Aggregate effects and measuring regional dynamics

Abstract

Empirical models of regional adjustment often control for aggregate effects when estimating the impact of region‐specific shocks on local economies. It is, however, difficult to filter out the effects of aggregate shocks—such as oil shocks, uncertainty shocks, or national recessions—because the incidence of these shocks varies across space and time. We propose an improved econometric method to control for this form of spatiotemporal heterogeneity, thereby yielding more accurate estimation of the effects of local shocks on regional economies. Applying the method to US states, we find that labour demand shocks are mostly absorbed through changes in participation; the migration response to these shocks is limited; and recoveries are highly protracted

The Impact of Immigration on Wage Dynamics: Evidence from the Algerian Independence War

“…This rate of adjustment is consistent with Ruist et al (2017) and Borjas (2016) who respectively find that region-and skill-specific wages recover from positive supply shocks after a decade or more. It is also in line with the literature on the dynamics of demand shocks which finds that U.S. state-level wages tend to return to their pre-shock level after 14 to 20 years (Blanchard et al, 1992;Greenaway-McGrevy and Hood, 2011).…”

The Impact of Immigration on Wage Dynamics: Evidence from the Algerian Independence War

“…After 6 years, manufacturing wages start the recovery and return to their pre-shock level after 20 years. By using the same methodology, Greenaway-McGrevy and Hood (2011) focus on common negative demand shocks rather than state-specific shocks and find that wages do not reach their long-run level until 14 to 18 years after the shock. The studies by Amior and Manning (2015); Monras (2015b) also indicate that spatial adjustments to local demand shocks take a decade or more.…”

Estimating the economic effects of US state and local fiscal policy: A synthetic control method matching‐regression approach

“…14 14 Replacing the time fixed effects with national cyclic components using the method of Greenaway‐McGrevy and Hood (2019) does not much affect the fiscal results for the two principal component regressions. The r‐squared increases from 0.54 to 0.75 in the first principal component regression and from 0.87 to 0.94 in the second principal component regression.…”