Knowledge that Transforms

tempted to go a little further and suggest that the degree of concentration as such does not contribute materially to the explanation of high profitability. Perhaps this is not surprising because although a highly concentrated industry may be associated with high profitability a large number of situations are possible depending, amongst other things, on whether the industry is expanding or contracting, and on the degree of internal, intra industry, and potential competition. Secondly, the analysis shows clearly the importance of very high barriers to entry arising for instance, from control over raw materials, patent protection and economies of scale. In these cases the means exist whereby firms can maintain high profitability over a long run of years. A concern for barriers to entry should certainly be central to the implementation of a monopoly policy. Thirdly, the highly significant relationship between growth and profitability is a well established one,3 and any monopoly policy which is based on realised profitability should at least distinguish between fast and slow growing industries or (ideally) firms, and attempt to assess the extent to which high profitability is 'justified' by a high rate of growth. STATISTICAL APPENDIX

T HE results presented in this paper are (1) A time series of personal savings is well explained by the dynamic savings function st = ast 1 + 8z A Yt + ut. This model was previously formulated (but tested only for the United States and Canada) by Professors Houthakker and Taylor [1]. (2) The steadystate or long-run savings function implicit in the above dynamic formulation, s/y = a2 + 12 (Ay/y) fits remarkably well to international cross-section data. (3) The coefficients of the long-run savings function, as obtained by application of least squares on the function, is statistically equal to the coefficient obtained indirectly from the dynamic savings function. This implies that both dynamic and steady-state savings behavior are adequately described by st = ast+ /3Ayt + ut. (4) A few pitfalls in estimation of economic relationships when employing cross-section time series data are revealed. It will be shown that the long-run savings function implied in the Houthakker-Taylor function supplemented by some additional conditions, is precisely the long-run savings function of Modigliani [2]. Therefore, the present attempt must be regarded as a synthesis of the two theories. We shall have the benefit of more observations per country than the above mentioned authors in deriving our empirical results. II The Dynamic Savings Function Let St, yt, and at denote (personal) savings, (personal disposable) income, and non-depreciating assets at time t, in per capita terms. The Houthakker-Taylor model in terms of these variables is: St = a +at+yyt (1) dat/dt = st (2) Thus, savings is a linear function of assets and income, and the rate of change of assets at any moment of time is the savings at that moment. The long-run implication of this model has not been explored and this we shall do below. To derive the long-run effects of equations (1) and (2), we need to know something about the growth of income. Let us assume 1 that: (3) y= p, where p is a positive constant. This equation thus implies that the rate of growth (of

where ut is a random disturbance with zero mean. Koyck pointed out that subtracting Xyt-i from (1) produced the equation yt = A'+xyt_i+ bxt+ut' (2) where A' = A(1 -X) and ut' = ut -Xut1. Thus instead of being forced to deal with the model in its distributed lag form (1), which involves the seemingly intractable task of estimating a relation with an infinite number of explanatory variables from a finite amount of data, we can estimate the parameters of the autoregressive form of the model given in equation (2). However, this apparent simplification is purchased only at a cost, for consistent estimation of relation (2) requires that we face several estimation problems associated with equations in which lagged dependent variables appear as explanatory variables. While ordinary least squares estimates of the parameters of (2) are consistent provided that the disturbances ut' are serially independent and follow a distribution which satisfies the assumptions of the central limit theorem, even in this case a small sample bias exists. If the disturbances are serially dependent, an asymptotic bias exists.' Moreover, the transformation from (1) to (2) has changed both the variance and the serial correlations of the disturbances. Hence, if the disturbances in (1) are serially independent, those in (2) are necessarily autocorrelated, which means that applying least squares to equation (2) yields inconsistent estimates of the parameters. In addition to ordinary least squares (OLS), several techniques for estimating such distributed lag relations are available. Generally these techniques have been recommended on the basis of their desirable asymptotic properties. However, for economists, who are forced to work in a world where data are scarce, asymptotic properties are frequently of little relevance. What is more often required is knowledge of the properties of the estimators in small samples. Unfortunately, it has proved difficult to investigate these properties analytically. In the absence of such results, sampling or Monte Carlo experiments provide an alternative, if less elegant, source of information. Accordingly, this paper presents the results of a Monte Carlo study of several lag estimators under conditions in which the disturbances ut' of relation (2) are serially correlated. In addition to ordinary least squares, the following five methods were studied. 1) Two Stage Regression (TSLS): This is an application of Leviatan's instrumental variable approach. Leviatan [12] has suggested that xti1 be used as an instrument for yt-i in estimating relation (2). In order to increase the efficiency of the technique, we employed a linear combination of lagged x's as the instrument. The linear combination was determined by first estimating the equation

