Academic journal article Economic Inquiry

Technology, Capital Spending, and Capacity Utilization

Academic journal article Economic Inquiry

Technology, Capital Spending, and Capacity Utilization

Article excerpt

I. INTRODUCTION

Capacity utilization is a variable of longstanding macroeconomic interest. Many studies have found it to be a valuable indicator of inflationary pressure. For example, Cecchetti (1995) finds that capacity utilization works as well as or better than other variables in predicting inflation over the next year or two. Similarly, in models of the level of resource utilization above which inflation accelerates, the utilization rate does as well as, and sometimes better than, the unemployment rate in predicting this level. (1) This predictive value may reflect capacity utilization's ability to do "double-duty," picking up the extent of slack in both labor and product markets (Corrado and Mattey 1997).

However, in recent years, the capacity utilization and unemployment rates have at times provided different signals about the degree of tightness in resource markets. Notably, in the late 1990s, the decline in the unemployment rate below 4% suggested a relatively tight labor market, but the capacity utilization rate remained unexpectedly flat (Figure 1). Part of this divergence may be due to effects of technology on capacity utilization, as the 1990s saw both an investment boom that broadly increased manufacturing capacity and a shift in the composition of capacity toward high-tech machinery and equipment. In the 1940s and 1950s, manufacturing methods typically involved assembly line production with large-scale fixed units of machinery and equipment.

[FIGURE 1 OMITTED]

Relationships between inputs and outputs were relatively fixed, and adjustments in capacity were both costly and slow. Modern manufacturing methods, however, build considerable flexibility into the management of capacity. Technologies like numerically controlled machines, programmable controllers, and modular assembly make it easier to adjust the level and composition of output. At the same time, the use of automated design and modular tooling lowers the cost and time needed to expand capacity. While the use of advanced technologies is far from universal, it is increasingly widespread. For example, about three-quarters of plants in equipment-producing industries used at least one advanced technology in 1993; about 30% used five or more. (2) With the investment boom that took place in the second part of the 1990s, these shares are likely higher now.

Conceptually, how these advances in technology would affect capacity utilization is not clear a priori. On the one hand, flexible manufacturing makes it easier to ramp production up and down. This may encourage firms to install a broader margin of excess capacity--that is, to operate at lower average utilization--in order to be able to handle upswings in demand. Such a strategy would be favored by declining prices of high-tech capital, which make excess capacity cheap. On the other hand, automated design and modular tooling make it faster and cheaper for firms to expand capacity. This may permit them to reduce the amount of excess capacity they maintain and to operate at higher utilization on average. With these two offsetting forces at work, determining how advances in technology affect capacity utilization at industry levels is ultimately an empirical question.

This paper investigates the relationship between capacity utilization and high-tech investment for U.S. manufacturing. The next section discusses conceptual considerations in the relationship between technological change, capital spending, and capacity utilization. We show how technological change may lead either to lower average utilization by making it cheaper to hold excess capacity or to higher utilization by making further changes in capacity less costly and time consuming. The third section discusses the data and specification used for our study. The extent of investment in high-tech machinery and equipment has varied importantly across industries and over time. Thus, we use data on 111 manufacturing industries from 1974 to 2000 and panel data techniques to investigate effects of technology on utilization. …

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