Knowledge that Transforms

Present-biased preferences engender a demand for commitment. Commitment is a problematic prediction, since we see so little of it. I quantitatively explore the reasons for the “missing” commitment. Extending the procrastination model in Carroll et al. (2009), I show how equilibrium commitment is related to (i) the standard deviation of the opportunity cost of time, (ii) the cost of delay, (iii) the degree of partial naivete, and (iv) the direct cost of commitment. The calibrated model demonstrates that the perceived benefits of commitment are often overwhelmed by the costs of commitment. Demand for commitment is a special case rather than the general case.

We consider estimation of and inference about coefficients on endogenous variables in a linear instrumental variables model where the number of instruments and exogenous control variables are each allowed to be larger than the sample size. We work within an approximately sparse framework that maintains that the signal available in the instruments and control variables may be effectively captured by a small number of the available variables. We provide a LASSO-based method for this setting which provides uniformly valid inference about the coefficients on endogenous variables. We illustrate the method through an application to demand estimation.

Traditional models of insurance choice are predicated on fully informed and rational consumers protecting themselves from exposure to financial risk. In practice, choosing an insurance plan is a complicated decision often made without full information. In this paper we combine new administrative data on health plan choices and claims with unique survey data on consumer information to identify risk preferences, information frictions, and hassle costs. Our additional friction measures are important predictors of choices and meaningfully impact risk preference estimates. We study the implications of counterfactual insurance allocations to illustrate the importance of distinguishing between these micro-foundations for welfare analysis.

A burgeoning “Climate-Economy” literature has uncovered many effects of changes in temperature and precipitation on economic activity, but has made considerably less progress in modeling the effects of other associated phenomena, like natural disasters. We develop new, objective data on floods, focusing on Bangladesh. We show that rainfall and self-reported exposure are weak proxies for true flood exposure. These data allow us to study adaptation, giving accurate measures of both long-term averages and short term variation in exposure. This is important in studying climate change impacts, as people will not only experience new exposures, but also experience them differently.

We explore how changes in ownership affect the productivity and profitability of producers. Using detailed data from the Japanese cotton spinning industry at the turn of the last century, we find that acquired firms' production facilities were not on average less physically productive than the plants of the acquiring firms before acquisition. They were much less profitable, however, due to higher inventory levels and lower capacity utilization—differences that reflected problems in managing the uncertainties of demand. After acquisitions, less profitable acquired plants saw drops in inventories and gains in capacity utilization that raised both their productivity and profitability levels.

We propose a new approach to test the full-information rational expectations hypothesis which can identify whether rejections of the null arise from information rigidities. This approach quantifies the economic significance of departures from the null and the underlying degree of information rigidity. Applying this approach to US and international data of professional forecasters and other agents yields pervasive evidence consistent with the presence of information rigidities. These results therefore provide a set of stylized facts which can be used to calibrate imperfect information models. Finally, we document evidence of state-dependence in the expectations formation process.

This paper studies, theoretically and empirically, the role of overconfidence in political behavior. Our model of overconfidence in beliefs predicts that overconfidence leads to ideological extremeness, increased voter turnout, and stronger partisan identification. The model also makes nuanced predictions about the patterns of ideology in society. These predictions are tested using unique data that measure the overconfidence and standard political characteristics of a nationwide sample of over 3,000 adults. Our numerous predictions find strong support in these data. In particular, we document that overconfidence is a substantively and statistically important predictor of ideological extremeness, voter turnout, and partisan identification.

Organizations are all around us. Culture is trickier—to analyze and even to see. We consider both the effect of management on culture and the effect of culture on performance. We begin by describing an intervention that dramatically improved outcomes and conspicuously included a culture-change component. We then use details from this intervention to describe potential empirical analyses of the association between organizational culture and performance in this and similar settings. Finally, we describe opportunities for theoretical models to explore how and why organizational culture might influence organizational performance.

This paper estimates historical intergenerational elasticities between fathers and children of both sexes in the United States using a novel empirical strategy. The key insight of our approach is that the information about socioeconomic status conveyed by first names can be used to create pseudo-links across generations. We find that both father-son and father-daughter elasticities were flat during the nineteenth century, increased sharply between 1900 and 1920, and declined slightly thereafter. We discuss the role of regional disparities in economic development, trends in inequality and returns to human capital, and the marriage market in explaining these patterns.

It is well established that consumption is “hump” shaped over an individual's lifecycle, peaking in middle age and then declining in the years that follow. Prior research has documented that consumption declines at retirement, which is inconsistent with the standard lifecycle model with consumption smoothing. Using a unique dataset with detailed administrative records of credit and debit card transactions, we show the hump shaped lifecycle consumption pattern as documented in the literature. Additionally, we show compositional changes in consumption expenditures across individuals in the years surrounding retirement confirming the results of Aguiar and Hurst (2005, 2013).