Quasi-Experimental Shift-Share Research Designs.

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.

High-Frequency Changes in Shopping Behaviours, Promotions and the Measurement of Inflation: Evidence from the Great Lockdown.

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.

Real-time price indices: Inflation spike and falling product variety during the Great Lockdown.

We characterize inflation dynamics during the Great Lockdown using scanner data covering millions of transactions for fast-moving consumer goods in the United Kingdom. We show that there was a significant and widespread spike in inflation. First, aggregate month-to-month inflation was 2.4% in the first month of lockdown, a rate over 10 times higher than in preceding months. Over half of this increase stems from reduced frequency of promotions. Consumers' purchasing power was further eroded by a reduction in product variety. Second, 96% of households have experienced inflation in 2020, while in prior years around half of households experienced deflation. Third, there was inflation in most product categories, including those that experienced output falls. Only 13% of product categories experienced deflation, compared with over half in previous years. While market-based measures of inflation expectations point to disinflation or deflation, these findings indicate a risk of stagflation should not be ruled out. We hope our approach can serve as a template to facilitate rapid diagnosis of inflation risks during economic crises, leveraging scanner data and appropriate price indices in real-time.