Abstract
Many studies in the immigration literature rely on geographic variation in the concentration of
immigrants to identify their impact on the labor market. National inflows of immigrants are
interacted with their past geographic distribution to create an instrument, in the hopes of
breaking the endogeneity between labor market conditions and the location choice of
immigrants. We present evidence that estimates based on this shift-share instrument are
subject to bias from the conflation of short- and long-run responses to local shocks. The bias
stems from the interplay of two factors. First, local shocks may trigger adjustment processes
that gradually offset their initial impact. Second, the spatial distribution of immigrant inflows
typically changes little over time. In the U.S., both the country-of-origin composition and
spatial distribution of immigrant arrivals have been almost perfectly serially correlated in
recent decades, with the same cities repeatedly receiving large immigrant inflows.
Estimates based on the conventional shift-share instrument are therefore unlikely to identify a
causal effect. We propose a “double instrumentation” solution to the problem that — by
isolating spatial variation that stems from changes in the country-of-origin composition on the
national level — produces estimates that are likely to be less biased than those in the previous
literature. Our results are a cautionary tale for a large body of empirical work, not just on
immigration, that rely on shift-share instruments for causal identification.