Knowledge that Transforms

We relate two main representations of the cost of acquiring information: a cost that depends on the experiment performed, as in statistical decision theory, and a cost that depends on the distribution of posterior beliefs, as in applications of rational inattention. We show that in many cases of interest, posterior-based costs are inconsistent with a primitive model of costly experimentation. The inconsistency is at the core of known limits to the application of rational inattention in games and, more broadly, in equilibrium analyses where beliefs are endogenous; we show that an experiment-based approach helps to understand and overcome these difficulties.

We study bargaining over contingent contracts in problems where private information becomes public or verifiable when the time comes to implement the agreement. We suggest a simple, two-stage game that incorporates important aspects of bargaining. We characterize equilibria in which parties always reach agreement, and study their limits as bargaining frictions vanish. Under mild regularity conditions, we show all interim-efficient limits belong to Myerson’s (1984) axiomatic solution. Furthermore, all limits must be interim efficient if equilibrium beliefs satisfy no-signaling-what-you-don’ t-know. Results extend to other bargaining protocols.

We quantify the importance of imperfect competition in the US labor market by estimating the size of labor market rents earned by American firms and workers. We construct a matched employer-employee panel dataset by combining the universe of US business and worker tax records for the period 2001–2015. Using this panel data, we identify and estimate an equilibrium model of the labor market with two-sided heterogeneity where workers view firms as imperfect substitutes because of heterogeneous preferences over nonwage job characteristics. The model allows us to draw inference about imperfect competition, worker sorting, compensating differentials, and rent sharing.

Increases in the minimum wage can substantially reduce earnings inequality. To demonstrate this, we combine administrative and survey data with an equilibrium model of the Brazilian labor market. We find that a 128 percent increase in the real minimum wage in Brazil between 1996 and 2018 had far-reaching spillover effects on wages higher up in the distribution. The increased minimum wage accounts for 45 percent of a large fall in earnings inequality over this period. At the same time, the effects of the minimum wage on employment and output are muted by reallocation of workers toward more productive firms..

We study the welfare effects of offering choice over coverage levels-"vertical choice"-in regulated health insurance markets. We emphasize that heterogeneity in efficient coverage level is not sufficient to motivate choice. When premiums cannot reflect individuals' costs, it may not be in consumers' best interest to select their efficient coverage level. We show that vertical choice is efficient only if consumers with higher willingness-to-pay have a higher efficient level of coverage. We investigate this condition empirically and find that as long as a minimum coverage level can be enforced, the welfare gains from vertical choice are either zero or economically small.

This paper assesses the effects of capital gains taxes on investment in the Republic of Korea (hereafter, Korea), where capital gains tax rates vary at the firm level by firm size. Following a reform in 2014, firms with a tax cut increased investment by 34 log points and issued more equity by 9 cents per dollar of lagged revenue, relative to unaffected firms. Additionally, the effects were larger for firms that appeared more cash constrained or went public after the reform. Taken together, these findings are consistent with the “traditional view” predicting that lower payout taxes spur equity-financed investment by increasing marginal returns on investment.

This paper studies how the impact of monetary policy depends on the distribution of savings from refinancing mortgages. We show that the efficacy of monetary policy is state dependent, varying in a systematic way with the pool of potential savings from refinancing. We construct a quantitative dynamic life-cycle model that accounts for our findings and use it to study how the response of consumption to a change in mortgage rates depends on the distribution of savings from refinancing. These effects are strongly state dependent. We also use the model to study the impact of a long period of low interest rates on the potency of monetary policy. We find that this potency is substantially reduced both during the period and for a substantial amount of time after interest rates renormalize.

We quantify the magnitude of R&D spillovers created by grants to small firms from the US Department of Energy. Our empirical strategy leverages variation due to state-specific matching policies, and we develop a new approach to measuring both geographic and technological spillovers that does not rely on an observable paper trail. Our estimates suggest that for every patent produced by grant recipients, three more are produced by others who benefit from spillovers. Sixty percent of these spillovers occur within the United States, and many of them occur in technological areas substantially different from those targeted by the grants.

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoff-relevant states of the world are unknown. The analyst observes behavior assumed to be rationalized by a Bayesian model, in which agents maximize expected utility, given partial and differential information about the state. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of the state and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional.