A recent paper in this REVIEW (LongRasmussen-Haworth, 1977) challenges the view that urban size is a source of income equality. L-R-H develop an econometric model that explains variation in the Gini coefficient of male incomes and family incomes. They conclude that urban size creates a more unequal distribution of urban income (as indicated by a significant negative sign on the SMSA population variable). This paper makes several contributions to our knowledge of urban income distribution. First, unlike previous papers, it develops a theoretical framework for separating the effects of urban size and level of development. Second, it provides a theoretical basis, as well as empirical justification, for the hypothesis that level of development (as measured by urban income level) bears a non-linear relationship to urban income inequality, becoming negative at high levels of income. Third, all authors except L-R-H anticipate to decline while moving up the urban hierarchy (size distribution of cities). This paper makes the point that the urban hierarchyequality hypothesis is partly supported but that the conclusion hinges on the choice of measure. Fourth, virtually all previous research on urban income distribution uses the Gini coefficient as the measure of income inequality. This paper uses three measures to test the sensitivity of conclusions to the choice of measure: the Gini coefficient, the incidence of poverty, and the percentile index. Despite the widespread belief by most economists that efficiency and equity do not go hand-in-hand, the urban size-equality view suggests there is no trade-off between the two. Richardson, for example (1973, p. 53), argues that inequality decreases as we move up the urban hierarchy.1 Most of the empirical work on state, county, and urban income distribution supports this view (Mattila and Thompson, 1968; Hoch, 1972; Farbman, 1974; Aigner and Heins, 1967; AlSumarrie and Miller, 1967; Conlisk, 1967; Murray, 1969; Frech and Burns, 1971; Burns, 1975; Danziger, 1975). Income inequality, as measured by the Gini coefficient, consistently declines with income level of the state, metropolitan area, or county. The incidence of poverty also appears to decline with urban income level (Omrati, 1968; Richardson, 1973). However, only three studies include both population and income level in their testing (Burns, 1975; Danziger, 1975; and Long-Rasmussen-Haworth, 1977), and only the L-R-H research finds both population and income to be significant variables. Both the theoretical argument and the empirical evidence presented in this paper point to the existence of an efficiency-equity trade-off. Previous research on urban income distribution seems to miss the trade-off because the various issues involved have not been clearly separated. Thus, several basic questions surrounding urban income distribution are investigated in this paper: 1. Does urban size, as measured by SMSA population, have a separate effect from level of development? A theoretical basis for a separate inequality-creating effect of urban size is an expected productivity-agglomeration effect which increases the productivity of skilled labor more rapidly than the productivity of unskilled labor. 2. Can we expect level of development, as measured by urban income level, to have a consistent equalizing effect on urban income distribution? A theoretical argument based on an expected amenity-compensation effect leads us

This paper presents a model to estimate the rate of capacity utilization (CU) in the manufacturing sector by state. Consistent measures of state-level CU rates have been unavailable since 1982. The lack of a capacity measure has hindered regional studies of capital formation, long-run output growth, and productivity growth. Our model employs a neoclassical approach to estimate CU. We estimate a model to determine the optimal level of production and compare it with the actual level in order to define our CU index. Our results show that states in the West North Central, South Atlantic, and Pacific census divisions tend to have a CU index consistently above the nation's average. On the other hand, states in the East North Central and West South Central census divisions tend to have a CU index below the nation's average.

High labor costs in large Midwestern metropolitan areas have significantly reduced their manufacturing capital stock. For the period 1974 to 1978, the authors estimate that sixteen metropolitan areas in the Midwest, taken together, had approximately $2.8 billion less capital stock than they would have had if their labor costs had been at the national average. This difference is equal to 4 percent of the capital stock in these areas. The results are simulated from the estimation of a labor demand equation that is derived from a generalized Leontief cost function.

This paper seeks to reconcile the growth empirics technique of Mankiw, Romer, and Weil (1992) with the empirical results of Barro and Sala-“i-Martin (1991) through the development of a new database covering the 1977-96 period. We create state-by-state capital stock and gross investment estimates by apportioning the national capital stock among the states. Using these estimates along with gross state product and employment data, we find evidence that the Solow growth model explains state-wide growth during this period. We consistently find a rate of convergence of around 2%. Our results, as a consequence, suggest that the empirical results of Barro and Sala-í-Martin are driven by the neoclassical growth process of Solow.