T HE recent development of spectral analysis as a tool for analyzing economic time series has provided a particularly neat method for independently testing the existence of Kuznets cycles. Researchers who have applied this technique, however, have produced mixed results. Adelman shows that such cycles do not exist in the United States data ' while Hatanaka and Howrey, in a criticism of Adelman's work, leave readers with, at best, an agnostic view.2 But both these studies have been poorly conceived with respect both to spectral analysis and to the particular version of the longswing hypothesis tested. The intention of this paper is to attempt to resolve the long-swing controversy, insofar as the Canadian data are concerned, by the application of spectral analysis to a large number of historical time series. For the purpose of comparison with results obtained in the United States this will initially mean applying what might be called the Adelman-Hatanaka-Howrey test. Finally, however, this test will be reformulated and reworked in a manner which is more compatible with spectral analysis and which makes more sense with respect to the long-swing hypothesis. Section II briefly describes and lists some important properties of spectral analysis while section III presents some of the practical considerations involved in applying this technique to the long-swing controversy. Section IV summarizes the results of the spectral estimates. Conclusions are in section V. IL

T HE volume under review is the result of the collective effort of twenty-five economists. It consists of a set of chapters each representing a building block for a large scale model of the United States economy, and an attempt at the end of the volume to put all these blocks together into one coherent and potentially useful structure. To review a volume which ranges so widely over almost all aspects of economics, the reviewer himself would have to be a committee. To review the final model adequately, one would need computer and programing resources which are beyond anything currently available to individuals. I shall, therefore, concentrate on reviewing the volume rather than the final model produced by the Brookings-SSRC group. This may be also desirable, since a final version of the model which the authors would stand by and take responsibility for may not yet exist.' One of the original ideas which led to this volume was that by parceling out different sectors and aspects of the economy to different one could integrate all of the best available theoretical and empirical knowledge into the model and thereby improve it greatly. To a great degree, therefore, the final quality of the model rests on the success with which the individual sectors were specified and estimated. Ideally, the job of a specialist would consist of first surveying the theoretical literature in a field (at the level of an AER or EJ review article), then surveying the associated quantitative-econometric knowledge and evidence, running a race between all the plausible econometric models which have not been eliminated on a priori grounds earlier, and submitting the winner to the final model. Taken seriously, this is a task of very great magnitude. By using the model builders implicitly assumed that much of this has already been accomplished by the specialists in the process of becoming specialists. This may have been a somewhat too optimistic assessment both of the processes of education in economics and the state of research in most of its subfields. Nevertheless, it must be assumed that each of the pieces would try to represent as well as possible the current state of quantitative economic knowledge about the various sectors of the economy. To be useful in constructing an econometric model of the whole economy, one would hope that each of the proffered relationships would be based as much as possible on existing (correct! ) economic theory (which derives the implications of purposive behavior of consumers, producers, and other economic actors from the interaction of their tastes and the constraints facing them) and will reflect those structural and institutional aspects of the economy which remain relatively stable from period to period and not the accidental confluence of different time series. It is in this light that I will examine below the sixteen individual chapters which constitute the bulk of the book.2

