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Countercyclical Price Movements during Periods of Peak Demand: Evidence from Grocery Retail Price for Avocados

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  • Li, Lan
  • Carman, Hoy F.
  • Sexton, Richard J.

Abstract

Using a unique micro dataset and advanced panel models, this study examines the effects of demand shocks on grocery retail price for avocados, a key Californian fresh produce commodity. Retail prices for avocados exhibited countercyclical movements over seasonal demand shocks for avocados associated with some holidays and events. Demand for avocados is shown to be higher during some holidays/events, e.g., Christmas/New Year, Super Bowl Sunday, and Cinco de Mayo. Super Bowl Sunday and Cinco de Mayo are identified as holidays/events associated with idiosyncratic demand peaks for avocados, but not associated with high aggregate consumer demand. Retail price and margin were significantly lower during some holidays/events associated with high demand for avocados, e.g., Christmas/New Year, Super Bowl Sunday, and Cinco de Mayo. The study also shows that the increase in demand and decrease in retail price during holidays/events with demand peaks for avocados was present for both large and small sizes of avocados, and the size of demand increases and the size of price reductions were not statistically different between large and small size of avocados. Furthermore, shipping price did not change or increased slightly, and hence moved opposite from retail the price during most holidays/events with high demand for avocados. We examine and test the predictions by four classes of theories that put forward to explaining countercyclical price movements over demand peaks. Overall, the evidence provides support for the Lal and Matutes (1994) model that retailers reduce retail prices and/or margins during a commodity's high-demand periods, but does not support alternative explanations for countercyclical price movements, such as Bernheim and Whinston (1990), Warner and Barskey (1995), or Nevo and Hatzitaskos (2006). The findings are consistent with the findings by Chevalier, Kashyap, and Rossi (2003). The study estimates the effects of the CAC's promotion programs on retail sales, retail price, and shipping price at disaggregate level. The analysis demonstrates that the CAC's promotion programs were associated with positive retail sales. In particular, the evidence from the long-panel data suggests that the CAC's promotion programs were successful in raising avocado sales. There is no evidence that retailers charged higher prices during the CAC's promotions.

Suggested Citation

  • Li, Lan & Carman, Hoy F. & Sexton, Richard J., 2008. "Countercyclical Price Movements during Periods of Peak Demand: Evidence from Grocery Retail Price for Avocados," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6251, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea08:6251
    DOI: 10.22004/ag.econ.6251
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    References listed on IDEAS

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    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    4. Lal, Rajiv & Matutes, Carmen, 1994. "Retail Pricing and Advertising Strategies," The Journal of Business, University of Chicago Press, vol. 67(3), pages 345-370, July.
    5. Christopher F Baum & Mark E Schaffer & Steven Stillman, 2002. "IVREG2: Stata module for extended instrumental variables/2SLS and GMM estimation," Statistical Software Components S425401, Boston College Department of Economics, revised 14 Aug 2024.
    6. B. Douglas Bernheim & Michael D. Whinston, 1990. "Multimarket Contact and Collusive Behavior," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 1-26, Spring.
    7. Judith A. Chevalier & Anil K. Kashyap & Peter E. Rossi, 2003. "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," American Economic Review, American Economic Association, vol. 93(1), pages 15-37, March.
    8. Elizabeth J. Warner & Robert B. Barsky, 1995. "The Timing and Magnitude of Retail Store Markdowns: Evidence from Weekends and Holidays," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 321-352.
    9. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group.
    10. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053.
    11. Rotemberg, Julio J & Saloner, Garth, 1986. "A Supergame-Theoretic Model of Price Wars during Booms," American Economic Review, American Economic Association, vol. 76(3), pages 390-407, June.
    12. James M. MacDonald, 2000. "Demand, Information, and Competition: Why Do Food Prices Fall at Seasonal Demand Peaks?," Journal of Industrial Economics, Wiley Blackwell, vol. 48(1), pages 27-45, March.
    13. Daniel Hosken & David Reiffen, 2004. "How Retailers Determine Which Products Should Go on Sale: Evidence From Store-Level Data," Journal of Consumer Policy, Springer, vol. 27(2), pages 141-177, June.
    14. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    15. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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