Knowledge that Transforms

A bstract-We discuss the problem of testing for constant versus time varying regression coefficients. Our alternative hypothesis allows the coefficients to follow a stationary AR(1) process with unknown autoregressive parameter. Standard testing procedures are inappropriate since this parameter is identified only under the alternative. We propose a test statistic which is a function of a sequence of Score statistics, and depends only on the regressors and the OLS residuals. The distribution of the test statistic is discussed, power and size are investigated using Monte Carlo methods, and an empirical example investigating stability in the gold and silver markets is presented.

Using cross-sectional data for U.S. manufacturing, the author identifies a negative effect of unions on the profits of highly concentrated industries and virtually no effect on the profits of unconcentrated industries. results for concentrated industries are explained in terms of the monopoly model which predicts a negative effect of unions on profits equivalent to the change in surplus. monopoly model also suggests that previous profit studies which omitted the union variable were likely to significantly underestimate the relationship between concentration and profits. This prediction is also supported by empirical evidence. M sANY studies have demonstrated the ability of unions to raise wages above the level of nonunion workers.' Higher union wages in turn may affect the level of prices, employment, capital intensity, productivity and profits. Recent studies by Clark (1982) and Freeman (1983) have found significant negative effects of unions on profit rates in the United States. Using line of business data from 1970 to 1980 for 900 firms, Clark found a negative effect of unions on the rate of return to capital. More specifically, the union effects were found to be significantly negative when market shares were low and insignificant when market shares were high. According to Clark, the absence of a union effect in the latter case can be explained by the ability of firms with market power to pass on higher union wages into higher prices (1982, p. 47). On the other hand, Freeman (1983), using data from the Survey of Manufactures from 1958 to 1976 and the Internal Revenue Service from 1965 to 1976 also found a negative effect of unions on profits. Contrary to Clark's results, however, the union profit effect was negative only in concentrated industries. point of contention appears to be the role of concentration in determining the union profit effect, rather than the overall negative impact of unions. results of Clark and Freeman are important because previous industrial organization research on profit rates generally ignored unions. For instance, of the forty-six profit studies surveyed by Weiss (1974), none included a union variable. More recently, a profit study in this Review by Ravenscraft (1983) using high quality, line of business data from the Federal Trade Commission, also ignored unionization. As will be demonstrated, the omission of the union variable leads to an understatement of the effect of industrial concentration on profits. This means that concentrated industries generate much greater profits than indicated by previous research. In order to measure the size of the understatement, a new concept, employer's surplus, is developed which illustrates the theoretical impact of a union wage increase on monopoly profits. empirical estimates of surplus in this paper indicate how monopoly profits are divided between firms and unions. Most of the data used in this study were obtained from James Medoff and Charles Brown.2 Unlike the previous two studies, each observation corresponds to a state by two digit SIC industry for manufacturing in 1972. Because of the large variations in unionization across states, this data set offers a useful test of the union profit effect. I. Employer's Surplus According to accepted theory, the profit maximizing monopolist will employ labor up to the point where the market wage equals the marginal revenue product, MRP.3 If for simplicity we assume that all other factors are variable, then the MRP curve represents the monopolist's long-run demand for labor. In the absence of unions, employment is determined by the intersection of the MRP curve and nonunion wage (Wn) as pictured in figure 1. If unions raise wages to Wa, employment will decrease from Ln to L, with the reducReceived for publication November 28, 1983. Revision accepted for publication July 6, 1984. *Eastern Washington University. I am very grateful to Dave Bunting, Lisa Brown, Bill Dickens, Claire Brown, and George Strauss for their valuable suggestions on this research. This paper is a revised version of chapter 3 from the author's Ph.D. thesis, The Union Impact on Profits, Productivity, and Prices, University of California, Berkeley. 1 See Parseley (1980) for a recent review of this literature. 2 I would like to thank Brown and Medoff for making these data available to me. 3 Marginal revenue product is equal to marginal revenue multiplied by marginal physical product. See Rees (1979).

The focus of the paper is to measure how consumption responds to changes in the interest rate. The equivalence between the effect of the interest rate, and the effect of mortality probabilities, on consumption is used to gain an estimate of the intertemporal elasticity of substitution between current and future consumption. Seemingly unrelated regressions in a cross-sectional model of consumption, earnings, and assets are used to provide efficient estimates of the intertemporal parameter. The regression results suggest that the elasticity is somewhat higher than previously thought.

The pattern of diversification within U.S. manufacturing between 1963 and 1977 are examined. Firms didn't diversify at random; they were more likely to enter rapidly growing industries, and industries that were related to their primary activities through supply relationships or marketing similarities. Research and development (R & D) expenditures also influence the observed patterns. R & D intensive industries generate outbound diversification and attract inbound diversification. However, the strongest influence is directional; R & D intensive firms channel their diversification toward R & D intensive industries. Much diversification reflects the transfer of sharable organization capital among related activities.

