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

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Author Info
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.

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Publisher Info
Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6251.

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Date of creation: 2008
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Handle: RePEc:ags:aaea08:6251

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Related research
Keywords: retail price; retail price determination; countercyclical price movement; dynamic panel model; GMM; Demand and Price Analysis;

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  1. MacDonald, James M, 2000. "Demand, Information, and Competition: Why Do Food Prices Fall at Seasonal Demand Peaks?," Journal of Industrial Economics, Blackwell Publishing, vol. 48(1), pages 27-45, March. [Downloadable!] (restricted)
  2. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Blackwell Publishing, vol. 58(2), pages 277-97, April. [Downloadable!] (restricted)
  3. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2002. "Instrumental variables and GMM: Estimation and testing," Boston College Working Papers in Economics 545, Boston College Department of Economics, revised 14 Feb 2003. [Downloadable!]
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  4. 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. [Downloadable!]
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  5. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July. [Downloadable!] (restricted)
  6. Lal, Rajiv & Matutes, Carmen, 1994. "Retail Pricing and Advertising Strategies," Journal of Business, University of Chicago Press, vol. 67(3), pages 345-70, July. [Downloadable!] (restricted)
  7. Warner, Elizabeth J & Barsky, Robert B, 1995. "The Timing and Magnitude of Retail Store Markdowns: Evidence from Weekends and Holidays," The Quarterly Journal of Economics, MIT Press, vol. 110(2), pages 321-52, May. [Downloadable!] (restricted)
  8. David Roodman, 2006. "How to Do xtabond2," North American Stata Users' Group Meetings 2006 8, Stata Users Group. [Downloadable!]
  9. 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. [Downloadable!] (restricted)
  10. 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. [Downloadable!] (restricted)
  11. 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 18 Jul 2008. [Downloadable!]
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