T HIS study investigates within a comrnon analytical framework the determinants of firm expenditures o;n capital investment, research and development and dividends. Its two basic objectives relative to past work are: first, to probe more deeply into the forces determining these outlays by taking into account the interdependencies among them,' and second, to provide a framework for evaluating alternative assumptions regarding firm motivation. A firm maximizing stockholder objectives will exhibit different behavior in its expenditure decisions from one pursuing managerial goals. Consequently, two main variants of a model of firm expenditures, based on these rival concepts of motivation, are developed and tested.

M ANY cross-section studies exist in the literature attempting to explain variations in per capita state and local expenditures on such services as highways, municipal services, health, and education. Differences in per capita expenditures across states are explained in terms of such factors as differences in population densities, urban-rural distributions, average income levels, age distributions, and numbers of school-age children.' In most of these studies, however, the underlying theory has not been carefully spelled out and, in particular, it is never made clear whether the demographic variables are thought to enter on the demand side or on the supply side of the market for state and local services. In the first part of this paper we look at the theory underlying such regression studies of the determinants of state and local expenditures. We attempt to distinguish between the demand side of the market for such services and the supply (cost) side. In the second part of the paper we obtain empirical estimates of price and income elasticities of demand for state and local services for three broad classifications of services. Finally we discuss a number of interesting applications of our results.

T HE purpose of this paper is to point out how the efficient instrumental-variables technique discussed by Brundy and Jorgenson (1971) can be modified to take into account auto-regressive properties of the error terms. The limited-information and full-information estimators proposed in this paper are consistent and have the same asymptotic distributions as the limited-information and full-information maximum likelihood estimators, respectively. The full-information estimation of simultaneous equations models with auto-regressive errors has been discussed by Sargan (1961), Hendry (1971), Chow and Fair (1973), and Dhrymes (1971). Sargan originally proposed the full-information maximum likelihood estimation of such models, and Hendry and Chow and Fair have recently developed computationally feasible methods for obtaining the maximum likelihood estimates. Hendry considered only the case of completely unrestricted auto-regressive coefficient matrices (i.e., no zero elements), whereas Chow and Fair considered the case of restricted auto-regressive coefficient matrices as well. Dhrymes has recently proposed the three-stage least squares estimator of simultaneous equations models with auto-regressive errors. Dhrymes also considered only the case of completely unrestricted auto-regressive coefficient matrices. The limited-information estimation of simultaneous equations models with auto-regressive errors has been discussed by Sargan (1961), Amemiya (1966), and Fair (1970), among others. Sargan proposed the limited-information maximum likelihood estimation of such models, and Amemiya and Fair considered various two-stage least souares estimators of such models. Most of the work on limited-information estimators has been concerned with the case of diagonal auto-regressive coefficient matrices. Brundy and Jorgenson's criticism of the twoand three-stage least squares estimators, namely, that the first stage involves estimating reduced form equations with a very large number of variables included in them, holds even more so for models with auto-regressive errors. For these models, the reduced form equations include not only all of the predetermined variables in the system but also all of the lagged endogenous and lagged predetermined variables. In fact, one of the main purposes of the work by Fair (1970) was to suggest ways in which the number of variables used in the first stage regressions of two-stage least squares might be decreased with perhaps small loss of asymptotic efficiency. The advantage of the instrumental-variables techniques proposed in the Brundy-Jorgenson paper and in this paper is that the first stage regressions need not be run.