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.

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.

In this study, we consider the impacts of dramatic regulatory reform during the 1980s on the efficiency of farms in New Zealand, using unbalanced panel data. A translog distance function representing the multiple output and input technology and incorporating nonneutral regulatory impacts is used for the analysis. Determinants of technical inefficiency, including a regulatory variable, a time term, and a debt/equity ratio, are also incorporated in a one-step model estimated by maximum-likelihood, stochastic production frontier methods. We find evidence of regulatory-induced changes in output composition—toward beef and deer, and away from wool, and especially lamb—but little associated technical inefficiency. These patterns motivated investment in complementary capital, land, and beef/deer livestock inputs. Firms that were more flexible in their adaptation toward these new mixes adjusted to regulatory changes with less upheaval, so any existing inefficiency appears linked to debt/equity levels.

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.

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