Modern Budget Forecasting in the American States : Precision, Uncertainty, and Politics 9780739168400, 9780739168394

Citation preview

Modern Budget Forecasting in the American States

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Modern Budget Forecasting in the American States Precision, Uncertainty, and Politics Michael J. Brogan

LEXINGTON BOOKS

Lanham • Boulder • New York • Toronto • Plymouth, UK

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Published by Lexington Books A wholly owned subsidiary of Rowman & Littlefield 4501 Forbes Boulevard, Suite 200, Lanham, Maryland 20706 www.rowman.com 10 Thornbury Road, Plymouth PL6 7PP, United Kingdom Copyright © 2014 by Lexington Books All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means, including information storage and retrieval systems, without written permission from the publisher, except by a reviewer who may quote passages in a review. British Library Cataloguing in Publication Information Available Library of Congress Cataloging-in-Publication Data Brogan, Michael J., 1972– Modern budget forecasting in the American states : precision, uncertainty, and politics / Michael J. Brogan. pages cm Includes bibliographical references and index. ISBN 978-0-7391-6839-4 (cloth : alk. paper)— ISBN 978-0-7391-6840-0 (electronic) 1. Budget—United States—States—Forecasting. I. Title. HJ2053.A1B695 2014 352.4'82130973—dc23 2013036292

™ The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI/NISO Z39.48-1992. Printed in the United States of America

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This book is dedicated to my wife Lorraine, sons Niall and Andreas, and family.

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Contents

Preface

ix

Part I 1

Introduction to the Public Budgeting Forecast Process

2 The Process and Politics of Generating Budget Forecast Errors

3 25

3

Inter-Temporal Variation, Fiscal Uncertainty, and State-Level Long-Term Revenue Budget Forecasts Errors

43

4

Projecting Expenditures: Revenue Uncertainty, Public Choices, Political Institutions, Elections, and Forecast Errors

59

Part II 5

Interpreting Budget Forecast Errors: Fiscal Shirking, Financial Uncertainty, and Public Opinion

77

6 The Financial Consequences of State-Level Budget Forecast Errors

103

7 The Electoral Consequences of Budget Forecast Errors

123

8

Budget Forecasting in the States—Reforms, Institutions, Politics, and Uncertainty

143

Bibliography

161

Index

175

About the Author

185 vii

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Preface

This is a book about public budget forecast errors. In examining this aspect of the budgetary process, it helps readers understand levels of political and financial risk that policymakers are willing to accept in estimating the likelihood of accurate budget projections. This title is noteworthy in its innovative, accessible approach to examining the budget process through an analysis of forecast errors. It differs from most public budgeting books, which focus primarily on the technical aspects of budgeting or on the politics of the budget process. This book, in contrast, bridges the technical and political aspects of budgeting, thereby providing a more comprehensive analysis of contemporary issues and research in public budgetary matters. The book is intended mainly for public budgeting scholars, graduate students, journalists, and practitioners. The text is designed to be accessible to both practitioners and graduate students, as well as to undergraduates who are pursuing a degree in public administration and/or finance. Rather than overwhelm readers with complex mathematical derivations used in developing forecasting technique, it features the underlying assumptions of forecasting approaches. Readers will better understand appropriate forecast tools, models, and outcomes in order to evaluate budget projections intelligently. In light of the current financial crisis in the United States, this text is crucial for providing readers with a comprehensive review of the limits of budget projections, and how political forces shape the forecasting process. Throughout the text, readers are presented with relevant state-specific mini-cases. The mini-cases highlight some of the difficulties in projecting future revenue and spending patterns, as well as the political conflict that can ensue. The empirical findings, mini-cases, and arguments presented throughout this text are intended to empower readers, giving them the expertise needed to better understand how uncertainty in public budget forecasts ix

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x

Preface

affects the budget process. Ultimately, this knowledge can help citizens connect the financial management of a state with its governing patterns. I have organized this book into two sections. The first part, chapters 1 to 4, explores the technical aspects of long-term and short-term forecasting models of statelevel expenditures and revenues. The modeling done in part 1 incorporates economic conditions, incremental spending, and revenue patterns; it also utilizes state- and national-level political variables in order to explain the errors generated by budget forecasts. The research evaluates how the timing of the electoral cycle and political institutions condition the accuracy of revenue and expenditure budget projections. The second part of the book, chapters 5 to 7, addresses whether incumbents are held accountable for inaccurate budget forecasts. Chapter 5 analyzes an experimental survey, conducted specifically for this title, on how voters interpret budget projections. The next chapter explores how financial markets respond to budget forecast errors. Last, chapter 7 concludes with an examination of whether voters punish incumbents for volatility in state budget forecasts. The final chapter provides readers with overall principles and lessons learned from the experiences of individuals responsible for putting together state budget forecasts, as well as from prior research in public budgeting, and on the results presented throughout this monograph. To ensure that the findings and principles that are presented to the reader are valid, I sought extensive peer reviews (blind and non-blind) for each chapter. I thank the following reviewers for their time, thoughtful critiques and recommendations for improving this work: Charles Tien, James Savage, Jonathan Mendilow, Frank Rusciano, Robert Skertich, Charles Barrilleaux, Aaron Wachhaus, Barbara Franz, Roberta Fiske-Rusciano, Harvey Kornberg, Barry Seldes, Rich Marsanico, and Robert Lowery. I would also like to thank Mac Taylor, Richard Stavneak, John Nixon, and David Rousseau for sharing their experiences in the budget forecast process. Elaine Pofeldt has provided crucial support in making sure the information and results presented in this work have been communicated effectively. Throughout the revising process, Elaine always found ways in which to improve the text. Rarely was there a section without her critical and insightful suggestions. I agreed with her observations and felt the work was much better as a result. She edited the entire book and I thank her for her time and effort. Finally I would like to thank my wife Lorraine Sova for her patience, comments, and insight into developing this work. The book would not have been written without her support.

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Part I

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1 Introduction to the Public Budgeting Forecast Process

OVERVIEW David Rosen, budget and finance director of the Office of Legislative Services (OLS) for the New Jersey Legislature, painted a grim picture for the state when he presented state revenue projections for FY2011 to the Assembly Budget Committee on April 7, 2010.1 He estimated a structural deficit of approximately $10.5 billion. The projections came in response to Republican Governor Chris Christie’s Governor Budget Message (GBM), presented before the New Jersey state legislature on March 16, 2010.2 The difference between the state’s revenue forecasts and Christie’s were approximately $167M or .59% of the governor’s projections.3 Though the variance between them appears quite small—based on an overall revenue budget of $28B for the state—the ensuing debate over the two is a classic example of how budget projections trigger intense partisan positioning and political rhetoric.4 Labeling the OLS’s budget projections as “fake” in a news conference on July 21, 2010, Governor Christie said the forecasts did not address the state’s dire fiscal situation—caused by structural deficits brought on by too much spending.5 Democratic Assemblyman Lou Greenwald, chair of the Budget Committee, countered Christie’s claims, arguing that the OLS projections confirmed that the state cannot continue to do the “same old things.” He said the state should implement income tax increases on high-income residents.6 This is one example of how budget projections are firmly entrenched in the political process. Policymakers use budget forecasts to shape the public’s perceptions of the incumbent government’s fiscal competence and also to redefine political choices over future taxation and spending policy.

3

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4

Chapter 1

This book presents a new way to examine public budgeting, by looking at the role of forecast errors. Sometimes, these errors are technical; in other cases, they are driven by the political considerations of policymakers who are willing to live with the political and financial consequences. Regardless, the implications for the public are significant. This book quantifies the degree to which faulty estimates occur and looks at how they affect public policy. Specific issues addressed in this book include: • What are appropriate planned revenue and expenditure levels for a state? • Do states systematically under-forecast revenue budgets? • Do state governments’ budget slack ratios (actual spending/projected budgets) change as a result of the timing of the next election? • Do forecast errors vary based on the timing of the next election cycle? • How are individual voters’ perceptions of budget forecasts shaped by policymakers and to what extent does the public evaluate budget projections? • To what degree do financial markets punish or reward state governments for their competence in predicting future revenue and spending patterns? • Do voters hold incumbents accountable for inaccurate budget forecasts? • What are some of the lessons learned from budget forecast errors at the state level that can be used to improve the process going forward? Furthermore, the book provides readers with the sense and scope of the real-life consequences of budget forecast errors. First, when policymakers are either too cautious, or aggressive, in setting states’ budget forecasts, this could cause political and financial problems later, when they must decide what to do with an unexpected surplus or deficit. If there is a budgetary surplus, policymakers must decide if they should reduce taxes or spend more. In a deficit, they are left with the decision to either raise taxes or cut spending–or both. Second, the relationship between volatile budget projections and financial markets can limit states’ ability to access capital markets, raise levels of interest paid on existing debt, and grow structural deficits. Any of these outcomes reduces the ability of the states to invest in education, infrastructure, and other priorities. The first part of this book, chapters 2 to 4, explores the technical aspects of long-term and short-term forecasting models used for state-level expenditures and revenues. The section analyzes the effects of political institutions on budget forecast errors. The modeling done in Part 1 incorporates economic conditions, as well as incremental spending and revenue patterns, state-level demographics and utilizes state-level and national-level political variables in order to explain errors generated by budget forecasts. The research evaluates how the timing of the electoral cycle and political institutions condition the accuracy of revenue and expenditure budget projections. The second part of the book, chapters 5 to 7, addresses whether incumbents are held accountable for inaccurate budget forecasts. It analyzes an experimental survey,

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Introduction to the Public Budgeting Forecast Process

5

conducted specifically for this title, on how voters interpret budget projections. The survey tests whether voters construct attitudes about forecast errors during the surveying process. Do the survey questions shape voters’ attitudes about forecast errors, public budgeting, and associated policy options? Do voters hold incumbent governors and state legislators accountable for erroneous forecasts—and subsequent policy decisions—developed by the incumbent administration? Public perceptions of budget forecast errors are further analyzed in an examination of the economic and electoral consequences for incumbent governors and state legislators that result from imprecise, and often egregious, budget forecasts. Moreover, in light of the current financial crisis in the United States, this text provides a crucial and comprehensive review of the limits of budget projections, and how political forces shape the forecasting process. Throughout the text, statespecific mini-cases highlight some of the difficulties in projecting future revenue and spending patterns, as well as the political conflicts that can ensue. The empirical findings, mini-cases, and arguments presented throughout this text are intended to empower readers, giving them the expertise needed to better understand how uncertainty in public budget forecasts affects the budget process. Ultimately, this knowledge can help citizens connect the financial management of a state with its governing patterns. Mini-Case 1.1: A Taxing or Spending Problem? The Fight over the FY2011 New Jersey State Budget Projections During the preparation of the FY2011 budget, New Jersey Governor Christie declared that the state had a spending problem, not a revenue problem. His budget projections reflected this claim. The governor issued balanced budget forecasts that didn’t depend on raising taxes. In the Governor’s Budget Message (GBM) on March 16, 2010, Christie argued that his budget for the upcoming year was a “blueprint for reform.” Shortly thereafter, the Office of Legislative Services (OLS), a non-partisan office of the New Jersey State Legislature, generated more pessimistic forecasts. The difference between the GBM forecast and OLS’s estimates was approximately $167M or .56%. How could the two forecasts be so different? Each projection addressed the state’s structural deficit in its own way. Though the computation of the structural deficit is specific to each state, all states address it in budget forecasts. In New Jersey, policymakers look at whether the state can fund all current programs and laws at current levels, or increased levels, based on current revenues, to determine if there is a structural deficit and how big it is. These estimates, of course, vary from year to year due to their sensitivity to economic conditions as well as tax collection rates. David Rosen, director of the OLS, noted: “The estimation of the State’s structural deficit is essentially an academic undertaking, dependent upon definitional assumptions and open to a range of defensible alternative conclusions.”7 Even though structural deficit estimates are speculative, based on

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6

Chapter 1

analysts’ interpretation of various assumptions and scenarios, they are highly unlikely to be realized because of the balanced budget requirement in the state constitution. A partisan battle ensued over how to count the state’s structural deficit. Democratic legislators relied on the OLS estimates. They argued that an income tax increase for high-income residents was necessary to fend off cuts in state programs and services. (Under Democratic Governor Jon Corzine, the state used this remedy on a one-time basis in the previous fiscal year.) Christie countered that the OLS forecasts were invalid because they counted all mandatory increases in state funding and assumed that all programs included in the budget would be funded next year at current levels. As a result, the OLS forecast indicated a shortfall for the following year. Christie argued that OLS estimates highlighted that the state spends too much already and tax rates are at unsustainable levels; such spending issues caused the budget gap in the first place. Christie continued that he would not go on spending at current levels, so an increase in taxes would not be warranted.8 New Jersey’s highly progressive tax structure makes it even harder to calculate the state’s structural deficit. Revenue budgets tend to fluctuate based on macroeconomic conditions, policy changes, and behavioral decisions among taxpayers. Though there this is no “fool-proof ” formula to accurately predict future revenue collections, typically the Governor’s Office bases its projections on current year collections and then applies an estimate of future growth. The second element of estimating the appropriate rate of future growth becomes a judgment that has political consequences. For Governor Christie, as well as for his predecessors, the risk of choosing an estimate that is either too high or low can be problematic. If the number is either too high or low then the governor needs to scramble to make appropriate spending cuts or revenue adjustments to make up the difference, and the electorate pins responsibility directly on the executive branch.9 As of the mid-point of fiscal year 2011—which runs from July 1, 2011, to June 30, 2012—New Jersey State Treasurer Andrew Sidamon-Eristoff, reported that overall state revenue outpaced FY2011 projections by 3.8%. He cautiously noted that the rise was due to an increase in income taxes. However, sales tax revenues dropped at the same time, which indicated that the increase in revenue might not be due to an improving economy but, rather, to the ability of high-income earners to shift what should be 2011 income to 2010 (e.g., taking early bonuses) to avoid potential increases in federal income tax rates in 2011. Nevertheless, the final tallies for the FY2011 budget found that revenues exceeded the GBM projections by 3.6%. (For the OLS estimates, the final FY2011 revenue budgets exceeded projections by 4.2%.) The final FY2011 budget estimates provoked partisan responses to the FY2012 budget. Assemblyman Joseph Malone (R-Burlington), the ranking member on the Assembly Budget Committee, took credit for rising revenue and indicated it was a sign of the effectiveness of the Republican Party’s initiatives. Robert Grady, Chairman of the Governor’s Council of Economic Advisors, indicated that the governor

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Introduction to the Public Budgeting Forecast Process

7

was exploring the possibility of cutting income and corporate taxes to try to spur growth, which would likely add to the structural deficit. Democrats have countered with their own strategy. It is likely that Democrats in the legislature will use these early estimates as justification for passing pension reform legislation, which would require the governor to approve a $500 million contribution to the state pension fund in the following budget cycle. The governor would thereby be forced to pay for a major part of the state’s structural deficit, which he did not plan on funding for the next fiscal year. He had hoped that such funding could be postponed until major reforms to the pension system were enacted by the legislature. The Democrats, therefore, appeared to call his bluff. In any event, the New Jersey case points to the challenges faced among state policymakers in balancing the “art” and “science” of budget forecasts.

