Knowledge that Transforms

Causal estimates of the benefits of increased schooling using US state schooling laws as instruments typically rely on specifications which assume common trends across states in the factors affecting different birth cohorts. Differential changes across states during this period, such as relative school quality improvements, suggest that this assumption may fail to hold. Across a number of outcomes including wages, unemployment, and divorce, we find that statistically significant causal estimates become insignificant and, in many instances, wrong-signed when allowing year of birth effects to vary across regions.

Imperfect information about product attributes inhibits efficiency in many choice settings, but can be overcome by providing simple, low-cost information. We use a randomized control trial to test the effect of high-frequency information about residential electricity usage on the price elasticity of demand. Informed households are three standard deviations more responsive to temporary price increases, an effect that is not attributable to price salience. Conservation extends beyond pricing events in the short and medium run, providing evidence of habit formation and implying that the intervention leads to greenhouse gas abatement. Survey evidence suggests that information facilitates learning.

We measure the pass-through of emissions costs to electricity prices. We perform both reduced-form and structural estimations based on optimal bidding in this market. Using rich micro-level data, we estimate the channels affecting pass-through in a flexible manner, with minimal functional form assumptions. Contrary to many studies in the general pass-through literature, we find that emissions costs are almost fully passed through to electricity prices. Since electricity is traded through high-frequency auctions for highly inelastic demand, firms have weak incentives to adjust markups after the cost shock. Furthermore, the costs of price adjustment are small.

This paper estimates the response of investment to changes in uncertainty using data on oil drilling in Texas and the expected volatility of the future price of oil. Using a dynamic model of firms' investment problem, I find that: (i) the response of drilling activity to changes in price volatility has a magnitude consistent with the optimal response prescribed by theory, (ii) the cost of failing to respond to volatility shocks is economically significant, and (iii) implied volatility data derived from futures options prices yields a better fit to firms' investment behavior than backward-looking volatility measures such as GARCH.

The Nobel Foundation asks that the Nobel lecture cover the work for which the Prize is awarded. The announcement of this year's Prize cites empirical work in asset pricing. I interpret this to include work on efficient capital markets and work on developing and testing asset pricing models—the two pillars, or perhaps more descriptive, the Siamese twins of asset pricing. I start with efficient markets and then move on to asset pricing models.

We use producer-level data to evaluate the role of financial frictions in determining total factor productivity (TFP). We study a model of establishment dynamics in which financial frictions reduce TFP through two channels. First, finance frictions distort entry and technology adoption decisions. Second, finance frictions generate dispersion in the returns to capital across existing producers and thus productivity losses from misallocation. Parameterizations of our model consistent with the data imply fairly small losses from misallocation, but potentially sizable losses from inefficiently low levels of entry and technology adoption.

Though risk aversion and the elasticity of intertemporal substitution have been the subjects of careful scrutiny, the long-run risks literature as well as the broader literature using recursive utility to address asset pricing puzzles has ignored the full implications of their parameter specifications. Recursive utility implies that the temporal resolution of risk matters and a quantitative assessment thereof should be part of the calibration process. This paper gives a sense of the magnitudes of implied timing premia. Its objective is to inject temporal resolution of risk into the discussion of the quantitative properties of long-run risks and related models.

Internet-based educational resources are proliferating rapidly. One concern associated with these (potentially transformative) technological changes is that they will be disequalizing—as many technologies of the last several decades have been—creating superstar teachers and a winner-take-all education system. These important concerns notwithstanding, we contend that a major impact of web-based educational technologies will be the democratization of education: educational resources will be more equally distributed, and lower-skilled teachers will benefit. At the root of our results is the observation that skilled lecturers can only exploit their comparative advantage if other teachers complement those lectures with face-to-face instruction. This complementarity will increase the quantity and quality of face-to-face teaching services, potentially increasing the marginal product and wages of lower-skill teachers.

This paper studies a New Keynesian business cycle model with agents who are averse to ambiguity (Knightian uncertainty). Shocks to confidence about future TFP are modeled as changes in ambiguity. To assess the size of those shocks, our estimation uses not only data on standard macro variables, but also incorporates the dispersion of survey forecasts about growth as a measure of confidence. Our main result is that TFP and confidence shocks together can explain roughly two thirds of business cycle frequency movements in the major macro aggregates. Confidence shocks account for about 70 percent of this variation.

We model the strategic provision of advice in environments where a principal's optimal action depends on an unobserved, binary state of interest. Experts receive signals about the state and each recommends an action. The principal and all experts dislike making errors in their decision and recommendations, respectively, but may have different costs of different errors. Is it in the principal's interest to let experts share information? Although sharing improves experts' ability to avoid errors, we identify a simple environment in which any principal, regardless of how he trades off the different errors, is worse off if he permits information sharing.