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How should we measure long-run changes in consumer welfare? This paper proposes a nonparametric approach that is valid under arbitrary preferences that depend on observable consumer characteristics, e.g. when expenditure shares vary with income. Our approach only requires data on the consumption baskets of a cross section of consumers facing a common set of prices. Using nominal expenditures under a constant set of prices as our money-metric for real consumption (welfare), we derive a consistent measure of its growth in terms of a correction to the conventional measures based on price index formulas. Our correction ac-counts for the cross-sectional dependence of the measured price indices on consumer income and other characteristics. We use nonparametric methods to approximate these corrections and provide bounds on the resulting approximation errors. Applying the approach to the measurement of growth in US real consumption per capita, we find a sizable correction to the standard measures of growth in the post-war era, a period of fast growth combined with substantial inflation gaps across income groups.

Innovators are intrinsically-motivated individuals who use ideas to create new goods and services. This raises the possibility that their social backgrounds may affect the direction of their innovative activity. Consistent with this "social push" channel, we document that innovators create products that are more likely to be purchased by customers similar to them along observable dimensions including gender, age, and socioeconomic status, both across and within detailed industries. Next, we provide causal evidence that social experience affects the direction of a person's innovative activity. Specifically, being exposed to peers from a lower-income group increases an entrepreneur's propensity to create necessity products, without affecting her rates of entrepreneurship and entrepreneurial income. We incorporate this channel into a general equilibrium model to assess its implications for cost-of-living inequality and long-run growth when there is unequal access to the innovation system.

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.

This article addresses a key point of contention in the ongoing UBI debate: given the way labour supply responds to tax changes, is it possible to fund a large UBI using income taxes? Using recent empirical estimates and quantitative tools from the public economics literature, we assess what level of UBI may be funded given the fall in labour supply that could be induced by the required larger taxes. Despite a prevalent belief that a large UBI would be fiscally irresponsible, we find that it is possible to fund a large annual UBI over £11,000 per person, that it could be funded through a 45% flat tax, but that increasing taxes on the most affluent alone would be insufficient. Our findings highlight an important tension: a large UBI is possible, but it requires large tax rates, including for those at the bottom of the income distribution.

We use real-time scanner data in Great Britain during the COVID-19 pandemic to investigate the drivers of the inflationary spike at the beginning of lockdown and to quantify the impact of high-frequency changes in shopping behaviours and promotions on inflation measurement. Although changes in product-level expenditure shares were unusually high during lockdown, we find that the induced bias in price indices that do not account for expenditure switching is not larger than in prior years. We also document substantial consumer switching towards online shopping and across retailers, but show this was not a key driver of the inflationary spike. In contrast, a reduction in price and quantity promotions was key to driving higher inflation, and lower use of promotions by low-income consumers explains why they experienced moderately lower inflation. Overall, changes in shopping behaviours played only a minor role in driving higher inflation during lockdown; higher prices were the main cause, in particular through a reduced frequency of promotions.

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.

Many countries provide financial incentives to spur innovation, ranging from tax incentives to research and development grants. In this paper, we study how such financial incentives affect individuals’ decisions to pursue careers in innovation. We first present empirical evidence on inventors’ career trajectories and income distributions using deidentified data on 1.2 million inventors from patent records linked to tax records in the United States. We find that the private returns to innovation are extremely skewed—with the top 1% of inventors collecting more than 22% of total inventors’ income—and are highly correlated with their social impact, as measured by citations. Inventors tend to have their most impactful innovations around age 40 and their incomes rise rapidly just before they have high-impact patents. We then build a stylized model of inventor career choice that matches these facts as well as recent evidence that childhood exposure to innovation plays a critical role in determining whether individuals become inventors. The model predicts that financial incentives, such as top income tax reductions, have limited potential to increase aggregate innovation because they only affect individuals who are exposed to innovation and have essentially no impact on the decisions of star inventors, who matter most for aggregate innovation. Importantly, these results hold regardless of whether the private returns to innovation are fully known at the time of career choice or are fully stochastic. In contrast, increasing exposure to innovation (e.g., through mentorship programs) could have substantial impacts on innovation by drawing individuals who produce high-impact inventions into the innovation pipeline. Although we do not present direct evidence supporting these model-based predictions, our results call for a more careful assessment of the impacts of financial incentives and a greater focus on alternative policies to increase the supply of inventors.

We show that examiner-driven variation in patent rights leads to quantitatively large impacts on several patent outcomes, including patent value, citations, and litigation. Notably, Patent Assertion Entities (PAEs) overwhelmingly purchase patents granted by "lenient" examiners. These examiners issue patents that are more likely to be litigated by both PAEs and conventional companies, and that also have higher invalidity rates. PAEs leverage a specific friction in the patent system that stems from lenient examiners and affects litigation more broadly. These patterns indicate that there is much at stake during patent examination, contradicting the influential "rational ignorance" view of the patent office.

