STATE TAX REVENUE FORECASTING ACCURACY. Technical Report. September 2014

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1 REVENUE FORECASTING STATE TAX REVENUE FORECASTING ACCURACY Technical Report September 2014 Donald J. Boyd and Lucy Dadayan Rockefeller Institute of Government State University of New York Support for this project was provided by The Pew Charitable Trusts The Public Policy Research Arm of the State University of New York 411 State Street Albany, NY (518)

2 Acknowledgements We thank revenue forecasters and analysts in the states for responding to the survey on revenue forecasting conducted in the course of this project. We graciously thank The Pew Charitable Trusts for providing funding for the research and release of this report. The views expressed herein are those of the authors and do not necessarily reflect the views of The 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 the economy, or of the weather, or of state tax revenue. It does not imply that the forecaster made an avoidable mistake or that the forecaster was somehow unprofessional. All forecasts of economic activity will be wrong to some extent, regardless of the expertise of the forecaster or the quality of 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 and dependent upon idiosyncratic behavior of individual taxpayers, which is notoriously difficult to predict; and tax revenue is subject to legislative and administrative changes that also are difficult to predict. Rockefeller Institute Page ii

3 STATE TAX REVENUE FORECASTING ACCURACY Technical Report September 2014 Contents Executive Summary....v Description and Summary of Data Used in This Study...v Revenue Forecasting Accuracy and Revenue Volatility...vi Revenue Forecasting Accuracy and the Timing and Frequency of Forecasts...vii Revenue Forecasting Accuracy and Institutional Arrangements...vii Introduction... 1 Description And Summary of Data Used in This Study...2 Description of Data....2 Measures of Forecasting Error...5 Forecasting Errors as Percentage of Actual Revenue...7 Absolute Value of Percentage Forecasting Errors...11 Constructing a Forecast Difficulty Measure...12 Conclusions and Policy Implications From Descriptive Analysis Revenue Forecasting Accuracy and Revenue Volatility...18 Prior Research on Revenue Volatility and Revenue Forecasting...18 Trends in Revenue Volatility and How It Relates to Forecasting Accuracy...20 Reducing Volatility by Changing Tax Structure...23 Managing Volatility and Errors...24 Conclusions and Policy Implications Regarding Forecasting Accuracy and Revenue Volatility...25 Revenue Forecasting Accuracy and the Timing and Frequency of Forecasts...27 Prior Research on Forecasting Accuracy and Forecast Timing and Frequency...27 Measuring Timing of Forecasts...27 Accuracy and the Timing of Forecast...28 Accuracy and the Frequency of Forecast Updates...31 Conclusions and Policy Implications Regarding Forecasting Accuracy and the Timing and Frequency of Forecasts...33 Revenue Forecasting Accuracy and Institutional Arrangements Prior Research on Forecasting Accuracy and Institutional Arrangements...34 Observations From the Survey...37 Descriptive Analysis...39 Conclusions and Policy Recommendations Regarding Forecasting Accuracy and Institutional Arrangements.. 39 Appendix A: Survey of State Officials...41 Endnotes Bibliography Rockefeller Institute Page iii

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6 Executive Summary This report updates data on state revenue forecasting errors that was initially presented in Cracks in the Crystal Ball, a 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 changed since 2009, which was the last data point in the prior project, we examine the relationship between revenue forecasting accuracy and: Tax revenue volatility; Timing and frequency of forecasts; and Forecasting institutions and processes. Our main conclusions and recommendations follow. Description and Summary of Data Used in This Study Our analysis for this report is based on four main sources. First, as with the 2011 report, we computerized data on revenue estimates and revenue collections for the personal income, sales, and corporate income taxes from the Fall Fiscal Survey of the States from the National Association of State Budget Officers (NASBO) for each year from 1987 through 2013, covering a total of twenty-seven years. We define the forecasting error as actual minus the forecast, and thus include both overforecasting and underforecasting. 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 difficulty that we used both in descriptive analyses of the data, and in statistical analyses to allow more accurate estimates of the influence of other factors on forecast error, after controlling for the difficulty of a forecast. The measure of forecast difficulty is the error that results from a uniformly applied simple or naïve forecasting model, which we then compare to forecasting errors that result from states idiosyncratic and more elaborate revenue forecasting processes. Third, as before, we included other secondary data in our analyses, including measures of year-over-year change in state tax revenue from the Census Bureau, and measures of the national and state economies from the Bureau of Economic Analysis. In addition, several other researchers provided us with data that they had developed on the institutional and political environment in which revenue forecasts are made, and we incorporated some of these data into our analyses. Fourth, we surveyed state government officials involved in state revenue forecasting. We did this to gain a more detailed understanding of the NASBO data on forecasts and results, and also to learn more about the institutional forecasting arrangements in Rockefeller Institute Page vi

