STATE TAX REVENUE FORECASTING ACCURACY
REVENUE FORECASTINGSTATE TAX REVENUEFORECASTINGACCURACYTechnical ReportSeptember 2014Donald J. Boyd and Lucy DadayanRockefeller Institute of GovernmentState University of New YorkSupport for this project was provided byThe Pew Charitable TrustsThe Public PolicyResearch Arm of theState Universityof New York411 State StreetAlbany, NY 12203-1003(518) 443-5522www.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical ReportAcknowledgementsWe thank revenue forecasters and analysts in the states for responding to the survey on revenueforecasting conducted in the course of this project.We graciously thank The Pew Charitable Trusts for providing funding for the research and releaseof this report.The views expressed herein are those of the authors and do not necessarily reflect the views ofThe Pew Charitable Trusts.A Note on the Term “Forecast Error”Throughout this report we refer to the difference between a forecast and actual results as a“forecast error.” This term is common in analyses of forecasts, whether they be forecasts of theeconomy, or of the weather, or of state tax revenue. It does not imply that the forecaster made anavoidable mistake or that the forecaster was somehow unprofessional. All forecasts of economicactivity will be wrong to some extent, regardless of the expertise of the forecaster or the qualityof the tools used. Forecasting errors are inevitable because the task is so difficult: Revenue forecasts are based in part upon economic forecasts, and economic forecasts prepared by professional forecasting firms often are subject to substantial error; tax revenue is volatile anddependent upon idiosyncratic behavior of individual taxpayers, which is notoriously difficult topredict; and tax revenue is subject to legislative and administrative changes that also are difficultto predict.Rockefeller InstitutePage iiwww.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical ReportContentsSTATE TAXREVENUEFORECASTINGACCURACYTechnical ReportSeptember 2014Rockefeller InstituteExecutive Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . vDescription and Summary of Data Used in This Study . . . . vRevenue Forecasting Accuracy and Revenue Volatility . . . . viRevenue Forecasting Accuracy and the Timing andFrequency of Forecasts . . . . . . . . . . . . . . . . . . . . viiRevenue Forecasting Accuracy and InstitutionalArrangements . . . . . . . . . . . . . . . . . . . . . . . . . viiIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Description And Summary of Data Used in This Study . . . . . . 2Description of Data. . . . . . . . . . . . . . . . . . . . . . . . . 2Measures of Forecasting Error . . . . . . . . . . . . . . . . . . 5Forecasting Errors as Percentage of Actual Revenue . . . . . . 7Absolute Value of Percentage Forecasting Errors . . . . . . . 11Constructing a Forecast Difficulty Measure . . . . . . . . . . 12Conclusions and Policy Implications FromDescriptive Analysis. . . . . . . . . . . . . . . . . . . . . . 15Revenue Forecasting Accuracy and Revenue Volatility . . . . . . 18Prior Research on Revenue Volatility and RevenueForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Trends in Revenue Volatility and How It Relates toForecasting Accuracy . . . . . . . . . . . . . . . . . . . . . 20Reducing Volatility by Changing Tax Structure . . . . . . . . 23Managing Volatility and Errors . . . . . . . . . . . . . . . . . 24Conclusions and Policy Implications RegardingForecasting Accuracy and Revenue Volatility . . . . . . . 25Revenue Forecasting Accuracy and the Timing andFrequency of Forecasts . . . . . . . . . . . . . . . . . . . . . . . 27Prior Research on Forecasting Accuracy andForecast Timing and Frequency . . . . . . . . . . . . . . . 27Measuring Timing of Forecasts . . . . . . . . . . . . . . . . . 27Accuracy and the Timing of Forecast . . . . . . . . . . . . . . 28Accuracy and the Frequency of Forecast Updates . . . . . . . 31Conclusions and Policy Implications RegardingForecasting Accuracy and the Timing and Frequencyof Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Revenue Forecasting Accuracy and InstitutionalArrangements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Prior Research on Forecasting Accuracy andInstitutional Arrangements . . . . . . . . . . . . . . . . . . 34Observations From the Survey . . . . . . . . . . . . . . . . . 37Descriptive Analysis . . . . . . . . . . . . . . . . . . . . . . . 39Conclusions and Policy Recommendations RegardingForecasting Accuracy and Institutional Arrangements . . 39Appendix A: Survey of State Officials . . . . . . . . . . . . . . . . 41Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Page iiiwww.