State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process
| Date | 01 March 2014 |
| Published date | 01 March 2014 |
| Author | Justin M. Ross,John L. Mikesell |
| DOI | http://doi.org/10.1111/puar.12166 |
John L. Mikesell is Chancellor’s
Professor of public and environmental
affairs at Indiana University. His published
research focuses on sales and property
taxation, budget systems, fi scal federalism,
and tax administration. His textbook Fiscal
Administration is widely used in graduate
public administration programs. He has
served on the Indiana Revenue Forecast
Technical Committee since the mid-1970s.
E-mail: mikesell@indiana.edu
Justin M. Ross is assistant professor of
public fi nance and economics in the School
of Public and Environmental Affairs at
Indiana University. Previously, he forecasted
state and local economic indicators while
working for the Bureau of Business and
Economic Research of West Virginia
University. His research focuses on local and
state public economics, with an emphasis
on empirical investigations of government
behaviors under alternative systems of tax
administration.
E-mail: justross@indiana.edu
188 Public Administration Review • March | April 2014
Public Administration Review,
Vol. 74, Iss. 2, pp. 188–203. © 2014 by
The American Society for Public Administration.
DOI: 10.1111/puar.12166.
John L. Mikesell
Justin M. Ross
Indiana University Bloomington
Concerns about political biases in state revenue forecasts,
as well as insuffi cient evidence that complex forecasts
outperform naive algorithms, have resulted in a nearly
universal call for depoliticization of forecasting. is
article discusses revenue forecasting in the broader context
of the political budget process and highlights the impor-
tance of a forecast that is politically accepted—forecast
accuracy is irrelevant if the budget process does not respect
the forecast as a resource constraint. e authors pro-
vide a case illustration in Indiana by showing how the
politicized process contributed to forecast acceptance in
the state budget over several decades. ey also present a
counterfactual history of forecast errors that would have
been produced by naive algorithms. In addition to show-
ing that the Indiana process would have outperformed
the naive approaches, the authors demonstrate that the
path of naive forecast errors during recessions would be
easily ignored by political actors.
Revenue forecasts play a critical role in the
development of state budgets because they
establish the resource baseline within which
expenditure programs must fall if operations are to be
executable and sustainable. As a result, many research-
ers have devoted attention to the revenue forecast-
ing process and its outcomes along two dimensions.
e fi rst literature approaches the revenue forecast
as a technical problem for which one process might
outperform another, either through the use of quali-
fi ed public employees or increasingly sophisticated
methodological models. is literature has typically
found little to no relationship between the sophisti-
cation of the revenue forecast methodology and its
subsequent accuracy.1 e second dimension of the
previous research is a political critique of the actors
in the process and tests for corresponding systematic
bias. is literature has generally found forecast errors
to contain evidence of political bias and risk aversion
stemming from political offi cials and the bureaucrats
they infl uence. ese dual fi ndings generally motivate
the recommendations for institutional reforms of the
forecasting process toward simple “naive” forecast
methods that would be no worse than more complex
approaches in terms of forecast error but presumably
would be free of ideological bias of the actors neces-
sary in causal methods.2 is article argues that when
the forecasting process is viewed in its full institutional
context, as one step in a budgetary process devoted to
the allocation of revenue, this emphasis on accuracy
alone is insuffi ciently narrow.
Fiscal sustainability requires that state lawmakers have
a baseline forecast of the amount of revenue expected
during the budget period. e selection of this base-
line fi gure is of direct importance to meeting the goals
of the political actors involved, and subsequently,
there exists an incentive for these actors to infl u-
ence the forecast toward producing fi gures that favor
their objectives. Some may wish to restrict the size of
state government by choosing the most conservative
forecast assumptions; others may select optimistic
assumptions that make additional spending programs
or tax cuts appear more aff ordable. In a rational model
of the entire budget process, however, these biases
should be anticipated and accounted for by the actors
whose spending choices are informed by the forecast.
For example, members of a legislature could reason-
ably infer that a governor who wants to cut spending
might propose a budget built from more conservative
forecast assumptions. Recent examples of these dis-
putes during 2013 include New Jersey and California
(Hamilton 2013; Megerian 2013).