7 each state, and how forecasts are used in the budget process. Among other things, this survey allowed us to develop a measure of the typical lag in each state between the time a revenue forecast is prepared and the start of the fiscal year, which we used in our analysis of the impact of the timing and frequency of forecasts on forecast accuracy. Our main conclusions from our initial descriptive analyses of these data sources are: Corporate income tax forecasting errors are much larger than 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 income tax, 4.4 percent for the personal income tax, and 2.3 percent for the sales tax. Smaller states and states dependent on a few sectors of the economy (particularly states reliant on oil or natural gas, or gambling) such as Alaska, Montana, Nevada, New Hampshire, 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 by Alaska, Delaware, Montana, New Hampshire, Vermont and Wyoming are a relatively small share of total taxes as measured by the Census Bureau. Their errors for total taxes in their budget, which also include other less volatile taxes, appear to be smaller than those examined here. However, these states also have large errors from naïve forecasting models using full Census data.) When taxes are particularly difficult to forecast, states tend to be more likely to underforecast revenue, suggesting that they 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 model does not become more likely to underforecast when forecasting becomes more difficult, suggesting that this phenomenon may reflect the behavior of forecasters rather than underlying factors in the economy or tax revenue structures. Errors become particularly large in and after recessions. Variation in errors across states also increases in and after recessions. Errors near the 2001 and 2007 recessions were much worse than in the recession of the early 1990s. States have returned to more normal forecasting errors with the 2007 recession now behind us. Revenue Forecasting Accuracy and Revenue Volatility Increases in forecasting errors have been driven by increases in revenue volatility, which in turn have been driven in large part Rockefeller Institute Page vii

8 by volatile capital gains, which have grown as a share of adjusted gross income over the last several decades. States have relatively little opportunity to reduce volatility simply by restructuring their tax portfolios. Only by virtually eliminating the corporate income tax and significantly increasing reliance on the sales tax relative to the personal income tax could the typical state reduce revenue forecasting errors, and even then most tax combinations would not reduce forecast errors very much. Changing the structure of individual taxes may be more promising, but it can raise difficult tax policy issues. For example, making income taxes less progressive might also make them less volatile 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 changing tax structure, it is especially important for states to be able to manage the effects of volatility. Rainy day funds are one important tool states can use. However, the data suggest that states with larger forecast errors and more difficult forecasting tasks do not have larger rainy day funds; perhaps they should. Revenue Forecasting Accuracy and the Timing and Frequency of Forecasts Further-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 lag between forecast preparation and the start of the forecast period that we developed based on the information collected from our survey. In both cases the effect is meaningful: the data suggest that accuracy worsens by well over a percentage point in the forecast for the second year of a biennium, and worsens by about a half a percentage point for every ten weeks of lag between the time 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 past research. The policy implications of the first part of our analysis are clear: States should minimize unnecessary lags between forecast preparation and the start of the fiscal year, and should update those forecasts as close as possible to the start of the fiscal year, as many states do. Even though there is no evidence that more frequent forecast updates during the forecast period will lead to more 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 available information. Revenue Forecasting Accuracy and Institutional Arrangements Our reading of the literature is that there is very little relationship between consensus forecasting and forecast accuracy. That is Rockefeller Institute Page viii

9 consistent with our examination of these data, also. However, as we have noted before, the evidence in favor of examining and combining forecasts is overwhelming: Combining forecasts tends to lead to lower errors. Processes that encourage this to happen may also lead to lower errors. Beyond that, it is good practice to try to insulate forecasting from the political process and consensus forecasting can help to achieve that. Rockefeller Institute Page ix

