Performance Evaluation of the New Connecticut Leading Employment Index Using Lead Profiles and BVAR Models

Dua and Miller (1996) created leading and coincident employment indexes for the state of Connecticut, following Moore's (1981) work at the national level. The performance of the Dua-Miller indexes following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out-of-sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new indexes show improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non-parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates the truth in the Granger and Newbold (1986) caution that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support.

Direct and Indirect Measures of Capacity Utilization: A Nonparametric Analysis of U.S. Manufacturing

We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

Wage Premia in Employment Clusters: Agglomeration Economies or Worker Heterogeneity?

The correlation between wage premia and concentrations of firm activity may arise due to agglomeration economies or workers sorting by unobserved productivity. A worker's residential location is used as a proxy for their unobservable productivity attributes in order to test whether estimated work location wage premia are robust to the inclusion of these controls. Further, in a locational equilibrium, identical workers must receive equivalent compensation so that after controlling for residential location (housing prices) and commutes workers must be paid the same wages and only wage premia arising from unobserved productivity differences should remain unexplained. The models in this paper are estimated using a sample of male workers residing in 33 large metropolitan areas drawn from the 5% Public Use Microdata Sample (PUMS) from the 2000 U.S. Decennial Census. We find that wages are higher when an individual works in a location that has more workers or a greater density of workers. These agglomeration effects are robust to the inclusion of residential location controls and disappear with the inclusion of commute time suggesting that the effects are not caused by unobserved differences in worker productivity. Extended model specifications suggest that wages increase with the education level of nearby workers and the concentration of workers in an individual's own industry or occupation.

Nonparametric Measures of Capacity Utilization: A Dual Approach

This paper develops a nonparametric method of obtaining the minimum of the long run average cost curve of a firm to define its capacity output. This provides a benchmark for measuring of capacity utilization at the observed output level of the firm. In the case of long run constant returns to scale, the minimum of the short run average cost curve is determined to measure short run capacity utilization. An empirical application measures yearly rates of capacity utilization in U.S. manufacturing over the period 1968-1998. Nonparametric determination of the short run average cost curve under variable returns to scale using an iterative search procedure is described in an appendix to this paper.