Recent empirical work has revived the Minsky hypothesis of boom-bust credit cycles driven by uctuations in investor optimism. To quantitatively assess this hypothesis, we incorporate diagnostic expectations into an otherwise standard business cycle model with heterogeneous firms and risky debt. Diagnostic expectations are a psychologically founded, forward-looking model of belief formation that captures over-reaction to news. We calibrate the diagnosticity parameter using microdata on the forecast errors of managers of listed firms in the US. The model generates countercyclical credit spreads and default rates, while the rational expectations version generates the opposite pattern. Diagnostic expectations also offer a good fit of three patterns that have been empirically documented: systematic reversals of credit spreads, systematic reversals of aggregate investment, and predictability of future bond returns. Crucially, diagnostic expectations also generate a strong fragility or sensitivity to small bad news after steady expansions. The rational expectations version of the model can account for the rst pattern but not the others. Diagnostic expectations offer a parsimonious account of major credit cycles facts, underscoring the promise of realistic expectation formation for applied business cycle modeling.
We explore the idea that judgment by representativeness reflects the workings of episodic memory, especially interference. In a new laboratory experiment on cued recall, participants are shown two groups of images with different distributions of colors. We find that i) decreasing the frequency of a given color in one group significantly increases the recalled frequency of that color in the other group, ii) for a fixed set of images, different cues for the same objective distribution entail different interference patterns and different probabilistic assessments. Selective retrieval and interference may offer a foundation for the representativeness heuristic, but more generally for understanding the formation of probability judgments from experienced statistical associations.
We examine the rationality of individual and consensus professional forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), which focuses on the predictability of forecast errors from earlier forecast revisions. We document two principal findings: forecasters typically over-react to information individual level, while consensus forecasts exhibit under-reaction. To reconcile these findings, we combine the diagnostic expectations model of belief formation from Bordalo, Gennaioli, and Shleifer (2018) with Woodford’s (2003) noisy information model of belief aggregation. The model accounts for the findings, but also yields a number of new implications related to the forward looking nature of diagnostic expectations, which we also test and confirm. Finally, we compare our model to mechanical extrapolation, rational inattention, and natural expectations.
A central challenge in securing property rights is the subversion of justice through legal skill, bribery, or physical force by the strong—the state or its powerful citizens—against the weak. We present evidence that undue influence on judges is a common concern in many countries, especially among the poor. We then present a model of a water polluter whose discharges contaminate adjacent land. If this polluter can subvert the assessment of damages caused by his activity, there is an efficiency case for granting the landowner the right to an injunction that stops the polluter, rather than the right to compensation for the harm. If the polluter can subvert even the determination of his responsibility for harm, there is an efficiency case for regulation that restricts pollution regardless of its effects. We then conduct an empirical analysis of water quality in the U.S. before and after the Clean Water Act, and show how regulation brought about cleaner water, particularly in states with higher corruption.