1 / 22

Market Potential , MAUP, NUTS and other spatial mysteries

11th International Workshop Spatial Econometrics and Statistics 15-16 November 2012 Avignon – France. Market Potential , MAUP, NUTS and other spatial mysteries. Fernando Bruna Jesus Lopez-Rodriguez Andres Faina. Motivation. SPATIAL INTERACTIONS WITH MARKET POTENTIAL

joy
Download Presentation

Market Potential , MAUP, NUTS and other spatial mysteries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 11th International Workshop Spatial Econometrics and Statistics 15-16 November 2012 Avignon – France Market Potential, MAUP, NUTS and other spatial mysteries Fernando Bruna Jesus Lopez-Rodriguez Andres Faina

  2. Motivation SPATIAL INTERACTIONS WITH MARKET POTENTIAL • Physics Magnetic or electric fields "Population potential" (Stewart, 1947) Market Potential function (Harris, 1954) Widely used in Regional Economics. • Krugman (1991), Fujita et al. (1999)… New Economic Geography (NEG): Micro-foundations of “market potential” Many tests of the wage equation. SPATIAL INTERACTION DEPENDS ON “SPACE”! • Modifiable Areal Unit Problem (MAUP): The results of the analysis depends on the modifiability of the spatial partitions (areal units) => We study it estimating an equation with a variable of Market Potential.

  3. Motivation: reasons for the MAUP ECONOMIC REASONS • The relative power of the various economic agglomerating and spreading forces are not scale-neutral but heterogeneous. • Different economic forces (theories) are active at different spatial scales => Analyses at different scales provide different insights: the MAUP is only a “problem” when it is not recognized (ESPON, 2006). STATISTICAL REASONS – The two sides of MAUP: • Scale effect (ecological fallacy): for a given space, results can depend on the number of units representing it. • Zoning (or “aggregation” ) effect: for a given scale, results can depend on how the study area is divided up.

  4. Motivation: empirical questions • Is a general form of the wage equation robust to different aggregation levels of European data and different non spatial econometric specifications?: • Long-term relationships: cross-section (variables in levels) • Short-term relationships: Panel data with fixed effects (growth rates) • Is the MAUP affecting the estimation of these relationships with spatial econometric models? • SEM and SAR • How does the sample selection affect the results? • Broad sample: 25 countries (260 NUTS 2 regions) • Restricted sample: 15 countries (206 NUTS 2 regions) Software – R packages: "spdep" (Bivand 2012); "plm" (Croissant and Millo, 2008) and "splm" (Millo and Piras, 2012). “Amelia II” (Honaker et al., 2011).

  5. New Economic Geography: The wage equation • The NEG's wage equation explains the equilibrium industrial nominal wages as a function of the sum of demands from other regions, weighted by prices and transport costs: NEG’s Market Potential (). • To go from the NEG’s Market Potential to the Harris’s (1954) initial formulation (), some simplifications are needed. • But two works using European data find similar results with than with a more complex measure derived from gravity equations: Breinlich(2006) and Head and Mayer (2006). • Both Breinlich (2006) and Ahlfeldt and Feddersen (2008) find similar results proxying trade costs with travel times or with geographical distances. • Many empirical applications use real per capita income instead of nominal wages. • We insert a NEG-type of equation with in a Makiw-Romer-Weil extension of Solow’s model (Mankiw et al., 1992).

  6. Specifications: variables and notation • Long-run (cross-section): pooling with time-varying intercept: • Short-run (growth): Panel with fixed individual and time effects: Time-demeaning estimation of fixed effects: • Spatial Error Model (SEM: ; • Spatial Autoregressive (Lag) Model (SAR): • Nomenclature of territorial units for statistics (NUTS): 0, 1, 2 • – Baseline weight matrix for each NUTS level and sample: symmetrized row-standardized binary matrix of the 5 nearest neighbors, pooled for 14 years when necessary.

