IDEAS home Printed from https://ideas.repec.org/p/ags/umaesp/13754.html
   My bibliography  Save this paper

Estimating Threshold Effects of Generic Fluid Milk and Cheese Advertising

Author

Listed:
  • Adachi, Kenji
  • Liu, Donald J.

Abstract

The purpose of this paper is to investigate the threshold effect of the U.S. generic fluid milk and cheese advertising programs. A threshold delineates the level of advertising intensity that has to be met to generate a specific level of sales effect. Given that promotional organizations face budget constraints, it is of particular interest to ascertain if there exists a minimum threshold that an advertising campaign has to overcome to yield a non-trivial sales effect. To the best of our knowledge, there is no study focusing on the threshold effects of generic advertising of agricultural products. The estimation results confirm that, for both fluid milk and cheese advertisings, there exist three thresholds which partition the quarterly observations between 1975 and 2004 into four possible regimes depending on the level of advertising intensity in each period. The generic fluid milk advertising goodwill coefficient is found to be relatively small for the regime with the lowest advertising intensity, building up to a higher level as the regime progresses, but eventually drops to lower levels as the intensity continues to grow, reflecting the eventual arrival of the diminishing returns of advertising. The pattern of initial build up, however, is not detected in the cheese equation in which the estimated goodwill parameter starts at a rather large magnitude in the first regime, but declines monotonically and drastically in the second and third regimes, only to become statistically not different from zero in the fourth regime. To evaluate the performance of the National Dairy Board (NDB) programs over time, the estimated demand equations are used to simulate the effect on sales of a change in the fluid milk and cheese advertising expenditure paths, focusing on the first ten years and the second ten years of the NDB's operation since 1984. While an increase in advertising expenditures has the effect of increasing sales (holding regime configuration constant), it is found that this scale effect of additional advertising may be outweighed by the negative effect of a downward shift in the goodwill coefficient arising from regime change. In other words, sales can be made lower as the result of increased advertising, a feature not supported by the conventional model of no threshold. Indeed, in the cheese case advertising elasticity is found to be negative when evaluated at some levels larger than the historical pattern. Compared to the result pertaining to the first ten years, the benefit-cost ratio of the fluid milk program suggests that the NDB has moved its fluid milk operation scale closer to optimal during the second ten years. The optimal fluid milk advertising expenditure level for the second ten years is found to be between 105 and 110 percent of the historical level. This paper will contribute to the discussion among researchers, program managers, and industry participants on the importance of entertaining the threshold effect of advertising when examining program effectiveness and the optimal allocation of program dollars. Researchers need to be aware of the potential pitfalls of their conventional econometric estimates (and, hence, the limitations of their policy suggestions) when the true model is of threshold type. Program managers have argued that advertising effectiveness may vary, depending on the intensity of their campaigns and have long sought this information in their spending allocation decisions. The insights provided in this paper will enhance our understanding of the threshold effect of advertising.

Suggested Citation

  • Adachi, Kenji & Liu, Donald J., 2006. "Estimating Threshold Effects of Generic Fluid Milk and Cheese Advertising," Staff Papers 13754, University of Minnesota, Department of Applied Economics.
  • Handle: RePEc:ags:umaesp:13754
    DOI: 10.22004/ag.econ.13754
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/13754/files/p06-08rev.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.13754?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    2. Chanjin Chung & Harry M. Kaiser, 2000. "Determinants of temporal variations in generic advertising effectiveness," Agribusiness, John Wiley & Sons, Ltd., vol. 16(2), pages 197-214.
    3. Kaiser, Harry M., 2000. "Impact Of Generic Fluid Milk And Cheese Advertising On Dairy Markets 1984-99," Working Papers 292855, Cornell University, Department of Applied Economics and Management.
    4. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    5. Liu, Donald J. & Kaiser, Harry M. & Mount, Timothy D. & Forker, Olan D., 1991. "Modeling The U.S. Dairy Sector With Government Intervention," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 16(2), pages 1-14, December.
    6. Kaiser, Harry M., 2000. "Impact of Generic Fluid Milk and Cheese Advertising on Dairy Markets," Research Bulletins 122670, Cornell University, Department of Applied Economics and Management.
    7. Todd M. Schmit & Harry M. Kaiser, 2004. "Decomposing the Variation in Generic Advertising Response over Time," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 139-153.
    8. Demetrios Vakratsas & Fred M. Feinberg & Frank M. Bass & Gurumurthy Kalyanaram, 2004. "The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds," Marketing Science, INFORMS, vol. 23(1), pages 109-119, April.
    9. Henry W. Kinnucan & Meenakshi Venkateswaran, 1994. "Generic Advertising; and the Structural Heterogeneity Hypothesis," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 42(3), pages 381-396, November.
    10. Jean-Pierre Dubé & Günter Hitsch & Puneet Manchanda, 2005. "An Empirical Model of Advertising Dynamics," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 107-144, June.
    11. Philip R. Vande Kamp & Harry M. Kaiser, 1999. "Irreversibility in Advertising-Demand Response Functions: An Application to Milk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 385-396.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuqing Zheng & Harry M. Kaiser, 2009. "Evaluating the effectiveness of generic advertising versus nonadvertising marketing activities on New York State milk markets," Agribusiness, John Wiley & Sons, Ltd., vol. 25(3), pages 351-368.

