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In the budget process of any unit of government, the revenue forecast sets the parameters for the allocation of dollars among competing pnontles. Because revenues are typically forecast 18 to 24 months prior to the beginning of each fiscal year, there is the potential for substantial error. If revenues are overestimated, disruptive midcourse corrections must be made. The recent recession in FY91 forced many states to increase revenues or cut spending in midyear because actua revenues (and spending) were out ofline with earlier forecasts. The most notorious examples were the states of California, Connecticut, and New Jersey, where well-publicized disputes between governors and legislatures ensued after the discovery of huge budget shortfals.
In this article, we will examine the proposition that state governments have consistently underforecast revenues every budget period in order to prouide a cushion in the event of an unanticipated downturn in economic conditions. We will evauate the extent and degree of underforecasting in all states during periods of economic expansion as well as periods of recession over an 18-year period.
First, we will explain how past research has treated forecasting error, and how our theory of underforecasting fits within the much wider context of previous research on revenue forecasting. Second, the hypothesis that states cushion their forecasts by underforecasting revenues will be tested by comparing data on forecast errors that were provided by state governments from FY87 through FY92, in addition to considerable evidence from states that provided us with forecast data for earlier years as well.
Explanations of Revenue Forecast Errors from Past and Current Research
The general focus of recent work on revenue forecasting has been on improving forecast accuracy, such that the smallest possible difference (or error) results beMreen the revenues that are forecast and the revenues that are collected. Most of the existing research consists of improving forecasting models, so that all of the factors that influence revenue collections are taken into account. This is a reasonable pursuit; indeed, such efforts have made an important contribution to improving the reliability of forecasts.
Despite the dedicated efforts of researchers and forecasting professionals to incorporate al factors into their models that have the potential to influence revenue streams, any single forecast may be wrong (Vasche and Williams, 1987; 66), sometimes...