Palgrave Handbook of Econometrics: Applied Econometrics

Sergio J. Rey and Julie Le Gallo 1253

While this recognition is promising, it is also indicative of the need for further
interaction between the growth econometric and spatial analysis literatures. As
we develop more fully in what follows, spatial correlation is not a simple spatial
analogue to temporal correlation, nor are the methods that have been developed
in the spatial literature simple extensions of time series methods. Moreover, spatial
correlation/dependence is but one of several types of spatial effects encountered in
the analysis of geographically referenced data.
This chapter explores the intersection of the growth empirics and spatial analysis
literature. Our objectives for doing so are threefold. First, while there have been
some efforts at providing overviews of regional convergence research, these have
tended to appear in the regional science and economic geography literature (e.g.,
Magrini, 2004; Abreuet al., 2005; Rey and Janikas, 2005) and not the wider growth
literature. Because of this the amount of cross-fertilization between the traditional
growth econometrics literature and the spatial analysis is not as advanced as it
could be. Thus we hope that by updating previous reviews we can bring this work
to a wider audience.
At the same time, existing reviews have divided the literature into those studies
adopting a confirmatory approach to formal growth modeling on the one hand
and, on the other, the “atheoretical” exploratory literature. In our view this is an
artificial distinction as we see both literatures as complementary, and thus our sec-
ond objective is to explore the potential synergies between these two fields. Finally,
there are a number of outstanding methodological issues that require further atten-
tion in order for the field of regional convergence analysis to move forward. We
identify these and suggest an agenda for future research.
The chapter is organized as follows. Section 27.2 provides a selective survey of
the treatment of space in empirical econometric work on convergence. Section
27.3 examines methods for distributional dynamics and related exploratory data
analysis and their application to spatially referenced data. Section 27.4 concludes
with a summary evaluation of progress to date and the identification of possible
directions for future research.

27.2 Space and econometric modeling of convergence

27.2.1 Models of economic growth

The primary basis for the analysis of spatial effects in empirical convergence studies
has been cross-country growth regressions, based on the seminal studies by Barro
and Sala-i-Martin (1992) and Mankiwet al. (1992). The main prediction of the
neoclassical growth models is that the growth rate of an economy is positively
related to the distance that separates it from its own steady-state. Let us take as a
starting point the canonical form for such regressions:

1

T

log(yi,t 0 +T/yi,t 0 )=α+βlog(yi,t 0 )+X1,iδ 1 +X2,iδ 2 +εi, (27.1)

whereyi,tis the per capita income of country or regioniat timet, andX1,iis a set
of additional structural regressors suggested by the Solow growth model (popula-
tion growth, technological change and physical and human capital savings rates).