10 REVENUE FORECASTING STATE TAX REVENUE FORECASTING ACCURACY State University of New York 411 State Street Albany, New York (518) Carl Hayden Chair, Board of Overseers Thomas Gais Director Robert Bullock Deputy Director for Operations Patricia Strach Deputy Director for Research Michael Cooper Director of Publications Michele Charbonneau Staff Assistant for Publications Technical Report Introduction This report is based upon work conducted in 2013 and 2014 and 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 in collaboration with The Pew Charitable Trusts. 1 Among other things, 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 past three recessions grew progressively larger as did the underestimates in the past two periods of economic growth. This analysis updates data on state revenue forecasting errors developed during that project and supplements it with data from a survey of state forecasting officials conducted by the Rockefeller Institute. In addition, this report examines the relationship between revenue forecasting accuracy and: Tax revenue volatility Timing and frequency of forecasts Forecasting institutions and processes Finally, we offer policy recommendations for how to improve revenue forecasting and to manage outcomes of the process. Nancy L. Zimpher Chancellor Rockefeller Institute Page 1

11 Description and Summary of Data Used in This Study Description of Data Data on Forecasting Errors From NASBO Each fall the National Association of State Budget Officers (NASBO) ask state budget officers for revenue estimates used at the time of budget enactment and for preliminary actual revenue collections. 2 They publish these results in the fall Fiscal Survey of States. The fall Survey typically is conducted in the months of July through September and published in December. Below is an example of the question as it was asked in August 2013 for fiscal year 2013 preliminary actuals and fiscal year 2014 estimates. Please complete the following table with respect to actual revenue collections for FY 2012, estimates used at FY 2013 budget enactment, preliminary actual revenue collections for FY 2013 & estimates used at budget enactment for FY 2014 ($ in millions). Taxes Actual Collections FY 2012 Estimates Used When Budget was Adopted FY 2013 Preliminary Actuals FY 2013 Estimates Used When Budget Adopted FY 2014 Sales Tax Collections Personal Income Tax Collections Corporate Income Tax Collections We computerized data on revenue estimates and revenue collections for the personal income, sales, and corporate income taxes from the NASBO Fall Fiscal Survey of States for each year from 1987 through 2013, covering a total of twenty-seven years. In the survey, the original estimates are intended to be the forecasts on which the adopted budget was based, and the current estimates are the preliminary actual estimates for the fall after the year was closed (e.g., the estimate in fall 2013 for the fiscal year that ended in June 2013). In 1987 and 1988 the survey covered personal income and sales taxes only: from 1989 forward it included personal income, sales, and corporate income taxes. Because NASBO data do not include the District of Columbia, we did not incorporate the 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 collected Rockefeller Institute Page 2

12 by a single source; they are intended to be collected under a common set of definitions; they are collected for all fifty states in most years; and they go back more than twenty-five years, covering all or part of three recessions (in 1990, 2001, and 2007). It would be impractical to assemble such a data set from scratch, collecting historical forecast and actual revenue data from individual states over nearly three decades; records disappear, memories fade, and staff move on, making it difficult to reconstruct these data. As with any self-reported numbers, there were some anomalies in 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; cases in which the original and current estimates were identical for two or more taxes, suggesting a possible reporting error; and cases in which we believed the estimating errors were too large to be plausible (the top 1 percent of cases with the highest absolute value of forecast error). California was missing from the NASBO data in 2001 and Because it was such a large state, we contacted state officials in California directly and supplemented the NASBO data with estimates of what would have been submitted to NASBO in those years. Data were also missing for Texas in 1996, 1997, and 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, for each state and year, for an additional 1,311 values. 3 Survey of State Officials We supplemented forecast-error data from NASBO with a survey we conducted of state government officials. 4 Our intent was to strengthen our ability to use the NASBO data in regression analyses, and to gain a richer understanding of state forecasting processes more generally. The survey had two main purposes: 1. To gain a clearer understanding of what, exactly, the forecast data provided by states to NASBO represent; to gather 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 states and how these processes relate to the budgeting process. We conducted the survey in two rounds. During the first round, we asked questions of state forecasting officials related to the data reported to NASBO. The survey questions were designed to help us get a better understanding of when estimates that underlie state budgets are developed, as that influences the difficulty of the forecasting job, and to be sure we understand how the forecasts are used in the budget process. Rockefeller Institute Page 3

