative to that associated with disability. Perhaps more surprising, the gaps associated with rural residence, and the large gap between rich and poor, are usually substantially smaller than that associated with disability. The exceptions are Burundi where wealth gaps dominate the gaps associated with disability, and Mozambique where rural/urban differences dominate. Typically, however, the gap in school participation between children with and without a disability is on the order of twice as large as those associated with rural residence or wealth.

_The interaction of disability with other characteristics in the association with schooling_ Comparing the magnitudes of the schooling gaps associated with disability and other characteristics helps to get a sense for orders of magnitude. But an interesting additional question is whether disability interacts with other characteristics in a way that reduces or exacerbates inequalities. A straightforward way to investigate this hypothesis is to estimate the multivariate model of school participation and include interaction terms between disability and each of the other covariates. In Romania, the disability deficit in school participation among boys is about 9 percentage points smaller than that among girls. In Mongolia the school participation deficit associated with disability is about 17 percentage points larger in rural areas than in urban areas. In Cambodia, the school participation deficit associated with disability is smaller in the poorer quintiles (largely because overall school participation is lower in those quintiles).

But other than these specific cases, there are no statistically significant interactions. There is an important caveat to this finding, however: small sample sizes make it hard to estimate these effects with much precision. Not only does one need enough observations to estimate average differences, one needs enough cases of the various combinations of characteristics in order to identify their association with enrollment. Given the small numbers overall (see Table 3) it is perhaps unsurprising that the models do not yield significant estimates.<sup>15</sup>

# **5. Conclusions**

This analysis of data from 11 nationally representative surveys has confirmed the many data problems that earlier discussions have identified as hampering the establishment of a broad empirical base for developing policies targeted to people with disabilities in poor countries. The fundamental variation across surveys in the definition of “disability” makes cross-country comparisons difficult. The small number of people identified as

> 15 In order to increase the power of these tests, the models were also run separately for each interaction. The pattern of statistically significant results is not affected under this alternative approach.

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- Figure 24: _Share of Government, Health, Education, and Scientific Services in Employment_

- Figure 25: _Share of Govermnent, Health, and Education Services in Employment_ See notes to Figures 1 and 2 above.

# C. Share of Services in Employment: 15 Soviet Republics and World Economies

World Economies data are from ILO yearbooks and include all countries for which data were available. Data are not uniform: can be for employment, labor force, total or only civilian and others. In addition, there are many differences in classification by branch. Soviet data are as above.

- Figure 14: _Share of Total Services in Employment_ See also Table 1, column 4

- Figure 16: _Share of Transport and Communication Services in Enployment_ See also Table 1, column 1.

- Figure 18: _Share of Trade, Restaurant, and Hotel Services in Employment_ See also Table 1, column 2

- Figure 21: _Share of Banking and Insurance Services in Employment_
(2006), Eichhorst et al. (2008), and Lehmann and Muravyev (2009) for extensive surveys of this literature. Some of the recent developments have been related to complementary policies and interaction between institutions (e.g., Belot and van Ours, 2001; Amable et al., 2007; Fiori et al., 2007; Bassanini and Duval, 2009; Lehmann and Muravyev, 2009); extending the empirical base and theoretical analysis of institutional framework to transition and developing countries (Boeri and Terrell, 2002; Heckman and Pagés, 2004; Svejnar, 2004; Eamets and Masso, 2004; Ederveen and Thissen, 2007; Hazans, 2007; Feldmann, 2009; Fialova and O. Schneider, 2009; Behar, 2009; Lehmann and Muravyev, 2009; Muravyev, 2010, among others); identifying differential impact of institutions on various demographic groups (e.g., Amable et al., 2007; Bertola et al., 2007; Hazans, 2007; Say, 2011); assessing labor market impact of product market and/or financial market regulations (Amable et al., 2007; Fiori et al., 2007; Loayza et al., 2009; Fialova and O. Schneider, 2011, among others); and robustness checks of the results using alternative panel data methods (e.g., Baccaro and Rei, 2005; Amable et al., 2007).

Most contributions in this body of the literature focus on unemployment and/or inactivity and ignore informal employment. To our best knowledge, Fialova and O. Schneider (2011) is the only paper in this literature addressing the informal employment issue (using proxy measures) in a more or less complete institutional framework. With regards to this strand of the literature, our contributions include: amending institutional variables with the shares of minorities and population with immigrant background; identifying a significant interaction between union density and minimum wage level; finding region-specific productmarket-related factors which affect employment informality; and applying mixed-effects and other panel data estimators which have not been used in this field.

Motivated by different data patterns found in Eastern and Southern Europe vs. Western European and Nordic countries (see Figures 1-3), we have conducted analysis separately for these two country groups, as well as for Europe at large.

The rest of the paper is organized as follows. Section 1 provides a simple multi-period model in which agents choose (in the random utility maximization framework) between various labor market states (including formal and informal employment), and firms decide on optimal mix of formal and informal workers. Theoretical predictions on the impact of institutional and other macro factors on informal employment are derived and discussed in the context of previous literature. Section 2 outlines econometric methodology. Section 3 (respectively, 4) describe in detail the model specifications and results for Eastern and Southern (respectively, Western and Northern) Europe. Results for Europe at large are presented in Section 5. Section 6 concludes.

