This paper studies the historical roots of the relationship between economics and psychology, and places recurring controversies between these disciplines in the context of the relationship between economics and the other human sciences, especially sociology. We focus on the formative years of contemporary economics, the early twentieth century, when psychologists and institutionalist economists attacked the unscientific nature of economics. Economists responded by (mistakenly) renouncing verstehen and claiming adherence to behaviorism, rather than by actually addressing the institutionalist critique. Behaviorist economics declared independence from psychology, and by analogy, from the other human sciences. Our illusion of independence continues to this day.

This study examines the effect of accounting flexibility on managers’ forecasting behavior prior to seasoned equity offerings (SEOs). Although SEO firms have a strong incentive to convey optimistic information to boost the pre-SEO stock price, they also face enhanced litigation risk arising from SEO-related regulations. Thus, I hypothesize that managers will release positive news through their forecasts (relative to the prevailing analyst consensus) prior to an SEO only if they have the accounting flexibility to manage subsequent reported earnings to meet or exceed their forecasts. I find that managers with greater accounting flexibility are more likely to issue a forecast prior to the SEO and that their forecasts are more likely to convey positive news and are more specific. Furthermore, I find no effect of accounting flexibility for non-SEO control firms or for non-SEO periods. My results suggest that when managers experience a tension between the incentive for voluntary disclosure and high litigation risk, accounting flexibility is an important factor that determines their forecasting behavior.

Authors often add covariates to a base model sequentially either to test a particular coefficient’s “robustness” or to account for the “effects” on this coefficient of adding covariates. This is problematic, due to sequence sensitivity when added covariates are intercorrelated. Using the omitted variables bias formula, I construct a conditional decomposition that accounts for various covariates’ role in moving base regressors’ coefficients. I also provide a consistent covariance formula. I illustrate this conditional decomposition with NLSY data in an application that exhibits sequence sensitivity. Related extensions include instrumental variables, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman test result.

This essay is the result of a long process involving many drafts and revisions. I am grateful to Duncan Foley for the careful and constructive criticisms offered throughout the project. The following people read the manuscript at different stages and, although they often disagreed with me, they generously provided helpful comments: two anonymous referees for this journal, Samuel Bowles, William Dickens, Greg Dow, Robert Drago, Richard Edwards, Kathleen Engel, Richard Freeman, James Galbraith, Herbert Gintis, Jody Hoffer-Gittel, SandfordJacoby, Tom Kochan, David Levine, Lisa Lynch, Paul Osterman, Michael Piore, Bob Rebitzer, Gil Skilman, Dan Slesnick, Robert Thomas, and Tsuyoshi Tsuru. Comments from participants in seminars at the Massachusetts Institute of Technology, University of Massachusetts at Amherst, and Hitotsubashi University are also gratefully acknowledged.

We analyze the role of federal funds rate volatility in affecting risk premium as measured by various money market spreads during the 2007–2009 financial crisis. We find that volatility in the federal funds market contributed to elevated Overnight Index Swap (OIS) spreads of unsecured bank funding rates during the crisis. Using OIS as a proxy for market expectations, we also decompose London Inter-Bank Offered Rate (Libor) into its permanent and transitory components in a dynamic factor framework and show that increased volatility in the federal funds market contributed to substantial transitory movements of Libor away from its long-run trend during the financial crisis.

IO economists often estimate demand for differentiated products using data sets with a small number of large markets. This paper addresses the question of consistency and asymptotic distributions of instrumental variables estimates as the number of products increases in some commonly used models of demand under conditions on economic primitives. I show that, in a Bertrand–Nash equilibrium, product characteristics lose their identifying power as price instruments in the limit in certain cases, leading to inconsistent estimates. The reason is that product characteristic instruments achieve identification through correlation with markups, and, depending on the model of demand, the supply side can constrain markups to converge to a constant quickly relative to sampling error. I find that product characteristic instruments can yield consistent estimates in many of the cases I consider, but care must be taken in modeling demand and choosing instruments. A Monte Carlo study confirms that the asymptotic results are relevant in market sizes of practical importance.

This article investigates the impact of changes in risk perception on bond markets triggered by the 2007–08 financial crisis. Using a methodology novel to empirical finance, we quantify the increase in credit spreads caused by changes in risk pricing and changes in risk factors. The lasting increase in credit spreads is almost exclusively due to time-varying prices of risk. We interpret this as a change in risk perception which provides a possible solution to the credit spread puzzle. Default premia spiked during the crisis and did not return to their pre-crisis levels. Liquidity premia increased during and after the crisis.

Anecdotal and indirect empirical evidence suggest that excitement and market bubbles are intertwined, such that excitement not only arises during bubbles but may also help fuel them. We directly test the impact of excitement on bubbles in a bubble-prone experimental asset-pricing market ( Capinalp, Porter, and Smith, 2001 ). Prior to trading, participants are assigned to emotion inductions through video clips The results of fifty-five markets show larger asset pricing bubbles in magnitude and amplitude in the excitement treatment relative to a treatment of same valence and lower intensity ( calm ) and a treatment of similar intensity and opposite valence ( fear ).

We propose a new method of testing asset pricing models that relies on quantities rather than just prices or returns. We use the capital flows into and out of mutual funds to infer which risk model investors use. We derive a simple test statistic that allows us to infer, from a set of candidate models, the risk model that is closest to the model that investors use in making their capital allocation decisions. Using our method, we assess the performance of the most commonly used asset pricing models in the literature.