This paper provides an econometric examination of geographic R&D spillovers among
countries by focusing on the issue of cross-sectional dependence, and in particular on the
dierent ways { weak and strong { it may aect the model. A preliminary analysis based
on the estimation of the exponent of cross-sectional correlation proposed by Bailey et al.
(2013), a, provides a very clear-cut result with an estimate of a very close to unity, not only
indicating the presence of strong cross-sectional correlation but also being consistent with the
factor literature typically assuming that a = 1. Moreover, second generation unit roots tests
suggest that while the unobserved idiosyncratic component of the variables under study may
be stationary, the unobserved common factors appear to be nonstationary. Consequently,
a factor structure appears to be preferable to a spatial error model and in particular the
Correlated Common Eects approach is employed since, among other things, it is still valid
in the more general case of nonstationary common factors. Finally, comparing the results
with those obtained with a spatial model gives some insights on the possible bias occurring
when allowing only for weak correlation while strong correlation is present in the data.