Knowledge that Transforms

HE most important motivation for T current study investigating lagged relationships in corporate demand for liquid assets is current controversy over lags in monetary policy. The literature is so wellknown that it need not be recapitulated here. The evidence that has been presented is of two kinds: analysis based on turning points in different series, i.e., that by Friedman; and an analysis based on estimation of distributed lag relationships, i.e., by Karaken and Solow. The latter authors estimate distributed lag relationships in different sectors and simply add these up. However, as Tucker [12] has pointed out, in a general equilibrium context summing lags in various sectors to measure lag in monetary policy is not a valid procedure. This implies that estimation of distributed lags should be done in a simultaneous equations context. But we feel that there are still several unresolved problems connected with estimation of single equations. The present study, therefore, concentrates on estimating distributed lags in a single equation context, with a specific view of analyzing merits and demerits of various alternative estimation techniques proposed till now for estimation of distributed lag models. We recognize that controversy over lags in monetary policy must be resolved in a general equilibrium context, with additional evidence on interactions among all variables, and especially on lags in each component function. For manufacturing corporations there is little evidence on lags in liquid asset demand, since most studies do not focus explicitly on this problem. Only Heston [7] and Anderson [2] enter lagged dependent variables in their regressions to examine adjustment. However, neither study discusses various problems of statistical estimation and interpretation. Anderson finds that approximately one-third of adjustment to equilibrium occurs in one quarter for cash while figure is about onefourth for government securities.' On other hand, Heston discovers that cash adjusts less than two-thirds of way to equilibrium in one year, and government securities less than one-half.2 The number of aggregate time-series studies of money demand dealing explicitly with lags is also not large, nor are statistical procedures particularly sophisticated. In one of earliest studies using a lagged model, Bronfenbrenner and Mayer [3] find that implied speed of adjustment toward equilibrium is onefourth to one-half per year. Treating money supply as an endogenous variable and using technique of two-stage least squares, Teigen [11] observes that during postwar period, one-third of adjustment to equilibrium occurs in a quarter, while for interwar period about one-half of adjustment takes place in a year.3 A study of money demand by Chow [4] also considers question of lagged adjustment at length, both by contrasting permanent income and wealth with current income and by including lagged dependent variables.4 Chow finds that speed of adjustment is less than one-half in first year. In a Federal Reserve study, De Leeuw [5] employs alternative estimation techniques in an attempt to deal with problems of serial correlation and lagged adjustment. He concludes his analysis saying the long lag hypothesis emerges from tests against post-war data

N thi's paper we describe an econometric model of the American tobacco industry for the period 1949 through 1966. The model contains 19 equations and is divided into three major blocks - (1) leaf production, (2) leaf price, and (3) cigarettes. The objective is to explain the behavior of the tobacco industry over an 18-year period. Ultimately, we hope to use the model to perform policy simulation experiments to evaluate the effects of alternative governmental and managerial policies on the behavior of the industry. We begin with a brief description of the industry. Next we discuss the theoretical specification of the model and the statistically estimated equations. We conclude with some example simulation results which provide additional evidence of the validity of the model for explaining the behavior of the tobacco industry over the period 1949 through 1966

war Economy (Washington: Brookings Institution, 1963). [4] Jaffee, D., Credit Rationing and the Commercial Loan Market, Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1968. [5] Keynes, J. M., General Theory of Employment, Interest and Money (New York: Harcourt, Brace and World, Inc., 1936). [6] Lipsey, R. G., The Relation Between Unemployment and the Rate of Change of Money in the United Kingdom, 1862-1957: A Further Analysis, Economica, 27 (Feb. 1960), 1-31. [7] Perry, G. L., Unemployment, Money Wage Rates, and Inflation (Cambridge: Massachusetts Institute of Technology Press, 1966). [8] , Wages and the Guideposts, American Economic Review, 57 (Sept. 1967), 897-904.