13 The main purpose of the second round was to get a better understanding of general procedures and practices of revenue estimating processes in the states. The survey questions were targeted at getting more information about the timing of forecasts, frequency of forecast updates, the long-term forecasting practices, parties involved in the forecasting processes, and whether there is a formal group that plays a major role in the revenue estimating process. Both surveys were conducted online, using Google Docs. The first round was conducted during July-August of 2013, while the second round was conducted during August-September of For the first round of the survey, we sent an online survey questionnaire to state budget officers in all fifty states, who were also the respondents for the NASBO s survey. The list of respondents was retrieved from the Fiscal Survey of the States report. The response rate for the first round of the survey was 68 percent (34 usable responses were received). For the second round of the survey, we sent an online survey questionnaire to both legislative and executive branch officials in all fifty states who were likely to be involved in the forecasting process. The response rate for this round was much higher: 90 percent (forty-five responses) for the executive branch and 92 percent (forty-six responses) for the legislative branch. In aggregate (executive and legislative branches combined), we have received responses from all fifty states. The responses from the first round indicate that in most states the estimates provided to NASBO are the final estimates used for the adopted budget. States vary widely in how far in advance of the budget they prepare their estimates. Some states prepare revenue estimates as early as thirty-five weeks ahead of the start of the fiscal year (e.g., Alabama), while other states use revenue estimates updated about two weeks before the start of the state fiscal year (i.e., Delaware, Pennsylvania, and Tennessee). The survey results also revealed that, in general, states with a biennial budget cycle provide updated estimates to NASBO for the second year of the biennium in order to account for legislative changes and updated economic forecast conditions. Nonetheless, on average, estimates for the second year of a biennium are prepared further in advance of the second year than are estimates for the first year. The responses from the second round indicate wide variation among the states in the timing of the forecasts prepared for the adopted budget as well as in frequency of revenue estimate updates. In terms of long-term forecasting, states indicated that they typically generate revenue estimates for one or more years beyond the upcoming budget period; most states release revenue estimates for two to five years beyond the budget period. The state officials were also asked to indicate who is involved in the revenue estimating process. The survey results indicate that in most states one or more agencies of the executive branch are involved in the revenue estimating process and in a handful of Rockefeller Institute Page 4

14 states the legislative branch also plays a major role in revenue estimating. However, only very few states also involve academicians and/or experts from the private sector in the revenue estimating process. Finally, in a few states the revenue estimating process is under the jurisdiction of the nonpartisan independent group/ agency, the members of which are normally appointed by the governor and/or legislative branch. According to the survey responses, in approximately two-thirds of the states the participants in the revenue estimating process come to an agreement on a single number. In general, the survey results indicate that there is a wide range of practices and processes involved among the fifty states in terms of revenue estimates. There are no universally agreed upon standards and practices among the fifty states. The survey responses, as well as various discussions with the state government officials, indicate that it is highly desirable to have more frequent forecasts as well as to have a minimum time gap between the forecast and the adoption of the budget so that the states can take into account the latest economic conditions as well as the impact of any legislative and/or administrative changes. Overall, it is also desirable to have a consensus revenue estimating practice in the state, so that various parties involved in the forecasting process can come into an agreement. Other Data In addition to the NASBO data, we used U.S. Census state tax collection figures in estimating the size of errors across years or across the type of tax. We also used data on national price changes (the gross domestic product price index), gross domestic product by state, and state personal income from the U.S. Bureau of Economic Analysis. We also obtained data from several other researchers, and describe as needed below. 5 Finally, we constructed a measure of forecast difficulty from U.S. Census state tax collection data, which we describe in the section, Constructing a Forecast Difficulty Measure. How do the revenue estimating data relate to Census tax revenue data? Not all states rely heavily on the three taxes that NASBO collects forecast data for. Six states relied on these three taxes for less than half of their tax revenue in the typical year: Alaska, Delaware, Montana, New Hampshire, Vermont, and Wyoming (see Table 1). Measures of Forecasting Error There are several common ways of measuring forecast error. Some of these measures do not distinguish positive from negative errors and are useful for gauging magnitude of error, and others maintain the sign and are useful for gauging bias and asymmetries. One common measure is the root-mean-squared error (RMSE), or more appropriate when comparing across states, the root-mean-squared percent error (RMSPE). Rockefeller Institute Page 5