AN OVERVIEW OF PUBLIC BUDGET FORECASTING A fundamental axiom of budget projections is that they tend to be wrong and subject to revision. Random error guarantees uncertainty in public budgeting. Forecasters use their technical know-how and expert judgment to reduce this uncertainty and to diminish the resulting financial and political risk associated with the process. Successful forecasts are focused on providing policymakers with various paths based on current trends, rather than on “predicting the future.” The imprecise nature of budget projections and policymakers’ tendency to be conservative in their future outlook of states’ finances contribute to biased forecasts. Typically, they under-forecast the next year’s budget. Normally, policymakers look at multiple forecasts regarding both the macroeconomy as well as the expected performance of state finances. When choosing scenarios in which to develop budget projections, they make judgments about the future that are based on approximations of economic performance and policy changes at the state and federal levels. State leaders must consider the possibility of error during the deliberation process. The magnitude and direction of the errors—that is, whether the errors are positive or negative—condition policymakers’ decisions about which scenario to use in their forecasting. Typically, policymakers tend to become more conservative in their outlook about future state finances when they take into account the possibility of forecast errors. Figure 1.1 illustrates how the distribution of budget forecast errors conditions policymakers’ considerations and judgments about a state’s projected finances. Based on the hypothetical distribution of forecast errors graphed in figure 1.1, policymakers typically face the following options when developing a state’s budget projections: Scenario A, which illustrates a positive skewed distribution; Scenario B, which shows a normal distribution; and Scenario C, where there is a negative skewed distribution.

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Chapter 1

Figure 1.1. Selecting Budget Projections: Based on the Distribution of Budget Forecast Errors.

If the economy has performed better than expected and as a result state revenue collections are higher than expected, policymakers would face Scenario C. Policymakers would likely choose the mean (illustrated as C (Mean) in figure 1.1), as they weighed various choices of budget point estimates when setting their initial projections for the following fiscal year. The selection of the mean estimate would allow policymakers to be more conservative in their forecasts than if they chose the median statistic for Scenario C as their projection.10 The forecast, however, might be too conservative if policymakers didn’t choose the median estimate for Scenario C and incorrectly underestimate growth in the economy. This outcome would result in a substantial revenue surplus, requiring significant adjustments to the budget. Thus, if policymakers selected the median estimate for Scenario C, which in this case would be greater than the mean, this would not result in an unexpected surplus. Scenario A in figure 1.1 presents policymakers with the opposite dilemma. In this case, economic conditions appear to be worse and revenue collections are down as a result. If policymakers wanted to be careful, they would select the median estimate in Scenario A. However, being too cautious would restrict their ability to fund existing programs and agencies, as well as additional priorities. Selecting the mean for Scenario A would likely result in the state continuing with most areas of existing funding, yet the estimates would still fall short of the status quo estimate indicated by the mean (peak) of Scenario B. In any event, Scenario A would leave policymakers with

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Introduction to the Public Budgeting Forecast Process

9

a budget deficit regardless of whether they selected the mean or median estimates. Their primary options would be to either cut the budget or to raise revenues to offset the deficit. The severity of the actions associated with these options would be dependent upon whether state leaders would choose the mean or the median estimate as their point of reference for developing near-term budget forecasts. Budget forecast errors guide policymakers’ ability to understand the ebb and flow of volatility in an economic downturn. Analyzing forecast errors can indicate whether the state has either hit the peak or trough and whether we would expect this trend to continue in the near future. Yet determining the lower or upper bounds of the direction of a state’s finances is a difficult process fraught with challenges. In 2009, Arizona state leaders faced this dilemma. In failing to project the severity of the impact of the Great Recession on the state’s finances, they had significant problems in funding baseline budgets.11 This resulted in state leaders having to adjust the FY2010 budget downwards by $500M (in addition to a projected shortfall of $1.5B) in the middle of the fiscal year.12 This case indicates the importance of state leaders’ understanding of the role of volatility, as measured by budget forecast errors, in providing a perspective regarding the depth and severity of an economic downturn. Pressures in financial markets add another layer of complexity for policymakers. Investors in state bonds require a premium for the risks they bear from systemically skewed forecasts.13 Uncertainty in budget forecasting, therefore, ensures a strong connection between economics and politics when setting and implementing government’s priorities.14 Policymakers tend to deal with this behavior through the use of ad hoc adjustments, recasting budgets, balanced budget rules, and/or rainy day funds.15 The inability of states to forecast revenues and expenditures exactly plays a critical role in their fiscal health.16 In the short term, forecasts are intended to reduce uncertainty in the proceeding year’s budget. However, because of the errors they contain, they make it harder to allocate public resources, particularly during times of severe economic volatility. When projections go far into the future, typically longer than two years, quantitative models “deliver unacceptably large errors.”17 This results in political pressures intervening in the budget process and inevitably places stress on the states’ future financial commitments. So, regardless of how technically competent or correct specific forecast models tend to be, the process of creating a budget is, nevertheless, influenced by competing actors’ attempts to shape it into a form that favors their needs. Incumbent politicians try to ensure certainty to make the public confident that they can be trusted to manage the public purse, while opponents try to win votes by capitalizing on the uncertainty in the process. Public Budgeting: Revenue and Expenditures Public budgeting sets priorities for government by determining how much money is available to spend and which policies and programs will be implemented.18 Public

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Chapter 1

budgets provide accountability to both voters and to investors, who provide financing and credit to state governments.19 Typically the budget process includes both revenue forecasts and the enactment of spending budgets into law.20 The process of developing revenue projections “has a heavy influence on budget execution,” in which forecasts can, at times, restrict current and future disbursements.21 From this perspective, revenue forecasts direct the “entire state government policy agenda.”22 In most states, the budget process is incremental. Any political conflict over financial resources tends to deal with discretionary funding (e.g., funding for literacy education programs, recreation programs, etc.).23 Such an approach allows for moderation of political conflict over budgeting, because incrementalism tends to stabilize budget functions and expectations.24 An incremental approach allows policymakers to focus only on proposed increases; typically, all other existing expenditures escape any “stringent review.”25 Defining Public Budgeting Forecasting Public budget forecasting is a risky endeavor that is part science and part art.26 The scientific aspect revolves around the application of econometric modeling techniques to predict future events. The creative element relies upon expert judgment. That includes the ability to communicate the estimates’ results, choose the right model and variables, disconfirm forecast bias, negotiate the estimates’ findings in order to reach consensus with various stakeholders, and apply projections to the appropriate scenario.27 The most important aspect of forecasting is accuracy. However, the ability of others to replicate a forecast, the theoretical considerations for specifying models, the discovery of optimal thresholds between projected and actual budgets, and the ability to compare forecasting models all help to improve the performance of such models. Forecasting errors are the difference between predicted budget estimates and actual budget estimates. There are multiple measures that include, but are not limited to, the following statistics: residual errors, mean error, mean square error, root means square error, mean absolute error, and the mean absolute percent error. Chapters 3 and 4 provide more detailed discussions about how these statistics are calculated. Differences between Public Sector and Private Sector Forecasts In many ways public sector forecasts are generated in similar ways to private sector forecasts (e.g., through the use of national and regional economic indicators, selected modeling techniques, and time horizons). Yet differences do persist. Public sector forecasts:

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Introduction to the Public Budgeting Forecast Process

11

• Are open to public deliberation and scrutiny. • Are often criticized and/or embraced by actors based upon political considerations between the major parties and/or state legislature and governor. • Are ultimately enacted into law. • Have a differing approach to dealing with structural deficits as a result of legislative decisions. • Are bound by rules such as balanced budget requirements, revenue and/or expenditure ceilings, and/or rainy day provisions. • Are subject to competing forecasts from the other branches of government. In most states, the governor’s office, as well as the state legislature, generates revenue and spending projections. Public Revenue Forecasts Revenue forecasts for personal and/or corporate income taxes, sales tax, cigarette taxes, and natural resource taxes, are calculated by state governments in order to project how much money will be in state coffers prior to setting spending plans for the following fiscal year. This is done primarily by the executive and legislative branches, or done by achieving consensus between both branches. When generating budget forecasts, policymakers must make a trade-off between being accurate and bearing the financial and political risks associated with the process.28 To increase forecast precision, policymakers may over-fit their data, resulting in biased estimates that can underestimate increased structural risk.29 Such models may appear accurate to an analyst, but they may rely on techniques that smooth variations of an historical trend rather than tell policymakers about future patterns. This dilemma comes back to the issue of the bias-variance trade-off used in statistical theory.30 If too much variance is explained in a highly accurate model, then it could result in the model simply explaining random fluctuations in the estimates. Therefore, if one case is removed from it, it is likely to be unstable, causing the initial precision of a projection model to be misleading. If estimates demonstrate too much bias (low risk), the model may be stable but may not be very effective in trying to predict future events.31 If policymakers are unable to effectively balance forecasting precision with risk, then in extreme cases, when major shortfalls occur, this would cause state governments to implement contingency plans (either tax increases and/or spending cuts) to close the budget gap.32 Typically, state governments will, over the long run, engage in strategies that lean towards reducing risk by underestimating revenue estimates, which would ideally limit a state’s chances of having to implement contingency plans.33 This allows policymakers to not only downplay overall cost increases but also to demonstrate to voters that they are responsible managers of public finances.34

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Chapter 1

However, governments that are too conservative in generating revenue estimates face a disadvantage: They “constrain gubernatorial recommendations and legislative appropriations for improvement of state services.”35 Practical issues such as existing state structural deficits and macro-economic volatility also challenge the claim that governments systematically under-forecast budgets.36 In contrast, governments typically engage in a process of developing their forecasts based on unbiasedness and forecast rationality (UFR).37 State governments do so to avoid having to rebalance their budgets. To avoid legal or political pressures to close a fiscal year with a balanced budget, they do not want projected revenue to fall short of or well above projected expenditures.38 Under-forecasting also creates political problems. It minimizes a state government’s ability to gain public confidence; perpetuates a moral hazard among policymakers in managing public finances and can cause an expected surplus to occur during the fiscal year. This, in turn, creates pressure on incumbents to either increase the budget or cut taxes.39 When under-forecasting does occur, it is more likely a result of macro-economic volatility that causes policymakers to overcompensate in their budget projections.40 Public Expenditure Forecasts Public expenditure forecasts lay out future state spending. Spending budgets are enacted into law and must closely follow revenue projections. Expenditure forecasts are based on a variety of sources. These include general funds (revenues received from broad-based state revenues), inter-governmental transfers (funds received primarily from the federal government), other state funds (e.g., a gasoline tax), and bonds (financing used primarily to pay for capital projects). Though revenue and expenditures differ by state, common categories of expenditures are education, public assistance, Medicaid, corrections, transportation (including capital projects), and other items (such as CHIP) (NASBO 2010). Expenditure projections, like revenues forecasts, are sensitive to economic conditions, and are subject to accountability pressures.41 As part of this effort, 30 states have enacted tax and/or expenditure limits (TELs). Expenditure limits in the states include capping revenue and expenditures to an index (e.g., CPI, population growth or personal income), limiting future appropriations to a percentage of revenue estimates, or some combination of these approaches.42 Expenditure forecasts typically follow incremental patterns: Programs and/or departments are not usually added or eliminated from year to year but rather tend to follow incremental patterns that focus on specific annual budgetary line-items. Though states employ varying budgeting techniques in their planning (zero-based or performance-based budgeting), incremental budgeting remains a dominant feature for developing future spending budgets. It provides a mechanism for policymakers to engage in various levels of program evaluation, allows for flexibility in case of extreme shifts in the macro-environment (e.g., major macro-economic events,

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Introduction to the Public Budgeting Forecast Process

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natural disasters, etc.), is effective in restricting spending, and encourages openness and accountability.43 There are limits in developing expenditure forecasts. Spending budgets tend to be constrained by the levels of interdependence among expenditures across budget categories. Spending levels in one category during a given year are influenced by external fiscal pressures, the expectations and availability of tax revenues, and/or expenditure pressures that are generated by other spending categories.44 Interdependence of spending categories therefore makes it difficult for policymakers to accurately gauge future revenue and spending patterns. In addition, budget categories tend to respond “counter cyclically” to variation in the macro-economy (e.g., unemployment). Budget categories can also be “procyclical” for certain expenditure items (e.g., middle class entitlements based on cost-of-living adjustments).45 In either event, simply making adjustments for incremental revenue and spending patterns may at times under or over allocate the necessary resources across budget categories, hindering the flexibility of policymakers to cope with fluctuations in the fiscal environment.

THE IMPACT OF POLITICAL INSTITUTIONS ON BUDGET PROJECTIONS State Governments Political incumbents use varying strategies to develop short-term and long-term projections to set the political agenda.46 In the short term, their annual forecasts of revenue and spending budgets tend to be more accurate and less biased by political factors, while their long-term forecasts tend to have higher levels of risk and are likely to be biased by political calculations. Therefore the process of developing revenue forecasts and spending budgets serves as a political tool for incumbents who seek to manage the electorate’s expectations of their overall job performance, particularly during an election cycle.47 State political institutions, as one might expect, shape and constrain the budget process, influencing the way projected revenue and expenditures are set. From this perspective, divided and unified control of a state’s political institutions, as well as systemic differences between the two major parties (Democrats and Republicans), influence the budget process.48 Indeed, state-level fiscal problems often result from “combinations of party and institutional control on the one hand and legal restrictions on fiscal policy on the other.” This, of course, limits the financial options available to state governments in developing future budgets.49 Corina et al. (2004) contend that competition over political institutions has a significant impact on the accuracy of revenue forecasts, after accounting for economic changes and fiscal stress. Research conducted by Poterba (1994) supports this view. He found that states’ balanced budget rules have a significant impact in how the state responds to unanticipated deficits or surpluses.