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical ReportExecutive SummaryThis report updates data on state revenue forecasting errorsthat was initially presented in “Cracks in the Crystal Ball,” a2011 report on revenue forecasting in the states produced incollaboration with The Pew Charitable Trusts. It supplementsthese data with additional data, including the results of a surveyof state forecasting officials conducted by the Rockefeller Institute.In addition to examining how revenue forecasting errors havechanged since 2009, which was the last data point in the prior project, we examine the relationship between revenue forecasting accuracy and:n Tax revenue volatility;n Timing and frequency of forecasts; andn Forecasting institutions and processes.Our main conclusions and recommendations follow.Description and Summary of Data Used in This StudyOur analysis for this report is based on four main sources.First, as with the 2011 report, we computerized data on revenueestimates and revenue collections for the personal income, sales,and corporate income taxes from the Fall Fiscal Survey of the Statesfrom the National Association of State Budget Officers (NASBO)for each year from 1987 through 2013, covering a total oftwenty-seven years. We define the forecasting error as actual minus the forecast, and thus include both overforecasting andunderforecasting. In other words, a positive number is an underestimate of revenue (actual revenue is greater than the forecast),and a negative number is an overestimate.Second, we developed a new measure of forecast difficultythat we used both in descriptive analyses of the data, and in statistical analyses to allow more accurate estimates of the influence ofother factors on forecast error, after controlling for the difficulty ofa forecast. The measure of forecast difficulty is the error that results from a uniformly applied simple or “naïve” forecastingmodel, which we then compare to forecasting errors that resultfrom states’ idiosyncratic and more elaborate revenue forecastingprocesses.Third, as before, we included other secondary data in ouranalyses, including measures of year-over-year change in state taxrevenue from the Census Bureau, and measures of the nationaland state economies from the Bureau of Economic Analysis. In addition, several other researchers provided us with data that theyhad developed on the institutional and political environment inwhich revenue forecasts are made, and we incorporated some ofthese data into our analyses.Fourth, we surveyed state government officials involved instate revenue forecasting. We did this to gain a more detailed understanding of the NASBO data on forecasts and results, and alsoto learn more about the institutional forecasting arrangements inRockefeller InstitutePage viwww.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical Reporteach state, and how forecasts are used in the budget process.Among other things, this survey allowed us to develop a measureof the typical lag in each state between the time a revenue forecastis prepared and the start of the fiscal year, which we used in ouranalysis of the impact of the timing and frequency of forecasts onforecast accuracy.Our main conclusions from our initial descriptive analyses ofthese data sources are:n Corporate income tax forecasting errors are much largerthan errors for other taxes, followed by the personal income tax and then the sales tax. The median absolute percentage error was 11.8 percent for the corporate incometax, 4.4 percent for the personal income tax, and 2.3 percent for the sales tax.n Smaller states and states dependent on a few sectors of theeconomy (particularly states reliant on oil or natural gas,or gambling) — such as Alaska, Montana, Nevada, NewHampshire, and North Dakota — tend to have larger errors. Those states’ errors also tend to be more variable.(Among these states, the taxes reported to NASBO byAlaska, Delaware, Montana, New Hampshire, Vermontand Wyoming are a relatively small share of total taxes asmeasured by the Census Bureau. Their errors for totaltaxes in their budget, which also include other less volatiletaxes, appear to be smaller than those examined here.However, these states also have large errors from naïveforecasting models using full Census data.)n When taxes are particularly difficult to forecast, states tendto be more likely to underforecast revenue, suggesting thatthey may try to do so in an effort to avoid large shortfalls.Thus, there is a pattern to the apparent bias in state revenue forecasts. By contrast, our naïve forecasting modeldoes not become more likely to underforecast when forecasting becomes more difficult, suggesting that this phenomenon may reflect the behavior of forecasters ratherthan underlying factors in the economy or tax revenuestructures.n Errors become particularly large in and after recessions.n Variation in errors across states also increases in and afterrecessions.n Errors near the 2001 and 2007 recessions were much worsethan in the recession of the early 1990s.n States have returned to “more normal” forecasting errorswith the 2007 recession now behind us.Revenue Forecasting Accuracy and Revenue VolatilityIncreases in forecasting errors have been driven by increasesin revenue volatility, which in turn have been driven in large partRockefeller InstitutePage viiwww.