15 Table 1. Personal Income, Sales, and Corporate One very common measure in the literature examining state revenue forecasting is the absolute value of the Income Taxes as Percent of Total Tax Revenue error as a percentage of the actual result (known as absolute percentage error), or variants on this (Voorhees 2004). Summary measures of estimating error across states or years often use the mean or median of the absolute value of the percentage error (mean absolute percentage error is quite common and is known as the MAPE). These measures, which treat positive and negative errors the same, may not be the most appropriate in all situations. In particular, other measures are needed to examine questions of whether forecasts are biased (e.g., more likely to underestimate revenue than to overestimate) or whether forecasters believe the costs of overestimates are greater than the costs of underestimates. As many observers have noted, the revenue estimating error is quite large relative to what policymakers might find acceptable. In 2002, when the previous fiscal crisis hit sharply, twelve states had revenue estimating errors of 10 percent or more (Willoughby and Guo 2008). To provide some perspective, a 4 percentage point error in the state of California would be more than $4 billion an amount that policymakers would find quite disconcerting. We define the forecasting error as actual minus the forecast. Thus, a positive number is an underestimate of revenue (actual revenue is greater than the forecast), and a negative number is an overestimate. Forecasters often view underestimates as better than overestimates. Our primary measure of error is the error as a percentage of the actual revenue. Thus, if a forecast of revenue was $90 million and the actual revenue was $100 million, then the error was $10 million (an underestimate), and the percentage error was 10 percent. We use two main versions of this percentage as is, where it can be either positive or negative, and the absolute value, which is always positive. In general, the absolute value is useful when we are interested in the concept of accuracy, without regard to whether revenue is above target or below, although the as-is value can be useful, too, and we use that in much of the analysis below. When we want to examine bias (not a significant focus of this report), the absolute value measure is not useful, because we care about the direction of error, and we focus solely on the as-is value. When we want to summarize error across states, years, taxes, or other groupings, we almost always use the median as a measure of central tendency. The reason is that our data are fairly noisy and we don t generally place great faith in any single data point, although we do believe the data tell truthful stories in aggregate. Because any single data point might be wrong or an Rockefeller Institute Page 6

16 outlier, it could have an undue impact on the mean, pulling it away from what we think is the true central tendency of the data. When we want to measure how much the errors vary across states, years, taxes, or other groupings we use either the standard deviation or the interquartile range. In the next section, we provide simple descriptive statistics of errors, first using the percentage error as is and then the absolute value. There are some useful insights, which we summarize in the concluding subsection of this section. Forecasting Errors as Percentage of Actual Revenue Below we provide summary tables of errors by tax, by fiscal year, and by state, and explore patterns with several plots. Table 2 displays selected summary statistics for forecasting errors by tax for The corporate income tax median forecasting error, at 2.8 percent, was much larger than the median for other taxes. The personal income tax median forecasting error was 1.8 percent, while the sales tax forecasting error was 0.3 percent. The standard deviation of the error a measure of variability in forecast error and a potential indication of forecasting difficulty was largest by far for the corporate income tax, nearly three times as large as for the personal income tax, and five times as large as for the sales tax. Table 2. State Revenue Forecasting Percentage Errors by Tax, Forecasterrorsaspercentageofactualrevenue Tax #of Mean Standard Median 25% observations error deviation (50%) 75% Personalincometax 1, (2.3) Salestax 1, (2.0) Corporateincometax 1, (9.3) SumofPIT,sales&CIT 1, (2.2) Source:RockefellerInstituteanalysisofNASBOforecasterrordata. Figure 1 on the following page displays the median forecast error by tax. There is some evidence that forecasting errors are serially correlated that is, that a revenue shortfall in one year is more likely to be followed by a shortfall in the next than by an overage, and vice versa. Rudolph Penner, a former director of the Congressional Budget Office, concluded this about federal government forecasts: if CBO makes an error in an overly optimistic or pessimistic direction one year, it is highly probable that it will make an error in the same direction the following year (Penner 2008). Inspection of NASBO data in time periods surrounding recessions shows that, at least for the states in aggregate, revenue overestimates tended to be followed by additional overestimates. Figure 2 provides median forecasting errors by fiscal year for twenty-seven years, spanning from 1987 to As depicted on Figure 2, states normally underestimate during expansions and overestimate during the downturns. Moreover, overestimating Rockefeller Institute Page 7