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Chapter 1

Research has found that differences between the executive and legislative branches of government significantly affect the process of generating budget forecasts.50 In states that have an executive-centered budget system, the executive sets, modifies, and approves future budgets. Within this type of system, government agencies submit their spending requests to the governor who, in turn, sends a single budget to the legislature. State legislatures respond and review the governor’s budget and may make adjustments as long as it stays under the existing spending ceiling.51 Furthermore, the line-item veto, which is not limited to executive-centered systems, grants governors an effective political and budgetary tool for developing, shaping, or constraining future budgets. Currently, 43 states have line-item vetoes for governors. Research to date has found that the line-item veto has frequently been used as an instrument to promote partisan considerations as often as it has been used to promote fiscal responsibility by governors.52 In contrast to executive-centered states, legislatures and governors in states with legislative dominant systems approach the budget process in a more deliberative fashion: Lawmakers receive competing budget proposals from the governor, as well as from the legislative leadership. The two branches develop budget instructions and procedures jointly, work toward consensus around revenue estimates, and formulate a common understanding of necessary resources to fund entitlement programs.53 A recent resurgence in state legislatures in the budget process has eroded the dominance of the executive branch in state budgeting.54 There is more sharing of power between the executive and legislative branches.55 Reasons include state legislatures’ increasing competence and their ability to get independent access to budget information. State legislators have also learned to package line-items so it is difficult for governors to veto them without also cutting the governor’s priorities.56 Like governors, state legislatures also play a prominent role in the development of future budgets. Stanford (1992) found that legislatures employ a “sophisticated decision calculus” in developing budgets.57 She concluded that legislatures were more likely to monitor spending on programs when budgets were tight and more lax in their oversight of programs in years of economic growth. The potential benefits of trying to boost net political returns from the strategic use of fiscal policy measures by state legislators are quite limited, after accounting for national economic trends and the behavior of governors. However, members have been able to engage in this practice due to their ability to successfully insulate themselves from electoral swings. And, as a consequence, legislators have increased flexibility and latitude in how they engage in the budget process, particularly when their party holds a legislative majority in a given state.58 Nonetheless, the behavior of governors and state legislatures indicates that political institutions have real effects on fiscal policy outcomes, in combination with incremental budget conditions, fiscal stress, national economic conditions, and state budget rules.59

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There are limits to the role of political institutions in developing budget projections. Incumbents cannot accurately choose the best budget forecast to suit their needs all of the time. Competing models, prepared by qualified and well-meaning individuals, present multiple scenarios to governors and legislatures. Thus, incumbents are at times “poorly positioned” to discern which model is correct and which should be ignored.60 Strategic behavior among incumbents is limited not only by the competence of the forecast model, but also by luck. This leads policymakers to ask what circumstances, independent of forecasts, have caused projections to be wrong, and what assumptions made in the models were not realized. If there is economic growth, budget makers face pressure to expand state finances, while if there is an economic decline, they are expected to be prepared for a shortfall. This makes it hard for politicians to be strategic in developing budget forecasts. National economic conditions also limit the role of political institutions in budget forecasts. The performance of the nation’s economy affects the president’s party in state legislatures.61 Therefore, the fate of incumbent governors and state legislators may not necessarily be based on their ability to manage the state fiscal policy but rather how voters perceive the president’s ability to manage the national economy.62 Mini-Case: 1.2 Power-Politics, Pay Raises, and Fiscal Hawks: The Short-andLong Term Consequences of Louisiana’s Budget Reform “Today I am correcting my mistake” stated Louisiana Governor Bobby Jindal in explaining his 2008 veto of Senate Bill 672, which sought to raise the state’s parttime legislators’ salary by 123%.63 His decision to veto the bill was contrary to his initial stance that he would stay out of the issue. His reversal signaled to legislators that he would be responsive to public opinion, which was fiercely against the pay raise, even if it would compromise his relationship with the legislature. Second, the governor would work in a bipartisan basis with the Louisiana state legislature on budgetary matters but would do so on his terms, not the legislature’s. While in office, the governor has sought to enact significant reforms to state government. His efforts have required bipartisan support in the state legislature where, in both chambers, Democrats outnumbered Republicans until 2010, the first time that both chambers have been controlled by the Republicans since Reconstruction. Getting political “buy-in” from legislators (who would be directly impacted by reform) would not be easy and was a primary reason for his initial hands-off approach to SB 672. The governor’s choice of Jim Tucker (R-Terrytown) to serve as house speaker at the start of the 2008 legislative session was one of his primary strategies for constructing legislative majorities to back his reforms. A majority of bipartisan House members supported Tucker’s candidacy for speaker. Though not part of the state constitution, and contrary to house rules,64 the selection of house speaker has been done with the governor’s consent. Initially, Governor Jindal noted that after being

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Chapter 1

elected he would no longer engage in this process, but shortly after making this statement, he changed his position and supported Tucker.65 Tucker seemed to have the ability to carry a bipartisan coalition to enact the governor’s reform. In the short-term, the veto of SB 672 was a smart political move by the governor. It set the terms of negotiation with the legislature during his term in office but also ended up weakening Speaker Tucker’s influence in the chamber as a result. In the long-term however, the veto of the proposed pay raise sparked an eventual backlash to the governor among House legislators of both parties. The second catalyst for the House’s backlash was the governor’s use of one-time funding gimmicks to cover reoccurring expenses. Though these measures put the state’s budget in balance at the start of the fiscal year, they led to mid-year budget cuts during Jindal’s tenure. After the 2008 legislative session, a minority of members in the lower house began to feel they were consistently politically outmaneuvered and beaten by Governor Jindal. State Rep. John Schroder (R-Mandeville) explained that members were confused over how to respond to the governor’s proposals where “the left hand didn’t know what the right hand was doing.” To fix this problem, he began working with other discontented Republican members to “come back better prepared on the budget.” By 2012, a coalition known as the Budget Reform Campaign (nicknamed the “Fiscal Hawks”) built a working majority of 40 Republican and 30 Democratic to act as an effective check on the governor’s actions.66 The Fiscal Hawks claimed victory in the FY2014 budget proposal by reducing the governor’s initial one-time request of $525M to $80M. However their victory was partial. Many of their proposed budgetary reforms were watered down by the state Senate.67 Understanding the state’s budget process puts the efforts of the Fiscal Hawks into context. The Louisiana state legislature typically funds the governor’s requests. The governor’s veto power has been an effective tool to check the legislature, either in line-item form or to reject a bill entirely. Though the legislature can override the governor’s veto, it has only been done twice in modern times.68 Only recently has the legislature been able to mount a legislative veto override coalition to temper Governor Jindal’s agenda. This is by far the most significant impact the Fiscal Hawks have had on Louisiana’s budget process and sends a clear message to the governor: The legislature is an equal player in enacting budgetary reform and is willing to reassert its institutional powers in the process.69 Federal Government Since the 1960s, there has been a significant increase in the interdependence of the federal, state, and local governments. The expansion of the federal government has forced states to carry out federal policies that are at times funded and, at other times, unfunded, thereby impacting the states’ ability to project future revenue and expenditures. 70 Fiscal considerations between levels of government also complicate

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the process of developing budget projections. Coordination issues that arise between federal, state, and local governments affect the accuracy of budget forecasts. These considerations include: determining which level of government to assign expenditure-revenue authority; whether there are shared revenue sources between levels of government; if inter-governmental transfers are available; if matching spending expectations are required; and finally, if there are significant differences in income and wealth between governments at the same level.71 Scholars point to two primary reasons for the expansion of the federal government and how it has impacted state finances. The first is based on the role of intergovernmental grants, which provide the states with additional federal dollars. The influx of federal dollars contributes to the growth in the size of state governments because, from the states’ point of view, it is highly unlikely that governors will return federal aid to Washington rather than spend it.72 Therefore, the process tends to be cyclical, in that states that have higher spending levels also attract more federal aid.73 Garand (1988) confirmed this behavior among state governments. He found that in more than two-thirds of the states, the size of state governments grew as a direct result of inter-governmental grants.74 States also use federal funds to reduce their own revenue burden without affecting total spending levels (Chubb, 1985).75 The financial collapse of 2008 provides a good example of this process. The infusion of federal dollars to the states as a result of the American Recovery and Reinvestment Act of 2009 (ARRA) covered an increase in state expenditures, while state revenue and general funds decreased from FY2008 to FY2010 (NASBO 2010).76 Local Governments There is also a significant relationship between state governments and local governments, which budget forecasters must consider when developing future revenue and spending budgets. In most states, state aid to local governments “is the largest element in the state budget.”77 In addition, states can also engage in “passing the buck” to local governments.78 Therefore, projecting future expenditure budgets for the states can include mandates, or commands, where the states do not fund a service, program, or project directly but rather make a local government responsible for the costs. The state government can claim local revenues in its projections or can cut aid to local governments in order to balance future budgets.79 Mini-Case 1.3: A Federal Bailout for the States: Saving the States or Prolonging the Pain? Have the states become too dependent on federal aid or has the federal government saved the states from collapse brought on by the current financial crisis? Since the Great Recession of 2008, there has been a significant drop in revenues across

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Chapter 1

the states while at the same time spending levels have stabilized and in some states, increased during this period. The American Recovery and Reinvestment Act (ARRA) provided approximately $200 million to the states to stabilize spending efforts. In August 2010, President Obama signed into law H.R. 1586, “The FAA Air Transportation Modernization and Safety Improvement Act,” which included an additional $26 billion in aid to the states ($10 billion for education and $16 billion for Medicaid). The additional federal funding was intended to help governors avoid shifting resources away from other priorities to fund these items. Proponents of the funding plans claimed both laws saved the states from financial collapse and prevented thousands of teachers, law enforcement officials, and state workers from losing their jobs. An opponent to the law, Senate Minority Leader Mitch McConnell, claimed the passage of this bill was a result of the states “simply becoming completely dependent” on the federal government with no end in sight.80 Historically, the states use federal aid as a replacement for finding additional revenues to cover state expenditures. Within the FY2011 forecasts, dozens of states assumed federal funding increases for Medicaid, for a projected total $84B nationwide.81 Have the ARRA and H.R. 1586 perpetuated this trend? If they did, then the next two fiscal years are troubling for the states. Funding from each law sunsets in FY2012. This ending brought about abrupt changes in the way the states formulate future budget projections. Nevertheless, the recession is likely going to continue in the states over the near-term, making it harder for states to maintain the status quo. State policymakers will no longer be able to continue relying on current levels of federal funding to replace state expenditures, particularly in light of dipping revenues in the foreseeable future.

DISCUSSION Forecasting is an uncertain business. The process of generating future budgets combines technical expertise and expert judgment. Public budgets share many characteristics with private sector forecasts in the variables they use to estimate projections, time horizons, and methodologies. However, public forecasts, more so than private ones, are open to public deliberation and scrutiny and have to be enacted into law, because they are bound by political institutions and rules. Public forecast methodologies vary by state. The process typically begins with revenue forecasts (taxes, fees, etc.) first and expenditure budgets are calculated thereafter. Forecasts are generated based on prior year performance (revenue and spending patterns), economic conditions, and on a state’s preexisting commitments. Political institutions affect the budget forecasting process. Again, the role of legislatures and governors in generating budget projections varies by state. In some states, the governor’s office is responsible for budget projections; other states use an

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approach in which the legislative and executive branches reach a consensus based on each branch’s projections. There are other states that have joint committees or where other organizations estimate their budget projections. The closer ties between the federal and state governments has had a significant effect on the ability of states to accurately project revenues and expenditure budgets. The growth of the federal government is also linked to the expansion of state and local governments. In addition, state budget forecasts use federal funding as a replacement for spending that is not funded by additional sources of revenue. The first part of the book estimates and analyzes the effects of forecasting errors on revenue and expenditure projections. Chapter 2 defines the model for estimating revenue and expenditure forecasting errors, as well as how voters and investors perceive inaccurate budget projections. Chapter 3 estimates long-term and shortterm revenue forecasts, and Chapter 4 uses a similar approach to the analysis of expenditure forecasts.

NOTES 1. David Rosen, NJ Assembly Budget Committee Testimony, April 7, 2010. For more detail on the New Jersey FY2011 projections, see mini-case 1.1 in the appendix. 2. In New Jersey, revenue projections are generated from the Treasury Department, which the governor certifies on the basis that revenues are appropriate for planned expenditure levels. The certification is deemed the official forecast until it is signed into law. OLS projections are informal and advisory. They are used by the state legislature when crafting the budget bill (NCSL, n.d., www.ncsl.org/default.aspx?tabid=12637). 3. The FY2011 GBM revenue forecast was $28.267B and the OLS revenue forecast was $28.099B. The actual revenue collections for FY2011 for the state were $29.283B. 4. For example in the FY2010 forecasts, the OLS and Corzine administration predicted an economic recovery that would produce growing tax revenues by the middle of the fiscal year (Rosen, NJ Assembly Budget Committee Testimony, April 7, 2010). The projections overestimated income tax revenue by approximately $2.4 billion. The budget gap that resulted from this forecast error caused an abrupt mid-year budget revision by the Christie administration (Siadman-Eristoff, NJ Senate Budget Committee Testimony, May 25, 2010). 5. Claire Heninger, “Gov. Christie says projection of $10.5B shortfall for next year is ‘fake,’” NJ.com (July 21, 2010). Accessed via www.nj.com/news/index.ssf/2010/07/gov_christie_says_projection_o.html. 6. Lisa Fleisher, “N.J. official says revenue from income tax could ‘fall significantly below’budget projections,” NJ.Com (May 20, 2010). Accessed via www.nj.com/news/index. ssf/2010/05/budget_chief_says_nj_revenue_f.html. One month after Dr. Rosen’s testimony, he wrote in an internal office e-mail that income tax could “fall significantly below” earlier projections by the governor’s office, which in his view may have been “too optimistic.” 7. David J. Rosen, “Estimate of the FY2011 Structural Deficit,” Office of Legislative Services: Memorandum (July 20, 2009).

