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In this article we review recent evidence showing how market forces and policies shape the rate and direction of innovation, with various implications for inequality. First, we characterize several market mechanisms whereby higher rates of innovation lead to higher inequality. Second, we highlight channels whereby inequality acts as an impediment to innovation and growth. Third, we highlight policies that can help achieve better outcomes for both innovation and inequality. We show that two types of policies, which are not traditionally viewed as part of innovation policy, play a key role to achieve the goal of enhancing innovation while reducing inequality: education and competition policy.

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 behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Extensions include time-varying controls, triple-differences, and certain non-binary treatments. We show the practical relevance of these insights in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent 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.

Das Projekt analysiert die Preiswirkungen der Mehrwertsteuersenkung im Juli 2020 sowie die der entsprechenden Anhebung im Januar 2021 im Rahmen des Konjunkturpakets der Bundesregierung im Zuge der Corona-Pandemie. Die Untersuchung basiert auf Scannerdaten der GfK, die Informationen über die gezahlten Preise und gekauften Mengen von mehr als hundert Millionen Transaktionen im Betrachtungszeitraum beinhalten und ein umfassendes Abbild des Konsumverhaltens der deutschen Verbraucherinnen und Verbraucher ermöglichen. Die Ergebnisse zeigen, dass im Zuge der Mehrwertsteuersenkungen sowohl die Preise von schnell als auch von langsamen drehenden Konsumgütern in Deutschland substanziell gesunken sind. Diese Preissenkung fand unmittelbar nach Implementierung der Steuersenkung, also im Juli 2020, statt. Somit hat die temporäre Steuersenkung die Haushalte entlastet - dies trifft auch auf ärmere Haushalte zu, die im Zuge der Krise potenziell besonders betroffen sind. Insgesamt wurden rund 86% der Steuersenkungen an die Verbraucher weitergegeben. Die Weitergabe bei den Gebrauchsgütern betrug etwa 96%, bei den Verbrauchsgütern 76%. Unter Annahme plausibler intertemporale Substitutionselastizität ergeben sich Mengeneffekte im Bereich von rund 0,25% und 2%. Daraus lassen sich direkte Stabilisierungseffekte in Bezug auf das Bruttoinlandsprodukt von bis zu 0,3% ableiten. Im Januar 2021 kommt es zu einem Anstieg der Verbraucherpreise, der für Gebrauchsgüter annähernd symmetrisch zum beobachteten Rückgang im Juli 2020 ist und für Verbrauchsgüter etwas geringer ausfällt als der Preisrückgang sechs Monate zuvor. Endbericht: Im Auftrag des Bundesministeriums für Justiz und Verbraucherschutz (BMJV).

What are the effects of automation in the labor and product markets? A host of factors may be at play. Automating the production process may displace certain workers, raising the possibility of technological unemployment, but these displacement effects could be offset by a productivity effect. Automation may induce productivity gains, increase market demand and the scale of production, and in turn increase labor demand. Depending on the extent to which productivity gains are passed through to consumers by producers, consumers could benefit from lower prices or producers could retain higher profits. Finally, because of business-stealing effects from firms that automate and displace their competitors, the industry-level employment and the price and profit effects of automation may differ from their firm-level or plant-level impacts. [First paragraph]

We use comprehensive micro data in the French manufacturing sector between 1994 and 2015 to document the effects of automation technologies on employment, wages, prices and profits. 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 industrial equipment. At all levels of analysis —plant, firm, and industry —the estimated impact of automation on employment is positive, even for unskilled industrial workers. We also find that automation leads to higher profits, lower consumer prices, and higher sales. The estimated elasticity of employment to automation is 0.28, compared with elasticities of 0.78 for profits, -0.05 for prices, and 0.37 for sales. Consistent with the importance of business-stealing across countries, the industry-level employment response to automation is positive and significant only in industries that face international competition. These estimates can be accounted for in a simple monopolistic competition model: firms that automate more increase their profits but pass through some of the productivity gains to consumers, inducing higher scale and higher employment. The results indicate that automation can increase labor demand and can generate productivity gains that are broadly shared across workers, consumers and firm owners. In a globalized world, attempts to curb domestic automation in order to protect domestic employment may be self-defeating due to foreign competition.

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 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.