The impact of regulation on technical change.

• Author: Granderson, Gerald

1. Introduction

   Input-based regulation restricts a firm's input choices to a subset of those that characterize the production technology. The Averch-Johnson (hereafter A-J) (1962) hypothesis states that rate-of-return regulation affects a firm's input demands for producing its selected output level. Given that firms can select the level of innovations in inputs through research and development, rate regulation can also impact a firm's selection of innovations among inputs. Regulation's influence on the choice of innovations also impacts the rate at which technical change occurs in an industry. The A-J hypothesis has been examined extensively in the literature. The impact of regulation on technical change has received less attention in the literature and is the focus of this paper.

   The subject matter is of some importance in that regulation, via the A-J hypothesis, leads to higher production costs. Regulation's impact on a firm's selection of innovations can magnify or dampen the rate at which technical change occurs. A magnification (dampening) of the rate of technical change leads to lower (higher) production costs in addition to regulation's impact on input demands. Regulation's impact on the rate of technical change can lead to small or large changes in prices for consumers. Regulators can use the information on regulation and technical change to evaluate the effectiveness of policies aimed at enhancing technical change. By how much do the policies affect production costs and how much would consumer prices change due to the policies? Regulators can also use the information to predict the impact deregulation would have on consumer prices. Both regulators and consumers can attempt to determine the amount by which consumer prices would change under deregulation.

   Smith (1974) and Okuguchi (1975) examined the impact of regulation on a firm's choice of innovations. They showed theoretically that regulation can alter a firm's choice of innovations as to further enhance the A-J effect. Macauley (1986a, b) used Smith's and Okuguchi's models to test the impact of regulation by the Federal Communications Commission (FCC) on technical change in communication satellites. Macauley found that regulations of the FCC, which differ from rate of return regulation, altered the rate at which technical change occurred.(1) Nelson (1984) examined the impact of regulation on technical change in the electric utility industry, an industry subject to rate of return regulation. Nelson found that tighter regulation led to a reduction in the rate at which technical change occurred.(2)

   This paper empirically examines the impact of regulation on induced technical change. The paper differs from previous studies in several ways. One, I attempt to derive theoretically the impact of a change in regulation (relaxation of regulation) on the innovation choices of the firm. Two, estimates of the regulated and standard (unregulated) cost minimizing input shares are used to examine the impact of regulation on factor-augmenting technical change. The regulated and unregulated cost input shares are compared in order to determine the degree to which regulation affects input-specific technical change. The unregulated cost input shares are utilized to examine factor-augmenting technical change under deregulation. Three, estimates of the regulated and standard (unregulated) cost functions are obtained to determine the impact of regulation on the rate at which technical change occurs. I estimate the rate at which technical change would occur under deregulation.

   I first review Smith's and Okuguchi's models of regulation and induced technical change and then examine the impact of a change in regulation on the firm's selection of innovation choices. Next, I estimate a regulated cost function and utilize the regulated and unregulated cost input shares derived from the cost function to measure the impact of regulation on technical change. The industry I investigate is the U.S. interstate natural gas pipeline industry from 1977 to 1987. This industry was subject to regulation over the sample period. I find that regulation led firms to adopt a technology that augmented (i) noncapital more than capital and (ii) noncapital more than the technology the unregulated firm would have adopted. I also find that regulation led to a small decline in the rate of technical change and that the reduction led to a 0.85% increase in production cost on average. With higher production cost leading to higher prices for consumers, consumer prices may well have changed by a larger amount under regulation compared to the price change that would have occurred if the industry was deregulated.

   The organization of the paper is as follows. Section 2 reviews Smith's and Okuguchi's models and investigates the impact of a change in regulation on the firm's selection of innovation choices. Section 3 describes how the regulated and unregulated cost-minimizing input shares can be utilized to examine the impacts of regulation on technical change. Section 4 presents the empirical model. Section 5 describes the data, while section 6 presents the empirical results. Section 7 concludes.

2. The Theoretical Model

   Let x = ([x.sub.nc], [x.sub.c]) denote a vector of inputs, with [x.sub.nc] and [x.sub.c] denoting subvectors of the noncapital and capital inputs, respectively. Let [Mu] = ([[Mu].sub.nc], [[Mu].sub.c]) denote a vector of input prices and q denote a firm's desired output quantity. Following Smith (1974) and Okuguchi (1975), a firm selects the level of innovations with regards to its inputs.(3) The firm faces a concave innovation frontier T = h([a.sub.nc], [a.sub.c]), where T denotes the amount of innovative effort and [a.sub.nc] and [a.sub.c] denote augmentation coefficients the firm selects for the noncapital and capital inputs, respectively. Let z = ([z.sub.nc], [z.sub.c]) denote a vector of the dollar per unit prices of innovative efforts. The firm's production function defined in terms of the augmentation factors is q = f([a.sub.nc][x.sub.nc], [a.sub.c][x.sub.c]).

