This paper contains an analysis of the effect of inflation on aggregate tax evasion in the United States over the period 1947-81. It is found that tax evasion in both absolute and relative terms is positively related to the inflation rate. Further, the results indicate that aggregate evasion has risen in both absolute and relative terms with increases in the marginal tax rate, but has fallen with increases in the detection probability, the penalty rate, and the wage share of income. Finally, evasion has risen in absolute terms but has fallen in relative terms when real true income has risen.

SUMMARY In this paper, it is argued that average tax rates exert an influence on income tax evasion separate from, and opposite to that of marginal tax rates. Failure to account for this effect in empirical evasion models biases the parameter estimate of the marginal rate in a predictable manner. Evidence from an aggregate empirical model of evasion in the US indicates that the marginal tax rate is positively related to evasion, whereas the average tax rate is negatively related. Further, exclusion of the average rate from the model does in fact bias the parameter estimate of the marginal tax rate.

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Procedures for tracking and forecasting economic conditions in regional economies have evolved significantly over the last 30 years. Much of this evolution has followed developments in macroeconomics, where techniques for tracking/forecasting key economic variables have tended to originate. This technique adoption and adaptation process continues today, as developments in the modeling of cointegrated macroeconomic time series have begun to appear in the regional modeling and forecasting literature. This paper presents an effort at modeling a segment of a regional economy using the cointegration testing procedures suggested by Johansen and Jusilius (1990) to develop a forecasting model for manufacturing employment in Milwaukee, WI. The paper demonstrates how Vector Error Correction (VEC) modeling can lead to gains in the accuracy of local manufacturing employment forecasts relative to more traditional VAR models in either levels or first-differenced form. In the process, it demonstrates procedures for developing a relatively simple VEC model that reveals something about the structure of the local manufacturing sector, including possible linkages to the national economy. This information can assist local policy makers in anticipating and adapting to business cycle-related fluctuations in this critical sector of the local economy.

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