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Modeling Productivity In Supermarket Operations: Incorporating The Impacts Of Store Characteristics And Information Technologies

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  • King, Robert P.
  • Park, Timothy A.

Abstract

Data from the 2002 Supermarket Panel are used to estimate a supermarket production function with weekly gross margin as the output measure and store selling area and total labor hours as variable inputs. The model also includes productivity shifters describing format and service offerings, store ownership structure, unionization, and adoption of new information technologies and related business practices. The null hypothesis of constant returns to scale cannot be rejected. Increases in ownership-group size, warehouse and supercenter formats, unionization of the workforce, and adoption of vendor-managed inventory and a frequent-shopper program are all associated with significantly higher productivity.

Suggested Citation

  • 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(02), July.
  • Handle: RePEc:ags:jlofdr:27235
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    File URL: http://purl.umn.edu/27235
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    References listed on IDEAS

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    1. Harvey, A C, 1976. "Estimating Regression Models with Multiplicative Heteroscedasticity," Econometrica, Econometric Society, vol. 44(3), pages 461-465, May.
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    4. Martin Neil Baily & Eric Zitzewitz, 2001. "Service Sector Productivity Comparisons: Lessons for Measurement," NBER Chapters,in: New Developments in Productivity Analysis, pages 419-464 National Bureau of Economic Research, Inc.
    5. Jean Kinsey, 2000. "A Faster, Leaner, Supply Chain: New Uses of Information Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(5), pages 1123-1129.
    6. 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.
    7. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, Oxford University Press, vol. 117(1), pages 339-376.
    8. Robert P. King & Paul F. Phumpiu, 1996. "Reengineering the Food Supply Chain: The ECR Initiative in the Grocery Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(5), pages 1181-1186.
    9. Farber, Henry S & Saks, Daniel H, 1980. "Why Workers Want Unions: The Role of Relative Wages and Job Characteristics," Journal of Political Economy, University of Chicago Press, vol. 88(2), pages 349-369, April.
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    Cited by:

    1. 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(03), pages 373-389, December.
    2. Jaenicke, Edward C. & Chikasada, Mitsuko, 2006. "Separate Decision-Making for Supermarket Leaders and Followers: The Case of Whether or Not to Offer Irradiated Ground Beef," Journal of Food Distribution Research, Food Distribution Research Society, vol. 37(03), November.
    3. Hinson, Roger A. & Sinoha, Ramona & Reaves, Dixie Watts, 2006. "Industry Concentration Impacts on Business Strategies Used by Small Produce Wholesalers," 2006 Annual Meeting, February 5-8, 2006, Orlando, Florida 35291, Southern Agricultural Economics Association.

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    Keywords

    Productivity Analysis;

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