  7. Specifications: variables and notation • lGVAp– log of per capita Gross Value Added (GVA). Units: 2000 euro / inhabitant. Source: Cambridge Econometrics. Dependent variable. • lKSp– log of per capita Capital Stock. Units: 2000 euro / inhabitant. Source: Cambridge Econometrics. Explanatory variable. • lhrstc_pop– log of the share of population with third level studies in Science and Technology (S&T) and working in a S&T occupation: core human resources in S&T. Source: Eurostat. Explanatory variable. Imputed missing data with hrstc_popit = ß0 + ß1t + ß2t2 • lMP2GVA – log of GVA Market Potential () defined as Harris (1954). Units: Units: millions of 2000 euro. Source: Own elaboration with GVA Cambridge Econometrics data. Explanatory variable. It is a measure of the region accessibility to both internal () and external () markets (), depending on distances () as a proxy of trade costs: • Here, the market size is measured as GVA (in real terms) and internal distances are based on the radius () of a circular region, corrected as in Keeble et al. (1982): 0.188

  8. Spatial distribution of the variables

  9. Spatial distribution of the variables

  10. Spatial distribution of the variables

  11. Pooled estimations 1996-2008 with time dummies: broad sample OLS • Market Potential (lagged one year) is meaningful but its presence does not alter dramatically the results. • Residuals are spatially autocorrelated for NUTS 1 and 2: a positive spatial autocorrelation tends to increase with the disaggregation level

  12. Lagrange Multiplier tests for spatial dependence In the pooled OLS estimations with time dummies and lagged Market Potential • And the winner is… the SEM model! => OLS estimates are not efficient Particular cases: • Contradiction Moran’s I-LM tests for NUTS 0 in the restricted sample • Both robuts tests are highly significant in some cases: thought the decision rule choses the SEM, caution with misspecification. Broad sample Restricted sample

  13. SEM: one year cross-section (1) and pooling with time effects (2) ML estimation Broad sample Restricted sample Broad sample

  14. SAR: one year cross-section (1) and pooling with time effects (2) ML estimation Broad sample Restricted sample Broad sample

  15. Pooled (1) and fixed effects (2) estimations with time effects OLS Broad sample Restricted sample Broad sample

  16. SEM: Pooled (1) and fixed effects (2) estimations with time effect ML Broad sample Restricted sample Broad sample

  17. SAR: Pooled (1) and fixed effects (2) estimations with time effect ML Broad sample Restricted sample Broad sample

  18. Preliminary conclusions • With the exception of the fixed effects estimation in the restricted sample, , residuals are autocorrelated and their autocorrelation and estimated spatial parameters increase with disaggregation. • The general wage equation is very robust to the short-and-long-run specifications, to this three NUTS levels and to the broad and the restricted sample. • Many test of the wage equation in the literature do not distinguish the short-and-long-run specifications. But the estimation with individual effects give a whole different view (Acemoglu et al., 2008).

  19. Preliminary conclusions • Results from NUTS 1 and 2: the estimated elasticities are very robust for the non spatial and the SEM and SAR models (FE non checked) => No problem with MAUP (but we have not studied NUTS 3!). • Results from NUTS 0 are more sensitive to sample selection. Maybe higher heterogeneity than when pooling regions from different countries at NUTS 1-3. • Some of the detected patterns in the change of estimates by NUT level are economically meaningful: at least from NUTS 1 to NUTS 2 the elasticity to Market Potential always increases => More severe problems if this variable is omitted at higher levels of disaggregation.

  20. Current research and possible extensions • Sensitivity analysis (at least in the pooled model): • Kelejian and Prucha’s(1998) instrumentation of the spatially lagged dependent variable in the SAR model • spatial heteroskedasticity and autocorrelation consistent (HAC) estimators • A graphical W instead of a matrix of the 5 nearest neighbours - but LeSageand Pace (2012)!- • Now annual data: Short-run models for several years panels • GWR ( “conditional parametric approach”) – local variation of estimates: At each NUTS level, what countries are de drivers of the fixed estimates? • The zoning effect internal to each MAUP – The areas by country at each NUTS level: Does size matters? • Weighted regression • Recalculate Market Potential: with distances among centroids, bigger regions are further apart from their markets

  21. Questions • Results change more using NUTS 0: thoughts welcomed. • Similar elasticities in the not spatial and in the SEM and SAR models in spite of being a simple equation. Thoughts: Is this because the SAR was not recommended by the LM tests?. So much effort with spatial models for this?.... • Endogeneity – Proper instruments for Market Potential. • Endogeneity – In the SAR model both market potential and the endogenous spatial lag of the dependent variable are endogenous: How to deal with this? • Which would be the best W matrix to compare models using data with different aggregation? • Results of the pooled estimation different when using “spdep” or “splm” R packages: why?

  22. COMMENTS WELCOMEDTHANK YOU Fernando Brunaf.bruna@udc.es) University of A Coruña, Spain

More Related