17 Forecastingpercentageerror Forecastingpercentageerror Figure 1. State Revenue Forecast Errors as Percent of Actual Tax, Median for % 2.5% 2.0% 1.5% 1.0% 0.5% 0.3% 1.8% 0.0% Sales PIT CIT Sumof Sales,PIT,CIT Source:RockefellerInstituteanalysisofdatafromNASBOFallFiscalSurveys. Figure 2. Median State Revenue Forecast Errors as Percent of Actual Tax, by Fiscal Year 8% 6% 4% 2% 0% 2% 4% 6% 8% 10% 2.8% recession 2001recession Great recession 1.5% 12% Source:RockefellerInstituteanalysisofdatafromNASBOFallFiscalSurveys. Note:Forecastingerrorisforthesumofpersonalincome,salesandcorporateincometaxes. errors were particularly large near the 2001 and 2007 recessions. States now returned to their pattern of underestimation after the worst of the fiscal crisis was behind them. Table 3 on the following page provides summary statistics of forecasting errors by state from 1987 to Overall, smaller states and states dependent on a few sectors of the economy (particularly states reliant on oil or natural gas, or gambling), such as Alaska, Montana, Nevada, New Hampshire, and North Dakota, tend to have larger errors. Those states errors also tend to be more variable, as measured by the standard deviation or the interquartile range. As already mentioned above, personal income, sales, and corporate income taxes in Alaska, Delaware, Montana, New Hampshire, Vermont, and Wyoming represent less than half of total taxes as measured by the Census Bureau. Their errors for total taxes in their budget, which also include other less volatile taxes, appear to be smaller than those examined here. We strongly caution not to read too much into the errors for any single state because the data are noisy and influenced by many forces, and larger errors need not indicate less effective revenue forecasting. Rockefeller Institute Page 8

18 Table 3. Forecast Errors as Percent of Actual Tax, by State State Mean Standard deviation 25% Median 75% US (1.1) AK* (9.0) AL (1.3) AR (0.2) AZ (0.4) 9.7 (1.6) CA (0.3) 7.4 (4.6) CO (0.2) CT (2.4) DE* (2.8) (0.1) 5.6 FL (1.0) 5.2 (2.3) (0.4) 2.1 GA (0.3) 5.0 (4.1) HI (2.7) IA (0.3) ID IL (2.3) IN (0.9) 4.9 (2.5) KS (0.0) KY (1.1) 5.1 (3.6) LA (3.8) MA (2.2) MD (2.9) ME (0.1) MI (1.7) 5.2 (4.2) (1.0) 2.3 MN (0.6) MO (0.2) 5.4 (1.3) MS (2.4) MT* (1.0) NC (1.2) ND NE (1.5) NH* (0.4) 16.1 (3.4) NJ (0.7) 5.6 (3.6) NM (2.5) NV (3.9) NY (2.5) 3.8 (5.2) (2.1) 0.3 OH (0.2) 5.3 (1.9) OK (1.5) OR (1.7) PA (0.2) 3.7 (1.9) RI (2.8) (0.4) 4.3 SC (0.6) 8.6 (5.1) SD (0.2) TN (1.4) TX UT VA (0.1) 6.7 (2.3) VT* (0.8) WA (1.0) WI (0.0) WV (1.1) WY* Source:RockefellerInstituteanalysisofdatafromNASBO. Note:Stateswith*reliedonPIT,CIT&salestaxesforlessthanhalfof theirtaxrevenueinatypicalyear. Figure 3 on the following page maps the distribution over time of state forecasting errors for the sum of the three taxes (personal income tax, sales tax, and corporate income tax), and also shows the extent of missing data. We are missing data for Texas in 1996, 1997, and 1999, but other than that no large state is missing in any year. It is difficult to obtain data from state documents that is comparable to data in the NASBO survey, and as noted earlier we were unable to obtain such data with reasonable effort for Texas. The figure shows how forecasting errors have rolled out over time across the country. The revenue shortfalls in the middle of the country in 1987 likely are related to the oil price bust that hit Texas and other oil-extraction states in the mid-1980s. The effects of the 1990 recession, which lingered for four years, appear to have led to significant revenue shortfalls on the coasts and in the north but not as much in the center of the country. The 2001 recession and the effects of the September 11th attacks are evidenced in revenue shortfalls that began on the east coast but spread to the rest of the country; revenue shortfalls of more than 8 percent were far more common than in the 1990 recession. The years were a golden period of revenue overages in most of the country. The 2007 recession appears to have hit early in the housing bust states of Arizona, Florida, and Nevada, as well as Michigan. It spread in 2008 and hit the rest of the country with force in 2009 and 2010, before state forecasters were once again on more-solid ground in 2011 through Forecasts may be intentionally biased more likely to be off in one direction than another because forecasters believe there is an asymmetric risk and that the most accurate estimate is not always the best estimate. Many executive-branch forecasters are assumed to believe that the costs of overestimating revenue are greater than the costs of underestimating revenue, because the political and managerial complications of managing deficits are believed to be worse than the consequences of managing surpluses. These forecasters, sometimes explicitly and other times unknowingly, may choose conservative forecasts of the revenue that is available to finance spending. By contrast, legislative forecasters or their principals, not generally responsible for financial management, might not be bothered in the least by revenue shortfalls that the governor has to manage, but may be troubled to learn of extra revenue that they could have spent on their priorities that is, they may be more willing to risk revenue overestimates and revenue shortfalls than is the executive. Rockefeller Institute Page 9

