Defense R&D represents the largest component of US public R&D spending and historically has promoted a wide range of civilian innovations. However, the empirical evidence on the impact of defense R&D is scant and it does not provide conclusive results on the possible crowding-in (-out) effects on private R&D investment. Exploiting a longitudinal dataset linking public R&D obligations to private R&D expenditures for US states, we investigate the impact of defense R&D on privately-financed R&D. To address potential endogeneity in the allocation of funds, we use an instrumental variable identification strategy leveraging the differential exposure of US states to national shocks in federal military R&D. We document considerable crowding-in effects with elasticities in the 0.11–0.14 range. These positive effects extend also to the labor market, when focusing on employment in selected R&D intensive industries and especially for engineers.

Does public R&D funding crowd-in private R&D investment? Evidence from military R&D expenditures for US states

Gianluca Pallante
;
Emanuele Russo;Andrea Roventini
2023-01-01

Abstract

Defense R&D represents the largest component of US public R&D spending and historically has promoted a wide range of civilian innovations. However, the empirical evidence on the impact of defense R&D is scant and it does not provide conclusive results on the possible crowding-in (-out) effects on private R&D investment. Exploiting a longitudinal dataset linking public R&D obligations to private R&D expenditures for US states, we investigate the impact of defense R&D on privately-financed R&D. To address potential endogeneity in the allocation of funds, we use an instrumental variable identification strategy leveraging the differential exposure of US states to national shocks in federal military R&D. We document considerable crowding-in effects with elasticities in the 0.11–0.14 range. These positive effects extend also to the labor market, when focusing on employment in selected R&D intensive industries and especially for engineers.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/541631
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