The Economics Of Cattle Supply

The primary goal of this paper is to build a more complete model of cattle supply, which could be used to both explain aggregate cattle dynamics and, ultimately, guide policy decisions. Toward that end, I build a dynamic rational expectations model describing the supply of cattle that improves on existing models by allowing cow-calf operators to make period-by-period investment decisions on both the cow and calf margins, separates the markets for fed and unfed beef, and considers a rich set of exogenous shocks. Several interesting observations have surfaced. First, it is shown that US cattle slaughter and prices do indeed exhibit cycles. The theoretical model provides mixed evidence with regard to slaughter and price cycles, with artificial slaughter data displaying more evidence of cyclical behavior than do artificial prices. To the extent that there are price cycles in the model, it is interesting to note that they are an equilibrium result from fully optimizing agents. As such, there is no opportunity to profit through countercyclical strategies (i.e., building up stocks when prices are near the trough of the cycle and selling when prices are near the peak of the cycle).Second, the model does not exhibit the short-term negative supply response noted in Jarvis (1982), even when the shock is permanent in nature. When ranchers are allowed to make decisions along both the calf and cow margins, the response to changes in relative prices will induce a positive short-run own supply response. The perverse supply response behavior noted in Jarvis instead shows up as a negative cross price response. That is, if the price of fed beef increases, ranchers optimally supply fewer cows and vice versa.And third, as shown by the impulse response functions, the dynamic response to the various cattle time series depends on the nature of the shock driving the response, whether it be a shock to retail demand, productivity, net exports, feed costs, etc. Therefore, when policymakers react to perceived changes in the cattle industry, it is critical that they understand the nature of the shock driving the dynamics.In addition to the observations above, a fully calibrated and simulated version of the model replicates several key features of US cattle time series. The model (i) produces a similar volatility ordering to that found in the US data, (ii) replicates the sign of the contemporaneous correlations between key US cattle time series, and (iii) generates cycles in cattle stocks.Although the model fits the data well in these dimensions, it fails in others. Most importantly, the model (i) understates the volatility of prices, (ii) understates the contemporaneous correlation between different stock measures, (iii) understates the length of the cycle in stocks, and (iv) only provides mixed evidence of slaughter and price cycles. In my estimation, it is these last two shortcomings that are the most pressing research items. By building in features to our existing models that "stretch" out the cattle cycle to replicate the observed cycle will be a major move forward in our understanding of cattle dynamics. The most promising extension in this regard is to formally model the age distribution of the stock of different animals, thereby allowing age effects to contribute to cyclical dynamics. Other promising extensions include credit constraints, rancher heterogeneity, variation in seasonal timing, noncompetitive behavior at the beef-packing level, and self-fulfilling prophecies.