LIKE any livestock industry, the Canadian beef and dairy cattle industry is characterized by a cyclical pattern in terms of the number of cattle on farm, the number of cattle slaughtered and the number of cattle exported. Traditionally, the analysis and forecast of cattle stocks are based either on econometric models which often include the biological life cycle of cattle or on the pure biological nature of cattle.' In this paper, we investigate the behavior of the cattle industry through the use of a third approach-the Markov chain technique. The Markov chain technique is by itself a very mechanical procedure, but one which can incorporate economic justifications. It can then, in our view, provide a very fruitful view of the industry. Basically, the Markov chain technique allows us to construct flow matrices of beef and dairy cattle according to their biological sequences. For instance, a male calf born during any time period t can, in the same period, be slaughtered, exported, die or remain on farm as a calf. The decision to retain a calf as a steer (for future slaughter) or as a bull (for future reproductive purposes), to export the calf, or to slaughter the calf (for veal), is basically an economic decision. The outcome of such decisions is translated into the elements of the Markovian transition flow matrix. Based upon the biological sequences of the different categories of cattle and the structure of the beef and dairy cattle industry, we can set up transition matrices for Western and Eastern Canada. Table I indicates the structure of such matrices for Western Canada. Cells representing possible flows are identified by numbers while cells representing impossible flows are left blank.2 Transition probability matrices of cattle movement can be constructed by dividing each row element in the matrix by its corresponding row total. These probabilities reflect the probabilities of cattle moving from one category to another. It is also through the use of such probabilities that we will carry out our simulation analysis. This paper is divided into seven sections. In the second section a model of demand, supply and inventory for beef and dairy cattle is presented. Section III discusses some general empirical results based on the transition probability matrices. Section IV discusses the procedures for simulation using the conditional transition probability matrices. Section V presents the results of the historical simulation while section VI presents the results of some sensitivity analysis experiments. The last section is for concluding remarks.

Richard W. Olshavsky, Bruce L. Jaffee, Responsiveness of Consumer Expectations and Intentions to Economic Forecasts: An Experimental Approach, The Review of Economics and Statistics, Vol. 63, No. 2 (May, 1981), pp. 298-302

The purpose of this paper is to examine demographic and socioeconomic determinants of migration for both employed and unemployed. Hypotheses concerning effects of age education and public services are developed and tested using data on interstate migration of U.S. labor force from 1965 to 1970. For individuals at-risk to either primary or repeat migration age and education selectivity of migration were in general confirmed. However for unemployed potential primary migrants education selectivity was not observed. It was also found that the provision of welfare services have little or no impact on migration decision of unemployed....However unemployment was shown to significantly affect role played by educational quality and training accessibility within migration decision. (EXCERPT)

A potentially important parameter that has not yet been convincingly estimated is the critical concentration ratio. This paper attempts to estimate it in three types of market. The estimation of a critical concentration ratio, if one exists, seems of potentially great importance because of its implication for antitrust policy. If a critical concentration ratio were found and if concentration had no effect below that level, it would seem to follow that a horizontal merger in a market where concentration was below the critical level and where the merger could not increase concentration to the critical level could not substantially lessen competition or tend to create monopoly.

James G. Beierlein, James W. Dunn, James C. McConnon, Jr., The Demand for Electricity and Natural Gas in the Northeastern United States, The Review of Economics and Statistics, Vol. 63, No. 3 (Aug., 1981), pp. 403-408

ECONOMISTS and statisticians who construct estimates of total factor productivity or who estimate production functions or systems of consumer demand functions are often forced to aggregate subsets of their data. In order to perform this aggregation, an index number formula is generally used. A price index P(pO, pl, x?, xI) is defined to be a function P of the prices of the N commodities to be aggregated in periods 0 and 1,p?-(pll, . . . , PNO) and pl (pl,.'.. PN'), respectively, and of the corresponding quantities utilized during periods 0 and 1, x? (xi?, . . .,XNO) andX1 _ (xi', . . .,XN1), respectively. A quantity index Q(p0, pl, x?, xl) is defined to be another function Q of the price and quantity vectors for the two periods. Generally, we assume that P and Q satisfy Fisher's (1922) weak factor reversal test: