Knowledge that Transforms

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.

The analysis of dynamic games hinges on assumptions about players' actions and beliefs at information sets that are not expected to be reached during game play. Under the standard notion of sequential rationality, these assumptions cannot be tested on the basis of observed, on‐path behavior. This paper introduces a novel optimality criterion, structural rationality , which addresses this concern. In any dynamic game, structural rationality implies weak sequential rationality (Reny (1992)). If players are structurally rational, assumptions about on‐path and off‐path beliefs concerning off‐path actions can be tested via suitable “side bets.” Structural rationality also provides a theoretical rationale for the use of a novel version of the strategy method (Selten (1967)) in experiments.

This essay describes the evolution and recent convergence of two methodological approaches to causal inference. The first one, in statistics, started with the analysis and design of randomized experiments. The second, in econometrics, focused on settings with economic agents making optimal choices. I argue that the local average treatment effects framework facilitated the recent convergence by making key assumptions transparent and intelligible to scholars in many fields. Looking ahead, I discuss recent developments in causal inference that combine the same transparency and relevance.

This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego's response. However, this assumption is violated in common models of social interactions. We propose a substantially weaker model of “approximate neighborhood interference” (ANI) under which treatments assigned to alters further from the ego have a smaller, but potentially nonzero, effect on the ego's response. We formally verify that ANI holds for well‐known models of social interactions. Under ANI, restrictions on the network topology, and asymptotics under which the network size increases, we prove that standard inverse‐probability weighting estimators consistently estimate useful exposure effects and are approximately normal. For inference, we consider a network HAC variance estimator. Under a finite population model, we show that the estimator is biased but that the bias can be interpreted as the variance of unit‐level exposure effects. This generalizes Neyman's well‐known result on conservative variance estimation to settings with interference.

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.

This study provides a new theoretical result that a decline in the long‐term interest rate can trigger a stronger investment response by market leaders relative to market followers, thereby leading to more concentrated markets, higher profits, and lower aggregate productivity growth. This strategic effect of lower interest rates on market concentration implies that aggregate productivity growth declines as the interest rate approaches zero. The framework is relevant for antitrust policy in a low interest rate environment, and it provides a unified explanation for rising market concentration and falling productivity growth as interest rates in the economy have fallen to extremely low levels.

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.

More than two decades ago, a well‐known study on heart attack treatments provided evidence suggesting that, when appropriately adjusted for quality, medical care prices were actually declining (Cutler, McClellan, Newhouse, and Remler (1998)). Our paper revisits this subject by leveraging estimates from more than 8000 cost‐effectiveness studies across a broad range of conditions and treatments. We find large quality‐adjusted price declines associated with treatment innovations. To incorporate these quality‐adjusted indexes into an aggregate measure of inflation, we combine an unadjusted medical‐care price index, quality‐adjusted price indexes from treatment innovations, and proxies for the diffusion rate of new technologies. In contrast to official statistics that suggest medical care prices increased by 0.53 percent per year relative to economy‐wide inflation from 2000 to 2017, we find that quality‐adjusted medical care prices declined by 1.33 percent per year over the same period.

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.

We investigate the impact of job loss on crime and the mitigating role of unemployment benefits, exploiting detailed individual‐level data linking employment careers, criminal records, and welfare registries for the universe of male workers in Brazil. The probability of committing crimes increases on average by 23% for workers displaced by mass layoffs, and by slightly less for their cohabiting sons. Using causal forests, we show that the effect is entirely driven by young and low‐tenure workers, while there is no heterogeneity by education and income. Regression discontinuity estimates indicate that unemployment benefit eligibility completely offsets potential crime increases upon job loss, but this effect vanishes completely immediately after benefit expiration. Our findings point to liquidity constraints and psychological stress as the main drivers of criminal behavior upon job loss, while substitution between time on the job and leisure does not seem to play an important role.