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8. Lisa Fleisher, “N.J. official says revenue from income tax could ‘fall significantly below’ budget projections,” NJ.Com (May 20, 2010). 9. David Rousseau, former state treasurer, state of New Jersey. In-person interview, October 4, 2012. 10. Gary C. Corina, Ray Nelson, and Andrea Wilko, “Fiscal Planning, Budgeting, and Rebudgeting Using Revenue Semaphores,” Public Administration Review (2004). 11. Richard Stavneak, director, Joint Legislative Budget Committee—State of Arizona. Telephone interview, October 9, 2012. 12. Matthew Benson, “Arizona budget shortfall projection reaches $2 billion,” AZ Central (October 23 2009). Accessed via www.azcentral.com/news/election/azelections/articles/2009/ 10/22/20091022statebudget-ON.html?nclick_check=1. 13. Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica (1979). 14. Allen Schick, The Capacity to Budget, 1990. 15. Corina, Nelson, and Wilko, “Fiscal Planning, Budgeting, and Rebudgeting Using Revenue Semaphores.” 16. Thomas A. Garrett and Gary A. Wagner, “State Government Finances: World War II to the Current Crises,” Federal Reserve Bank of St. Louis Review (2004). 17. Howard A. Frank, Budget Forecasting in Local Government: New Tools and Techniques (Washington, DC: Quorum Books, 1992), 33. 18. Edward Clynch and Thomas Lauth, Governors, Legislators, and Budgets: Diversity across the American States (New York: Greenwood, 1991). Irene S. Rubin, “The Politics of Budgeting: Getting and Spending, Borrowing and Balancing: 6th Edition.” Congressional Quarterly (2009). 19. Robert D. Lee, Ron W. Johnson, and Philip G. Joyce, Public Budgeting Systems: 8th Edition, 2010. 20. William Buiter, Principles of Budgeting and Financial Policy, 1990. 21. George Hale and Scott R. Douglass, “The Politics of Budget Execution: Financial Manipulation in State and Local Government,” Administration & Society (1977): 370. 22. Corina, Nelson, and Wilko, “Fiscal Planning, Budgeting, and Rebudgeting Using Revenue Semaphores” (2004): 165. 23. Charles Lindblom, “The Science of Muddling Through,” Public Administration Review (1959). Aaron Wildavsky and Naomi Caiden, The New Politics of the Budgetary Process, 1988. 24. Wildavsky and Caiden, The New Politics. Schick, The Capacity to Budget. 25. Schick, The Capacity to Budget, 24 26. S. Madridakis, “The Art and Science of Forecasting: An Assessment and Future Directions,” International Journal of Forecasting (1986). Robert D. Lee, Ron W. Johnson, and Philip G. Joyce, Public Budgeting Systems: 8th Edition, 2010. 27. Frank, Budget Forecasting in Local Government. 28. Gary C. Cornia and Ray D. Nelson, “Rainy Day Funds and Value at Risk” Tax Analysis State Tax Notes (2003). 29. V. N. Vapnik, “An Overview of Statistical Learning Theory,” Neural Networks, IEEE Transactions (1999). 30. William H. Greene, Econometric Analysis: 5th ed. 31. Peter Kennedy, A Guide to Econometrics: 4th ed., 1998. 32. Robert J. Barro, “Are Government Bonds Net Wealth?” The Journal of Political Economy (1974). James E. Alt and Robert C. Lowery, “Divided Government, Fiscal Institutions, and Budget Deficits: Evidence from the States,” American Political Science Review (1994).

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33. Robert Rodgers and Philip Joyce, “The Effect of Underforecasting on the Accuracy of Revenue Forecasts by State Governments,” Public Administration Review (1996). 34. Rodgers and Joyce, “The Effect of Underforecasting.” 35. Clynch and Lauth, Governors, Legislators, and Budget. 36. Rodgers and Joyce, “The Effect of Underforecasting.” 37. Paul R. Blackley and Larry DeBoer, “Bias in OMB’s Economics Forecasts and Budget Proposals” Public Choice (1993). George A. Krause and James W. Douglas, “Institutional Design versus Reputational Explanations of Agency Performance: Evidence from U.S. Government Macroeconomic and Fiscal Projections,” Journal of Public Administration Research and Theory (2005). 38. Corina, Nelson, and Wilko, “Fiscal Planning, Budgeting, and Rebudgeting Using Revenue Semaphores.” James Poterba, “States Responses to Fiscal Crisis: The Effects of Budgetary Institutions and Politics. Journal of Political Economy (1994). 39. Corina, Nelson, and Wilko, “Fiscal Planning, Budgeting, and Rebudgeting.” 40. George A. Krause and James W. Douglas, “Institutional Design versus Reputational Explanations of Agency Performance: Evidence from U.S. Government Macroeconomic and Fiscal Projections,” Journal of Public Administration Research and Theory (2005). Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica (1979). 41. Lee, Johnson, and Joyce, Public Budgeting Systems. 42. Irene S. Rubin, The Politics of Budgeting: Getting and Spending, Borrowing and Balancing: 6th Edition, 2009. Bert Waisanen, “State Tax and Expenditure Limits-2008,” National Conference of State Legislatures (2008). 43. Allen Schick, The Capacity to Budget, 1990. 44. Tsai-Tsu Su, Mark S. Kamlet, and David C. Mowery, “Modeling U.S. Budgetary and Fiscal Policy Outcomes: A Disaggregated, Systemwide Perspective,” American Journal of Political Science (1993). 45. Su, Kamlet, and Mowery, “Modeling U.S. Budgetary and Fiscal Policy Outcomes,” 237. 46. J. Kevin Corder, “Managing Uncertainty: The Bias and Efficiency of Federal Macroeconomic Forecasts,” Journal of Public Administration Research and Theory (2005). 47. Tilman Brück and Andreas Stephan, “Do Eurozone Countries Cheat with their Budget Deficit Forecasts?” Kyklos (2006). 48. James E. Alt and Robert Lowery, “Divided Government, Fiscal Institutions, and Budget Deficits: Evidence From the States,” American Political Science Review (1994). 49. Alt and Lowry, “Divided Government, Fiscal Institutions, and Budget Deficits,” 823. 50. David Nice, “The Item Veto and Expenditure Restraint,” Journal of Politics (1988). David Nice, “The Impact of State Policies to Limit Debt Financing,” Publius (1991). Thomas P. Lauth, “The Line-Item Veto in Government Budgeting,” Public Budgeting and Finance (1996). James W. Douglas and Kim U. Hoffman, “Impoundment at the State Level: Executive Power and Budget Impact,” American Review of Public Administration (2004). 51. Clynch and Lauth, Governors, Legislators, and Budgets. 52. Catherine C. Reese, “The Line-Item Veto in Practice in Ten Southern States” Public Administration Review (1997). Glenn Abney and Thomas P. Lauth, “The Line-Item Veto in the States: An Instrument for Fiscal Restraint or an Instrument for Partisanship?” Public Administration Review (1985). Glenn Abney and Thomas P. Lauth, “The Item Veto and Fiscal Responsibility,” Journal of Politics (1997).

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53. Clynch and Lauth, Governors, Legislators, and Budgets. 54. Corina, Nelson, and Wilko, “Fiscal Planning, Budgeting, and Rebudgeting.” James J. Gosling, “The State Budget Office and Policymaking,” Public Budgeting and Finance (1987). 55. See Mini-Case 1.2 for a detailed account of some of the challenges in developing future budgets, which occur in a strong shared governance system of budgeting. 56. Douglas and Hoffman, “Impoundment at the State Level.” Thomas P. Lauth, “The Line-Item Veto in Government Budgeting,” Public Budgeting and Finance (1996). 57. Karen A. Stanford, “State Budget Deliberations: Do Legislators Have a Strategy?” Public Administration Review (1992): 24. 58. John E. Chubb, “Institutions, The Economy, and the Dynamics of State Elections,” American Political Science Review (1988). 59. James Poterba, “Budget Institutions and Fiscal Policy in the U.S. States,” American Economic Review (1996). 60. Henry J. Aaron, “Seeing through the Fog: Policymaking with Uncertain Forecasts,” Journal of Policy Analysis and Management (2000): 194. 61. James C. Garand, “Explaining Government Growth in the U.S. States,” American Political Science Review (1988). 62. Carl Klarner, “Forecasting Control of State Governments and Redistricting Authority After the 2010 Elections,” Forum (2010). 63. AP, “In Turnaround, Louisiana Governor Vetoes Bill Doubling Lawmakers’ Pay” New York Times (July 1, 2008). Accessed at www.nytimes.com/2008/07/01/us/01jindal.html?_r=0. 64. Rule 2.3 states “The Speaker of the House shall be elected by the members of the House from among the members thereof. Such election shall be viva voce and the favorable vote of fifty-three members shall be required to elect the Speaker. The Speaker shall be elected every four years at the organizational session of the legislature provided for in Article III, Section 2(D) of the Constitution of Louisiana at which the newly elected members take the oath of office. The election of the Speaker shall be the next order of business following the election of the Clerk. Vacancies in the office of Speaker shall be filled in the manner of the original selection.” 65. Jeff Brady, “Jindal Faces a Test in Taming Louisiana Politics,” NPR (2007). 66. Tyler Bridges, “Fiscal Hawks make difference in budget battle against Jindal, Louisiana Senate,” Bayoubuzz (2013). 67. Best of New Orleans, “Hawking Louisiana Budget Reform,” BestofNewOrleans.com, June 4, 2013. 68. Louisiana.gov., “Executive Branch,” Louisiana.gov. 69. Best of New Orleans, “Hawking Louisiana Budget Reform.” 70. Rubin, The Politics of Budgeting. 71. Robert D. Lee, Ron W. Johnson, and Phillip G. Joyce, Public Budgeting Systems: 8th Edition, 2010. 72. Poterba, “Budget Institutions and Fiscal Policy in the U.S. States.” 73. Nice, “The Item Veto and Expenditure Restraint.” 74. Garand, “Explaining Government Growth in the U.S. States.” 75. Chubb, “Institutions, The Economy, and the Dynamics of State Elections.” 76. Refer to Mini-Case 1.3 for a more detailed discussion of this process in the appendix. 77. Lee, Johnson, and Joyce, Public Budgeting Systems. 78. Rubin, The Politics of Budgeting, 212.

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79. Carol W. Lewis and W. Bartley Hildreth, Budgeting: Politics and Power, 2010. 80. Brian Faler, “State Aid Plan Seeking $26 Billion Survives Procedural Vote in U.S. Senate,” Bloomberg (2010). 81. NCSL. “Legislative Update: Extension of ARRA Enhanced Medicaid Match,” NCSL (2010).

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2 The Process and Politics of Generating Budget Forecast Errors

OVERVIEW “Nobody knows what the future’s going to be,” explained Texas governor and former Republican presidential candidate Rick Perry to reporters standing outside of the Texas state house in April 2011; he was answering a question about whether it was prudent to use any of the state’s rainy day funds to cover unanticipated costs associated with natural disasters Texas would face in the near-term.1 Perry intended to convey a message of competence and preparedness to voters. During the summer of 2011, the state had already faced wildfires and was expecting a major tropical storm or hurricane. He also wanted to pressure state legislators involved in developing the state’s biennium budget for fiscal years 2012–2013. By mentioning disasters that were out of his control, he aimed to push them to make further cuts to the state budget, which is facing long-term structural debt projected for the foreseeable future, instead of relying on rainy-day funds to plug gaps. The governor’s Texas two-step, positioning himself as both a pragmatic leader and deficit hawk, allowed him to shape the debate over the state’s budget projections in his favor. Texas is not alone in its struggle to deal with structural deficits. Governors in other states, such as California, Illinois, and New Jersey—which all have sizeable projected structural deficits— have woven together funding to cover unpredictable events into their proposed budgets as they have tried to shape the debate over how to spend public money. But what makes Texas so unique is Governor Perry’s singular focus on managing the state’s bleak financial future through a plan that refuses to use contingency funding to ameliorate the current economic downturn. It relies solely on spending cuts, without raising revenue, in order to fix the budget. Only time will tell if this experiment works.2 25

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Chapter 2

Like those in Texas, state lawmakers must cope with uncertainty in long-term economic forecasts as they project public expenditures and revenues. Trying to model future outcomes is a risky endeavor that is prone to error. Often, legislators attempt to reduce uncertainty in different ways: Using a variety of techniques to forecast budgets, under-forecast future budgets, and consensus approaches to project future revenues and expenditures. Yet technical strategies cannot eliminate uncertainty from the budget process. Political pressures can reduce or amplify uncertainty in the process in major ways, as competing actors engage in an ideological debate over how much government should tax and spend. The Texas case demonstrates how political actors, who engaged in a war between politically conservative and liberal ideologies, use uncertainty as a weapon. Politicians ask voters to put aside concerns about issues like solvency and liquidity and to focus on their beliefs about the ideal scope, size, and shape of government. Though this strategy may work in the immediate term, policymakers are inevitably constrained by uncertainty in the budget process. Ambiguity in the direction of a state’s finances of course limits the political rhetoric about what could be done in terms of a state’s fiscal policy. Politics aside, the consequences of uncertainty in the budget process result in state leaders being bound by stochastic elements of the forecasting process in order to formulate policy.3 This chapter introduces the forecasting techniques and modeling used to estimate state-level budget forecast errors. The focus of the models employed in this book is on the concept of uncertainty. Throughout the chapter, I present empirical models to estimate uncertainty within the budget process and how political institutions attempt to manage errors within budgeting. In addition, this work develops a behavioral model showing how individuals perceive budget forecast errors and how uncertainty shapes their perceptions. Chapters 6 and 7 focus on how both credit markets and voters respond to state budget forecast errors. Mini-Case 2.1: Waiting for a Rainy Day: Lawmakers Differ in How to Fix Texas’s Budget Deficit What should Texas lawmakers do to fix the state’s projected budget deficit? The debate in Austin has found the governor and state legislative leaders putting forth two sharply contrasting approaches. Republican Texas Governor and former presidential candidate Rick Perry pushed to finalize the Fiscal Year 2012–2013 state budgets through a “cuts only” approach to expenditures.4 However, leaders of the Republican-controlled state legislature were not ready to wholeheartedly endorse the governor’s plan. As a result, the legislature sought to delay action on passing the state’s biennial budget for fiscal year 2012 and 2013. Joe Straus (R), speaker of the Texas House of Representatives, stifled the governor’s efforts to quickly pass a budget. He said that many of the governor’s proposed $50 million in additional cuts for the next year had already been included in the $1 billion in suggested reductions to the state budget that the governor had already put forth. Skeptical of the governor’s

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The Process and Politics of Generating Budget Forecast Errors

27

plan, many legislative leaders were unsure if the governor’s office double counted future savings. Going into the budget cycle, the state faced a projected budget deficit of $4.3 billion in fiscal year 2012 and a revenue shortfall of $27 billion over the next two years. Covering the budget deficit in fiscal year 2011 required the state to use $3.2 billion from its Economic Stabilization Fund (ESF), also known as the rainy-day fund. If lawmakers did not use the fund to cover the gap, there would likely be severe cuts to all areas of state government, most heavily to public education and low-income and elderly health care programs. A major problem with the “cuts only” approach was that it would be unsustainable in the near future as a result of a rising population in the state. State representative Drew Darby (R-San Angelo), a member of the House Appropriations Committee, defended current efforts to solve the budget gap through significant reductions in expenditures in the near term. He did, however, caution that additional revenues are needed to cover expected rises in government services in the future.5 Nonetheless, at the end of the two-year budget cycle, of approximately $4B in the FY2012–2013 budget that was cut from education, about $3.4B was restored in the FY2014–2015 budget.6 Throughout the process, the governor discouraged the use of rainy day funds to close the budget gap over the FY2012–2013 period. He argued that his all-cuts approach would be the best way to deal with the state’s deficit and that the voters gave him a mandate to take this course of action. So far this strategy has been successful for the governor both politically and financially. The governor’s staunchest supporters contend that any use of the state’s rainy day funds is irresponsible, neglects the fundamental problems that got the state into this situation, and ignores the will of the people, who demand that the state’s finances be in order. Ultimately, the governor and state legislature compromised on the use of ESF funds to cover the FY2011 budget deficit by using $3B in rainy day funds to “backfill” the gap.7 Overall, markets appear to like the state’s approach to manage its finances. The ratings agency, Fitch, cited the state’s conservative approach to revenues and expenditures in the upcoming years in deeming its finances stable. However, one caveat to the agency’s ratings is that current revenue streams—which are primarily consumption based—may not meet the growing pressures for the state to provide increases in services in the long-term. Nonetheless, the outcome of the strategies employed by the Texas lawmakers and the governor remains uncertain as to their impact on the state’s future finances.

