Knowledge that Transforms

T HEORETICAL models of inventory inl vestment including Belsley (1969), Holt et al. (1960), Whitin (1953) and others invariably suggest the opportunity cost of inventories should be included as a key explanatory variable in any empirical study of inventory behavior. In the absence of an opportunity cost (or other holding costs), the theoretically optimal inventory holding is infinitely large. Even so, it is rather rare any financial variable emerges as statistically significant in empirical studies of inventories.1 Michael Lovell, who has written extensively on inventory investment, goes so far as to comment that the probability of obtaining an interest-rate coefficient with negative sign is 50 percent (1976, p. 400). Even the MITPennsylvania-Social Science Research Council model, which represents an explicit attempt to specify in detail the channels of monetary policy, fails to include monetary variables in its inventory equation.2 The absence of empirical evidence in support of a cost of capital effect on inventory investment renders uncertain what many economists regard as a major channel of monetary policy. It is often commented roughly three quarters of the variance in GNP is accounted for by changes in inventory investment. Since monetary policy is commonly viewed as very powerful, it is truly remarkable so little econometric evidence exists to indicate a cost of capital effect on the most variable component of GNP. This study attempts to re-examine the size and significance of the theoretically important cost of capital effect on inventory investment by utilizing firm specific cost of capital measures, as suggested by the finance theory literature, in a pooled cross section econometric analysis of inventory behavior. The cost of capital measure is computed using the actual balance sheet capitalization particular to each firm in the sample for each point in time. Use of a firm specific cost of capital measure instead of a market interest rate avoids the measurement errors introduced into the analysis by the latter procedure. Risk differences among firms, such as between General Motors and Chrysler, imply substantial differences in capital costs. The errors in measurement problem introduced by a market interest rate will bias towards zero the cost of capital effect. Thus a firm specific cost of capital measure may serve as a more effective opportunity cost variable in an econometric analysis of inventory investment. Perhaps even more critical than the use of firm specific cost of capital measures, the econometric analysis is conducted using two samples of firms with each sample disaggregated by stage of fabrication. The first sample, which includes heavy machinery producing companies, attempts to explain inventory investment behavior for companies produce output in response to orders. The second sample consists of textile companies produce output predominantly to stock in anticipation of orders. Aggregation of firms produce to stock and produce to order-and, in addition, aggregation of inventories across stages of fabrication-may obscure the underlying behavioral characteristics operate, in fact, at the individual firm level. As suggested by theory, the findings of this study indicate the cost of capital is a highly sigReceived for publication May 30, 1978. Revision accepted for publication October 30, 1979. * Federal Reserve Bank of New York. A preliminary draft of this paper was presented at the August 1978 meetings of the Econometric Society in Chicago, Illinois. Financial support for the formative stages of this research was provided by the Computer Science Center of the University of Maryland and from the University Research Board in the form of a faculty research award. The data were provided by the College of Business and Management of the University of Maryland. The author thanks Clopper Almon, Robert Eisner, Irwin Friend, Robert J. Gordon and Joel Popkin for their comments and David Dossetter for very able research assistance. Neither they nor the Federal Reserve Bank of New York nor the Federal Reserve System are responsible for the errors or views contained in this paper. I Studies by Kuznets (1964) and Liu (1963) are among the very few report statistically significant interest rate effects. 2 The paucity of econometric evidence on behalf of cost of capital effects is nevertheless consistent with the prewar survey of Meade and Andrews (1938) (and others) and the postwar surveys of Crockett, Friend and Shavell (1967) and Shavell and Woodward (1971), which questioned managers on the degrees to which they adjusted inventories in response to changes in financial conditions.

In this paper the labor force entry and exit by married women are examined using longitudinal data that enable one to observe actual changes in economic behavior and characteristics. The symmetry assumption is investigated by estimating separate equations for the entry and exit choice. Section II briefly analyzes the wifes labor supply decision and discusses the issue of symmetry. The data and analytical procedure are described in section III and the empirical results are presented in section IV. Some implications of our findings are presented in section V. (excerpt)

Nathaniel H. Leff, Kazuo Sato, Macroeconomic Adjustment in Developing Countries: Instability, Short-Run Growth, and External Dependency, The Review of Economics and Statistics, Vol. 62, No. 2 (May, 1980), pp. 170-179

gence on fertility and of household size on offspring's intelligence remain the subject of public debate. Most recently, attention has centered on the decline in Scholastic Aptitude Test scores and the possible effects of the postwar baby boom. These concerns suggest that economists would do well to introduce intelligence into the household utility maximization model used to explain differential fertility. We attempt to do this here.

