Knowledge that Transforms

An intertemporal optimization condition follows from any model of optimal growth. Such an equation usually contains two parameters which cannot be directly observed: the discount rate of future utility and the elasticity of marginal utility. These two parameters can be empirically estimated if they remain constant over time and if the assumed criterion function is maximized in the real economic system. In this paper a method of estimating these two parameters is presented and it is applied to the United States, Japan, and Canada. Although the results are not conclusive, in view of the assumptions involved, they tend to confirm the hypothesis that they are fairly stable over time, at least during a sociologically well-defined short period.

Archen Minsol, Some Tests of the International Comparisons of Factor Efficiency with the CES Production Function: A Reply, The Review of Economics and Statistics, Vol. 50, No. 4 (Nov., 1968), pp. 477-479

T HE postwar growth performance of several European economies has been the cause of coincident feelings of awe, guilt, and envy. It has also been the reason for a number of attempts to explain what has allowed, say, the Germans, French, and Italians to go merrily on their way with only minor interruptions of remarkable growth records, while others, such as the British, have been faced with little other than stagnation. The studies have varied a good deal in the level of sophistication and abstraction, as well as in the relative weight given to empirical in contrast to theoretical considerations. Nevertheless, a common theme is the profound importance of capital formation as a source of growth. Neither of the two recently published studies is an exception, although the heavy weight given to capital formation in one is certainly inadvertent.' Each book is important in its own right, and together they provide an excellent opportunity to examine the main trends in an important and growing body of literature. Therefore, though emphasis will be on the two latest contributions in this area, we will include other studies for comparison and contrast. Together, they provide a sharp contrast to much current analysis which downgrades the importance of capital formation as a source of growth.

EVERY adult consciously or unconsciously determines the extent to which he supplies labor for economic activities. Every business determines the extent to which it wishes to increase its work force by new hiring or the extent to which it wishes to reduce its work force by layoffs. The outcome of these decisions is recorded in a monthly tally of the employed (E), unemployed (U), new hires (h), layoffs and quits (s),' and the number of adults outside the labor force (N). As a first step towards understanding the supply and demand relationships that determine the flows of new hires and separations and the stock of unemployed, the authors have examined the Markov matrix A, that determines transitions between the state of Employment, Et, Unemployment of less than one month's duration Uot, Unemployment of more than one month's duration Ult, and non-labor force participation Nt. By definition

Z VI GRILICHES has written an interesting and provocative article on the first volume on the Brookings model. He does not report on later materials (cf. his footnote 2) that are pertinent to reviewing this continuing research project.1 In some places his own preconceptions and specializations caused some loss of perspective. In a few places too, he obtains seemingly contradictory or implausible results by making extreme assumptions about coefficients or by extrapolating to distant time points, inappropriate for testing a system designed for short-run business cycle analysis. This comment is designed to assure the reader a balanced view. At a general level, Griliches does not indicate the progress in model building associated with the Brookings project. It should be recalled that most of the prior models were small (on the order of 30 equations), had a limited degree of disaggregation of expenditure components and virtually no industry detail, lacked a financial sector, and did not explicitly include government policy parameters.2 The Brookings model has several noteworthy features which advance the state of the art of model building, solution, and simulation. 1. Scale: The model is substantially larger than its predecessors. Estimates of single equations do not necessarily give reasonable complete system results. We have solved a 200 equation condensed version of the model and obtained sensible cyclical and growth path predictions. Future large scale models may give better predictive results; but, at least we have shown that it is feasible to work with systems of several hundred equations. 2. Government Policy Parameters: Rather than simply dealing with variables such as tax yields and required reserves, the model treats the many instruments of government policy action explicitly. That is, tax yields are not controlled by the government but only tax rates. This makes for a more realistic description of the actual structure of the economy; it also permits more sensible simulation of policy changes. 3. Sector and Industry Detail: There is more extensive treatment of several sectors such as housing, financial, agricultural and foreign trade. At the industry level, there are now eight production sectors and an expansion to thirty-three is in progress. For each of these sectors, there are price, wage, employment, hours, investment, and so forth, equations. Such extensive treatment in a macro model has not been attempted previously. 4. Monetary Influences: With the exception of T. C. Liu's model, other formulations deemphasized the role of monetary factors.3 And, even in the Liu model, the monetary sector was quite limited. In the Brookings model, the full monetary sector comprises over thirty equations. 5. Input-output: This is the first attempt to integrate an input-output structure into a cyclical model. While input-output is only a limited approximation of the technical structure of the economy, it does permit the translation of GNP component demands into industry gross outputs and industry prices into GNP component prices. As has been shown elsewhere, within the same framework, it is possible to relax the input-output elasticity of substitution assumptions and apply more general CES production functions.4 Yet, an input-output type structure is still required because production from

