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Vassilis Tselios PhD candidate in Urban and Regional Planning

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).

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Vassilis Tselios PhD candidate in Urban and Regional Planning

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  1. 19th Advanced Summer School in Regional ScienceGIS and spatial econometricsUniversity 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

  2. 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: 1995-2000 • Cross-section analysis: 102 regions (NUTS I or NUTS II)

  3. 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?

  4. 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

  5. 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 1995-00) (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

  6. 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?

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