19 th Advanced Summer School in Regional Science GIS and spatial econometrics University of Groningen, 4-12 July 2006 “Income and human capital inequalities and regional economic growth in the EU: urban-rural and North-South pattern” A description of the methodological problems (Q) Vassilis Tselios PhD candidate in Urban and Regional Planning London School of Economics and Political Science

Research question: ‘Do income and human capital inequalities matter for growth?’ focused on the role of spatial effects: spatial dependence and spatial heterogeneity Data: ECHP & Eurostat’s regional database Variables: –Income inequalities (individuals) Income inequality for all people Income inequality for normally working people –Human capital inequalities Inequality on education level completed Inequality on age when the highest education level was completed Panel data: –Time-series analysis: –Cross-section analysis: 102 regions (NUTS I or NUTS II)

Exploratory Spatial Data Analysis Mapping the data Boxplots Spatial effects (1) Spatial dependence –Spatial weights matrix (1) Rook first order contiguity; (2) 3-nearest neighbours; (3) threshold distance Q: What is the appropriate choice of the spatial weight matrix? –Spatial autocorrelation (Moran’s I statistic) –Space-time correlation Q: Spatial dependence is positive. If Z-value of spatial autocorrelation > Z- value of space-time correlation (i.e. for income per capita), what does it mean? (2) Spatial heterogeneity –Cluster map (LISA) –Two forms of spatial heterogeneity: Urbanisation (time-invariant variable) Latitude (time-invariant variable) Q: How can we calculate the latitude of a region?

Econometric analysis (1) MODEL –Determinants of income and human capital inequalities and regional economic growth (GDP per capita growth) –One-way error component model (large N and small T) –Robust and non-robust inferences (1) Static regression models –OLS, FEs and REs (2) Dynamic regression models –GMM-DIFF (Arellano and Bond, 1991) –GMM-SYS (Arellano and Bover, 1995; Blundell and Bond, 1998) –Short-run vs long-run coefficients –Explanatory variables are assumed to be Strictly exogenous Predetermined Endogenous

Econometric analysis (2) (3) Spatial regression models Missing observations Q: How can we deal with missing observations? (i.e. ‘different map’ each year) (a) Spatial effects in the form of a spatial average –Cross-section analysis (average between ) (Obs=102) –Standard OLS regression (basic diagnostics) –Spatial dependence: The general spatial model ML (Anselin and Bera, 1998) and GMM (Kelejian and Prucha, 1999) Q: Can we distinguish the importance of spatial externalities with the geographical location in regression analysis? What if we use trend surface regressions? –Spatial heterogeneity: Generic heteroskedasticity (a) Urbanisation degree (b) Latitude

Econometric analysis (3) (b) Spatial effects in the form of spatial filters –Spatial autoregressive and spatial moving average filters –Re-estimate static and dynamic regression models (4) Dynamic regression models in space and time Anselin (2001) –Deals with both serial and spatial dependence –ML (Hsiao, Pesaran and Thamiscioglu, 2002) –Examples: Elhorst, 2005; Arbia, Elhorst and Piras, 2005 Q: Which is the appropriate regression model?