Working Paper: NBER ID: w19374
Authors: Bettina Peters; Mark J. Roberts; Van Anh Vuong; Helmut Fryges
Abstract: This paper estimates a dynamic structural model of discrete R&D investment and quantifies its cost and long-run benefit for German manufacturing firms. The dynamic model incorporates linkages between the firm's R&D choice, product and process innovations, and future productivity and profits. The long- run payoff to R&D is measured as the proportional difference in expected firm value generated by the R&D investment. It increases firm value by 6.7 percent for the median firm in high-tech manufacturing industries but only 2.8 percent in low-tech industries. Simulations show that reductions in maintence costs of innovation significantly raise investment rates and productivity while reductions in startup costs have little effect.
Keywords: R&D investment; dynamic structural model; innovation; productivity; firm performance
JEL Codes: L60; O30; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| R&D investment (O32) | probability of achieving product or process innovations (O36) |
| R&D investment (O32) | firm value (G32) |
| product innovations (O35) | productivity gains in high-tech industries (O49) |
| process innovations (O31) | productivity gains in low-tech industries (O49) |
| prior R&D experience (O36) | current innovation costs (O31) |
| 20% reduction in maintenance costs (R42) | probability of R&D investment (O39) |
| 20% reduction in maintenance costs (R42) | mean productivity after ten years (O49) |
| startup costs reduction (M13) | impact on established firms (L19) |
| startup costs reduction (M13) | new R&D entrants in low-tech industries (O39) |
| firm characteristics (L20) | benefits of R&D (O32) |