The Effect of Underforecasting on the Accuracy of Revenue Forecasts by State Governments

In the budget process of any unit of government, the revenue forecast sets the parameters for the allocation of dollars among competing priorities. 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 actual revenues (and spending) were out of line 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 shortfalls. In this article, we will examine the proposition that state governments have consistently underforecast revenues every budget period in order to provide a cushion in the event of an unanticipated downturn in economic conditions. We will evaluate 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 between 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 all factors into their models that have the potential to influence revenue streams, any single forecast may be wrong (Vasche and Williams, 1987; 66), sometimes significantly (Roberds, 1988). When revenues are overestimated, program cuts or revenue increases may be necessary (Schroeder, 1982; 122). In the case of underestimates, revenues exceed expectations and the door is open to criticisms of excessive taxation (Vasche and Williams, 1987). In either case, inaccurate estimates have the potential to cause nightmares for both government officials and political leaders. Recent advances in forecasting technology, while significant in their own right, have focused on evaluating the impact of a wide variety of economic, political, and institutional factors on forecast accuracy (e.g., Bahl, 1980; Bretschneider & Gorr, 1987). For example, the relationship between unemployment and revenues is fairly clearly established (Belongia, 1988; Kamlet, Mowery, and Su, 1987). If the timing of an economic downturn is misforecast, the effect of unemployment on revenues in any single year may be substantially misforecast as well. This type of error evens out over the long run, since it tends to be equally likely that the economy will perform better than the forecast or worse than the forecast in any particular year. The presence of uncertainty in revenue forecasts means that it is in the best interests of revenue forecasters to provide themselves with a cushion to guard against a drop in revenues that is unanticipated (Rubin, 1987). If forecasts of revenues incorporate a safety valve in the form of a cushion, then revenues will be consistently underestimated every forecast period, whether the forecast reflects the expectation of expansion or one of decline. …