ECONOMISTS have recently begun to devote an increasing amount of attention to the relationships among interest rates on various instruments. Rates on instruments which differ with respect to maturity alone, all other features supposedly being held constant, have in particular received a great deal of attention since the publication of Meiselman's [7] work in 1962. While the term structure has certainly received the most concern, import-ant contributions have been made to the broader problem of studying relationships among rates on bonds which differ with respect to features other than maturity.' An especially important aspect of this broader area of research lies in examining the relationships between rates on government and corporate bonds. Knowledge of the characteristics of these relationships is important for an understanding of the paths through which monetary policy affects yields on corporate bonds, and hence, perhaps, expenditures on investment. This paper presents the results of an examination of the relationships among several interest rates on government and corporate bonds for the period January 1951 through December 1960. Monthly data are used, and series for the rates on three-month treasury bills, one, two, three, four, five, ten, and twentyyear government bonds, commercial paper, and Moody's Aaa's and Baa's are studied.2 This list represents an array of instruments ranging over a broad maturity spectrum and featuring various levels of quality. Tools of spectral and cross-spectral analysis are used to study the behavior of the series and the relationships among the series at various important components of oscillation.3 In addition to calculating the standard statistics associated with the spectrum and cross-spectrum, the relatively new tool of complex demodulation is employed to study the seasonal behavior of selected rates.4

The article shows that innovations are induced, since they become more profitable with the expansion of output. The amount of resources devoted to innovating activity, however, is in general not the optimal one because of the pressure of two opposing forces. On the one hand, competition between potential innovators tends to make this amount too large, on the other, the inability of innovators to capture all the benefits tends to make the amount too small. When all benefits are captured by the innovator either there is no economic growth due to innovations or else innovators are the sole beneficiaries from that growth. When benefits are diffused the innovation will always lead to economic growth, but only by sheer coincidence will it lead to maximum growth, which may be missed because the innovation is introduced either too early or too late. The rate of growth is always positive if the innovation is introduced too late. It may fall to zero with too-early introduction or even become negative if innovational activity is subsidized.

T HE appropriations series collected by the National Industrial Conference Board (NICB) come closer than any other data to pinpointing the time of investment decisions. This paper reports a study of the time lags between the basic stimulants of investment output, cash flow, and interest rate and these appropriations. We find that appropriations respond quickly to these incentives. Three points summarize the results for the all manufacturing:

N ONE of the procedures used in the measurement of investment has generated more controversy than the deflation of construction expenditures, for the measured rate of growth of real investment in structures in the United States varies over a wide range depending on which price index is chosen. Deflators proposed for United States construction range from the official Department of Commerce Cost (CCC) used in the United States National Accounts, which implies that real investment in nonresidential structures grew by 228 per cent from 1919 to 1966, to the Bureau of Public Roads (BPR) Composite Highway Index suggested in [7], which implies a much higher 19191966 increase of 47& per cent. Far from achieving a reconciliation between these two alternatives, conflicting conclusions were reached in the most recent detailed studies of the subject. Dacy [2] [3] has proposed a method which yields a 1947-1963 price increase much closer to that of the BPR highway series than that of the CCC index, while Kendrick, R. A. Gordon, and other have cited evidence supporting the much faster long-run upward trend of the CCC.1 A resolution of this conflict would allow us to evaluate and improve United States capital stock data, which have recently been criticized for ignoring an alleged bias in the construction price deflators.2 Can the apparent decline in the United States capital-output ratio between the 1920's and 1950's, for instance, be partly explained by defects in the official construction price indexes? In addition, improved information on construction prices would yield new output and productivity indexes suitable for testing the commonplace proposition that construction is a primitive industry in which efficiency improvements are almost as rare as in barbershops and the United States Post Office.3 This paper examines primary source data on the subject and proposes a new construction price index for the period 1919-1966. The new index is based partly on actual buyers' prices paid for several structural components and partly on an improved version of Dacy's indirect method using aggregative data for the entire contract construction industry. To anticipate the conclusion of the paper, the new index exhibits a long-run trend which is, appropriately enough, halfway between those of the CCC and BPR indexes and suggests that the official data overstate investment in structures in the mid-1920's by more than 35 per cent. The paper also suggests that productivity advances in construction have been surprisingly rapid in the postwar years, in contrast to an apparent stagnation of efficiency in the prewar period.