Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Heterogeneous effects of subsidies on farms’ performance: a spatial quantile regression analysis
Marusca De Castris

Last modified: 2018-05-18

Abstract


Italian agricultural sector is characterized by a wide heterogeneity which can affect the effectiveness of rural policies and, by consequence, economic performances. Indeed, wide differences arise both at farm (i.e. sector, dimension, etc.) and regional levels. In particular, Giannakis and Bruggeman (2015) show how agricultural policies can provide enlarge regional disparities between advanced and lagged regions. In this paper, we analyse the differential impact of the policies by considering Italian lagged regions. The introduction of a Spatial Autoregressive Quantile model allows to take into account both spatial and farm-specific characteristic. Evidences are found in favour of significant and positive spatial spillovers of the policies, especially for the less performing farms.

References


  1. Baltagi, B. H., Fingleton, B., and Pirotte, A. (2014). Spatial lag models with nested random effects: An instrumental variable procedure with an application to english house prices. Journal of Urban Economics, 80(Supplement C):76 – 86.
  2. Chernozhukov, V. and Hansen, C. (2006). Instrumental quantile regression inference for structural and treatment effect models. Journal of Econometrics, 132(2):491 – 525.
  3. Giannakis, E. and Bruggeman, A. (2015). The highly variable economic performance of European agriculture. Land Use Policy, 45(Supplement C):26 – 35.
  4. Kelejian, H. H. and Prucha, I. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. The Journal of Real Estate Finance and Economics, 17(1):99–121.
  5. Kim, T.-H. and Muller, C. (2004). Two-stage quantile regression when the first stage is based on quantile regression. Econometrics Journal, 7(1):218–231.
  6. Koenker, R. and Hallock, K. (2001). Quantile Regression. Journal of Economic Perspectives, 15(4):143–156.
  7. LeSage, J. and Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.
  8. McMillen, D. and Shimizu, C. (2017). Decompositions of spatially varying quantile distribution estimates: The rise and fall of Tokyo house prices. Urbana, 51:61801.

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