D ESPITE the rapid development of techniques of regional economic analysis during the past fifteen years, most regional models have continued to focus upon selected aspects of the regional economy rather than upon its totality. Economic base models and regional input-output models have concentrated upon the relationships between the output and employment in the export sectors and the local sectors;' comparative cost models have concentrated upon the response of the export sectors to changes in relative regional production costs;2 and regional econometric models have concentrated upon the determinants of employment in the export sectors and the relationships between regional economic activity and that of the nation.3 This disparate collection of partial-equilibrium models generally does not make it possible to determine the full general-equilibrium effects of a given economic change on the total regional economy. For example, although economic base/input-output models permit the estimation of the indirect and induced employment and output effects arising from a change in final demand or the level of activity in the export sector, they treat the level of activity in the export sector as exogenous and do not permit factor substitution. Similarly, although comparative cost models explicitly recognize that the location of export industries is largely determined by relative production costs, they do not consider the interrelationships among the industries within the export and local sectors or the role that factor substitution can play in regional employment levels. Finally, although regional econometric models generally use a neoclassical labor demand function, and hence explicitly consider factor substitution, they do not fully differentiate between the factor-substitution and production cost effects of a change in regional input prices. Furthermore, they do not account for the full set of linkages among the industries in the export and local sectors. The growing need for comprehensive regional models for planning and policy analysis suggests that there would be substantial value in having models that synthesize the relevant aspects of existing regional economic theory into a single integrated construct. Such an integrated model would be useful for both forecasting and policy evaluation and should include the following fea-. tures: First, it should recognize that factor substitution is possible and that an increase in the regional price of any given factor will tend to cause substitution in favor of other factors (the factorsubstitution effect); Second, it should recognize that an increase in any input price in a region relative to that in other regions will tend to increase production costs in the region in question. The result will be a reduction in the comparative locational advantage for the affected region and a tendency toward a relative shift in employment in national-market industries away from that region to lower-cost regions (the location effect); Third, it should be able to quantify the relative magnitudes of the factor-substitution effect and the location effect arising from any given change in regional input prices; Fourth, it should recognize that a complex set of interrelationships exists not only between the export sector and the local sector, but also among the various industries within each sector. Received for publication May 17, 1978. Revision accepted for publication December 7, 1978. * University of Massachusetts at Amherst, Massachusetts Institute of Technology, and Regional Science Research Institute, respectively. Work on this model has been supported by the Commonwealth of Massachusetts. The authors are grateful to Edward M. McNertney for contributions to the development and estimation of many of the equations and to Roy E. Williams for a mathematical and statistical review of the model and for programming the model. ' See, for example, Isard (1960), Tiebout (1962), Bourque et al. (1967), Miernyk (1970), and Polenske (1974). 2 See, for example, Weber (1928), Hoover (1937), Isard (1956) and Borts and Stein (1964). 3See, for example, Friedlaender et al. (1975), Adams et al. (1976), and Glickman (1977).

In previous work (Zellner, 1978) structural coefficient estimates that minimize the posterior expectation of a generalized quadratic loss function were derived. On computing these minimum expected loss (MELO) estimates for coefficients of Klein's Model I and the Girschick-Haavelmo supply and demand model for food, it was found that the MELO coefficient estimates have values between corresponding direct least squares (DLS) and two stage least squares (2SLS) estimates (see Zellner and Park, 1979). This note explores the relation of MELO, DLS and 2SLS estimates. Below it is shown,that the MELO estimate of a vector of coefficients of endogenous variables in an equation of a linear, interdependent econometric model can be expressed as a matrix weighted average of the DLS and 2SLS estimates. In the scalar case, the MELO estimate can be expressed as a simple weighted average of the DLS and 2SLS estimates. Some properties of these matrix weighted averages are discussed using the results in Chamberlain and Leamer (1974).