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Assessing Performance Impacts in Food Retail Distribution Systems: A Stochastic Frontier Model Correcting for Sample Selection

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  • Park, Timothy A.

Abstract

A key organizational decision for retailers is whether to self-distribute or rely on a wholesaler-supplied network and yet little is known about the impact of this strategic choice on store-level productivity. We estimate a stochastic frontier model for food retailers that accounts for selectivity effects linked to the choice of distribution strategy. We find that adoption of data-sharing technologies has a positive impact on store-level gross margins of stores in self-distributing chains. Technical inefficiency among U.S. food retailers leads to a gross margin that is around $5,000 less for a conventional food retailer and about $7,670 less for a supercenter.

Suggested Citation

  • Park, Timothy A., 2014. "Assessing Performance Impacts in Food Retail Distribution Systems: A Stochastic Frontier Model Correcting for Sample Selection," Agricultural and Resource Economics Review, Cambridge University Press, vol. 43(3), pages 373-389, December.
    • Handle: RePEc:cup:agrerw:v:43:y:2014:i:03:p:373-389_00
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    1. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    2. Martin Neil Baily & Robert M. Solow, 2001. "International Productivity Comparisons Built from the Firm Level," Journal of Economic Perspectives, American Economic Association, vol. 15(3), pages 151-172, Summer.
    3. Steven J. Davis & R. Jason Faberman & John Haltiwanger, 2006. "The Flow Approach to Labor Markets: New Data Sources and Micro-Macro Links," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 3-26, Summer.
    4. Dubelaar, Chris & Bhargava, Mukesh & Ferrarin, David, 2002. "Measuring retail productivity: what really matters?," Journal of Business Research, Elsevier, vol. 55(5), pages 417-426, May.
    5. Roger Betancourt & Margaret Malanoski, 1999. "An Estimable Model of Supermarket Behavior: Prices, Distribution Services and Some Effects of Competition," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 26(1), pages 55-73, March.
    6. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    7. Susan Cohen Kulp & Hau L. Lee & Elie Ofek, 2004. "Manufacturer Benefits from Information Integration with Retail Customers," Management Science, INFORMS, vol. 50(4), pages 431-444, April.
    8. Ellickson, Paul B., 2006. "Quality competition in retailing: A structural analysis," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 521-540, May.
    9. King, Robert P., 2003. "Is There a Future for Wholesaler-Supplied Supermarkets?," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 18(4), pages 1-4.
    10. Keh, Hean Tat & Chu, Singfat, 2003. "Retail productivity and scale economies at the firm level: a DEA approach," Omega, Elsevier, vol. 31(2), pages 75-82, April.
    11. Timothy Park & Robert King, 2007. "Evaluating food retailing efficiency: the role of information technology," Journal of Productivity Analysis, Springer, vol. 27(2), pages 101-113, April.
    12. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    13. Walter Y. Oi, 1992. "Productivity in the Distributive Trades: The Shopper and the Economies of Massed Reserves," NBER Chapters, in: Output Measurement in the Service Sectors, pages 161-193, National Bureau of Economic Research, Inc.
    14. Volpe, Richard J., III, 2011. "Evaluating the Performance of U.S. Supermarkets: Pricing Strategies, Competition from Hypermarkets, and Private Labels," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(3), pages 1-16, December.
    15. Kinsey, Jean D. & Jacobson, Elaine M. & Behl, Ajay S. & Seltzer, Jonathan M., 2003. "The 2003 Supermarket Panel Annual Report," Supermarket Panel Reports 14354, University of Minnesota, The Food Industry Center.
    16. David A. Matsa, 2011. "Competition and Product Quality in the Supermarket Industry," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(3), pages 1539-1591.
    17. King, Robert P. & Park, Timothy A., 2004. "Modeling Productivity In Supermarket Operations: Incorporating The Impacts Of Store Characteristics And Information Technologies," Journal of Food Distribution Research, Food Distribution Research Society, vol. 35(2), pages 1-14, July.
    18. Joseph Terza, 2009. "Parametric Nonlinear Regression with Endogenous Switching," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 555-580.
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