Essays on Fiscal Institutions, Public Expenditures, and Debt

This three-essay dissertation focuses on the political economy of fiscal rules in a comparative context and highlights their unintended consequences – an issue that has received relatively little attention in public financial management literature. The first essay examines whether numerical limits on deficits, or balanced budget rules, influence the composition of public spending, particularly in the social sector. Using a combination of fixed effects and GMM regressions on a large panel of developed and developing economies, this essay finds that while deficit targets are effective in improving fiscal balances, they also tend to reduce social spending on health and social protection. This effect is particularly prominent in democratic countries, which often witness overspending problems. Countries that are considering adoption of such rules should carefully examine the effects of these requirements on expenditures that may have long-term positive externalities. Policymakers should explore mechanisms to minimize the distortionary effects of fiscal limits on spending composition. The second essay focusses on whether the adoption of deficit targets by subnational governments in India influenced the composition of public spending. Using a combination of fixed effects and GMM regressions, this essay finds that the adoption of Fiscal Responsibility and Budget Management (FRBM) legislation by Indian states improved their budget balances significantly. However, the post-FRBM period also witnessed significant cuts in development spending. Furthermore, states have reduced their capital outlay and social spending after the adoption of fiscal responsibility laws. Reduced expenditure on development, and capital projects may affect long-term economic growth, therefore future amendments to the FRBM law should explore mechanisms to minimize the distortionary impacts of fiscal targets on the composition of subnational spending. The third essay shifts attention to the effect of supermajority voting requirements on credit ratings and borrowing costs in the subnational debt market in the United States. Using a combination of generalized ordered logit and linear regression analyses on a sample of general obligation bonds issued by American state governments between 2001 and 2014, this essay finds that states with supermajority voting requirements for tax increases are more likely to receive a lower credit rating on their bonds. Furthermore, on average, the states with a supermajority voting requirement pay a premium of 18 to 21 basis points in true interest cost for their bonds. States that are considering adopting supermajority requirements should consider the unintended effects in terms of lower credit ratings and higher borrowing costs while adopting or designing such fiscal rules. The findings of this dissertation inform the policy debate on the subject and improve our understanding of the impact of fiscal institutions that are being increasingly adopted to regulate the behavior of governments across the world

Discovering Representations for Black-box Optimization

The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge -- between exploring a wide variety of solutions, and ensuring that those solutions are useful. Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Variational Autoencoder) from that dataset. Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions -- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators. We demonstrate these capabilities by learning an low-dimensional encoding for the inverse kinematics of a thousand joint planar arm. The results show that learned representations make it possible to solve high-dimensional problems with orders of magnitude fewer evaluations than the standard MAP-Elites, and that, once solved, the produced encoding can be used for rapid optimization of novel, but similar, tasks. The presented techniques not only scale up quality diversity algorithms to high dimensions, but show that black-box optimization encodings can be automatically learned, rather than hand designed.Comment: Presented at GECCO 2020 -- v2 (Previous title 'Automating Representation Discovery with MAP-Elites'