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Clustering of Auto Supplier Plants in the United States

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  • Klier, Thomas
  • McMillen, Daniel P

Abstract

A linearized logit version of Pinkse and Slade's spatial GMM estimator reduces estimation to two steps—standard logit followed by two-stage least squares. Linearization produces a model that can be estimated using large datasets. Monte Carlo experiments suggest that the linearized model accurately identifies the presence of spatial effects and is capable of producing accurate estimates of marginal effects. In an application to the location of supplier plants in the U.S. auto industry, the results imply no additional clustering of new plants beyond the level of clustering of existing plant locations.

Suggested Citation

  • Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
  • Handle: RePEc:bes:jnlbes:v:26:y:2008:p:460-471
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    1. Songsermsawas, Tisorn & Baylis, Kathy & Chhatre, Ashwini & Michelson, Hope & Prasanna, Satya, 2015. "Friends or traders? Do social networks explain the use of market mechanisms by farmers in India," 2015 Conference, August 9-14, 2015, Milan, Italy 211206, International Association of Agricultural Economists.
    2. Cho, Sung Ju & McCarl, Bruce A. & Wu, Ximing, 2015. "Climate Change Adaptation via U.S. Land Use Transitions: A Spatial Econometric Analysis," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196684, Southern Agricultural Economics Association.
    3. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
    4. Schuetz, Jenny, 2015. "Why are Walmart and Target Next-Door neighbors?," Regional Science and Urban Economics, Elsevier, vol. 54(C), pages 38-48.
    5. Laurent Davezies & Xavier D'Haultfoeuille & Denis Fougère, 2009. "Identification of peer effects using group size variation," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 397-413, November.
    6. Buczkowska, Sabina & de Lapparent, Matthieu, 2014. "Location choices of newly created establishments: Spatial patterns at the aggregate level," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 68-81.
    7. Sabina Buczkowska & Nicolas Coulombel & Matthieu de Lapparent, 2015. "Euclidean distance versus travel time in business location: A probabilistic mixture of hurdle-Poisson models," ERSA conference papers ersa15p1060, European Regional Science Association.
    8. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    9. Luís Silveira Santos & Isabel Proença, 2017. "The Inversion of the Spatial Lag Operator in Binary Choice Models: Fast Computation and a Closed Formula Approximation," Working Papers REM 2017/11, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    10. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
    11. Haiqing Xu, 2010. "Social Interactions: A Game Theoretic Approach," Department of Economics Working Papers 130914, The University of Texas at Austin, Department of Economics.
    12. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Discussion Paper 2013-061, Tilburg University, Center for Economic Research.
    13. Marzano, Riccardo, 2015. "One more ride on the merry-go-round! Public ownership and delayed competition in local public services," Journal of Comparative Economics, Elsevier, vol. 43(4), pages 981-996.
    14. Li, Man & Wu, JunJie & Deng, Xiangzheng, 2011. "Unintended effects of urbanization in China: Land use spillovers and soil carbon loss," IFPRI discussion papers 1138, International Food Policy Research Institute (IFPRI).
    15. Anping Chen & Marlon Boarnet & Mark Partridge & Raffaella Calabrese & Johan A. Elkink, 2014. "Estimators Of Binary Spatial Autoregressive Models: A Monte Carlo Study," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 664-687, September.
    16. repec:eee:regeco:v:64:y:2017:i:c:p:57-67 is not listed on IDEAS
    17. Badi H. Baltagi & YingDeng & Xiangjun Ma, 2017. "Network Effects on LaborContracts of Internal Migrants in China- A Spatial Autoregressive Model," Center for Policy Research Working Papers 207, Center for Policy Research, Maxwell School, Syracuse University.
    18. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    19. Raffaella Calabrese & Johan A. Elkink & Paolo Giudici, 2014. "Measuring Bank Contagion in Europe Using Binary Spatial Regression Models," DEM Working Papers Series 096, University of Pavia, Department of Economics and Management.
    20. Shelley M. Kimelberg & Elizabeth Williams, 2013. "Evaluating the Importance of Business Location Factors: The Influence of Facility Type," Growth and Change, Wiley Blackwell, vol. 44(1), pages 92-117, March.

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