GETTING STARTED: DEFINING THE EMPIRICAL FORECASTING ERROR MODELS The modeling used in this book builds on current research in budget forecasting.8 It tests how political factors tend to push revenue forecasts downward.9 It also examines how serial correlation among forecast errors is conditioned by inter-temporal varia-

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Chapter 2

tions based on the political-business cycle.10 (For example, it is highly likely that if a forecaster is too cautious, or too favorable, in one year, he or she is likely to also be the same in the following year.) Last, the work also explores whether economic uncertainty improves or diminishes the accuracy of policy-makers’ public budget forecasts.11 I have developed two approaches (the naïve and incentive approaches) to analyze patterns of variation in errors in state-level budget forecasts. These methods rely on forecasting models of state-level expenditures and revenues, similar to what the states currently use to project their future budgets. Though each state has its own way of estimating future budgets, the econometric models used in this book to generate projections of state level public finances incorporate national and state economic conditions, demographic changes, incremental spending and revenue patterns within the states, and also allows for random variation to occur between states due to differences in methods of developing future budgets.12 Next, we look at the errors generated from the projections by utilizing state-level and national-level political variables to explain uncertainty within them. This combined approach allows for a more comprehensive understanding of the economic and political factors that drive forecasts, and provides a means to improve upon the public’s understanding of the complex interactions between economics and politics in developing state governments’ priorities. It also shows what types of structural factors influence the public budgeting process.

LOWBALLING THE BUDGET: THE INCREASED PRACTICE OF UNDER-FORECASTING Prior studies of state-level forecasting practices have found that state governors are likely to provide themselves with cushions in order to adjust to shifts in the economy.13 This research has been successful in explaining why states are likely to under-forecast to contend with future economic uncertainty.14 By 2004, 20 states underestimated their revenue projections as a result of a “precipitous drop in state revenues from 2000 to 2002.”15 To better understand the practice of under-forecasting (a practice that is closely connected with what I define as being conservative in setting budget forecasts), chapters 3 and 4 provide an in-depth analysis of bias, or cushion, in short-term and longterm techniques used by the states in generating revenue and expenditure budgets. It expands on prior research by including data up to fiscal year 2010. The analysis addresses the limits of underforecasting revenue and expenditure forecasts. Namely, if the states tend to build a substantial cushion into their projections, it is likely that political forces calling for accountability, competence, and transparency would make it difficult for policymakers to systemically lowball public finances, particularly during an election cycle. In addition, increases in demand for government services and

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programs require that revenue projections fall in line with reality because of political pressures to meet these needs. That requires adjustments to the existing revenue base (e.g., raising taxes). Of course, most politicians are averse to such undertakings.

DEFINING THE NAÏVE AND INCENTIVE APPROACHES TO FORECASTING STATE REVENUES AND EXPENDITURES Overall, the first step in generating state-level revenue and expenditure forecast errors is to estimate the baseline for budget projections. One can then evaluate the effects of political variables on errors generated by budget projections and determine if the political-business cycle leads to the underestimation of budget forecasts during election years. To accomplish this, I have incorporated two approaches, with each one building upon the previous. The first approach is defined as the naïve model. It simply estimates budget forecast errors based on economic and demographic data. It develops forecasts for both revenues and expenditures based on the following variables: lagged state gross product (GSP), unemployment, the previous year’s tax revenue, and expenditures for a given year.16 The model picks up trends within the budgeting processes because prior year revenues influence current year revenue and spending levels.17 These estimates are designed to closely model the current budget process to generate forecast errors that will serve as the dependent variables in the next two approaches. The intent is to obtain the smallest forecast error possible, in order to minimize unexplained effects for estimating the strategic and incentive approaches. To estimate expenditure and revenue forecasts for the states, I utilize a least squares regression method, which accounts for random effects between as well as fixed effects within states, to estimate cross-sectional time-series data.18 The reason for choosing this technique is to correct for heteroskedasticity. The technique does so by adjusting the estimation of given weights of each of the model’s independent variables based on the model’s error predictions. The dependent variables for the revenue and expenditure estimates are based on the natural log of each variable. This is to reduce skewness in the variables. The equations are weighted by variables that measure state population, lagged state revenues, lagged GSP, and state unemployment. In addition, a dummy variable for each state except Nebraska (which has a unicameral system) and Wyoming (which is treated as a reference category) is included as direct effects in each equation. The forecasts allow for state differences in methods for forecasting revenues and expenses. I have specified a disturbance term that is divided into short-term forces and random observations.19 Short-term forces are included in estimating the next period’s expenditures. For instance, when estimating changes in current expenditures, I include short-term changes in lagged revenues, as well as lagged gross state product. The multivariate model for estimating party volatility is summarized in equation 2.1.

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Chapter 2

(Eq. 2.1) (State Level Revenue [Expenditures])k =constantk + φUnemployment1,i + θLagged GSPk-1 + θLagged Revenues k-1k,+ + βState1…+ βState49 +ς2k-i + εk,1 The expected direction of the revenue model’s coefficients is positive for lagged GSP and lagged state budget revenues and negative for state-level unemployment. The rationale for these expectations is that prior year growth is likely to result in current year growth and, during periods of economic downturn, revenues are likely to drop. For the expenditure model, the expected direction of the model’s coefficients is positive for lagged GSP, lagged revenue, and unemployment. Expectations suggest that pressures for increased expenditures come from both periods of economic growth and also economic downturns, as demand increases for state services (e.g., unemployment benefits, food stamps, insurance, etc.). After estimating state budget forecasts, I then incorporate political variables into explaining variation in forecast errors (e.g., party control of the legislature and governorship, national political factors, and timing of the next election). The dependent variable in this approach is based on forecast errors from the prior approach. The next step is to transform states’ forecast errors into two-year absolute percent errors. This allows for the size of the error to be comparable across states and provides a metric for understanding levels of risk associated with the imprecision embedded in the budget process. The two-year window takes into account states that have a biennium process, as well as those that budget on an annual basis. The theoretical expectation of this model is that differences persist between the political parties in budget forecasts. State legislatures controlled by either of the two main political parties are more likely to under-forecast revenue and expenditures.20 This causes differences in the distribution of states’ forecast errors. It is also expected the two major governing political parties will have distinct approaches to public policy, resulting in different positions on how to fund them.21 The major expected difference between parties is that Democrats will push for higher expenditures and Republicans will seek lower spending levels. In terms of revenues, it is expected that both parties will seek alternative funding sources and fees to offset increased expenditures while being very averse to raising income taxes, property taxes, and/or corporate taxes.22 The strategic benefit for the political parties in having two opposing budgetary platforms is that the parties provide voters with distinct alternatives by taking competing views about projected funding levels and revenue estimates. Alt and Lowery (1993) found a roughly 6.7% difference between the parties in desired spending levels. In addition, it is expected that the accuracy of budget forecasts diminishes in states with strong one-party rule of the state legislature.23 Last, this approach should also show differences based on the branch of government. Governors are more likely to resist pressures from the state legislature to change revenue and expenditure forecasts, even when the same party controls the institution. Governors tend to be held more accountable for budget management

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than members of the state legislatures by voters. State voters tend to use national economic conditions as a measure of the president’s performance; thus, a connection is made first between governors and the president for these conditions and then between state legislatures and the president.24 It is the president’s party in the state legislature that is held accountable by voters, who tend to be focused more on national conditions than on state conditions. In these instances, it is expected that divided government will increase the size of forecasting errors when estimating longterm forecasts. This is due to a lack of clarity among voters about who should be held accountable for inaccurate projections. Furthermore, it is expected that the political-electoral cycle also explains variation in budget forecast errors. The model tests whether incumbents change their behavior regarding budget forecast errors the closer they are to the next election for both short-term (two years out) and long-term (three to five years out) forecasts.25 A likely result of this strategy is a decrease in forecast errors for both revenues and expenditures during a gubernatorial election year. Incumbents are likely to want to demonstrate to the electorate that they are effective managers of the public purse. During non-election years, forecast errors are likely to be larger as a means to reward supporters by funding “pet” projects and/or programs. The increase in forecast errors results in more incumbents funding these types of projects during off-election years than what one would expect if projections were based solely on macro-economic conditions and prior year revenue.26 The Incentive model is defined in equation 2.2. It builds on the naïve model by using political variables to explain variation in state-level forecast errors. The technique for estimating the incentive model is based on a multi-level design (with fixed and random effects). This estimation measures differences in budget forecast errors for revenues and expenses within and between states. Errors within each of the two levels are likely correlated, thus requiring a random effects model in order to make more accurate inferences about the fixed effects of state level budget forecast errors.27 This model is defined by equation 2.2: (Eq. 2.2) Y(Absolute Percent of Revenue [Expenditure] Forecast Error)ij = U00 + U01 (Partisan Intensity Index) + U02 (Governor Electoral Change) + U03 (Divided Government) + U05 (Annual State Gross Domestic Product) +U06(ACIR Index) +U07(Years until Next Gubernatorial Election) +U08(Expenditure Limit) + u0j + u1j (State) + rij The fixed effects model element of Equation 2.2 utilizes covariates to control for changes in a state’s forecast errors. The random effects elements in Equation 2 estimate the variation in all of the states’ forecast errors. Furthermore, the Incentive model integrates the political-electoral cycle into estimating variation in budget forecast errors. This model also uses a mixed-level design. The intent of these estimates is to assess the effects of the timing of the next statewide election on budget forecasts.

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The model also tests whether incumbents change their behavior regarding budget forecast errors the closer they are to the next election. The size of state-level forecast errors for both revenues and expenditures is expected to decrease as the gubernatorial election approaches, as incumbents want to showcase their skills in managing the public purse. During non-election years, forecast errors are likely to be larger as governors reward supporters by funding projects and programs tied to their agenda. More incumbents fund these types of projects during non-election years than what one would expect if projections were based solely on macro-economic conditions and prior year revenue. Mini-Case 2.2: California’s Proposition 25: Do Hard Times Cause Budget Reform? Over the past few decades, California has been a laboratory of democracy. The recent passage of Proposition 25—an amendment to the state constitution that changed the state legislature’s rule requiring a two-thirds majority to pass state budget and spending bills to a simple majority—was heralded by supporters as an end to gridlock in Sacramento. Advocates said it holds legislatures accountable, and reduces waste.28 However, in preparation for the fiscal year 2012 budget, the first budget year under the new system, a new wrinkle to the budget process emerged. The bill contains a loophole that allows lawmakers to get paid on time, even if the budget is late. The fine print in the amendment, drafted by a labor coalition to support the Democratic majority in the state legislature, was to merely “pass a budget” regardless of whether or not it was a balanced budget, as required in the California state constitution.29 So what motivated this constitutional reform? Partisanship was a primary determinant. In recent years, party polarization within the state legislature forced Sacramento to a legislative standstill. The financial crisis of 2008 reminded the public of a dysfunctional governing system that is not only not able to deal with its existing $25 billion dollar structural deficit but also unable to successfully manage its way out of the economic downturn. Yet, will passing a budget in a timely process fix the state’s structural deficit? The likely answer is no. Rather, requiring a simple majority vote to pass budgets does not mean a majority of legislators, and by extension their constituents, would be willing to enact significant spending cuts and raise revenues to fix the state’s financial standing. The fiscal year 2012 budget demonstrated the difficulty in reaching such an agreement. Though Governor Brown signed a budget bill that resulted in a $15 billion dollar spending reduction, he did so without raising new sources of revenue. The governor’s revenue projections were more optimistic forecasts than what were presented by the non-partisan Legislative Analyst’s Office. However, the overall differences between forecasts were statistically insignificant. So, though it is unlikely that Proposition 25 reform will improve the state’s structural deficit in the near term,

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there is optimism among its supporters that it makes California governable once again. Time will tell.