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical Reportby volatile capital gains, which have grown as a share of adjustedgross income over the last several decades.States have relatively little opportunity to reduce volatilitysimply by restructuring their tax portfolios. Only by virtuallyeliminating the corporate income tax and significantly increasingreliance on the sales tax relative to the personal income tax couldthe typical state reduce revenue forecasting errors, and even thenmost tax combinations would not reduce forecast errors verymuch. Changing the structure of individual taxes may be morepromising, but it can raise difficult tax policy issues. For example,making income taxes less progressive might also make them lessvolatile and easier to forecast, but that may run counter to distributional objectives that some policymakers hope for.Because it is so hard to reduce forecasting errors by changingtax structure, it is especially important for states to be able to manage the effects of volatility. Rainy day funds are one importanttool states can use. However, the data suggest that states withlarger forecast errors and more difficult forecasting tasks do nothave larger rainy day funds; perhaps they should.Revenue Forecasting Accuracy andthe Timing and Frequency of ForecastsFurther-ahead forecasts are more error prone, whether we examine the forecasts for the second year of a biennium, which generally are prepared further in advance, or use a measure of the lagbetween forecast preparation and the start of the forecast periodthat we developed based on the information collected from oursurvey. In both cases the effect is meaningful: the data suggestthat accuracy worsens by well over a percentage point in the forecast for the second year of a biennium, and worsens by about ahalf a percentage point for every ten weeks of lag between thetime a forecast is prepared and the start of the forecast period.There is no evidence from our data that frequent forecast updates lead to greater accuracy. This is consistent with pastresearch.The policy implications of the first part of our analysis areclear: States should minimize unnecessary lags between forecastpreparation and the start of the fiscal year, and should updatethose forecasts as close as possible to the start of the fiscal year, asmany states do. Even though there is no evidence that more frequent forecast updates during the forecast period will lead tomore accurate forecasting, it is good management practice to update forecasts regularly as the year progresses so that finance officials are managing the budget using the best availableinformation.Revenue Forecasting Accuracyand Institutional ArrangementsOur reading of the literature is that there is very little relationship between consensus forecasting and forecast accuracy. That isRockefeller InstitutePage viiiwww.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical Reportconsistent with our examination of these data, also. However, aswe have noted before, the evidence in favor of examining andcombining forecasts is overwhelming: Combining forecasts tendsto lead to lower errors. Processes that encourage this to happenmay also lead to lower errors.Beyond that, it is good practice to try to insulate forecastingfrom the political process and consensus forecasting can help toachieve that.Rockefeller InstitutePage ixwww.rockinst.org
REVENUE FORECASTINGSTATE TAX REVENUEFORECASTINGACCURACYTechnical ReportState University of NewYork411 State StreetAlbany, New York 12203(518) 443-5522www.rockinst.orgCarl HaydenChair, Board of OverseersThomas GaisDirectorRobert BullockDeputy Director forOperationsPatricia StrachDeputy Director for ResearchMichael CooperDirector of PublicationsMichele CharbonneauStaff Assistant forPublicationsIntroductionThis report is based upon work conducted in 2013 and 2014and updates and extends analysis by the Rockefeller Institute of Government for “Cracks in the Crystal Ball,” a 2011 report on revenue forecasting in the states produced incollaboration with The Pew Charitable Trusts. 1 Among otherthings, the 2011 report concluded: errors in the annual revenue estimates have worsened.Revenues have become more difficult to predict accurately . [R]evenue overestimates during the nation’s pastthree recessions grew progressively larger as did the underestimates in the past two periods of economic growth.This analysis updates data on state revenue forecasting errorsdeveloped during that project and supplements it with data froma survey of state forecasting officials conducted by the RockefellerInstitute. In addition, this report examines the relationship between revenue forecasting accuracy and:n Tax revenue volatilityn Timing and frequency of forecastsn Forecasting institutions and processesFinally, we offer policy recommendations for how to improverevenue forecasting and to manage outcomes of the process.Nancy L. ZimpherChancellorRockefeller InstitutePage 1www.