This paper finds that U.S. consumer prices fell substantially due to increased trade with China. With comprehensive price micro-data and two complementary identification strategies, we estimate that a 1pp increase in import penetration from China causes a 1.91% decline in consumer prices. This price response is driven by declining markups for domestically-produced goods, and is one order of magnitude larger than in standard trade models that abstract from strategic price-setting. The estimates imply that trade with China increased U.S. consumer surplus by about $400,000 per displaced job, and that product categories catering to low-income consumers experienced larger price declines.

Many countries provide financial incentives to spur innovation, ranging from tax incentives to research and development grants. In this paper, we study how such financial incentives affect individuals' decisions to pursue careers in innovation. We _first present empirical evidence on inventors' career trajectories and income distributions using de-identified data on 1.2 million inventors from patent records linked to tax records in the U.S. We find that the private returns to innovation are extremely skewed - with the top 1% of inventors collecting more than 22% of total inventors' income - and are highly correlated with their social impact, as measured by citations. Inventors tend to have their most impactful innovations around age 40 and their incomes rise rapidly just before they have high-impact patents. We then build a stylized model of inventor career choice that matches these facts as well as recent evidence that childhood exposure to innovation plays a critical role in determining whether individuals become inventors. The model predicts that financial incentives, such as top income tax reductions, have limited potential to increase aggregate innovation because they only affect individuals who are exposed to innovation and have no impact on the decisions of star inventors, who matter most for aggregate innovation. Importantly, these results hold regardless of whether the private returns to innovation are known at the time of career choice. In contrast, increasing exposure to innovation (e.g., through mentorship programs) could have substantial impacts on innovation by drawing individuals who produce highimpact inventions into the innovation pipeline. Although we do not present direct evidence supporting these model-based predictions, our results call for a more careful assessment of the impacts of financial incentives and a greater focus on alternative policies to increase the supply of inventors.

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 ha

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 characterize the factors that determine who becomes an inventor in America by using de-identified data on 1.2 million inventors from patent records linked to tax records. We establish three sets of results. First, children from high-income (top 1%) families are ten times as likely to become inventors as those from below-median income families. There are similarly large gaps by race and gender. Differences in innate ability, as measured by test scores in early childhood, explain relatively little of these gaps. Second, exposure to innovation during childhood has significant causal effects on children's propensities to become inventors. Growing up in a neighborhood or family with a high innovation rate in a specific technology class leads to a higher probability of patenting in exactly the same technology class. These exposure effects are gender-specific: girls are more likely to become inventors in a particular technology class if they grow up in an area with more female inventors in that technology class. Third, the financial returns to inventions are extremely skewed and highly correlated with their scientific impact, as measured by citations. Consistent with the importance of exposure effects and contrary to standard models of career selection, women and disadvantaged youth are as under-represented among highimpact inventors as they are among inventors as a whole. We develop a simple model of inventors' careers that matches these empirical results. The model implies that increasing exposure to innovation in childhood may have larger impacts on innovation than increasing the financial incentives to innovate, for instance by cutting tax rates. In particular, there are many “lost Einsteins” - individuals who would have had highly impactful inventions had they been exposed to innovation

What makes countries engage in reforms of mass education? Motivated by historical evidence on the relation between military threats and expansions of primary education, we assemble a panel dataset from the last 150 years in European countries and from the postwar period in a large set of countries. We uncover three stylized facts: (i) investments in education are associated with military threats, (ii) democratic institutions are negatively correlated with education investments, and (iii) education investments respond more strongly to military threats in democracies. These patterns continue to hold when we exploit rivalries in a country’s neighborhood as an alternative source of variation. We develop a theoretical model that rationalizes the three empirical findings. The model has an additional prediction about investments in physical infrastructures, which finds support in the data.

Does inflation vary across the income distribution? This article reviews the growing literature on inflation inequality, describing recent advances and opportunities for further research in four areas. First, new price index theory facilitates the study of inflation inequality. Second, new data show that inflation rates decline with household income in the United States. Accurate measurement requires granular price and expenditure data because of aggregation bias. Third, new evidence quantifies the impacts of innovation and trade on inflation inequality. Contrary to common wisdom, empirical estimates show that the direction of innovation is a significant driver of inflation inequality in the United States, whereas trade has similar price effects across the income distribution. Fourth, inflation inequality and non-homotheticities have important policy implications. They transform cost-benefit analysis, optimal taxation, the effectiveness of stabilization policies, and our understanding of secular macroeconomic trends—including structural change, the decline in the labor share and interest rates, and labor market polarization.

Many studies in economics use instruments or treatments that combine a set of exogenous shocks with other predetermined variables via a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification. We compare this design-based approach with conventional estimation strategies based on conditional unconfoundedness, and contrast it with alternative strategies that leverage a model for unobservables.

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.