D ISTRIBUTION of the population by urban, rural nonfarm and farm residence is one of the oldest and most established causes of differentials in fertility cited by demographers.' Geographical region is also frequently mentioned in demographic studies as a source of variation in fertility in the United States. Race and social class, the latter often measured by occupational status, are additional variables popular with both demographers, and less specialized sociologists, as sources of variation in fertility.2 In most of the studies by demographers, a major difficulty with the factors proposed as causes of variation in fertility is that there is no way of knowing whether the variables are separate and independent explanations of birth rate differentials. To some extent, this is due to the fact that the statistical methodology consists of simple correlations, or more frequently, tabular and graphical presentations, which limit the analysis to two or three dimensions. A more fundamental criticism is that the discussion of the causal relationship between the independent variables and the fertility differentials does not attempt to assay whether basic factors, such as income and economic conditions, and the costs and benefits of having children, are common explanations of the variations in fertility by community of residence, geographical region, class, race, etc. Analyses of birth rate differentials by economists usually are based on a stronger statistical methodology than the studies by demographers but suffer from a similar weakness in their analytical formulation. independent contribution of the variables to fertility differentials is determined by means of multiple regression or partial correlation analysis. Although meaningful statistically, the regression results usually afford little insight into the fundamental relationship between population growth and economic development and/or economic conditions. One source of confusion in economic crosssection studies of birth rates may be the unfortunate choice of data. Some economists apparently ignore the demographers' findings that a considerable portion of the variance in fertility within a country is due to geographical differentials, and attempt a cross-section analysis of fertility on a very heterogeneous sample composed of different countries.3 It would seem that a more homogeneous sample of observations within a country is a more propitious beginning for an interpretation of the relationship between birth rates and economic variables. Economists have benefited from one of the findings of demographers. A popular variable for inclusion is the fraction of the population classified as farm or the per cent of the labor force employed in nonagricultural industries. Weintraub suggests that the ratio of the popu* This paper is a revision of an earlier version presented at the annual meeting of the Western Economic Association at Corvallis, Oregon, August, 1968. It has benefited from a critical reading by our colleague Jerzy F. Karcz who made a number of helpful suggestions. Our appreciation goes to Dana Burtness who assisted us in all phases of this study but especially in making our interactions with the IBM 360 Computer pleasant. Additional credit goes to John Danforth and Ken Gralla who assisted us in the early stages of this study. 'Ben Franklin noted this causal relationship between birth rate differentials and population distribution as early as 1786. 2The following references are typical of the analysis of differential fertility by demographers. Donald J. Bogue, of the United States, Free Press of Glencoe, Illinois (1959), chapter 12 -The Fertility of the United States Population (contributed by Wilson H. Grabill). Warren S. Thompson, Problems, McGraw-Hill Book Co. (1965), chapter 11 -Some Factors Affecting Fertility. As an example in the same vein by a sociologist, refer to the book by T. Lynn Smith, Fundamentals of J. P. Lippincott Co. (1960), chapter 13Differential Fertility. 3 Typical of the multiple regression cross-section analysis of fertility differentials in different countries conducted by economists are Irma Adelman, Econometric Analysis of Growth, American Economic Review, 52, no. 3 (1963); Robert Weintraub, The Birth Rate and Economic Development, An Empirical Study, Econometrica, 40, no. 4 (Oct. 1962).

Robert Ferber, John Forsythe, Harold W. Guthrie, E. Scott Maynes, Validation of a National Survey of Consumer Financial Characteristics: Savings Accounts, The Review of Economics and Statistics, Vol. 51, No. 4 (Nov., 1969), pp. 436-444

W HEN the surface of the economist is 14/1 7 scratched we generally find a belief that vertical integration in the corporate sector has increased during the past few decades, if not longer. This proposition, however, has not been put to a rigorous empirical test for the entire corporate sector. According to Professor Bain, We must, in the present state of knowledge, confine ourselves to a few remarks based on miscellaneous scraps of evidence. I In this note a measure of vertical integration in the corporate sector is developed. The measure is calculated for the year 1929 and for the period 1948 through 1965. The conclusion reached on the basis of this empirical evidence is that there has not been any discernible increase in the degree of vertical integration in the corporate sector. If anything, there might have been a slight decline. The index we use is the ratio of corporate sales to gross corporate product standardized to abstract from the changes in output mix. A rise in this index implies a decline in corporate vertical integration and vice versa.2 Because industry sales data are on a consolidated basis by corporation and most of the gross corporate product is on an establishment basis, this series reflects a preponderance of any general movements on the part of corporations to merge with suppliers or customers. If, for example, firm A has a gross corporate product of 500 and sales to firm B of 1000 (firm A's purchased material inputs are 500) and firm B has a gross corporate product of 500 and sales of 1500, then total corporate sales for both firms equal 2500 and total gross corporate product equals 1000. In this instance the ratio of sales to gross corporate product equals 2.5. If these two firms merge, total corporate sales will then be 1500 and gross corporate product will still be 1000. The new ratio of corporate sales to gross corporate product will be 1.5. Vertical integration has caused a decline in our ratio. As is readily aDDarent. neither pure horizontal integration nor a pure conglomerate movement will affect our ratio.3 There are natural differences among industries which preclude the meaningfulness of comparing the degree of vertical integration in one industry with that of any other industry. Thus a corporation in the service or mining industry will naturally have a much lower sales to gross corporate product ratio than a corporation in the retail or wholesale trade industry. If the proportional mix of total gross product is changing, we could very easily find a change in the aggregate sales to gross product ratio without any changes in this ratio for any specific industry. Any conclusions about changes in the ratio which are due to such changes in the proportional mix implies interindustry comparisons. In order to avoid the mix problem we calculate the aggregate ratio using the proportional mix of one base period. More explicitly our methodology is as follows: For any year t, total corporate sales, St, is equal to the sum of total corporate sales for each industry i. Thus,