VOTERS: FISCAL ILLUSIONS, NON-ATTITUDES, PSEUDO-OPINIONS, AND IDEOLOGY This book also develops an individual-level model of perceptions of budget projections. Chapter 5 develops an empirical model that tests whether individuals who are subject to information on a state’s short-term budget projections will change their perceptions of the state’s budget. From the vantage point of individual perceptions of state-level budget projections, I present a model of fiscal shirking among individuals in their preferences over how the states should deal with structural budget deficits. The framework also tests whether individuals hold incumbent lawmakers and governors accountable for inaccurate budget projections. Fiscal Shirking is defined as a process where individuals’ perceptions of budget projections result from short-term information on the economy that conditions their responses. At the core of this process is an increasing desire for individuals to find any, or all, alternate sources of revenue—increasing income taxes on households earning $250,000 or more per year, sin taxes, gasoline taxes, and/or federal aid, and so forth— to pay for public expenditures. This process differs from the Fiscal Illusion Hypothesis; the latter is a process in which individuals systemically underestimate the costs and overestimate the benefits of public goods, thus demanding higher levels of public spending.30 The major point of difference between the two theories is that fiscal shirking is dynamic and does not require voters to disconnect the costs and benefits of governmental goods and services. Individuals use cognitive tools such as anchoring, heuristics, and self-projection to conceptualize public finances. The work explores how individuals’ perceptions of public financial data are biased by cognitive illusions. Specifically, it tests whether individuals use techniques such as anchoring—where their responses are biased by the cues provided in survey questions31—and through heuristics, in which individuals use short-cuts in processing information to deal with uncertainty, relying on political partisanship and/or ideology in developing their opinions of future budgets.32 I hypothesize that if individual perceptions of budget projections are formed by these cognitive tools, we should therefore be more likely to find individuals engaged in fiscal shirking. The decision-making process of fiscal shirking works in the following manner: First, individuals use heuristics, or information short-cuts, such as partisanship or political ideology, as a starting point to take in new information.33 Thus, these cognitive tools are used by individuals to reduce uncertainty and synthesize complex ideas in the budget process.34 Individuals are also more likely to consider arguments and policy options when they come from sources that conform to their existing preferences.35 Second, once an individual processes new information on the

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Chapter 2

state of the budget, his or her perceptions of it are contingent on the most recent information available. Information and possible judgments about its implications are therefore processed in a manner that does not require too much effort on the part of the individual. Therefore, as part of managing uncertainty in this process, a citizen will likely prefer policy options that indirectly impact his or her own financial situation. Fiscal shirking differs from the Fiscal Illusion Hypothesis. Fiscal shirking is caused by short-term stimuli, such as a financial crisis, and does not require systematic behavior among individuals. The process is also shaped by cognitive heuristics, such as political partisanship, rather than an overall distortion of the true costs of running government. Last, fiscal shirking assumes that even though individuals have imperfect information, as is also the case in the Fiscal Illusion Hypothesis, the difference is that fiscal shirkers do in fact conceptualize that public budget deficits require additional levels of revenue. However, they want overall increases in revenue that do not cover long-term demands for public spending. As a result, their shirking perpetuates structural deficits into the future.

INVESTORS AND INSTITUTIONS: BUDGET FORECAST ERRORS, MARKETS, AND ELECTIONS Chapter 6 focuses on how credit markets respond to state-level budget forecast errors. Of particular interest to this study is whether volatility in state-level forecasts provides signals, albeit noisy ones, to investors in a state that is likely to incur a short-term or long-term operating deficit that may ultimately impact the state’s costs of borrowing.36 The market discipline hypothesis is used to model investor behavior regarding state-budget forecast errors. This is defined as markets raising interest rates and limiting states’ access to credit as a result of a perceived default. This theory can be modified to evaluate whether volatility in forecasts causes an increase in premiums to states, as investors perceive this volatility as a result of running excessive deficits, persistent deficits, or falling revenue sources and increased expenditures.37 Furthermore, this work builds on prior research that has found that actions by political institutions have a significant effect on interest rates of state bonds.38 A key finding relates to whether a state has a balanced budget requirement.39 Lowery and Alt (2001) found that states with more restrictive balanced budget rules preventing them from carrying a deficit into another fiscal year had lower costs of borrowing than states that did not have such requirements. Even though states may have a balanced budget rule, this does not mean that they do not try and “skirt” the requirements of balanced budget laws.40 Other fiscal rules also impact the costs of borrowing for a state. Researchers have found that in states with tax and expenditure limits (TELs), these rules impact the costs of taking on additional debt. Poterba and Rueben (1999) found that borrowing costs were significantly lower in states that did not have revenue ceilings and

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were also lower in states that had expenditure limits. Also, restrictions on how much a state can borrow tend to have significant and independent effects on the costs of borrowing.41 Results from these studies confirm the impact of financial rules and political institutions on borrowing costs for states. This research builds on prior work by including controls for fiscal rules and political institutions, as well as economic conditions, when assessing the impact of budget forecast errors on credit markets. It also adds to the existing body of research by looking at how inter-temporal variations in budget forecast errors impact investors’ perceptions of bond yields. Furthermore, chapter 6 assesses the impact of variations in revenue and expenditure forecast errors on the issuance of general obligation debt in the states. To do this, the analysis uses the following dependent variables: The ratio of debt service to current revenues, the ratio of the amount of debt issued to state personal income, and a composite average of state bond credit rating scores. To estimate the impact, I employ a two-stage least squares random effects instrumental variable regression on the first two dependent variables and a two-stage Tobit regression model to estimate state credit ratings. Overall the reason for using an instrumental variables approach is to take into account endogeneity issues surrounding the impact of political variables in conditioning revenue forecast errors as well as their impact on financial markets. The selection of the Tobit regression technique is due to the nature of the dependent variable that measures state credit ratings, where adjusted credit ratings scales are naturally bounded from below by zero and from above by one, thus causing censoring of the dependent variable. In the estimation, I treat absolute percent revenue budget forecast errors as an instrumental variable, based on two-year averages. This is to account for states that use an annual or biennium budget process. I use political and economic factors (e.g., lagged revenue, GSP, party control of the legislature and governorship, state-level differences, fiscal year, and timing of the next gubernatorial election) to explain variation in it. Once the instrument has been estimated, it is then added into a second equation used to explain variation in bond market ratings for the states. I define the equation to estimate state credit ratings in equation 2.3: (Eq. 2.3) State Bond Ratings/ Debt Service to Current Revenues/ Debt Issued Per State Personal Income =Constant + ß1State-Level Unemployment + ß2Debt Limits + ß3ACIR Score + Z4State Revenue [Expenditure] Errors+ B5 Gross State Product+ B6 Lag State Revenue Budget + B7State… B57State + B58Fiscal Year… B74Fiscal Year + e To construct the dependent variable for credit rating scores, I utilize a similar methodology to Depken and LaFountain (2006) regarding how to convert the credit ratings used by Standard & Poor’s, Moody’s, and Fitch into a numeric scale. I do so by converting the scale for each firm, with higher ratings receiving higher scores (e.g., for an AAA rating for Standard & Poor’s I would assign a score of 25), and then

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Chapter 2

dividing by all of the possible ratings for each score. In the same example, a Triple A rating would be divided by 25. In addition to measuring separate ratings by firm, I have also averaged across states ratings by the three companies in order to normalize ratings per state, even though not all states offer public debt or have ratings from all three firms in a given year.

ELECTIONS, ECONOMIC VOTING, AND BUDGET FORECAST ERRORS Chapter 7 explores the impact of forecast errors on state-level elections. It analyzes legislative and gubernatorial contests, looking at what changes result from forecast errors and the extent to which they occur. The work builds upon prior research on economic voting where voters reward (or punish) incumbents for a good (or bad) economy.42 The theoretical argument in constructing election models of legislative and gubernatorial contests is to test the impact of financial uncertainty on electoral outcomes. Thus, the more uncertain a state’s financial conditions, other things being equal, the more likely voters are to punish incumbents. Equation 2.4 determines the share of votes received by a state’s governor (or, if there is no incumbent running, share won by the governor’s party) and the share received by the majority party in the state legislature in both the upper and lower chambers. (Eq. 2.4) Percent Vote Governor [Majority Legislative Party] =Constant + ß1StateLevel Unemployment + ß3 State Partisanship Index + ß3ACIR Score + Z4State Revenue [Expenditure] Errors+ B5 Gross State Product+ B6 Lag State Revenue Budget + B7State… B57State + B58Fiscal Year… B74Fiscal Year + e Mini-Case 2.3: Michigan’s 2011 Budget: Signs of Optimism for a Troubled State? “It is still true that bad news for the U.S. is bad news for Michigan—just maybe not as much as it once was,” said Michigan State University economist Charles Ballard.43 The state has faced dire financial circumstances since the last recession in 2001. The Great Recession has prolonged the pain. However, in light of current economic conditions, some state leaders feel that things may be getting better. This year’s state budget was developed to try to minimize economic uncertainty. University of Michigan economists are forecasting a slight uptick in job creation for 2011; such predictions may be a boost to the business community, which has been cautious over the past few years. The debate in Lansing, however, continues to be over how the state should budget to accommodate state economic conditions. Republican governor Rick Snyder attempted to minimize economic uncertainty in his upcoming budget proposal for Fiscal Year 2012. Major parts of his plan included a flat corporate tax and very substantial cuts to the state’s public sector.44 The

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governor has called his budget part of an effort of “shared sacrifice.”45 Predictably, the governor’s plan has been met with both praise and criticism. Proponents have pointed to the governor’s proposed changes as a means to increase job growth and promote economic stability in the state. Recent endorsements by pro-business groups, like Michigan Economic Development, have given some leeway in the governor’s choices about who wins and who loses in his upcoming budget. Critics counter that such dramatic changes threaten a very fragile economy and reduce economic competitiveness and quality of life, all of which require a more balanced approach to managing government spending. Public sector labor unions have protested en masse against proposed pension/retirement taxes in the governor’s plan.46 Nevertheless, as arguments about the governor’s proposed budget continue, whether this proposal will make things better for the state is a wide-open issue.47

DISCUSSION This chapter provides various methods to model uncertainty in state-level budget forecasts. Uncertainty is defined by two approaches: The Naïve and Incentive models. The Naïve model estimates state-level revenues and expenditures and serves as the baseline in which to estimate budget forecast errors. The method is estimated for both short-term forecasts one to two years ahead and long-term ones, looking three to five years ahead. Budget forecast errors are calculated as the absolute difference between actual revenues and expenditures minus predicted revenues and expenditures. After calculating state budget revenue and expenditure forecasts, budget forecast errors are measured as the absolute percent difference between actual budgets from predicted budgets. The transformation of forecast errors into absolute percent error allows for the size of the error to be comparable across states and also provides a metric for understanding levels of risk associated with the imprecision embedded in the budget process. The Incentive model uses a fixed-random effects approach that incorporates, as fixed effects, political variables into explaining variation in forecast errors (e.g., state partisan intensity index and governorship, national political factors, and timing of the next election). State-level differences are treated as random effects. The incentive model also includes electoral cycle variables in the fixed effects portion of the estimate as well as random effects for the state-level differences. Testing and analyzing the results of these models can be found in chapters 3 and 4. Chapter 5 conducts individual-level experimental surveys of the general public to test respondents’ perceptions of the impact of budget forecast errors on the budget process and how their preferences shape the policy options available to lawmakers to mitigate structural deficits faced in some states. The chapter introduces the fiscal shirking hypothesis, which finds that individuals tend to demand high levels of government spending on programs that directly affect them while also preferring options that do not tax them directly to cover the costs.

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38

Furthermore, econometric models are used to estimate the impact of state-level budget forecast errors on state bond yields and the issuance of general obligation debt in the states. These estimates control for political institutions and economic conditions. The results test the market discipline hypothesis on budget forecast errors. These estimates are discussed in chapter 6. Last, chapter 7 examines the impact of forecast errors on state legislative and gubernatorial elections. It does so by evaluating the connection between economic conditions, fiscal policy, and electoral outcomes in the states.

APPENDIX Table 2.1.

Defining the Variables

Dependent Variables

Comments

State Annual Expenditures

All state governmental spending for a given year except for federal aid. The natural log of this variable is used to estimate the forecast model. Forecast errors from these projections are used to estimate the strategic and incentive models. Data for this variable come from the NASBO Fiscal Survey of the States from 1991 to 2010.1 All state government revenues for a given year except for federal aid. The natural log of this variable is used to estimate the forecast model. Forecast errors from these projections are used to estimate the strategic and incentive models. Data for this variable come from the NASBO Fiscal Survey of the States from 1991 to 2010. The variable is the absolute percent average error measured as the difference between a state’s actual revenue and its expected revenue for a given year. This index is based on credit ratings by Standard & Poor’s, Moody’s, and Fitch. The index converts the alpha ranking used by the agencies into a numeric scale so that higher numbers on the index indicate higher ratings. The ratios based on states’ revenues are sufficient by current debt service. The ratio measures the issuance of new debt in a state as it relates to the level of per capita income support to support it. The percent vote share for the governor’s party during a gubernatorial election year as well as for the majority party in the upper and lower chambers in a given state. The data come from state registrars and U.S. statistical abstract.

State Annual Revenues

Revenue Forecast Errors

Bond Ratings

Ratio of debt service to current revenues Ratio of the amount of debt issued per state personal income Percent Vote Governor’s Party [Majority Legislative Parties in Upper and Lower Chambers]

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Independent Variables

Comments

State-level Variables

A binary variable is used for each state in estimating the forecasting model and mixed effects models. State variables are not included for Nebraska and Wyoming. The natural log of previous year revenues in a given state. Coded as “1” for states that have Debt Restriction limits and “0” for all other states. Coded as an index that ranges from “0” for states with lax balanced budget rules to “10” for states with strict rules. Unemployment rate per state per year compiled by the U.S. Department of Labor. It is part of the Local Area Unemployment Statistics (LAUS) data series. Current gross state product for a state per year as estimated by the U.S. Department of Commerce. A principal component factor of the following variables: the percent of the lower and upper houses held by the Democratic Party; the percent of votes the Democratic Party received in the state in the previous presidential election; and the average Americans for Democratic Action (ADA)2 scores of the members of Congress per state.3 States that have high scores on this index represent more Democratically partisan states while states with lower scores are Republican partisan states. States that score close to zero tend to be more politically competitive with neither party completely dominating state political institutions. A binary variable in which “1” equals divided government (where party control is split in the upper/lower chamber in the state legislature and/or governor) else “0.” A binary variable where “1” equals a Democratic governor else “0.” The governor’s term in office and ranges from 1 to 4.

Lagged State Annual Revenues Debt Restriction Limits ACIR Index

Annual State Unemployment Rate Gross State Product State Partisan Intensity Index

Divided Government

Democratic Governor Term in Office 1

The years included in this study are based on the current availability of data. The data series provides comprehensive information on state and local governmental revenue, expenditures, debt and assets (cash and security holdings).

2 ADA scores are defined as the 20 most important annual votes, ranging from social and economic issues both domestically and internationally, deemed by the ADA’s Legislative Committee during a legislative session. The index measures political liberalism of members of Congress (House and Senate) by combining the 20 key votes into the Liberal Quotient (LQ) which gives each member 5 points if he/she voted with ADA, and 0 points if he/she voted against or was absent. The total possible score per member, per session, is 100. 3 The Eigen value for this estimate is 2.47 and explains 62% of total variation. Principal Components for this factor are: Percent Democratic Lower House .546; Percent Democratic Upper House .544; ADA scores .43; and State Presidential Vote for Democratic Candidates is .48.

