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This chapter examines the effect of changes in the public debt–gross domestic product (GDP) ratio on long, 10 year, interest rates in a panel of 17 countries over the period 1870–2016 controlling for other variables, in particular the world interest rate. Over this long period, one can argue that most of the big changes in public debt were the product of factors largely exogenous to national interest rate determination, such as war, depression or financial crisis. The issue is of current relevance since the Covid-19 pandemic has caused large increases in the ratio of public debt to GDP in many countries. The estimates suggest that it is the change in debt, rather than the level of debt or the deficit that matters for long interest rates. World interest rates have long- and short-run effects on interest rates which are very well determined and close to one. Current inflation has a small but significant effect.

Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of the data. In this chapter, the authors look at how one might assess the fit of these models using a variety of measures, rather than what seems to be an increasingly common device – the Marginal Data Density. The authors apply these in the context of models by Christiano, Motto, and Rostagno (2014) and Ireland (2004), finding they fail to make a match by a large margin. Both of these models feature more shocks than observed variables, resulting in the empirical shocks having a singular density, and so making them correlated. When correlated one can neither interpret impulse responses nor perform variance decompositions. Against this, there is a strong argument for having a straitjacket, as it enforces some desirable behavior on models and makes researchers think about how to account for any non-stationarity in the data. The authors illustrate this with examples drawn from the SVAR literature and also more eclectic models such as Holston, Laubach, and Williams (2017) for extracting an estimate of the real natural rate.

Purpose: This literature review aims to answer the calls for further exploration of scaling challenges and opportunities for social entrepreneurs (SEs). We address the scaling issue of social entrepreneurship through the theoretical lens of sustainable business models. Methodology: This paper investigates, on a multilevel approach, 340 journal articles published in one of the 20 peer-reviewed journals in management, entrepreneurship, CSR, organizational behavior, and nonprofit. It also considers influential articles due to their relatively high citation count (i.e., more than 150 times) outside of those selected journals. This paper furthermore analyses in-depth 32 scaling articles. Findings: This study positions the topics of social entrepreneurship over the last decades, together with the six types of scaling strategies: scaling up, scaling down, scaling across, scaling deep, scaling out, and diversification. It also discusses 15 challenges related to the scaling efforts by SEs. It furthermore elaborates on potential leads for research and practice regarding scaling social impact. Social Implications: There are many pathways for SEs to increase their impact on society, even though it remains quite challenging to achieve for most. Understanding what possibilities or limitations apply to individual SEs is but a first step in developing the full potential of social entrepreneurship. Originality: This paper approaches scaling from three complementary levels of analysis, i.e., individual, organizational, and institutional. Thus we provide more clarity and a nuanced perspective on past and future research regarding scaling challenges and opportunities.

This chapter contributes to the growing global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multicountry setting, and the results are compared to those obtained from standard single country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations are exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.45 when the rise in output is due to a fiscal expansion as compared to 0.76 when the rise in output follows from a favorable output shock.