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical ReportDescription and Summary of Data Used in This StudyDescription of DataData on Forecasting Errors From NASBOEach fall the National Association of State Budget Officers(NASBO) ask state budget officers for revenue estimates used atthe time of budget enactment and for preliminary actual revenuecollections.2 They publish these results in the fall Fiscal Survey ofStates. The fall Survey typically is conducted in the months of Julythrough September and published in December.Below is an example of the question as it was asked in August2013 for fiscal year 2013 preliminary actuals and fiscal year 2014estimates.Please complete the following table with respect to actualrevenue collections for FY 2012, estimates used at FY2013 budget enactment, preliminary actual revenue collections for FY 2013 & estimates used at budget enactment for FY 2014 ( in millions).TaxesActualCollectionsFY 2012EstimatesUsed WhenBudget wasAdopted FY2013PreliminaryActuals FY2013EstimatesUsed WhenBudgetAdopted FY2014Sales TaxCollectionsPersonalIncome TaxCollectionsCorporateIncome TaxCollectionsWe computerized data on revenue estimates and revenue collections for the personal income, sales, and corporate income taxesfrom the NASBO Fall Fiscal Survey of States for each year from1987 through 2013, covering a total of twenty-seven years. In thesurvey, the “original estimates” are intended to be the forecasts onwhich the adopted budget was based, and the “current estimates”are the preliminary actual estimates for the fall after the year wasclosed (e.g., the estimate in fall 2013 for the fiscal year that endedin June 2013). In 1987 and 1988 the survey covered personal income and sales taxes only: from 1989 forward it included personalincome, sales, and corporate income taxes. Because NASBO datado not include the District of Columbia, we did not incorporatethe District in this analysis.The NASBO data held several great advantages for the purposes of our analysis: They are self-reported by states and thus reflect states’ own assessment of appropriate data; they are collectedRockefeller InstitutePage 2www.rockinst.org
Revenue ForecastingState Tax Revenue Forecasting Accuracy: Technical Reportby a single source; they are intended to be collected under a common set of definitions; they are collected for all fifty states in mostyears; and they go back more than twenty-five years, covering allor part of three recessions (in 1990, 2001, and 2007). It would beimpractical to assemble such a data set from scratch, collectinghistorical forecast and actual revenue data from individual statesover nearly three decades; records disappear, memories fade, andstaff move on, making it difficult to reconstruct these data.As with any self-reported numbers, there were some anomaliesin the NASBO data, which we were diligent in cleaning. We eliminated data in the following situations: cases in which only the original estimate was reported but not the current, and vice versa; casesin which the original and current estimates were identical for twoor more taxes, suggesting a possible reporting error; and cases inwhich we believed the estimating errors were too large to be plausible (the top 1 percent of cases with the highest absolute value offorecast error). California was missing from the NASBO data in2001 and 2009. Because it was such a large state, we contacted stateofficials in California directly and supplemented the NASBO datawith estimates of what would have been submitted to NASBO inthose years. Data were also missing for Texas in 1996, 1997, and1999. However, as noted below, it was not practical to obtain comparable data for missing values for Texas.After these adjustments, we have 3,347 observations of forecast errors for individual taxes. In addition, where data allowed,we computed forecast errors for the sum of the three taxes, foreach state and year, for an additional 1,311 values. 3Survey of State OfficialsWe supplemented forecast-error data from NASBO with a survey we conducted of state government officials. 4 Our intent wasto strengthen our ability to use the NASBO data in regressionanalyses, and to gain a richer understanding of state forecastingprocesses more generally. The survey had two main purposes:1. To gain a clearer understanding of what, exactly, theforecast data provided by states to NASBO represent; togather information on when those forecasts are prepared; and to understand how they are used in the budget process in the states.2. To gain a richer understanding of the variety of forecasting arrangements in the sta
2011 report on revenue forecasting in the states produced in collaboration with The Pew Charitable Trusts. It supplements these data with additional data, including the results of a survey of state forecasting officials conducted by the Rockefeller Institute. In addition to examining how revenue forecasting errors have
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