19 Figure 3. Percentage Forecast Errors for Sum of PIT, CIT, and Sales Taxes, Figure 4 on the following page displays boxplots of each state s percentage forecasting error for the sum of personal income tax, sales tax, and corporate income tax. The top horizontal line of each box marks the 75th percentile of forecast errors, and the lower horizontal line marks the 25th percentile. Each box also has vertical lines (sometimes called whiskers ) extending from its top and bottom, indicating variability in the top and bottom 25 percent of the data (longer lines indicate more variability in this part of the data), and dots marking extreme outliers. 6 The taller the box (the greater the difference between the 75th and 25th percentiles), and the longer the lines, the greater the variability in forecast errors for a given state. Only five states have a negative median forecasting error suggesting again that states may be trying to have small positive forecasting errors underforecasts of revenue. However, in a common formal but rather strict test of unbiasedness known as the Mincer-Zarnowitz test we reject unbiasedness at the 5 percent level for only eight states: Arkansas, Iowa, Montana, New York, North Dakota, Texas, Vermont, and Wyoming. 7 For each of these states, virtually all of the box in Figure 4 is above the line (about three quarters of the errors are positive), or else below the line (in the case of New York). Rockefeller Institute Page 10

20 Figure 4. Forty-Five States Underestimated Revenue in the Typical Year Distribution of revenue forecasting % errors, , by state. Sum of PIT, CIT, and sales taxes. Vertical axis truncated at +/-20 percent error. Source: Rockefeller Institute Analysis of Data From NASBO Fall Fiscal Surveys. Judging by the graph, there appears to be a slight tendency for states with greater variability in the middle half of their data, measured by vertical length of the box, to have slightly morepositive errors (more underforecasting) than those with lesser variability, suggesting that when the possibility of large negative errors is greater, states may be more likely to protect against downside risk by aiming for small positive errors i.e., bias. Absolute Value of Percentage Forecasting Errors As noted earlier, the absolute value of the percentage error is useful in examining accuracy. Figure 5 on the following page, which shows median errors by tax, reinforces the idea that the corporate income tax has been the hardest tax to forecast, by far. Table 4 on the following page provides another view, showing the size distribution (across states) of the median absolute percentage error, by tax. It is clear that large errors are extremely common for the corporate income tax, and much more common for the personal income tax than the sales tax. 8 Figure 6 on page 13 shows median absolute percentage errors by fiscal year. The trends shown on indicate that: Errors become particularly large in and after recessions. Variation in errors across states also increases in and after recessions. Errors near the 2001 and 2007 recessions were much worse than in the recession of the early 1990s. Rockefeller Institute Page 11

INTRODUCTION. Figure 1. Contributions by Source and Year: 2012 2014 (Billions of dollars)

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