Working Paper: CEPR ID: DP1924
Authors: Angel de la Fuente
Abstract: Recent estimates of convergence equations using panel data techniques tend to produce theoretically unpalatable results which run counter to the views prevailing in the literature. This paper argues that these results may be partly due to the difficulty of empirically separating short-term fluctuations around trend from long-term growth dynamics. Using data for the Spanish regions, it is found that explicitly allowing for short-term noise reduces the estimated convergence rate to values which are roughly consistent with an extended neoclassical model. On the other hand, the dispersion of estimated steady states remains high, although these estimates do not seem to be particularly reliable.
Keywords: convergence; panel data
JEL Codes: C23; O40
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| high convergence rates derived from panel data (C23) | biased estimates of true speed of convergence (C51) |
| short-term noise accounted for (C58) | estimated convergence rate decreases (F62) |
| application of fixed effects models (C23) | overestimation of convergence rates (O47) |
| cyclical fluctuations treated as measurement errors (E32) | overestimation of convergence rates (O47) |
| choice of model (C52) | impact on estimated convergence rates (F62) |
| high dispersion of estimated steady states (C51) | significant regional differences persist (R23) |