Catherine Morrison, Quasi-fixed Inputs in U.S. and Japanese Manufacturing: a Generalized Leontief Restricted Cost Function Approach, The Review of Economics and Statistics, Vol. 70, No. 2 (May, 1988), pp. 275-287

This study builds on recent research giving the notion of capacity utilization clearer economic foundations. In this research optimal output Y* is defined as the minimum point on the firm's short-run average total cost curve, and capacity utilization is then computed as CU=Y/Y*, where Y is actual output. Here I extend these concepts to include adjustment costs due to changes in the stock of capital, and nonstatic expectations of future output demand and input prices. The more general notion of CU is shown to depend on the shadow values of the firm's quasifixed inputs, and is decomposed to isolate the effects of anticipatory expectations. An empirical comparison is then made between traditional indices and alternative economic CU measures, using annual U.S. manufacturing data 1954-80. The calculated indices exhibit plausible patterns, which can be interpreted as the effects of nonstatic expectations and adjustment costs.

We assess the cost-reducing impacts of increasing stocks of “high-tech” equipment (O capital). Our empirical analysis is based on a dynamic production theory model and annual data for two-digit U.S. manufacturing industries (1952–1991). We find evidence of overinvestment in O capital in the mid to late 1980s, following a period of strong investment incentives in the late 1970s. By the end of the 1980s, however, the returns to investment and falling prices for O capital more than justified the high investment levels in nondurable-goods industries, and the benefit–cost ratio was also increasing for durable-goods industries. The underlying substitution patterns suggest that high-tech capital expansion increases demand for most capital and noncapital inputs overall, but saves on materials inputs. In durables industries, however, both energy and “other” capital appear somewhat substitutable with O capital, and in nondurables industries increasing high-tech intensity may be a factor underlying stagnating labor demand.“We see computers everywhere except in the productivity statistics.”Attributed to Robert M. Solow

Catherine J. Morrison, Unraveling the Productivity Growth Slowdown in the United States, Canada and Japan: The Effects of Subequilibrium, Scale Economies and Markups, The Review of Economics and Statistics, Vol. 74, No. 3 (Aug., 1992), pp. 381-393

Recent research on productivity growth has focused on public infrastructure and its impact on economic growth and productivity. We construct a model of firms' technology and behavior, taking advantage of the analytical framework provided in the cost-function-based applied production-theory literature, and apply it to state-level data for U.S. manufacturing. We find that infrastructure investment provides a significant return to manufacturing firms and augments productivity growth. The net benefits of infrastructure investment may or may not be positive, depending upon the social costs of infrastructure investment and the relative growth rates of output and infrastructure.

Increasing size of establishments and resulting concentration in U.S. industries may stem from various types of cost economies. In particular, scale economies arising from technological factors embodied in plant and equipment may be a driving force for such market structure changes. In this case, typical market power measures like Lerner indices can be misleading: if scale (cost) economies prevail, cost efficiencies rather than market deficiencies may actually underlie the observed patterns. In this study, I provide measures of scale economies and market power for the U.S. meat packing industry, whose increased consolidation and concentration have raised great concern in policy circles. The results suggest that this trend has been motivated by cost economies, but that little excess profitability exists, and on the margin the potential for taking further advantage of such economies has become minimal.

Theoretical models of endogenous growth identify capital accumulation and returns as a potential stimulus to economic growth. Existing empirical studies, however, are based on a limited notion of these returns, which follows from the simple production function framework used for estimation. The purpose of this study is to examine growth issues using dynamic cost function estimation. This methodology enables us to broaden the concept of returns to include returns arising from short-run quasi-fixity of private capital, long-run (internal) scale economies, and external “knowledge” factors—overall investment in research (R&D), technology (high-tech capital), and education (human capital). Based on detailed industry-level data, we find evidence of increasing returns to scale arising from cost savings on variable inputs, although diminishing returns to capital are prevalent. Our results also show that knowledge factors augment growth. More importantly, they appear to explain a substantial proportion of measured scale economies.

Measures of industrial capacity utilization (hereafter, CU) have been used extensively in helping to explain changes in the rate of investment, labor productivity and inflation. The CU measures have also been used to obtain indices of capital in use, as distinct from capital stock in place. A number of alternative measures of CU are periodically calculated and published; the 1980 Economic Report of the President, for example, contains three series, that by the Federal Reserve Board, the U.S. Department of Commerce (Bureau of Economic Analysis) and the Wharton School of Finance. Other publicly available series are those prepared by McGraw-Hill Publishing Company, the U.S. Department of Commerce (Bureau of the Census), and Rinfret-Boston Associates, Inc. Although a host of CU measures is publicly available, it is not at all clear how one should interpret changes over time in each measure or variations among them. A principal reason underlying these interpretation problems is that the crucial link between underlying economic theory and the constructed measure of CU is weak. One way in which this issue has manifested itself in the policy domain over the last five years has been with respect to the uncertain effects of dramatic increases in energy prices on capacity output and on CU. Each of the CU measures noted above is computed in such a way that explicitly ignores any role for energy prices. Yet several times during the last decade, though growth to apparently high rates of CU had taken place, investment and average labor productivity were much lower than expected, and the rate of price increase much greater. In brief, during the last decade the explanatory power of alternative CU measures has dropped sharply. Some have conjectured that post-OPEC energy price increases may have brought about major changes in the U. S. economy so that old quantitative relationships between measured CU and investment, labor productivity, and price inflation may have been altered substantially. In order to assess effects of changes in PE on CU, a re-examination of the notion and measurement of CU is needed, based on the framework of the economic theory of the firm. That is the focus of this paper.

Effects of public infrastructure investment on the costs and productivity of private enterprises have proven difficult to quantify empirically. One piece of this puzzle that has received little attention is spatial spillovers. We apply a cost-function model to 1982–1996 state-level U.S. manufacturing data, to untangle the private cost-saving effects of inter- and intrastate public infrastructure investment. We implement two spatial adaptations—including a spatial spillover index in the theoretical model, and allowing for spatial autocorrelation in the stochastic structure. Recognizing such spillovers both increases the estimated magnitude and significance of cost savings from intrastate public infrastructure, and augments these productive effects.