Knowledge that Transforms

This paper demonstrates the crucial role that firms' mandatory disclosures play in determining their voluntary disclosure strategies. It also shows how a firm's propensity for providing voluntary disclosures relates to various features of the mandatory disclosure environment and disclosure regulation. The special case of choosing between aggregated and disaggregated disclosures serves as an illustration of the model's applicability.

This study tests an implication of the real‐options theory of investment, that uncertainty leads firms to prefer technologies with low fixed and high variable costs. In 1983, a change in Medicare reimbursement increased the uncertainty of revenues for hospitals. Using a sample of 831 departments in 59 Washington State hospitals over the 1977–1994 period, we find that the ratio of variable to total costs increased after 1983. This increase is not attributable to a gradual increase in the ratio over time: We estimate a significant increase after 1983 even after controlling for a time trend. Further, we find a greater increase in the variable‐to‐total cost ratio for hospitals that had higher percentages of Medicare patients, increasing our confidence in the conclusion that the change in cost behavior is attributable to Medicare's change in reimbursement.

This paper examines the cross-sectional implications of the inflation illusion hypothesis for the post-earnings-announcement drift. The inflation illusion hypothesis suggests that stock market investors fail to incorporate inflation in forecasting future earnings growth rates, and this causes firms whose earnings growths are positively (negatively) related to inflation to be undervalued (overvalued). We argue and show that the sensitivity of earnings growth to inflation varies monotonically across stocks sorted on standardized unexpected earnings (SUE) and, consistent with the inflation illusion hypothesis, show that lagged inflation predicts future earnings growth, abnormal returns, and earnings announcement returns of SUE-sorted stocks. Interestingly, controlling for the return predictive ability of inflation weakens the ability of lagged SUE to predict future returns of SUE-sorted stocks.

This paper addresses how changing the admission and financial aid rules at colleges affects future earnings. I estimate a structural model of the following decisions by individuals: where to submit applications, which school to attend, and what field to study. The model also includes decisions by schools as to which students to accept and how much financial aid to offer. Simulating how black educational choices would change were they to face the white admission and aid rules shows that race-based advantages had little effect on earnings. However, removing race-based advantages does affect black educational outcomes. In particular, removing advantages in admissions substantially decreases the number of black students at top-tier schools, while removing advantages in financial aid causes a decrease in the number of blacks who attend college.

This chapter introduces the institutional setting of over-the-counter (OTC) markets and raises some of the key conceptual issues associated with market opaqueness. An OTC market does not use a centralized trading mechanism, such as an auction, specialist, or limit-order book, to aggregate bids and offers and to allocate trades. Instead, buyers and sellers negotiate terms privately, often in ignorance of the prices currently available from other potential counterparties and with limited knowledge of trades recently negotiated elsewhere in the market. OTC markets are thus said to be relatively opaque; investors are somewhat in the dark about the most attractive available terms and about whom to contact for attractive terms. Prices and allocations in OTC markets are, to varying extents, influenced by opaqueness and by the role of intermediating brokers and dealers.

We extend the standard model of general equilibrium with incomplete markets to allow for default and punishment by thinking of assets as pools. The equilibrating variables include expected delivery rates, along with the usual prices of assets and commodities. By reinterpreting the variables, our model encompasses a broad range of adverse selection and signalling phenomena in a perfectly competitive, general equilibrium framework. Perfect competition eliminates the need for lenders to compute how the size of their loan or the price they quote might affect default rates. It also makes for a simple equilibrium refinement, which we propose in order to rule out irrational pessimism about deliveries of untraded assets. We show that refined equilibrium always exists in our model, and that default, in conjunction with refinement, opens the door to a theory of endogenous assets. The market chooses the promises, default penalties, and quantity constraints of actively traded assets.

This paper is concerned with accuracy properties of simulations of approximate solutions for stochastic dynamic models. Our analysis rests upon a continuity property of invariant distributions and a generalized law of large numbers. We then show that the statistics generated by any sufficiently good numerical approximation are arbitrarily close to the set of expected values of the model's invariant distributions. Also, under a contractivity condition on the dynamics, we establish error bounds. These results are of further interest for the comparative study of stationary solutions and the estimation of structural dynamic models.

This paper analyzes the conditions under which consistent estimation can be achieved in instrumental variables (IV) regression when the available instruments are weak and the number of instruments, Kn, goes to infinity with the sample size. We show that consistent estimation depends importantly on the strength of the instruments as measured by rn, the rate of growth of the so-called concentration parameter, and also on Kn. In particular, when Kn→∞, the concentration parameter can grow, even if each individual instrument is only weakly correlated with the endogenous explanatory variables, and consistency of certain estimators can be established under weaker conditions than have previously been assumed in the literature. Hence, the use of many weak instruments may actually improve the performance of certain point estimators. More specifically, we find that the limited information maximum likelihood (LIML) estimator and the bias-corrected two-stage least squares (B2SLS) estimator are consistent when , while the two-stage least squares (2SLS) estimator is consistent only if Kn/rn→0 as n→∞. These consistency results suggest that LIML and B2SLS are more robust to instrument weakness than 2SLS.

Exploiting a rich panel data set on anti-ulcer drug prescriptions, we measure the effects of uncertainty and learning in the demand for pharmaceutical drugs. We estimate a dynamic matching model of demand under uncertainty in which patients learn from prescription experience about the effectiveness of alternative drugs. Unlike previous models, we allow drugs to have distinct symptomatic and curative effects, and endogenize treatment length by allowing drug choices to affect patients' underlying probability of recovery. We find that drugs' rankings along these dimensions differ, with high symptomatic effects for drugs with the highest market shares and high curative effects for drugs with the greatest medical efficacy. Our results also indicate that while there is substantial heterogeneity in drug efficacy across patients, learning enables patients and their doctors to dramatically reduce the costs of uncertainty in pharmaceutical markets.

This paper shows that the bootstrap does not consistently estimate the asymptotic distribution of the maximum score estimator. The theory developed also applies to other estimators within a cube-root convergence class. For some single-parameter estimators in this class, the results suggest a simple method for inference based upon the bootstrap.