Knowledge that Transforms

This paper develops a model of Bayesian learning from online reviews and investigates the conditions for learning the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. observe the ratings of a product and decide whether to purchase and review it. We study learning dynamics under two classes of rating systems: full history , where customers see the full history of reviews, and summary statistics , where the platform reports some summary statistics of past reviews. In both cases, learning dynamics are complicated by a selection effect —the types of users who purchase the good, and thus their overall satisfaction and reviews depend on the information available at the time of purchase. We provide conditions for complete learning and characterize and compare its speed under full history and summary statistics. We also show that providing more information does not always lead to faster learning, but strictly finer rating systems do.

Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high‐dimensional, making machine learning useful. Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions. The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high‐dimensional methods. In addition to providing the bias correction, we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.

I prove an envelope theorem with a converse: the envelope formula is equivalent to a first‐order condition. Like Milgrom and Segal's (2002) envelope theorem, my result requires no structure on the choice set. I use the converse envelope theorem to extend to general outcomes and preferences the canonical result in mechanism design that any increasing allocation is implementable, and apply this to selling information.

We develop a tractable model of banks' liquidity management with an over‐the‐counter interbank market to study the credit channel of monetary policy. Deposits circulate randomly across banks and must be settled with reserves. We show how monetary policy affects the banking system by altering the trade‐off between profiting from lending and incurring greater liquidity risk. We present two applications of the theory, one involving the connection between the implementation of monetary policy and the pass‐through to lending rates, and another considering a quantitative decomposition behind the collapse in bank lending during the 2008 financial crisis. Our analysis underscores the importance of liquidity frictions and the functioning of interbank markets for the conduct of monetary policy.

We study the role of commitment in communication and its interactions with rules, which determine whether information is verifiable. Our framework nests models of cheap talk, information disclosure, and Bayesian persuasion. It predicts that commitment has opposite effects on information transmission under the two alternative rules. We leverage these contrasting forces to experimentally establish that subjects react to commitment in line with the main qualitative implications of the theory. Quantitatively, not all subjects behave as predicted. We show that a form of commitment blindness leads some senders to overcommunicate when information is verifiable and undercommunicate when it is not. This generates an unpredicted gap in information transmission across the two rules, suggesting a novel role for verifiable information in practice.

We study the role of global financial intermediaries in international lending. We construct a model of the world economy, in which heterogeneous borrowers issue risky securities purchased by financial intermediaries. Aggregate shocks transmit internationally through financial intermediaries' net worth. The strength of this transmission is governed by the degree of frictions intermediaries face in financing their risky investments. We provide direct empirical evidence on this mechanism showing that around Lehman Brothers' bankruptcy, emerging‐market bonds held by more distressed global banks experienced larger price contractions. A quantitative analysis of the model shows that global financial intermediaries play a relevant role in driving borrowing‐cost and consumption fluctuations in emerging‐market economies, during both debt crises and regular business cycles. The portfolio of financial intermediaries and the distribution of bond holdings in the world economy are key to determine aggregate dynamics.

We study the transmission of monetary policy through risk premia in a heterogeneous agent New Keynesian environment. Heterogeneity in households' marginal propensity to take risk (MPR) summarizes differences in portfolio choice on the margin. An unexpected reduction in the nominal interest rate redistributes to households with high MPRs, lowering risk premia and amplifying the stimulus to the real economy. Quantitatively, this mechanism rationalizes the role of news about future excess returns in driving the stock market response to monetary policy shocks and amplifies their real effects by 1.3–1.4 times.

In the U.S., large firms now account for a greater share of economic activity, new firms are being created at slower rates, and workers are receiving a smaller share of GDP. Changes in population growth provide a unified quantitative explanation. A decrease in population growth lowers firm entry rates, shifting the firm‐age distribution toward older firms. Firm aging accounts for (i) the concentration of employment in large firms, (ii) and trends in average firm size and exit rates, key determinants of firm entry rates. Feedback effects from firm demographics generate two‐thirds of the effect. Prior to the decrease, entry rates rose steadily reflecting the earlier baby boom. The glut of firms due to the baby boom lead to rich transitional dynamics within the feedback effects, accounting for more than half the total change. Baby boom induced changes in the firm‐age distribution provide a driving force for the post‐WWII rise and fall in the aggregate labor share. Ignoring changes in population growth attributes all the long run decline in entry rates to a decrease in firm exit rates, which in reality have been only one‐third as large.

We propose an analytical method to analyze the propagation of an aggregate shock in a broad class of sticky‐price models. The method is based on the eigenvalue‐eigenfunction representation of the dynamics of the cross‐sectional distribution of firms' desired adjustments. A key novelty is that we can approximate the whole profile of the impulse response for any moment of interest in response to an aggregate shock (any displacement of the invariant distribution). We present several applications for an economy with low inflation and idiosyncratic shocks. We show that the shape of the impulse response of the canonical menu cost model is fully encoded by a single parameter, just like the Calvo model, although the shapes are very different. A model with a quadratic hazard function, arguably a good fit to the micro data on price setting, yields an impulse response that is close to the canonical menu cost model.

We explore patterns of price competition in an oligopoly where consumers vary in the set of firms they consider for their purchase and buy from the lowest‐priced firm they consider. We study a pattern of consideration, termed “symmetric interactions,” that generalizes models used in existing work (duopoly, symmetric firms, and firms with independent reach). Within this class, equilibrium profits are proportional to a firm's reach, firms with a larger reach set higher average prices, and a reduction in the number of firms (either by exit or by merger) harms consumers. However, increased competition (either by entry or by increased consumer awareness) does not always benefit consumers. We go on to study patterns of consideration with asymmetric interactions. In situations with disjoint reach and with nested reach, we find equilibria in which price competition is “duopolistic”: only two firms compete within each price range. We characterize the contrasting equilibrium patterns of price competition for all patterns of consideration in the three‐firm case.