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in both selected sub-districts (this NGO will be denoted as NGO-M). Community-level meetings and participatory social mapping exercises introduced the interventions in treatment villages and allowed project staff to familiarize themselves with the community.

Under supportive supervision, the NGOs provided intensive supervision and support to ASHA workers through visits, at least twice monthly, by an accredited NGO worker. Each NGO worker was responsible for 10 ASHA. A supportive visit included some combination of the following activities: (1) review of relevant malaria treatment protocols and retraining when necessary, (2) supervision of follow-up visits by ASHA to at least two households with recent cases of fever in order to inquire about health status, satisfaction with care, and to encourage proper preventive behavior, (3) review and discussion of ASHA record-keeping since the last supervisory visit, and (4) facilitation of communication between the ASHA and the broader health system with a focus on RDT and ACT supply chain management.

In the community mobilization component, NGOs were assisted by village health and sanitation committees (VHSC) and women’s self-help groups (SHG) to promote appropriate malaria preventative and care-seeking behavior. Door-to-door visits by the NGO and SHG members attempted to motivate the consistent use of bed nets and timely care seeking from the ASHA for fever. Each SHG member was encouraged to monitor nighttime bed net usage in 10 to 15 households, thus achieving approximately comprehensive coverage. Information campaigns through local media and street plays also delivered the following messages: (1) whenever you have fever, visit the ASHA as soon as possible; (2) have your blood tested and take medication from the ASHA if you test positive for malaria; (3) always consume the full course of medication given to you by the ASHA; (4) use bed nets every night; and (5) prioritize net use by pregnant women and young children if you do not have enough bed nets for everyone. These activities were intensified before the malaria transmission season. Following standard government practice, all villages in both intervention arms and the control received Long Lasting Insecticide Treated Nets (LLINs) as well as standard government information campaigns, and all ASHA received training in RDT and ACT usage. Further implementation details on both interventions are given in Das et al. [2014].

6
A large section of the questionnaire is dedicated to the topic of finance. This section asks about sources of financing for day-to-day operations, purchases of assets, and sources of financing of these purchases, along with multiple questions regarding access to credit. Questions about the use of financial services such as mobile money are also included. Data is also collected on fixed assets used by informal businesses (whether owned or not) along with costs and even profits from the business. These more delicate questions are placed towards the end of the interview following standard practices of questionnaire design.

A sizeable portion of the interview is devoted to the topic of informality itself. In particular, the questions examine potential reasons for the business to be in the informal sector, along with a battery of questions exploring potential costs and benefits of formalization as perceived by the owner or manager of the informal business.

The topic of business practices is also covered in the questionnaire, with the wording of the questions closely following the work of McKenzie and Woodruff (2017) and aligned broadly with studies on management practices among formal firms in developed economies (e.g., Bloom and Van Reenen 2007, 2010 and Bloom et al. 2013). The standard set of questions on management practices used in the study by McKenzie and Woodruff (2017) was adapted for simplified and less formal wording to be more accessible to the target respondents of the Informal Sector Enterprise Surveys. Further, the battery was also considerably shortened to accommodate the implementation of these questions together with the rest included in the Informal Sector Enterprise Surveys within the average of 25 minutes allotted to the entire interview. Principal components analysis was used to determine which of the questions to include from the original set developed by McKenzie and Woodruff (2017). In addition to the topic of management practices, the interview also covers informal businesses' relations with their suppliers and customers.

# **2.4 Construction of Databases**

This paper combines the data collected under the methodology described in Section 2.1, using the questionnaire described in Section 2.3, across seven countries. The resulting database comprises 15,497 interviews from a randomly selected sub-sample of the total of over 70,000 informal businesses discovered through the survey methodology. Table 1 provides details of the survey implementation for each of the seven countries included.

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positive effects of pay‐for‐performance schemes on teacher behaviors and student learning, while others<sup>35,36</sup> have found no effects.

The health meta‐analysis is exploring the causes of the heterogeneity in results, which may be due to differences in the design of the pay‐for‐performance scheme (e.g., the generosity of the payments relative to cost) and its implementation. Evidence from the education sector is already pointing in this direction. A recent study<sup>37</sup> found a pay‐for‐performance scheme in Tanzania’s schools was more successful if combined with additional spending on school inputs.

In summary, all payment schemes shape service provider incentives, and all three basic schemes have advantages and disadvantages. Blending elements from one or more holds promise but getting the design and implementation of a blended provider payment scheme right is not straightforward.

> 35 Fryer RG. Teacher Incentives and Student Achievement: Evidence from New York City Public Schools. _Journal of Labor Economics_ 2013; 31(2): 373‐407.

> 36 Barrera‐Osorio F, and Raju D. Teacher Performance Pay: Experimental Evidence from Pakistan. _Journal of Public Economics_ 2017; 148: 75‐91.

> 37 Mbiti I, Muralidharan K, Romero M, Schipper Y, Manda C, and Rajani R. Inputs, Incentives, and Complementarities in

> Education: Experimental Evidence from Tanzania. National Bureau of Economic Research, Inc, NBER Working Papers: 24876; 2018.

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