   The standard (unregulated) firm selects the input quantities and the level of innovation (augmentation coefficients) to minimize its costs subject to the production technology and the innovation frontier constraints. Following Smith's notation, the Lagrange function for this optimization problem is

   [pounds] = [Mu]x + [[Lambda].sub.1](q - f([a.sub.nc][x.sub.nc], [a.sub.c][x.sub.c])) + [[Lambda].sub.2](T - h([a.sub.nc], [a.sub.c])), (1)

   where [Mu]x is the observed cost of producing output level q and [[Lambda].sub.1] and [[Lambda].sub.2] are multipliers. Differentiating the Lagrange function with respect to the input quantities and the augmentation coefficients and carrying out some manipulation yields the following expression for [([a.sub.c]/[a.sub.nc]).sub.u]:

   [Mathematical Expression Omitted]. (2)

   The term [([a.sub.c]/[a.sub.nc]).sub.u] is the ratio of the standard (unregulated) firm's selection of innovations; [Mathematical Expression Omitted] and [Mathematical Expression Omitted] denote the unregulated cost minimizing input expenditures on noncapital and capital, respectively; [h.sub.nc] = [Delta]h/[Delta][a.sub.nc]; [h.sub.c] = [Delta]h/[Delta][a.sub.c]; and [h.sub.nc]/[h.sub.c] = -[Delta][a.sub.c]/[Delta][a.sub.nc] is the slope of the innovation frontier.

   The regulated firm faces a rate-of-return constraint on capital.(4) Regulators determine an allowed rate of return a firm can earn on capital, with the allowed rate assumed to exceed the financial cost of capital but to be less than the rate of return attainable by an unregulated monopoly. Let p, R = pq, s, and r denote the output price, total revenue, the allowed rate of return on capital, and the financial cost of capital, respectively. The regulatory constraint can be written as R - [Mu]x [less than or equal to] [Theta][x[prime].sub.c], where [Theta] = s - r, [x[prime].sub.c] = [Sigma] [p.sub.c][x.sub.c] and [p.sub.c] is a vector of prices of the physical units of capital.(5) The Lagrange function for the optimization problem the regulated firm faces is

   [pounds] = [Mu]x + [[Lambda].sub.1](q - f([a.sub.nc][x.sub.nc], [a.sub.c][x.sub.c])) + [[Lambda].sub.2](T - h([a.sub.nc], [a.sub.c])) + [[Lambda].sub.3](R - [Mu]x - [Theta][x[prime].sub.c]), (3)

   where [[Lambda].sub.3] is a multiplier.(6)

   Differentiating the Lagrange function in Equation 3 with respect to the input quantities and the augmentation coefficients and carrying out some manipulation yields the following expression for [([a.sub.c]/[a.sub.nc]).sub.r]:

   [Mathematical Expression Omitted]. (4)

   The term [([a.sub.c]/[a.sub.nc]).sub.r] is the ratio of the regulated firm's selection of innovations; [Mathematical Expression Omitted] and [Mathematical Expression Omitted] denote the regulated cost minimizing quantities of noncapital and capital inputs, respectively; [Mathematical Expression Omitted]; and [Mathematical Expression Omitted]. Comparing Equations 2 and 4, Baumol and Klevorick (1970) and Petersen (1975) showed that under a binding regulatory constraint, 0 [less than] [[Lambda].sub.3] [less than] 1. If the A-J bias occurs, then [Mathematical Expression Omitted]. With [a.sub.c] [greater than or equal to] 0, [a.sub.nc] [greater than or equal to] 0, and [Theta] [greater than] 0 (s [greater than] r), as Smith (1974, 1975) and Okuguchi (1975) showed, it is possible that [([a.sub.c]/[a.sub.nc]).sub.r] [less than] [([a.sub.c]/[a.sub.nc]).sub.u].(7) The regulated firm may select a technology that augments noncapital inputs more than capital inputs, thus magnifying the A-J bias.

   Now consider the impact of a change in regulation on the regulated firm's selection of innovations. For example, what impact would deregulation have on the nature of technical change? The impact of a change in regulation on the nature of technical change can be examined by determining the sign of...