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40

Chapter 2

NOTES 1. AP, “Perry Says Disaster Could Blow a Hole in the State Budget,” CBS: Dallas Fort Worth (August 27, 2011). http://dfw.cbslocal.com/2011/04/27/perry-says-disaster-couldblow-hole-in-state-budget/. 2. See mini-case 2.1 for a discussion of the debate over Texas state budget projections for fiscal year 2011–2012. 3. Rudolph Penner, “Federal Revenue Forecasting,” in Government Budget Forecasting: Theory and Practice, edited by Jinping Sun and Thomas D. Lynch, 2008. 4. www.gosanangelo.com/news/2011/aug/13/growing-state-requires-revenue-darby-says/. 5. http://trailblazersblog.dallasnews.com/archives/2011/08/post-29.html. 6. Corrie MacLaggan, “Texas Budget Restoring Funds Cut From Schools Approved By State Lawmakers,” Reuters (May 26, 2013). Accessed via www.huffingtonpost. com/2013/05/26/texas-budget_n_3340949.html. 7. Bill Peacock, “The Great Texas Budget Debate of 2013,” Texas Public Policy Foundation (June 19, 2013). Accessed via www.texaspolicy.com/center/economic-freedom/blog/greattexas-budget-debate-2013. 8. Robert D. Lee, Ron W. Johnson, and Philip G. Joyce, Public Budgeting Systems: 8th Edition, 2010; Irene S. Rubin, “The Politics of Budgeting: Getting and Spending, Borrowing and Balancing: 6th Edition,” Congressional Quarterly (2009); Charles E. Menifield, The Basics of Public Budgeting and Financial Management: A Handbook for Academics and Practitioners (Lanham, MD: University Press of America, 2008); Robert W. Smith and Thomas D. Lynch, Public Budgeting in America: 5th Edition (New York: Prentice Hall, 2005); Paul R. Blackley and Larry DeBoer, “Bias in OMB’s Economics Forecasts and Budget Proposals,” Public Choice 76 (1993): 215–32; Howard A. Frank, Budget Forecasting in Local Government: New Tools and Techniques (Washington, DC: Quorum Books, 1992); William Earle Klay, “Revenue Forecasting: An Administrative Perspective,” in Handbook on Public Budgeting and Financial Management, edited by Jack Rabin and Thomas D. Lynch (pp. 287–315) (New York: Marcel Dekker, 1983). 9. Robert Rodgers and Philip Joyce, “The Effect of Underforecasting on the Accuracy of Revenue Forecasts by State Governments,” Public Administration Review 56, no. 1 (January–February 1996): 48–56. 10. Gary C. Corina, Ray Nelson, and Andrea Wilko, “Fiscal Planning, Budgeting, and Rebudgeting Using Revenue Semaphores,” Public Administration Review 64, no. 2 (2004): 164–79; Rudolph Penner, “Federal Revenue Forecasting,” in Government Budget Forecasting: Theory and Practice, ed. Jinping Sun and Thomas D. Lynch (pp. 11–26) (Boca Raton, FL: Taylor & Francis, 2008). 11. Jinping Sun, “Forecast Evaluation,” in Government Budget Forecasting: Theory and Practice, ed. Jinping Sun and Thomas D. Lynch (pp. 223–240) (Boca Raton, FL: Taylor & Francis, 2008). 12. Tom Stinson, “State Revenue Forecasting: An Institutional Perspective,” Government Finance Review 18, no. 3 (2002): 12–15; Irene S. Rubin, “The State of State Budgeting,” Public Budgeting & Finance, Silver Anniversary Edition (2005): 46–67. 13. Howard A. Frank and Gianakis A. Gerasimos. “Raising the Bridge Using Time Series Forecasting Models,” Public Productivity 6 Management Review 14, no. 2 (1990): 171–188; William Earle Klay, “Revenue Forecasting: An Administrative Perspective,” in Handbook on Public Budgeting and Financial Management, ed. Jack Rabin and Thomas D. Lynch (pp. 287–315) (New York: Marcel Dekker, 1983).

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41

14. Robert Rodgers and Philip Joyce, “The Effect of Underforecasting on the Accuracy of Revenue Forecasts by State Governments,” Public Administration Review 56, no. 1 (January–February 1996): 48–56. 15. Willoughby and Guo, “Forecasting in the US States,” in Government Budget Forecasting: Theory and Practice, ed. Jinping Sun and Thomas D. Lynch (p. 33) (Boca Raton, FL: Taylor & Francis, 2008). 16. Michael Dothan and Fred Thompson, “Optimal Budget Rules: Making Government Spending Sustainable Through Present-Value Balance,” Public Finance and Management 9 (3) (2009): 439–69. 17. Allen Schick, The Capacity to Budget (Washington, DC: Urban Institute, 1990). 18. This is the xtreg procedure used in Stata to analyze panel data. 19. William H. Greene, Econometric Analysis: 5th ed. (New York: Prentice Hall, 2003). 20. Katherine Willoughby and Guo Hai, “The State of the Art: Revenue Forecasting in the US States,” 2008. 21. James C. Garand, “Explaining Government Growth in the U.S. States,” American Political Science Review 8, no. 3 (1988): 837–49; James E. Alt and Robert C. Lowery, “Divided Government, Fiscal Institutions, and Budget Deficits: Evidence From the States,” American Political Science Review 88, no. 4 (1994): 811–28. 22. Irene S. Rubin, “The State of State Budgeting,” Public Budgeting & Finance (2005). 23. Katherine Willoughby and Guo Hai, “The State of the Art: Revenue Forecasting in the US States,” 2008. 24. John E. Chubb, “Institutions, The Economy, and the Dynamics of State Elections,” American Political Science Review (1988): 149. 25. For the sake of simplicity, I refer to fiscal periods as years. This categorization includes states that have both annual and biennial budget cycles. 26. Q. Xu, H. Kayser, and L. Holland, “Forecast Errors: Balancing the Risks and Costs of Being Wrong,” in Government Budget Forecasting: Theory and Practice (2008). 27. Greene, Econometric Analysis. 28. http://articles.latimes.com/2010/sep/30/opinion/la-ed-props2526-20100930. 29. http://articles.latimes.com/2011/may/20/local/la-me-legislature-pay-20110520. 30. Wallace E. Oates, “On the Nature and Measurement of Fiscal Illusion: A Survey,” 1988. 31. John R. Zaller, The Nature and Origins of Mass Opinion (Cambridge: Cambridge University Press, 1992). 32. Samual L. Popkin, The Reasoning Voters: Communications and Persuasion in Presidential Campaigns (Chicago: University of Chicago Press, 1994). 33. Phillip Converse, “The Nature of Belief Systems in Mass Publics,” in Ideology and Discontent, ed. David Apter (New York: Free Press, 1964). 34. Daniel Kahnman, “Maps of Bounded Rationality: Pyschology for Behavioral Economics,” American Economic Review 93, no. 5 (2004): 1449–75. 35. John R. Zaller, The Nature and Origins of Mass Opinion (Cambridge: Cambridge University Press, 1992). 36. Robert C. Lowery and James E. Alt, “A Visible Hand? Bond Markets, Political Parties, Balanced Budget Laws and State Government Debt,” Economics and Politics 13, no. 1 (2001): 49–72. 37. Maria Cornachione Kula, “Credit Market Discipline: Theory and Evidence,” International Advances in Economic Research 10, no. 1 (2004): 58–71; Tanim Bayoumi, Morris

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Goldstein, and Geoffrey Woglam, “Do Credit Markets Discipline Sovereign Borrowers? Evidence from US States,” Journal of Money, Credit, and Banking 27, no. 4 (1995): 1046–59. 38. James Poterba and Kim S. Reuben, “State Fiscal Institutions and the U.S. Municipal Bond Market,” in Fiscal Institutions and Fiscal Performance, ed. J. Poterba and J. von Hagen (Chicago: University of Chicago Press, 1998); Alberto Alesina and Roberto Perotti, “Budget Deficits and Budget Institutions,” in Fiscal Institutions and Fiscal Performance, ed. James M. Poterba and Jurgen von Hagen (Chicago: University of Chicago Press, 1998), 13–36; Robert C. Lowery and James E. Alt, “A Visible Hand? Bond Markets, Political Parties, Balanced Budget Laws and State Government Debt,” Economics and Politics 13, no. 1 (2001): 49–72. 39. Poterba and Reuben, “State Fiscal Institutions and the U.S. Municipal Bond Market.” 40. Maria Cornachione Kula, “Credit Market Discipline: Theory and Evidence,” International Advances in Economic Research 10, no. 1 (2004): 58–71. 41. Poterba and Reuben, “State Fiscal Institutions.” 42. Michael S. Lewis-Beck and Mary Stegmaier, “Economic Models of Voting,” in Oxford Handbook of Political Behaviour, ed. Russell J. Dalton and Hans-Dieter Klingemann (Oxford: Oxford University Press, 2007). 43. www.annarbor.com/business-review/will-us-economic-uncertainty-zap-michigansjobs-recovery/. 44. www.freep.com/article/20110810/NEWS15/108100390/Are-Michigan-s-lawmakersreally-sharing-sacrifice-too–. 45. www.annarbor.com/business-review/rick-snyders-budget-proposal-is-it-fair/. 46. www.msnbc.msn.com/id/42115121/ns/politics-more_politics/t/angry-protestersrally-against-mich-governors-budget-plans/. 47. www.areadevelopment.com/newsItems/2-22-2011/michigan-economic-developmentgovernor-budget02225.shtml.

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3 Inter-Temporal Variation, Fiscal Uncertainty, and State-Level LongTerm Revenue Budget Forecasts Errors

OVERVIEW “It’s a steady-as-she-goes budget,” explained Arizona Senate Appropriations Committee Chairman Don Shooter (R-Yuma) in describing the state legislature’s future budget proposal for fiscal years 2013–2015. It contains “no sweeps, no restorations and no cuts.”1 Though the chairman’s statement might seem innocuous, it was controversial. The Arizona state legislature’s conservative revenue projections, which were about $300 million less than the governor’s,2 contrasted sharply with the governor’s more optimistic proposal. Republican Governor Jan Brewer had released her budget about a month prior to Shooter’s remarks, and the contents of the Brewer budget proposal ensured that House and Senate GOP members were steadfast in resisting any future increases in state spending in the near term.3 The legislature’s proposal left Brewer with few options other than to haggle over specific line items with lawmakers over the fiscal year 2013–2015 budget proposal. Conventional wisdom suggests that there is a straightforward bargaining process between the legislature and governor in crafting a budget, with give-and-take over the specifics of each branch’s proposals. What complicates this assumption is the future economic outlook of the state and uncertainty surrounding it. In the Arizona case, competing projections resulted in variation in forecasters’ estimates of economic growth. The governor’s economists gave a zero chance of the state going into a recession over the next two years. Legislators’ outlook was not as rosy. Even though projections from both the governor’s office and the state legislature estimated short-term surpluses in fiscal year 2013, the two branches of government differed over the impact of the temporary one-cent tax ending at the close of fiscal year 2013 and the threat of a double-dip recession. For Republican legislators, uncertainty about the economy’s performance over the next two years, as well as 43

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Chapter 3

projected deficits in fiscal year 2014 caused by increases to entitlement spending, left little room for adjustments to the fiscal year 2015 budget that would not include spending cuts and tax increases. Differences in future perceptions were not solely about the economy but also concerned long-term governing strategies among state Republicans regarding how to manage Arizona’s finances. These strategies ended up biasing the forecasts policymakers would use in setting the state’s future budget. Republicans wanted to keep together a coalition committed to limited and small government—in light of economic volatility which constrains the state’s ability to tax and spend—and convey to voters long-term economic competence. This example points to the political implications of developing revenue forecasts that fit into the political philosophy of the majority party. The further into the future revenue projections go, the more likely they are biased—not only by risk averse politicians but also by their political considerations. In Arizona, a temporary one-cent sales tax increase was a political liability to both the governor and Republican state legislators. It was sold to voters by Governor Brewer as a one-time fix to plug the state’s budget deficit and would go away in fiscal year 2013. Begrudgingly, Republican lawmakers went along with the governor’s plan. They did so only on the conditions that it was temporary, an adequate rainy-day fund would be set up, and the state’s long-term finances would be sustainable. Their fear was that the governor did not share their urgency over the precipitous drop in the state’s revenues in fiscal year 2014, when the temporary tax was set to end. As a result, the legislature’s long-term economic outlook for the state was predictably bearish. A related concern was about how transparent the methodology and assumptions used for the two competing forecasts were (see Mini-case 3.1 for a fuller discussion of this problem). The opacity in the underlying methods used to generate the projections played a significant role in the dismay of both the governor and legislative leaders regarding their projections. This chapter examines the implications of uncertainty in long-range state-level revenue budget forecast errors. Leading research on public budgeting has found that political institutions’ actions condition revenue and expenditure levels at the state level.4 However, little attention has been paid to how inter-temporal variation in fiscal, social, political, and environmental factors condition forecasting revenue budget performance. When modeling public revenues, stability in future growth patterns tends not to be based solely on fixed time preferences, but also tends to be endogenous to the political-business cycle. To test the impact of the political-business cycle on revenue projections this chapter evaluates the precision of forecasts based upon various time horizons. In developing long-term financial decisions for a state, lawmakers must contend with uncertainty about a variety of factors which impact states’ finances.5 To manage uncertainty, forecasting errors provide policymakers with various levels of

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45

risk used to guide their decisions regarding future public finances for the states. 6 Reducing errors in budget forecasts, and, by extension, reducing risk levels, is the most likely approach for incumbents in developing future revenue budgets. Based on this motivation, political institutions in most states have been designed to mitigate volatility in a state’s finances by attempting to stabilize the budget process through the adoption of such policy tools as the line-item veto, balanced budget rules, and impoundment authority.7 Strategically, policymakers can learn from prior mistakes when developing and revising future projections. From this perspective, forecasting errors provide levels of deviation from baseline estimates, which present the best and worst case scenarios for the future.8 Typically, forecasters use ex post simulations. However, these results can be limited in their practicality beyond the sample used in the initial estimates,9 in order to develop ex ante forecasts. At a minimum, this technique provides some guidance about how a state should move forward.10 Nevertheless, time horizons present lawmakers with differing foci: Short-term revenue forecasts, one to two years ahead, are about control and precision, while long-term forecasts are more about planning and general direction for a state’s future.11 An obstacle for policymakers in developing long-range forecasts is that the further into the future they go, the larger the size of the forecast error.12 Forecasting models that estimate revenue budgets beyond a two-year time period “deliver unacceptably large errors,” and therefore reduce the precision of most long-range projections.13 As a result, attempts to manage unexpected fluctuations in future budgets present a significant political and financial challenge for policymakers. Politically, long-range forecasts are more likely to be subject to political pressures, when compared to shortterm estimates. In Arizona, policymakers’ primary concern was not whether they were being too optimistic about the future economic outlook for the state. It was over being perceived by voters as competent and forward-looking. Thus they were biased to choose the appropriate forecast to reduce any uncertainty about the state’s economic future and to prepare for the worst. In focusing on contingencies, state policymakers considered two options: Should the state use its current surplus to fund increases to targeted programs and agencies, or should appropriate funds from the surplus be put aside in a rainy day fund to be used as a contingency in case revenue projections were far less than what was expected? These alternatives became the point of conflict between the governor and legislature in setting the state’s budget for fiscal years 2013–2015. This chapter presents results from near-term and long-term revenue projections in the states. It also provides a comparative analysis of revenue forecast errors by states and examines the process of under-forecasting in developing their budgets. The next section discusses the results in light of bias in long-term revenue forecasts. Finally, the chapter presents two mini-cases which examine the political consequences of long-term revenue projections in Arizona and South Carolina.

