This article examines how product innovations led to inflation inequality in the United States from 2004 to 2015. Using scanner data from the retail sector, I find that annual inflation for retail products was 0.661 (std. err. 0.0535) percentage points higher for the bottom income quintile relative to the top income quintile. When including changes in product variety over time, this difference increases to 0.8846 (std. err. 0.0739) percentage points a year. In CEX-CPI data covering the full consumption basket, the annual inflation difference is 0.368 (std. err. 0.0502) percentage points. I investigate the following hypothesis: (i) the relative demand for products consumed by high-income households increased because of growth and rising inequality; (ii) in response, firms introduced more new products catering to such households; (iii) as a result, the prices of continuing products in these market segments fell due to increased competitive pressure. Using a shift-share research design, I find causal evidence that increasing relative demand leads to increasing product variety and lower inflation for continuing products. A calibration indicates that the hypothesized channel accounts for a large fraction (over 50%) of observed inflation inequality.

How should we measure changes in consumer welfare given observed data on prices and expenditures? This article proposes a nonparametric approach that holds under arbitrary preferences that may depend on observable consumer characteristics, for example, when expenditure shares vary with income. Using total expenditures under a constant set of prices as our money metric for real consumption (welfare), we derive a principled measure of real consumption growth featuring a correction term relative to conventional measures. We show that the correction can be nonparametrically estimated with an algorithm leveraging the observed, cross-sectional relationship between household-level price indices and household characteristics such as income. We demonstrate the accuracy of our algorithm in simulations. Applying our approach to data from the United States, we find that the magnitude of the correction can be large because of the combination of fast growth and lower inflation for income-elastic products. Setting reference prices in 2019, we find that (i) the uncorrected measure underestimates average real consumption per household in 1955 by 11.5%, and (ii) the correction reduces the annual growth rate from 1955 to 2019 by 18 basis points, which is larger than the well-known “expenditure-switching bias” over the same time horizon.

Many studies use shift-share (or "Bartik") instruments, which average a set of shocks with exposure share weights. We provide a new econometric framework for shift-share instrumental variable (SSIV) regressions in which identification follows from the quasi-random assignment of shocks, while exposure shares are allowed to be endogenous. The framework is motivated by an equivalence result: the orthogonality between a shift-share instrument and an unobserved residual can be represented as the orthogonality between the underlying shocks and a shock-level unobservable. SSIV regression coefficients can similarly be obtained from an equivalent shock-level regression, motivating shock-level conditions for their consistency. We discuss and illustrate several practical insights of this framework in the setting of Autor et al. (2013), estimating the effect of Chinese import competition on manufacturing employment across U.S. commuting zones.

We present five facts from an experiment on the marginal propensity to consume (MPC) out of transitory transfers: (1) the one-month MPC on a cash-like transfer is 23 percent; (2) it is substantially higher (61 percent) on a transfer administered via a card where remaining funds expire after three weeks, inconsistent with money fungibility; (3) the consumption response is concentrated in the first three weeks; (4) MPCs vary with household characteristics but are high even for the liquid wealthy; (5) unconditional MPC distribution exhibits large variation. Our findings inform the design of stimulus policies and pose challenges to existing macroeconomic models.

We establish the importance of team-specific capital in the typical inventor's career. Using administrative tax and patent data for the population of US patent inventors from 1996 to 2012, we find that an inventor's premature death causes a large and long-lasting decline in their co-inventor's earnings and citation-weighted patents (−4 percent and −15 percent after 8 years, respectively). After ruling out firm disruption, network effects, and top-down spillovers as main channels, we show that the effect is driven by close-knit teams and that team-specific capital largely results from an “experience” component increasing collaboration value over time.

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behaviour of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the U.S., we find that the notional marginal propensity to consume is between 8 and 11% in the first quarter—about half as large as benchmark estimates used to calibrate macroeconomic models—and predominantly occurs in the first month after the rebate.

We characterize the factors that determine who becomes an inventor in the United States, focusing on the role of inventive ability (“nature”) versus environment (“nurture”). Using deidentified data on 1.2 million inventors from patent records linked to tax records, we first show that children's chances of becoming inventors vary sharply with characteristics at birth, such as their race, gender, and parents' socioeconomic class. For example, children from high-income (top 1%) families are 10 times as likely to become inventors as those from below-median income families. These gaps persist even among children with similar math test scores in early childhood-which are highly predictive of innovation rates-suggesting that the gaps may be driven by differences in environment rather than abilities to innovate. We directly establish the importance of environment by showing that exposure to innovation during childhood has significant causal effects on children's propensities to invent. Children whose families move to a high-innovation area when they are young are more likely to become inventors. These exposure effects are technology class and gender specific. Children who grow up in a neighborhood or family with a high innovation rate in a specific technology class are more likely to patent in exactly the same class. Girls are more likely to invent in a particular class if they grow up in an area with more women (but not men) who invent in that class. These gender- and technology class-specific exposure effects are more likely to be driven by narrow mechanisms, such as role-model or network effects, than factors that only affect general human capital accumulation, such as the quality of schools. Consistent with the importance of exposure effects in career selection, women and disadvantaged youth are as underrepresented among high-impact inventors as they are among inventors as a whole. These findings suggest that there are many “lost Einsteins”-individuals who would have had highly impactful inventions had they been exposed to innovation in childhood-especially among women, minorities, and children from low-income families.

What are the implications of unequal access to innovation careers for the direction of innovation and inequality? Leveraging novel linked datasets in the United States and Finland, we document that innovators create products more likely to be purchased by consumers like them in terms of gender, socioeconomic status, and age. Homophily exists both within narrow product categories and across industries and has been stable in recent decades. Incorporating this “social push” channel into a growth model, we estimate that unequal access to innovation careers has a large effect on cost of living inequality and long-run growth.