One important channel through which real interest rates affect aggregate demand is consumer expenditure on durable goods.This paper examines empirically the link between interest rates and consumer durables.Solving for the decision rule relating income and interest rates to consumer demand is an intractable task.This paper avoids this problem by examining the first-order conditions necessary for maximization by the representative consumer.Structural parameters of the representative utility function are thus recovered.The estimated model suggests that expenditure on consumer durables is far more sensitive to changes in the interest rate than is expenditure on nondurables and services.

We examine six integration schemes and decompose their ability to increase inter-member trade into environmental, policy and system effects. Environmental factors caused the greatest variation in trade creation, with inter-member distance the most important environmental variable. The CACM and EFTA have followed more effective integration policies than the EEC, LAFTA and the Andean Pact. Although integration can thus benefit developed and developing countries alike, for some, such as those in Latin America, inter-member distances severely limit its effectiveness. While the combination of policy and system has kept the CMEA fromrr achieving its full potential for increasing inter-member trade, its effectiveness does not differ from that of unions among market economies.

The purpose of this paper is to present new evidence on employer search to fill a position. The study is based on data for recent hires collected in the 1980 Employer Opportunity Pilot Project (EOPP) survey of employers. The paper investigates the effect of factors such as training, employer size, and labor market conditions on employer search. Employer search is measured by the number of applicants interviewed prior to an employment offer and the average number of hours spent by an employer recruiting, screening, and interviewing per applicant interviewed. The paper also documents the relationship between employer search and wages.

This paper investigates the effect of shifts in output composition on the slowdown of productivity growth in the United States between 1947-67 and 1967-76. I employ a Leontief input-output framework and a Divisia index of aggregate productivity growth to separate the effects of changes in sectoral rates of technical progress from the effects of changes in output composition and interindustry flows on the change in overall productivity growth. Of the approximately 2 percentage point decline in overall total factor productivity growth, 17% to 22% was due to compositional effects and the remainder to other factors. T HE importance of shifts in input or output composition in explaining the recent productivity slowdown in the United States has been a source of some controversy. Estimates of such effects vary considerably. For example, Gollop (1982) calculated that resource shifts were actually an offset to the slowdown in productivity growth; Kutscher, Mark, and Norsworthy (1977) estimated that employment shifts had no effect on productivity growth; Thurow (1979) ascribed half of the slowdown between 1965-72 and 1972-77 to employment shifts; and Nordhaus (1972) attributed 77% of the decline from 1948-55 to 1965-71 to employment shifts. In a related paper, the possible reasons for these disparate results were discussed at length (see Baumol and Wolff, forthcoming). Briefly, these differences stem primarily from the use of different concepts and measures. Actually, three different concepts are used in the literature. The first is a resource or equilibrating shift which measures the increase in productivity that can result from a more efficient allocation of resources (cf. Denison (1979a, 1979b, and 1984); Norsworthy, Harper, and Kunze (1979); and Gollop (1982)). While interesting in itself, this resource reallocation effect is a somewhat limited notion, measuring the movement toward the efficient frontier instead of the outward movement of the frontier over time. The second concept is the so-called level which assesses the effect of resource shifts on overall productivity growth by holding constant the productivity levels of the various sectors of the economy (cf. Nordhaus (1972); Kutscher, Mark, and Norsworthy (1977); and Thurow (1979)). This measure was found to be quite arbitrary, depending on the (arbitrary) choice of base year used in the computation. The third is the so-called effect, which assesses the effect of shifts in resources by holding constant sectoral rates of productivity growth (cf. Nordhaus (1972); Baily (1982), and Gollop (1982)). All three authors found that the rate effect had a negligible influence on the productivity slowdown. This measure is the most theoretically sound of the three, and my measure will fall in this category, though differ in significant ways from previous formulations, and show a greater effect on overall productivity growth from compositional changes. I shall first develop a general model to measure such shifts or composition effects from a Leontief input-output framework (sections I and II). Results for the U.S. economy over the 1947-76 period will then be reported, with particular emphasis on accounting for the productivity slowdown after 1967 (sections III, IV, and V). Conclusions and a comparison with other results will be discussed in section VI, VII and VIII. I. The Standard Model Following the work of Peterson (1979), let us define: X,= (column) vector of gross output by sector at time t Y, = (column) vector of final demand by sector at time t at= matrix of inter-industry technical coefficients at time t It = (row) vector of labor coefficients at time t, showing employment per unit of output kt = (row) vector of capital stock coefficients at time t, showing the capital stock required per unit of output p,= (row) vector of prices at time t, showing the price per unit of output of each industry. Received for publication June 27, 1983. Revision accepted for publication October 19, 1984. * New York University. I would like to express my appreciation to Wassily Leontief, William Baumol, M. I. Nadiri, Mark Schankerman, Martin Baily, and Andrew Sharpe for helpful comments and to the Division of Information Science and Technology of the National Science Foundation for financial support.