Hostname: page-component-89b8bd64d-9prln Total loading time: 0 Render date: 2026-05-10T03:15:45.007Z Has data issue: false hasContentIssue false
  • English
  • Français

Entry, Exit, and Structural Change in Pennsylvania's Dairy Sector

Published online by Cambridge University Press:  15 September 2016

Jeffrey R. Stokes
Jeffrey R. Stokes*
Affiliation:
Department of Agricultural Economics and Rural Sociology at the Pennsylvania State University in University Park, Pennsylvania
Get access Rights & Permissions [Opens in a new window]

Abstract

Data on the number of Pennsylvania dairy farms by size category are analyzed in a Markov chain setting to determine factors affecting entry, exit, expansion, and contraction within the sector. Milk prices, milk price volatility, land prices, policy, and cow productivity all impact structural change in Pennsylvania's dairy sector. Stochastic simulation analysis suggests that the number of dairy farms in Pennsylvania will likely fall by only 2.0 percent to 2.5 percent annually over the next 20 years, indicating that dairy farming in Pennsylvania is likely to be a significant enterprise for the state in the foreseeable future.

Keywords

dairymaximum entropyfarm sizeMarkov chainsimulation

Information

Type
Contributed Papers
Information
Agricultural and Resource Economics Review , Volume 35 , Issue 2 , October 2006 , pp. 357 - 373
Copyright
Copyright © 2006 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Arnold, L. 1974. Stochastic Differential Equations: Theory and Applications. New York: John Wiley and Sons.Google Scholar
Chan, M. 1981. “A Markovian Approach to the Study of the Canadian Cattle Industry.” The Review of Economics and Statistics 63(1): 107–116.CrossRefGoogle Scholar
Chavas, J., and Magrand, G. 1988. “A Dynamic Analysis of the Size Distribution of Firms: The Case of the U. S. Dairy Industry.” Agribusiness 4(4): 315–329.3.0.CO;2-2>CrossRefGoogle Scholar
Disney, W., Duffy, P., and Hardy, W. 1988. “A Markov Chain Analysis of Pork Farm Size Distributions in the South.” Southern Journal of Agricultural Economics 20(2): 57–64.Google Scholar
Ethridge, D., Roy, S., and Myers, D. 1985. “A Markov Chain Analysis of Structural Changes in the Texas High Plains Cotton Ginning Industry.” Southern Journal of Agricultural Economics 17(2): 11–20.Google Scholar
Federal Reserve. 2006. Statistical Release H. 15 Selected Interest Rates (1980–2003). Washington, D. C.: Federal Reserve. Available at www.federalreserve.gov (accessed June 6, 2006).Google Scholar
Garcia, P., Offutt, S., and Sonka, S. 1987. “Size Distribution and Growth in a Sample of Illinois Cash Grain Farms.” American Journal of Agricultural Economics 69(2): 471–476.CrossRefGoogle Scholar
Gillespie, J., and Fulton, J. 2001. “A Markov Chain Analysis of the Size of Hog Production Firms in the United States.” Agribusiness 17(4): 557–570.CrossRefGoogle Scholar
Golan, A., Judge, G., and Miller, D. 1996. Maximum Entropy Econometrics: Robust Estimation with Limited Data. New York: John Wiley and Sons.Google Scholar
Hallberg, M. 1969. “Projecting the Size Distribution of Agricultural Firms: Application of a Markov Process with Nonstationary Transition Probabilities.” American Journal of Agricultural Economics 51(2): 289–302.CrossRefGoogle Scholar
Kim, C., Lin, W., and Leath, M. 1991. “The Changing Structure of the U. S. Flour Milling Industry.” The Journal of Agricultural Economics Research 43(3): 18–25.Google Scholar
Lee, T., and Judge, G. 1996. “Entropy and Cross-Entropy Procedures for Estimating Transition Probabilities from Aggregate Data.” In Berry, D., Chaloner, K., and Geweke, J., eds., Bayesian Analysis in Statistics and Econometrics. New York: John Wiley and Sons.Google Scholar
Lee, T., Judge, G., and Takayama, T. 1965. “On Estimating the Transition Probabilities of a Markov Process.” Journal of Farm Economics 47(3): 742–762.CrossRefGoogle Scholar
Lee, T., Judge, G., and Zellner, A. 1973. Estimating Transition Probabilities of the Markov Chain Model from Aggregate Share Data. Amsterdam: North-Holland.Google Scholar
Malliaris, A. and Brock, W. 1991. Stochastic Methods in Economics and Finance. New York: North-Holland.Google Scholar
Padberg, D. 1962. “The Use of Markov Processes in Measuring Changes in Market Structure.” Journal of Farm Economics 44(1): 189–199.CrossRefGoogle Scholar
Rahelizatovo, N., and Gillespie, J. 1999. “Dairy Farm Size, Entry, and Exit in a Declining Production Region.” Journal of Agricultural and Applied Economics 31(2): 333–347.CrossRefGoogle Scholar
Tauer, L. 2004. “When to Get In and Out of Dairy Farming: A Real Option Analysis.” Working Paper No. 2004-12, Department of Applied Economics and Management, Cornell University, Ithaca, NY.Google Scholar
U. S. Department of Agriculture. Various issues. “Milk Production.” USDA, Washington, D. C. (February issues, 1980–2004).Google Scholar
Wolf, C., and Sumner, D. 2001. “Are Farm Size Distributions Bimodal? Evidence from Kernel Density Estimates of Dairy Farm Size Distributions.” American Journal of Agricultural Economics 83(1): 77–88.CrossRefGoogle Scholar
Zellner, A. 1962. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests of Aggregation Bias.” Journal of the American Statistical Association 57(298): 348–368.CrossRefGoogle Scholar
Zepeda, L. 1995a. “Asymmetry and Nonstationarity in the Farm Size Distribution of Wisconsin Milk Producers: An Aggregate Analysis.” American Journal of Agricultural Economics 77(4): 837–852.CrossRefGoogle Scholar
Zepeda, L. 1995b. “Technical Change and the Structure of Production: A Non-Stationary Markov Analysis.” European Review of Agricultural Economics 22(1): 41–60.CrossRefGoogle Scholar