Working Paper: CEPR ID: DP9425
Authors: Harald Badinger; Peter Egger; Maximilian von Ehrlich
Abstract: This paper assesses the strength of productivity spillovers non-parametrically in a data-set of 12 industries and 231 NUTS2 regions in 17 European Union member countries between 1992 and 2006. It devotes particular attention to measuring catching up through spillovers depending on the technology gap of a unit to the industry leader and the local human capital endowment. We find evidence of a non-monotonic relationship between the technology gap to the leader as well as human capital and growth. Spillovers are strongest for units with a small technology gap to the leader and with abundant human capital.
Keywords: absorptive capacity; nonparametric estimation; technology spillovers; total factor productivity
JEL Codes: C14; N10; N14; O33; O47; R11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technology gap to the leader (O33) | TFP growth (O49) |
| human capital (J24) | TFP growth (O49) |
| technology gap to the leader + human capital (D29) | TFP growth (O49) |
| human capital abundance (small technology gap) (J24) | positive TFP growth (O49) |
| human capital scarcity (J24) | negative TFP growth (significant technology gap) (O49) |
| medium-sized technology gaps (O33) | no positive spillovers (F69) |
| leapfrogging at larger gaps (F12) | negative TFP growth (O49) |
| non-parametric estimator (C51) | greater variance in marginal effect of human capital (D29) |
| technology gap and TFP growth (O49) | not monotonic (C69) |
| industry-region dyads (R12) | no convergence to technology leader (O39) |