    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. Schmit, Todd M. & Kaiser, Harry M., 2002. "Modeling The Effects Of Generic Advertising On The Demand For Fluid Milk And Cheese: A Time-Varying Parameter Application," 2002 Annual meeting, July 28-31, Long Beach, CA 19754, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Caner,M. & Hansen,B.E., 1998. "Threshold autoregression with a near unit root," Working papers 27, Wisconsin Madison - Social Systems.
    3. Candelon, Bertrand & Lieb, Lenard, 2013. "Fiscal policy in good and bad times," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2679-2694.
    4. Albert J.F. Yang & William N. Trumbull & Chin Wei Yang & Bwo‐Nung Huang, 2011. "On The Relationship Between Military Expenditure, Threat, And Economic Growth: A Nonlinear Approach," Defence and Peace Economics, Taylor & Francis Journals, vol. 22(4), pages 449-457, April.
    5. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2002. "A simple test for PPP among traded goods," Econometric Institute Research Papers EI 2002-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Capps, Oral, Jr. & Williams, Gary W. & Dang, Trang, 2010. "Effects of Lamb Promotion on Lamb Demand and Imports," Reports 90492, Texas A&M University, Agribusiness, Food, and Consumer Economics Research Center.
    7. Houda Haffoudhi & Rihab Bellakhal, 2020. "Threshold Effect of Globalization on Democracy: the Role of Demography," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(4), pages 1690-1707, December.
    8. Po, Wan-Chen & Huang, Bwo-Nung, 2008. "Tourism development and economic growth–a nonlinear approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5535-5542.
    9. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    10. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    11. Dang, Viet Anh & Kim, Minjoo & Shin, Yongcheol, 2014. "Asymmetric adjustment toward optimal capital structure: Evidence from a crisis," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 226-242.
    12. Christoph Rothe & Philipp Sibbertsen, 2006. "Phillips-Perron-type unit root tests in the nonlinear ESTAR framework," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(3), pages 439-456, September.
    13. Lanouar Charfeddine & Dominique Guegan, 2008. "Is it possible to discriminate between different switching regressions models? An empirical investigation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368358, HAL.
    14. Chung‐Hua Shen & Hsing‐Hua Hsu, 2022. "The determinants of Asian banking crises—Application of the panel threshold logit model," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 248-277, March.
    15. Lütkepohl, Helmut & Milunovich, George & Yang, Minxian, 2020. "Inference in partially identified heteroskedastic simultaneous equations models," Journal of Econometrics, Elsevier, vol. 218(2), pages 317-345.
    16. Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
    17. Munehisa Kasuya, 2003. "Regime-Switching Approach to Monetary Policy Effects: Empirical Studies using a Smooth Transition Vector Autoregressive Model," Bank of Japan Working Paper Series Research and Statistics D, Bank of Japan.
    18. Che, Chou Ming, 2013. "Panel threshold analysis of Taiwan's outbound visitors," Economic Modelling, Elsevier, vol. 33(C), pages 787-793.
    19. Sergio Da Silva & Mauricio Nunes, 2008. "Explosive and periodically collapsing bubbles in emerging stockmarkets," Economics Bulletin, AccessEcon, vol. 3(46), pages 1-18.
    20. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.

    More about this item

    Keywords

    Livestock Production/Industries; Marketing;

    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:umaesp:13754. 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/daumnus.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.