This paper highlights a problem in using the first-differenced GMM panel
data estimator to estimate cross-country growth regressions. When the time series
are persistent, the first-differenced GMM estimator can be poorly behaved,
since lagged levels of the series provide only weak instruments for subsequent firstdifferences.
Revisiting the work of Caselli, Esquivel and Lefort (1996), we show
that this problem may be serious in practice. We suggest using a more efficient
GMM estimator that exploits stationarity restrictions, and this approach is shown
to give more reasonable results than first-differenced GMMin our estimation of an
empirical growth model.