What is the nature of the distributional effects of trade? This paper demonstrates conceptually and empirically the importance of “trade-induced horizontal inequality,” i.e. inequality that occurs among workers with the same level of earnings before the trade shock. This type of inequality does not affect the income distribution but generates winners and losers at all income levels. To quantify the horizontal inequality and changes in the income distribution induced by trade in a data-driven way, we develop a characterization of the welfare impacts, governed by simple and intuitive statistics of labor market and consumption exposure to trade. In the U.S., we find substantial heterogeneity in exposure and thus in the welfare effects of trade shocks across workers. Over 99% of the variance of welfare changes from trade shocks arises within income deciles. These findings run against a popular narrative that “trade wars are class wars.”

This paper finds that U.S. consumer prices fell substantially due to increased trade with China. With comprehensive price micro-data and two complementary identification strategies, we estimate that a 1pp increase in import penetration from China causes a 1.91% decline in consumer prices. This price response is driven by declining markups for domestically-produced goods, and is one order of magnitude larger than in standard trade models that abstract from strategic price-setting. The estimates imply that trade with China increased U.S. consumer surplus by about $400,000 per displaced job, and that product categories catering to low-income consumers experienced larger price declines.

Many countries provide financial incentives to spur innovation, ranging from tax incentives to research and development grants. In this paper, we study how such financial incentives affect individuals' decisions to pursue careers in innovation. We _first present empirical evidence on inventors' career trajectories and income distributions using de-identified data on 1.2 million inventors from patent records linked to tax records in the U.S. We find that the private returns to innovation are extremely skewed - with the top 1% of inventors collecting more than 22% of total inventors' income - and are highly correlated with their social impact, as measured by citations. Inventors tend to have their most impactful innovations around age 40 and their incomes rise rapidly just before they have high-impact patents. We then build a stylized model of inventor career choice that matches these facts as well as recent evidence that childhood exposure to innovation plays a critical role in determining whether individuals become inventors. The model predicts that financial incentives, such as top income tax reductions, have limited potential to increase aggregate innovation because they only affect individuals who are exposed to innovation and have no impact on the decisions of star inventors, who matter most for aggregate innovation. Importantly, these results hold regardless of whether the private returns to innovation are known at the time of career choice. In contrast, increasing exposure to innovation (e.g., through mentorship programs) could have substantial impacts on innovation by drawing individuals who produce high-impact inventions into the innovation pipeline. Although we do not present direct evidence supporting these model-based predictions, our results call for a more careful assessment of the impacts of financial incentives and a greater focus on alternative policies to increase the supply of inventors.

What is the nature of the distributional effects of trade? This paper demonstrates conceptually and empirically the importance of "trade-induced horizontal inequality," i.e., inequality brought about by trade shocks that occurs among workers with the same level of earnings prior to the shock. While this type of inequality does not affect the income distribution, it generates winners and losers at all income levels and may thus affect political support for trade policy. To quantify the horizontal inequality and changes in the income distribution induced by trade in a data-driven way, we develop a characterization of the welfare impacts, governed by simple and intuitive statistics of labor market and consumption exposure to trade. This characterization holds in a class of quantitative trade models allowing for a broad set of preferences, including non-homothetic, and production functions. Taking this framework to U.S. data, we find substantial heterogeneity in exposure and thus in the welfare effects of trade shocks across workers, with horizontal inequality as the dominant force. Over 99% of the variance of welfare changes from trade shocks arise within income deciles, rather than across. This finding runs against a popular narrative that "trade wars are class wars".

We use comprehensive micro data in the French manufacturing sector between 1995 and 2017 to document the effects of a fall in the cost of investments in modern manufacturing capital, including modern automation technologies, on employment, wages, sales, prices, and business stealing. Causal effects are estimated with event studies and a shift-share IV design leveraging pre-determined supply linkages and productivity shocks across foreign suppliers of manufacturing capital. At all levels of analysis - plant, firm, and industry - the estimated impact of capital investments on employment is positive, even for unskilled industrial workers. Further-more, we find that capital investments lead to higher sales and exports, higher profits, and lower consumer prices, while wages and wage inequality remain unchanged. We estimate a positive industry-level employment response to manufacturing capital investments only in industries that are exposed to import competition, due to business-stealing across countries. Thus, typical investments in modern manufacturing capital lead to an increase in domestic labor demand and promote competitiveness in international markets.

How much do consumption patterns matter for the impact of international trade on inequality? In neoclassical trade models, the effects of trade shocks on consumers' purchasing power are governed by the shares of imports in consumer expenditures, under no parametric assumptions on preferences and technology. This paper provides in-depth measurement of import shares across the income distribution in the United States, using new datasets linking expenditure and customs microdata. Contrary to common wisdom, we find that import shares are flat at throughout the income distribution: the purchasing-power gains from lower trade costs are distributionally neutral. Accounting for changes in wages in addition to prices in a unified nonparametric framework, we find substantial distributional effects that arise within, but not across, income and education groups. There is little impact of a fall in trade costs on inequality, even though trade shocks generate winners and losers at all income levels, via wage changes.