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.

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Mini-Case 3.1: A Little More Sunshine: The Push for a Fair and Honest Process for Generating Revenue Budget Projections in Arizona In fiscal year 2009, Arizona was faced with one of the nation’s worst budget deficits.14 One cause was that revenues performed significantly below expectations. Forecasters projected revenues would be 9% below revenues for fiscal year 2008. However by mid-year, receipts were about 21% below prior year collections. Yet the 2008 projection errors were not a one-year anomaly. Forecasts for the prior four years continued to indicate a drop in state revenues but state policymakers could not predict the severity of the drop; it was not until fiscal year 2010 that the state saw a rise in revenue collections.15 As a result, lawmakers felt revenue projections were part of the problem in dealing with the state’s fiscal crisis because they were deemed to be too unreliable, lacking in both transparency and accountability. To fix this problem, Republican Governor Jan Brewer proposed structural budget reforms that would include an increase to the state’s “rainy day” fund as well as create a transparent formula for funding it. She also called for a commitment to a “fair and honest” process of generating revenue projections.16 Though the governor did not provide specific details about how to reform the forecasting process, her primary tool to help offset fluctuations in revenue projections in the near-term would be to implement a one-cent temporary sales tax increase that would take effect in fiscal year 2011 and sunset at the end of fiscal year 2013. Her plan helped stabilize revenues in the first year the tax hike took effect. Although the temporary tax hike provided the governor with a short-term solution to stabilize the state’s revenues, a major hurdle for policymakers in improving Arizona’s future revenue projections still exists. Currently state revenue sources are highly cyclical, reflecting overall economic conditions. This makes the process of projecting state revenues particularly difficult during times of economic crisis.17 Declining incomes and a precipitous drop in the housing market caused by the Great Recession significantly contributed to why projections were so way off the mark. Another apt criticism of the revenue projection process is its lack of transparency. Currently the Joint Legislative Budget Committee (JLBC) crafts the state’s budget, which does take into account the governor’s recommended budget. However, complaints about the JLBC’s process of creating the state budget say that it is too opaque, which makes understanding revenue projections difficult for those who do not sit on the committee. The declining economy, and lack of clarity in how revenue projections were generated by the JLBC, provided the impetus for reformers to improve the budget process. Their aim was to reduce uncertainty in future revenues by making them more transparent. In 2011, state lawmakers introduced HB 2572 to provide citizens with a comprehensive public database of state and local government expenditures and revenue activity. The primary assumption behind the reform was that if information were made available and accessible it would make the budget process more accountable. This

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approach echoes the sentiments of the late Supreme Court Justice Louis Brandeis who wrote, “Sunlight is said to be the best of disinfectants.”18 Though the reform does not address the underlying structural problem of the cyclical nature between the state’s revenue sources and the overall economy, it does provide a means by which to improve the budget process by making it more transparent. However, in the deliberations for the development of the fiscal year 2013–2015 budget, the lack of transparency in the projection process continued. Legislators and the governor’s office had differing views of the economic outlook for Arizona, which resulted in starkly contrasting revenue projections over the next two years. Though the budget did pass in May 2012, it is unlikely the legislature would likely yield their power over the development of future revenue projections by making all of its information public. Chairman of the House Appropriations Committee John Kavanagh (R-Fountain Hills) remains skeptical of the benefit of providing the nuts and bolts of competing budget proposals. He feels that publically disclosing every budget proposal presented to his committee would be counterproductive and result in making everyone “go nuts” before a final product is assembled. At least in the near term, it appears state lawmakers are not yet ready to open up the budget process to a little more sunshine.

THE FORECAST: SHORT-TERM AND LONG-TERM REVENUE PROJECTIONS Every state has its own methodology when developing revenue projections. These estimates are generated for various revenue sources (e.g., sales tax, fees, income taxes, etc.). They are typically based on historic performance, on changes in economic indicators in the state and macro-economy, and on demographic shifts in the states. Revenue projections determine how much funding a state will have during a given period and are used by decision-makers to develop expenditure budgets for the same time period. The forecasts are rolled into the overall state budget, which must be enacted into law prior to the start of the fiscal year.19 To develop a comparative analysis of revenue forecasts in the states, this research uses total revenue projections and compares them to actual receipts (this does not include federal aid). The estimations take into account variations within states, as well as between them, in understanding differences in how they were generated. The chapter focuses primarily on uncertainty within the states’ forecasts and assesses how political institutions, elections, and political actors respond to and condition bias in states’ near-term and long-term revenue projections. The analysis is based on methodology presented in chapter 2. The following section provides an in-depth evaluation of differences in the accuracy of states’ revenue budgets and how political institutions condition revenue budget forecast errors.

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48

EVALUATING THE RESULTS The results of the forecast model of state revenues are summarized in table 3.1. Overall, the forecast model fit for revenues estimates is statistically significant. The revenue model explains approximately 97% of the variation (98% of variation within states and 92% of variation between states) in total receipts. The mean square error of the model is ± .14. To simulate long-range forecasts, I have used revenue forecasts two years into the future as the baseline to estimate three-year, four-year, and five-year projections. For example, estimates that were generated for the following year would be used for year three as an instrumental variable, and this process would be replicated in subsequent years.20 Furthermore, all of the variables used in these estimates are statistically significant except for the parameters that specify North Dakota and South Dakota.21 Last, the signs of the estimate’s coefficients are in their expected directions. To assess overall accuracy of the forecasts, figure 3.1 plots the mean absolute percent error (MAPE). Though the MAPE tends to be biased by low forecasts, it provides reliable estimates of validity as well as the measure’s ability to do a good job of picking up changes in sensitivity of forecast errors.22 Furthermore, since a majority of states under-forecast their revenues, concerns about downward bias in the measure make it an appropriate metric for analyzing budget forecast errors in the states. For a full discussion of the limits and benefits of this error measurement compared against other measures of forecasting accuracy, see Armstrong and Collopy (1992).23 Overall the MAPE for state-level revenue forecast errors for one-year forecasts is 3.5%; two-year forecasts is 5.6%; for three years, 6.7%; for four years, 7.6%; and for five years, 8.5%. These results indicate that the further into the future one goes,

Table 3.1. Fixed Effects and Random Effects Least Squares Regression Estimates of State-Level Revenue Budget Forecasts (1991–2010): Reduced Form Variables

Coef.

Lagged GSP State Unemployment Rate Lagged State Revenue Constant

.10** -0.07** 0.65** 6.9**

  Model Fit

 

R2 MSE N

0.97 0.14 950

Sig. Level: .01**

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Figure 3.1. Summary of Mean Absolute Percent Errors in State-Level Revenue Forecasts, 1991–2010. Courtesy of NASBO Fiscal Survey of the States, 1988–2010.

the greater the drop in the accuracy of revenue forecasts. Though long-term forecasts tend to be less precise than those generated in the near term by policymakers, they do nevertheless remain important baselines for lawmakers in setting future priorities for the state. Table 3.2 presents the results from the mixed level models for estimating the two to five year forecasting models for state-level revenue errors. Both of the economic covariates for the fixed effects—gross state product and unemployment—are significant. Among the political variables, time in office is significant in early-year forecasts but the variable does not reach the critical threshold in years four and five. The finding indicates that incumbents are less likely to be worried about being held accountable by the electorate when producing budgets that are beyond four to five years or past the timing of the next election. Overall, party strength, as measured by the state partisanship factor, is significant in all years, and the effect size of this variable increases in later years. These findings suggest that there is an incentive among the dominant party in a state to minimize

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Table 3.2. Results from Short-range and Long-range Estimates for State-Level Forecast Revenue Errors (1991–2010)  

Two-Years Ahead Forecast

Three-Years Ahead Forecast

Four-Years Ahead Forecast

Five-Years Ahead Forecast

Coef.

Coef.

Coef.

Coef.

0.01** -0.07** -0.01 -0.03* -0.18** 0.06** 0.07** 0.20**

0.01** -0.05** -0.01 -0.01 -0.22** 0.11** 0.11** 0.12**

Fixed Effects Gross State Product Unemployment Term Year Governor Change State Partisanship Index Divided Government (DG) State Partisanship Index by DG Constant

0.02** –0.09** –0.02** –0.09** –0.13** –0.01 0.01 0.32**

0.01** -0.09** -0.01** -0.09** -0.13** 0.02 0.043** 0.30**

Random Effects

 

 

 

 

State-level Effects:

Est.

Est.

Est.

Est.

Constant Residual

0.11** 0.09**

0.11** 0.08**

0.11** 0.08**

0.11** 0.08**

Model Fit

 

 

 

 

AIC

–2913

-3508

-3351

-3281

BIC

–2847

-3459

-3302

-3233

% Variance Explained By Forecast Errors by State-level Effects

53.3%

57.9%

57.9%

59.5%

Sig. Level: .05*, .01**

budget errors to satisfy its core supporters and also to project to the electorate some level of economic competence. Another political variable of interest is divided government. This variable is significant in years four and five and contributes to an increase in the probability of errors in future year forecasts. A likely explanation of this outcome is that because voters are not sure who to hold responsible in future years, there is little incentive for either party to improve forecasting precision. How much a party controls the institutions of state government (as measured by the State Partisanship Index by Divided Government) is also significant. The interaction between this variable and divided government indicates that the two variables tend to condition a decrease in the probability of forecasting errors. To assess levels of risk aversion that are embedded in the forecasting process, figure 3.2 illustrates the change in distribution in revenue forecast errors by time horizon. Of particular interest is the increase in the variability in the distribution of forecast

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Figure 3.2. Probability Distribution of State-Level Revenue Forecast Errors. Courtesy of NASBO Fiscal Survey of the States, 1988–2010.

errors when going further into the future. For forecasts four and five years ahead, there is increasing variability in the errors’ distributions. The distributions tend to be skewed right, suggesting that estimates for the future may be more conservative than current year projections. There are practical and political advantages and disadvantages to being more conservative in estimating future year revenues. On the one hand, a benefit of low-balling future revenue receipts allows for a reduction in the probability of having to revise a budget downwards in the middle of a fiscal year. On the other hand, it can add further constraints to existing structural deficits in the states, resulting in a significant cost to both lawmakers and taxpayers. The next section will discuss the impact of under-forecasting of revenue projections in more detail.

UNDER-FORECASTING: IDENTIFYING THE PATTERNS AND UNDERSTANDING THE IMPLICATIONS Overall there is a pattern of under-forecasting revenues in the states. Figure 3.3 plots the average percentage of revenue errors by the states. The errors are based on the pooled average of near-term projection errors one year and two years ahead.

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Chapter 3

Figure 3.3. Average Revenue Forecast Errors, 1988–2010. Courtesy of NASBO Fiscal Survey of the States, 1988–2010.

For comparative purposes, I have sorted the graph from smallest to largest average percent error from 1988 to 2010. The graph identifies a significant pattern among state-level executives. There is an overall average of revenue forecasts being -1.9 percent below actual revenues. There are three likely reasons the governor would have an incentive to underforecast future revenue budgets. First, being more conservative in generating revenue budgets provides governors with leeway to adjust to changes in current-year economic projections.24 Second, under-forecasting future revenues allows governors to more easily revise current and future budgets when current-year forecasts are above initial estimates. This re-budgeting process is less complicated than having to reduce budgets in mid-year as a result of overestimating revenue collections.25 Finally, under-forecasting allows governors room to balance political demands for the creation of new government programs with a continued commitment to existing programs in light of any requirements of closing existing or projected budget gaps.26

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Yet, when conducting long-term revenue forecasts (three-to-five years ahead), projections tend to be more conservative, and inaccurate than near-term projections (one-two years ahead). For long-term revenue forecasts, the pooled average among the states is approximately -9.2 percent. Again, this finding suggests that policymakers exert a strong sense of caution in setting their revenue budgets beyond the two-year window. When evaluating states with large structural deficits, under-forecasting can make a bad problem worse. For instance, in fiscal year 2010, under-forecasting equates to approximately $652 million of $32.3 billion in total structural deficits in the states. This represents a significant amount of resources that would help policymakers manage not only the financial downturn, but more fundamental structural problems in the states’ financial health. Furthermore, policymakers tend to be biased by prior year forecasts. This causes state governments to be too cautious in estimating the future performance of revenue collection while at the same time, state-level expenditures have continued to rise, during good and bad economic times, thus putting significant pressure on politicians to manage this imbalance. The bias can be found in serial correlations of the revenue forecast errors in estimates. The impact of this overly cautious behavior is that state policymakers are likely to replicate it in the next year and so on. Table 3.3 summarizes bivariate correlations between revenue forecast errors one-year ahead to five-years ahead. The relationships summarized in table 3.3 indicate a serial correlation between time period forecasts. In fact, over a four-year period, each year’s forecast error is significantly correlated to the initial forecast year. By year five, the impact of the initial forecast is no longer significant. And forecasts in years four and five tend to be correlated in the same direction (.41). Nevertheless, the existence of the correlation between time periods is a result of bias among policymakers in crafting annual budget projections. It also implies that by being cautious in setting future revenues for the states, governors and lawmakers

Table 3.3. Pairwise Pearson Correlation Coefficients of Revenue Forecast Percent Errors (1991–2010) Revenue Raw Errors

One-Year Ahead Two-Year Ahead Three-Year Ahead Four-Year Ahead Five-Year Ahead

One-Year Ahead

Two-Year Ahead

Three-Year Ahead

Four-Year Ahead

Five-Year Ahead

1 0.74** 0.64** 0.45** 0.07**

1 0.99** 0.37** 0.03**

1 0.29** 0.2***

1 0.41**

1

**Sig. level p