Introduction, 149. — A simple production-smoothing model, 150. — Empirical findings, 152. — Conclusions, 157.

One of the leading hypotheses concerning the dynamics of production over time is the production smoothing hypothesis. Given a planning horizon which spans a number of production periods, the firm need not produce in each period an amount equal to expected sales. Rather, resorting to inventory accumulation and liquidation, the firm may follow a production plan temporally smoother than the path of demand. If firms faced with convex cost functions chose to smooth the rate of output in order to minimize costs, one would expect to observe that the rate of output would vary less than the rate of sales, with variations in inventory stocks absorbing some of the fluctuations in sales. Recently, work on the testing of the production smoothing hypothesis has cast doubt on its empirical validity. The evidence presented by Alan Blinder seems to indicate that the variance of production exceeds that of sales in seven out of eight two-digit retail industries (1981) and in eighteen out of twenty two-digit manufacturing industries (1983 and 1986). The purpose of this paper, then, is to examine the validity of such tests when seasonally adjusted aggregated data are used. The evidence presented show that the relative size of the variances of the seasonally adjusted production and sales does not provide valid tests of the production smoothing hypothesis. In addition, aggregating over firms where the seasonal patterns differ may also distort tests of production smoothing. Blinder realized that the use of seasonally adjusted data may not provide an adequate test of the hypothesis, stating Had they been available, I would have preferred to use data that were not seasonally adjusted since the production smoothing model presumably applies to seasonal fluctuations in sales. However, such data are not (1983, fn. 19). In this paper I focus on the cement industry because the unadjusted disaggregated data are available for the direct testing of the conjecture that aggregate seasonally adjusted data mask production smoothing phenomenon. Aggregate monthly data on five other industries will also be examined.

THE effects of factor mobility on the regional differences in factor returns and on regional growth have been central to the theoretical analysis of regional' growth. Those who believe that economic growth of a region is dependent on the growth of inputs (either because aggregate demand for the region's output is not a constraint as in the neoclassical growth models or because a demand type model of the post-Keynesian variety is not sufficient to explain the growth of an open region) cannot ignore the role played by factor mobility. The interest in the effects of factor mobility on regional growth through its effect on the rates of growth of inputs is more than academic. Appropriate regional growth policies cannot be designed without prior determination of the role played by input growth and the variables which influence those growth rates. Yet,' empirical studies of these issues are scarce. So far only three studies have investigated the relationships between input growth and regional growth, and factor returns and input growth. The seminal work has been that of Borts and Stein (1964), who studied the response of factor mobility to factor price differentials and the relationship between output and the aggregate capital-labor ratio. However, empirical results are inconsistent with standard neoclassical growth theorems... (Smith, 1974, p. 166). These inconsistencies are attributed by Smith (1974, 1975) to the use of data on one sector (nonagriculture) without considering the possibility of intersectoral factor movements. Smith has argued that one can either consider total regional output and ignore intersectoral factor movement (1974), or consider sectoral output and allow for intersectoral factor mobility (1975). Although the empirical results reported by Smith (1974) for his two sector model are interesting,they are of limited value for our purpose since the parameters estimated and tested are those of the reduced form and no indication of the explanatory power of those equations are reported. From those results one cannot assess the contribution of factor mobility to regional growth nor the value of the model in explaining the variations in growth rates among states. The coefficients of determination are reported in the second study (1975) for the reduced form equations; however, the same limitations on the value of the results are encountered. Furthermore, the explanatory power of the reduced form equations are distressingly low. In this article we report on the results of estimating a simple neoclassical model of regional growth. The estimated model is validated by comparing its simulations with observed values of the variables and their rates of growth between 1963 and 1973. We then use the model to derive the long run implications with respect to growth rates of output, output per worker and the level of output per worker. These are obtained from the dynamic simulation of the model over a period of time. The results of these simulations reveal a strong tendency for the rates of growth output and output per worker to converge. No such convergence is obtained for the level of output per worker.

In his comment, Mark Ladenson contends that substantial differences in wages still exist between the North and the South even after accounting for differences in regional prices. We find his results objectionable on three grounds: a) his arbitrary exclusion of available data, b) his choice of a low budget cost of living as a deflator, and c) the possibility of heteroscedasticity which would render his testing procedure invalid. Ladenson objects to four of the five cities we used as our southern sample, all previous empirical work notwithstanding. These five cities were used in our analysis because they were the only five cities for which the necessary 1963 data were available. Similarly, the five cities included in our Northeast sample were the only Standard Metropolitan Statistical Areas (SMSA) for which complete 1963 data existed.' If we consider 1967 data, there are many more SMSAs in both regions than are utilized by Ladenson. (Our data sources are identical to Ladenson's: Census of Manufacturers 1967 and Handbook of Labor Statistics 1970.) In this study we use all the data available by: 1) examining wage differentials throughout the entire United States, 2) offering alternative definitions for the Northeast and South, and 3) using different deflators to measure differentials in the regional cost-of-living. The statistical techniques used are similar to those employed by Ladenson and in our previous paper, p. 934, except that we have added two additional dummy variables to account for the additional geographical regions, the North Central and western regions of the United States. Dummy variables, then, exist for the South, North Central, and West of the United States; consequently we are comparing wages in these regions to wages in the Northeast.2 Industry 21 of the Standard Industrial Classification