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2023 | OriginalPaper | Chapter

7. The Financial Side of the Social Impact Bond: The Determinants of the Returns

Authors : Rachele Hyerace, Maria Mazzuca, Sabrina Ruberto

Published in: Contemporary Issues in Sustainable Finance

Publisher: Springer International Publishing

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Abstract

Using a financial lens, we study which factors affect the financial return of a Social Impact Bond (SIB). The hypothesis underlying our empirical strategy is that the SIBs’ diffusion also depends on their attractiveness for the investors that, in turn, typically depends on the (financial) return. However, SIBs are very special schemes that permit to achieve social outcomes using the structured finance. Therefore, it is plausible to imagine that their financial return is blended with a social return. It becomes interesting to investigate the determinants of the financial return to shed light on the interest and the role of the (traditional) finance in these (social finance) schemes. We develop an empirical analysis considering an extensive sample of 181 SIBs since 2010 and using an original dataset. The results are mixed. Overall, findings suggest that these schemes function according to logics different from those driving the other financial schemes/instruments.

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Footnotes
1
Due to the paucity of data on the effective returns, we compute the variable MRI. The MRI is an estimated return calculated assuming that each SIB was a multi-year zero coupon bond. We argue that this hypothesis is plausible as most SIBs provide for the repayment of the principal plus the interests only upon reaching predetermined (minimum) levels of success of the project. The MRI is calculated as follows: \({\varvec{M}}{\varvec{a}}{\varvec{x}}\boldsymbol{ }{\varvec{R}}{\varvec{e}}{\varvec{t}}{\varvec{u}}{\varvec{r}}{\varvec{n}}\boldsymbol{ }{\varvec{f}}{\varvec{o}}{\varvec{r}}\boldsymbol{ }{\varvec{I}}{\varvec{n}}{\varvec{v}}{\varvec{e}}{\varvec{s}}{\varvec{t}}{\varvec{o}}{\varvec{r}}{\varvec{s}}=\sqrt[{\varvec{t}}]{\left(\frac{{\varvec{M}}{\varvec{a}}{\varvec{x}}{\varvec{O}}{\varvec{u}}{\varvec{t}}{\varvec{c}}{\varvec{o}}{\varvec{m}}{\varvec{e}}{\varvec{P}}{\varvec{a}}{\varvec{y}}{\varvec{m}}{\varvec{e}}{\varvec{n}}{\varvec{t}}}{{\varvec{C}}{\varvec{a}}{\varvec{p}}{\varvec{i}}{\varvec{t}}{\varvec{a}}{\varvec{l}}{\varvec{R}}{\varvec{a}}{\varvec{i}}{\varvec{s}}{\varvec{e}}{\varvec{d}}}\right)}-1\), where t is the period of the SIB’s implementation, and Max outcome payment is the maximum capital payment due to the investors (that has to be considered as a cap). Intuitively, the MRI is the maximum amount offered to investors (incorporating the repayment of the principal plus the interest). In an SIB the interest rate is often variable (within a certain range) and strictly dependent on the level of outcome achieved. This is why results based on the similarity hypothesis between a zero-coupon bond and an SIB must be considered with caution.
 
2
We evaluate this estimated marginal effect by considering a linear estimation in order to avoid the complexities of interpreting interaction terms in nonlinear model (see Agostino et al., 2022).
 
3
We will appraise the effect of this variable in a linear model reported below.
 
4
To mitigate potential endogeneity issues, we include several fixed effects that may absorb factors that simultaneously affect the dependent variable and the potentially endogenous explanatory variables.
 
5
We restrict our sample to the following countries: Australia, Austria, Belgium, Canada, Chile, Finland, France, Germany, Israel, Japan, the Netherlands, New Zealand, Portugal, Sweden, Switzerland, the United Kingdom, the United States and the United Arab Emirates, classified by the World Bank as high-income economies.
 
6
These results are also confirmed by the test, reported at the bottom of Table 7.6 column 2 that show the statistically significant differences in the estimated impact of all the determinants across different quantiles.
 
7
When generating a graph analogous to Fig. 7.1 (and available upon request), the marginal effect of MATURITY on MRI for the sample belonging to the first 25th percentile of the MRI distribution is similar to the results mentioned above.
 
8
Similarly, when generating a graph analogous to Fig. 7.1 (and available upon request) for the sample belonging to the first 75th percentile of the MRI distribution, it emerges that at low level of MATURITY, the effect of MATURITY is positive but not statistically significant. When MATURITY increases, the impact of MATURITY on MRI decreases, turning to be negative and statistically significant beyond a threshold value of about 5 years.
 
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Metadata
Title
The Financial Side of the Social Impact Bond: The Determinants of the Returns
Authors
Rachele Hyerace
Maria Mazzuca
Sabrina Ruberto
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-22539-0_7