RISK TAKING BY THRIFT INSTITUTIONS: A FRAMEWORK FOR EMPIRICAL INVESTIGATION

The discussion surrounding the recent reregulation of the thrift industry suggests that (i) thrifts engaging in "risky" activities are more likely to become insolvent and that (ii) thrifts already near insolvency are likely to take on more risks resulting in increased loss. This paper considers the relationship between insolvency and risk taking in a simultaneous framework and uses 1978–1983 data for Illinois thrifts to investigate the relationship. The paper explores the likelihood that risk taking by thrifts increases as the probability of insolvency increases, that risk taking increases as the probability of failure (i.e., closure by the regulator) increases, and that the probability of insolvency increases as risk taking increases. Preliminary empirical results suggest that an increase in the probability of insolvency increases risk taking and that an increase in risk taking increases the likelihood of insolvency. This latter result is (statistically) significant only when one measures risk by an index of diversification. If sustained in more extensive testing, this result implies that regulatory restrictions on asset diversification are counterproductive.     

Comparative Advantage and Heterogeneous Firms

This paper examines how country, industry, and firm characteristics interact in general equilibrium to determine nations' responses to trade liberalization. When firms possess heterogeneous productivity, countries differ in relative factor abundance, and industries vary in factor intensity, falling trade costs induce reallocations of resources both within and across industries and countries. These reallocations generate substantial job turnover in all sectors, spur relatively more creative destruction in comparative advantage industries than in comparative disadvantage industries, and magnify ex ante comparative advantage to create additional welfare gains from trade. The improvements in aggregate productivity as countries liberalize dampen and can even reverse the real-wage losses of scarce factors.     

Networks in Production: Asset Pricing Implications

In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and ?3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.