1284 Spatial Analysis of Economic Convergence
“measurement without theory,” such applications may actually lead to theoretical
advances.
The gap between the restrictive nature of most growth theory on the one hand
and empirical complexity of regional datasets on the other has clearly been a moti-
vating factor in the turn to EDA methods in convergence analysis. But rather than
abandoning the classical regression based approach, we suggest that using ESDA
and EDA methods to refine model specifications could work to relax some of the
restrictions. In this regard, there is a rich conceptual literature on spatial poverty
traps (cf. Bowleset al., 2006) that could, in our view, be tied to recent work in ESDA
to develop operational measures for these theoretical constructs. A final area is the
use of ESDA methods in enhanced diagnostic methods for spatial econometric
modeling (Ord, 2008).
Notes
- We focus here on the essential properties of these models, as they have been applied in
the growth econometrics literature. For extensive technical discussion on these models,
see Anselin and Bera (1998), Anselin (2003, 2006). - In assessing the effect of structural funds on the European regional convergence process,
Dall’erba and Le Gallo (2008) use a spatial lag model and estimate it with IV by considering
that both the spatial lag variable and the structural funds variable are endogenous. - See Fingleton and López-Bazo (2006) for a more complete description of papers including
spatial effects in growth and Verdoorn regressions. - This assumption corresponds to one of Kaldor’s stylized facts.
- For a recent overview of software for ESDA, see Rey and Anselin (2006).
- Since the probability of moving out of the current income class is 1 in each period and
class. - The solid black circle indicates the first year in the time path for each state.
- The conditional kernel and HDR plot were generated using the HDRCDE package
(Hyndman, 1996; Hyndmanet al., 1996).
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