Iranian Agricultural Economics Society (IAES)

Document Type : Research Article

Authors

Razi University, Kermanshah

Abstract

Introduction: Today uncertainty has a special place in the economic literature, shock and uncertainty can affect the market structure and the performance of policies and macroeconomic by changing behavior of actors at micro level. A demand shock is a sudden surprise event that temporarily increases or decreases demand for goods or services. A positive demand shock increases demand, while a negative demand shock decreases demand. Both a positive demand shock and negative demand shock have different effect on the prices of goods and services. Terrorist event, technological advances and government stimulus programs are examples of events that can cause demand shocks. This paper intends to investigate the structure and behavior of the meat industry to the market unexpected changes in demand (demand shocks). Because having a reliable policy at the micro level is an important precondition for better performance at the macro levels. This industry was selected for some reasons. First, cyclical fluctuations promised substantial demand-side variation. Second, firms in this industry most likely learned about marketplace behavior primarily through contacts with their customers and suppliers. Because this information was limited, the meat producers may well have been unable to distinguish between demand declines and rival cheating if they were colluding.
Materials and Methods: This article makes inferences about oligopoly behavior by observing industry’s respond to unexpected changes in the market price. In particular, the econometric methodology identifies firms that act more competitively following unexpected declines in demand. This behavior is interpreted as a pricing strategy of Green and Porter “price-trigger” oligopoly model. In the "trigger price" literature, this behavior is interpreted as a punishment mechanism practiced by members of an industry who cannot distinguish between negative-demand shocks and rival cheating. In the Green and Porter model, the dynamic oligopoly equilibrium fluctuates between static cartel behavior and static non-cooperative behavior. The timing of these shifts is uncorrelated with movement in observable exogenous demand-shift or supply-shift variables. In other word Green and Porter described how firms, when faced with unobservable demand, use an unobservable external price to detect whether rivals are adhering to an implicit collusive outcome. If price falls below some level, the trigger price, firms infer that a rival “cheated” by expanding output. They then retaliate by also expanding output to reduce the gains from cheating for the rival firm. Rotenberg and Saloner (1986) also focus on the gains to cheating, but notes that these gains are greatest during periods when demand is high, or booms. So, this paper intends to investigate the structure and behavior of the meat industry to the market unexpected demand changes. Because having a reliable policy at the micro level is an important precondition for better performance at the macro level. The model hypothesizes that packer oligopoly markup decrease following unexpected declines in demand for beef. The empirical task is to test this hypothesis
The demand shocks that cause margin to decline will make firms act more competitively only if these shocks lead firms to suspect an increase in output by the competing firms.
Empirical analysis is presented based on a system of simultaneous equations model (functions of supply and demand) ordinary least squares estimation method (Because of over identification of both equations), in which the vector of residuals from function of demand, consistently estimates the vector of demand shock. The variable DUM takes a value of 1 when RES is a large negative number in absolute value and 0 for small negative values and for positive values of RES. Thus, DUM is triggered in periods during which the competing firms feel the greatest threat of increase in output by competing firms, hence the greatest incentive to increase their own output. The hypothesis of interest can be tested by examining whether this coefficient on DUM is negative.
Results and Discussion: The purpose of this paper is evaluating behavioral and structural changes under unexpected demand fluctuations by using the quarterly data of 1996-2016. Quarterly observations provide a plausible approximation to the lag in this industry between firms observing unexpected price declines and their output response. The results showed that the demand shock with a small coefficient of 0.1 has a negative and significant effect on the logarithmic beef price variable, this result confirms trigger price policy and shows that the structure of the Iranian meat market has the potential for changing to become more competitive, following the negative shock of demand, so that the results will enable policy makers, by designing and presenting a suitable pattern, to make the way easy for the industry toward development goals and implementing competitive and anti-monopoly policies.
Conclusions: The econometric methodology is showed that firms in Iranian meat industry act more competitively following unexpected declines in demand

Keywords

1- Ardelean A., Leon-Ledesma M., and Puzzello L. 2017. Volatility of Industrial Output, Demand Fluctuations, and International Trade. School of Economics Discussion Papers, 1709.
2- Bahmani M., and Youssefi Q. 2010. Industry sector demand shocks on output in manufacturing industries of Iran .Journal of development and investment, 3(6):111-138. (In Persian)
3- Barros G., Polador H., and Bacchi M. 2009. Supply and Demand Shocks and the Growth of the Brazilian Agriculture. RBC Rio de Janeiro, 63(1):35-50.
4- Brorsen B.W. 1985. Marketing Margins and price uncertainty: The case of the U.S. Wheat market. American Journal of Agricultural Economics, 67(3):521-528.
5- Breuch T.S., and Pagan A.R. 1980. The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47:239-253.
6- Baker J. 1986. Identifying Cartel Policing under Uncertainty: The U.S. Steel Industry, 1933-1939. 1989. J. Law and Econ, 32:47-76.
7- Cover J. P. 1992. Asymmetric effects of positive and negative money-supply shocks. The Quarterly Journal of Economics, 107(4): 1261-1282.
8- Daneshjafari D. 1991. Economic times are determined by using vector auto regression approach .Thesis of faculty of Economics, University of Allameh Tabatabai. (In Persian)
9- Green E.J., and Porter R.H. 1984. Noncooperative Collusion under Imperfect Price Information Econometrica. Econometric Society, 52(1):87-100.
10- Hanazono M., and Yang H. 2007. Collusion, Fluctuating Demand, and Price Rigidity. International Economic Review, 48(2):483-515.
11- Hausman J.A. 1976. Testing the error components model with non-normal disturbances. Review of Economic Studies, 52:681-690.
12- Holtiwanger J., and Harrington JR. 1991. The Impact of Cyclical Demand Movement on Collusive Brhavior. The RAND Journal of Economics, 22(1):86-106.
13- Kandil M. 2006. Asymmetric Effects of Aggregate demand shocks Across U.S. industries: Evidence & implications. Eastern Economic Journal, 32(2):259-284.
14- Knittel C. R., and Lepore J. J. 2010. Tacit collusion in the presence of cyclical demand and endogenous capacity levels. International Journal of Industrial Organization, 28(2):131-144.
15- Oleksandr S., and NaokiWakamori. 2009. A simple way to identify the degree of collusion under proportional reduction. Governance and the Efficiency of Economic Systems (GESY), 497:1-40.
16- Perren R. 1979. Oligopoly and competition: price fixing and market sharing among timber firms in northern Scotland. Business History, 213(25):1890–1939.
17- Porter R. 1983. A study of cartel stability: the Joint Executive Committee 1880–1886. Bell Journal of Economics, 14:301–25.
18- Rey P. 2002. Collective Dominance in the Telecommunications Industry. University of Toulouse, mimeo.
19- Rotemberg J., and Saloner G. 1986. A Supergame-Theoretic Model of Price Wars during Booms. American Economic Review, 76:390–407.
20- Samadi S., and Jalaei A. 2004 .Analysis of business cycles in the Irans economy. Economic researches, 139-15 (In Persian)
21- Sahikitash M., Shaydaei Z., and Mohammadzadeh A. 2016. Investigating market power and risk caused by price uncertainty in Iranian economy. Journal of Agricultural Economics and Development, 30(2):107-116. (In persion)
22- Yousefi M.Q., Muhammadi T., and Bahmani M. 2011. Effects of industry sector demand shocks on the price of the product Iranian manufacturing industris. Quarterly of Quantitative Economics, 8(1):99-122. (In Persian)
23- Weliwita A., and Azzam A. 1996. Identifying implicit collusion under declining output demand. Journal of Agricultural and Resource Economics, 235-246.‏
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