One of the most fascinating aspects of Soviet economic development has been the remarkable pace in growth of aggregate output maintained over the substantial period of more than three decades. The pace has been remarkable, though not completely unprecedented, and there need be little doubt about its authenticity. Thanks largely to Professors Abram Bergson, Warren Eason, and Raymond Powell, to Dr. Richard Moorsteen, and to Nancy Nimitz there exists a carefully prepared and consistent record of Soviet Russia's gross national product, capital stock, and labor inputs for the period 1928 through 1961 [4] [13]. In table 1 a portion of this basic record on Soviet economic development is reproduced. The primary purpose of this paper is to explore the usefulness of the hypothesis of embodied technical change for providing insight into sources of the growth in output. II Alternative Aggregate Production Functions and Soviet Growth

originally proposed by Arrow, Chenery, A Minhas and Solow, the CES function was constrained to constant returns to scale. It has since been generalised to allow for any degree of homogeneity in the inputs. But the function is still constrained: if returns to scale are a when output is low, they are equally a when output is high. It is shown later in this paper that if this assumption is untrue, if what may be called point returns to scale are themselves functionally related to output, a common procedure for estimating the elasticity of substitution will generally be inconsistent, even if it would not otherwise have been so. To prove this, a modified CES function is derived in which point returns to scale are functionally related to output.

JN an earlier paper we have reported that manufacturing plants in two digit industries tend to cluster around an expansion path characterised by constant input output elasticities [4]. We have also argued that the nature of the path provides a motivation for the plants to grow, and this motivation has been further strengthened by the shifts of the path that took place. The question of differential rates of growth exhibited by the different plants still remains. Given an expansion path that excludes any optimal plant size, why should some plants exhibit a higher rate of growth than others? The question may be paraphrased in other ways. What determines a change in market share? 1 What is the source of competitive advantage enjoyed by some plants and not by others so that they grow at different rates? As we have shown elsewhere [5], part of the answer is in the nature of the expansion path and the relative position of the plants along the path. In this paper we will investigate whether the deviation of plants from the expansion path is also relevant in explaining their growth performance. The paper is in three sections. In the first section we analyze the duration of competitive advantage enjoyed by the plants. Where plants possess only a temporary competitive advantage randomly distributed among all the plants, a lognormal size distribution of plants ensues. On the other hand, where plants enjoy a persistent competitive advantage, the resulting systematic relationship between size and the rate of growth leads ultimately to a concentration of the market in the hands of a few large plants. The duration of competitive advantage depends on the source of competitive advantage. In the second section we focus on technology difference-measured by the different deviations of plant observations from the expansion path in a certain directionas a possible source. It is found that advanced technology contributes to the likelihood that a plant could maintain or expand its share of the market. However, superiority in technology of a given plant turns out to be generally transitory and appears to be related to the age of its machines and equipment. This suggests the hypothesis that, with technological progress, the natural life of machinery and equipment dominates the extent and the outcome of technology competition. Competition for market also comes from new entries. In the final section we find that new plants in an industry are characterized by greater capital intensity and therefore enjoy a competitive advantage at times of rising wages. In fact, the great significance of entry and exit indicates that the market share change is to a large extent related to the building of new plants and the abandoning of old plants. It is in this connection that technological progress and the change in the relative price of factors have the most significant impact on the competition for market.

aggregate scrappage rate divided by the rate expected from aging alone (M*t). The coefficient of determination is 0.64, indicating almost two-thirds of the variation of the actual scrappage rate from its trend in explained by changes in turnover and prices. In chart 2, values of the trend (M*tKt) are plotted as a dotted line and values of expected scrappage (St) as a dashed line. The continuous line represents the Polk aggregate observed scrappage values. Correlation of the estimates of the dashed line with the Polk estimates yields a coefficient of determination of 0.90.

T HIS paper analyzes negotiated settlements over a 17-year period. The analysis uses annual data for manufacturing as a whole, for individual manufacturing industries, and for building construction as a special case. Various comparisons are made. The wagesettlement series for manufacturing is compared with increases in straight-time average earnings in manufacturing and the unemployment rate in manufacturing; settlements are also compared with earnings increases for individual manufacturing industries, suggesting some conclusions with respect to wage drift and the wage-price guidelines of the Kennedy and Johnson administrations. Likewise, settlements are compared for eleven manufacturing industries and construction, indicating the contrasts among them and the effects of changes in industry differentials upon the structure. Such comparison raises questions concerning the influences of the wage-price guidelines on negotiated increases in various types of industry beginning in 1962. Finally, negotiated increases in building construction are compared with such increases for manufacturing as a whole and with the relative unemployment rate for construction workers, indicating disparate developments and the consequent pressures on negotiations in manufacturing, especially the mass production industries.