IDEAS home Printed from https://ideas.repec.org/p/ags/aaea08/6251.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/6251/files/469829.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.6251?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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 30 Jul 2023.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexis Antoniades & Sofronis Clerides & Mingzhi Xu, 2023. "Micro‐responses to shocks: pricing, promotion, and entry," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(3), pages 584-615, July.
    2. Martinsson, Gustav, 2009. "Finance and R&D Investments - is there a debt overhang effect on R&D investments?," Working Paper Series in Economics and Institutions of Innovation 174, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    3. Holzer, Patrick Sebastian, 2020. "The effect of time-varying factors on promotional activity in the German milk market," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    4. Janine Empen & Stephen F. Hamilton, 2015. "How Do Retailers Price Beer During Periods of Peak Demand? Evidence from Game Weeks of the German Bundesliga," Southern Economic Journal, John Wiley & Sons, vol. 81(3), pages 679-696, January.
    5. Luisa Corrado & Roberta Distante & Majlinda Joxhe, 2019. "Body mass index and social interactions from adolescence to adulthood," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(4), pages 425-445, October.
    6. Laura Birg & Anna Goeddeke, 2016. "Christmas Economics—A Sleigh Ride," Economic Inquiry, Western Economic Association International, vol. 54(4), pages 1980-1984, October.
    7. Harald Oberhofer & Marian Schwinner, 2017. "Do Individual Salaries Depend On the Performance of the Peers? Prototype Heuristic and Wage Bargaining in the NBA," WIFO Working Papers 534, WIFO.
    8. Timothy Richards, 2007. "A nested logit model of strategic promotion," Quantitative Marketing and Economics (QME), Springer, vol. 5(1), pages 63-91, March.
    9. Piper, Alan T., 2014. "An Investigation into Happiness, Dynamics and Adaptation," MPRA Paper 57778, University Library of Munich, Germany.
    10. Maryam Farhadi & Rahmah Ismail & Masood Fooladi, 2012. "Information and Communication Technology Use and Economic Growth," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    11. Emmanuel Owusu-Sekyere & Reneé van Eyden & Francis M Kemegue, 2014. "Remittances and the Dutch Disease in Sub-Saharan Africa: A Dynamic Panel Approach," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 8(3), September.
    12. Majid M. Al-Sadoon & Sergi Jiménez-Martín & Jose M. Labeaga, 2019. "Simple methods for consistent estimation of dynamic panel data sample selection models," Economics Working Papers 1631, Department of Economics and Business, Universitat Pompeu Fabra.
    13. 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.
    14. Guler, Ali Umut, 2021. "Seasonal price effects of mergers," Economics Letters, Elsevier, vol. 209(C).
    15. Piper, Alan T., 2014. "The Benefits, Challenges and Insights of a Dynamic Panel assessment of Life Satisfaction," MPRA Paper 59556, University Library of Munich, Germany.
    16. David Kucera & Marco Principi, 2014. "Democracy and foreign direct investment at the industry level: evidence for US multinationals," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 150(3), pages 595-617, August.
    17. Zhou, Jidong, 2011. "Multiproduct search," MPRA Paper 37139, University Library of Munich, Germany.
    18. Pablo Brañas-Garza & Marisa Bucheli & Teresa Garcia-Muñoz, 2011. "Dynamic panel data: A useful technique in experiments," ThE Papers 10/22, Department of Economic Theory and Economic History of the University of Granada..
    19. Li, Tingting & Wang, Yong & Zhao, Dingtao, 2016. "Environmental Kuznets Curve in China: New evidence from dynamic panel analysis," Energy Policy, Elsevier, vol. 91(C), pages 138-147.
    20. Habiyaremye, Alexis, 2016. "Is Sino-African trade exacerbating resource dependence in Africa?," Structural Change and Economic Dynamics, Elsevier, vol. 37(C), pages 1-12.

    More about this item

    Keywords

    Demand and Price Analysis;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea08:6251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.