An accurate and binding forecast serves fi scal sus-
tainability by providing the hard budget constraint
of resources available for allocation across public
services without shifting the cost of programs to the
future. An inaccurate forecast or a distrusted forecast
both represent ways in which the forecast process
can contribute to a violation of sustainability. ese
dueling concerns over accuracy and perceived biases
potentially require a forecast process that fi nds the
method of minimizing error that is accepted by the
actors, perhaps even managing trade-off s between
accuracy and acceptance. For instance, a highly
sophisticated forecast model may provide an excellent
State Revenue Forecasts and Political Acceptance: e Value
of Consensus Forecasting in the Budget Process
State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process 189
of these errors to the forecasting process. To some extent, state
revenue forecasting is just an application of economic forecasting,
and as a result, there has been some tendency for scholars to view
the evidence from a broader set of literature than only state revenue
forecasts (e.g., ompson and Gates 2007).3 A variety of statisti-
cal techniques have been applied to numerous economic variables
(e.g., stock prices, gross domestic product, fi rm sales, employment,
consumer sentiment, etc.) in part to deter-
mine which approaches, a priori, might be the
most reliable in minimizing forecast error.4
e most famous evaluations are probably
from the M-Competitions that have repeat-
edly pitted forecasts against one another in
a tournament-style setting on common data
(e.g., Makridakis and Hibon 2000). e
conclusion of this competition series, which replicates the general
fi ndings across much of the forecasting literature, is that increasingly
sophisticated forecasting techniques do not necessarily improve
performance.
State revenue forecasts usually build from a causal model that
links forecasted economic conditions and state tax revenue. e
complexity of the causal model varies across states, but it involves
determining a relationship between the economy, the tax base, and
tax revenue.5 On the basis of previous research on methods and
accuracy in forecasting, some have challenged this conceptualization
as not being worth the eff or t. Kliesen and ornton (2012) demon-
strate that the defi cit and debt projections prepared by the federal
Congressional Budget Offi ce are simply no better than a random
walk forecast, that is, using last year’s actual as a forecast for the next
year.6 At the state level, ompson and Gates suggest, drawing on
the literature of business and fi nance forecasts, the implementation
of a simplifi ed approach to identifying state revenue growth: “the
simple average of past growth rates is the best estimator of expected
growth rates” (2007, 826). In sum, important parts of the forecast-
ing literature question the value added by attempting complex
causal modeling of the variable to be forecast and propose that naive
models do just as well, if not better, and are less prone to interfer-
ence with the forecast result.
Much of the existing literature also reports evidence of systematic
bias in revenue forecast errors. One common fi nding is that state
revenue forecasters systematically bias their forecasts downward, and
a considerable stock of the existing academic research assumes that
forecasters deliberately guard against criticism from governors and
legislatures by conservatively underforecasting.7 Forecasters gener-
ally work with a forecasting range (even if they do not provide that
range in their presentations), and, as Rodgers and Joyce state, “It
is seen as much too foolish to use the high-end forecast, risky to
accept the best estimate forecast, and fi scally responsible to endorse
the low-end forecast” (1996, 49). is underforecast is their fi nd-
ing for all states from 1975 through 1992. Feenberg et al. (1989)
fi nd similar underestimation in New Jersey, Massachusetts, and
Maryland over the years from roughly the end of World War II to
1987. Heins (1975) fi nds a negative bias in forecasts over 58 years
in nine states (Alabama, California, Delaware, Illinois, Indiana, New
Jersey, New York, Rhode Island, and Wisconsin). Rose and Smith
(2012) fi nd underestimation in a 47-state panel over 1986–2007.
Williams (2012), looking at the work of fi ve diff erent entities that
fi t to the historical data, but it may also aff ord insiders the opportu-
nity to employ model assumptions that suit a particular ideological
preference in the forthcoming budget cycle. By contrast, a simple
naive forecast that uses only the previous year’s actual collections is
transparent and therefore hard to manipulate, but it also can pro-
duce predictable enough errors to generate additional debates over
whether certain programs are truly “aff ordable” or not.
is article is the fi rst to bring the concern for
the political acceptance of revenue forecasting
to the forefront. It proposes that forecasting
must be observed and understood within the
broader context of the budgeting deliberation,
in which there are multiple points for various
actors to revise away from the original base-
line values. It is conceivable that an independent and depoliticized
forecast committee could produce widely accepted revenue projec-
tions, but we suggest that is a hasty recommendation: a depoliti-
cized forecast does not ensure that there are no political gains from
criticizing and rejecting the forecast. We similarly argue that naive
algorithms for producing forecasts, which may or may not improve
upon causal forecasting approaches, are relatively easy for budget
actors to reject on the occasions when doing so produces political
gains. e absence of a human stakeholder without a reputational or
political interest in defending the process causes the forecast to be a
politically inexpensive and unresponsive target.
For purposes of concreteness, we demonstrate these points by
employing the state of Indiana as a case study. Indiana’s budget
process is reasonably representative of other states, involving delib-
eration within the legislature, between branches of government,
and across competing political parties. Indiana is also interesting for
the study of forecast acceptance because it has been recognized as
having one of the nation’s most accurate forecasts and has a deliber-
ately political consensus process. e next two sections expand our
outline of the previous literature of state revenue forecasting and
provide the appropriate background on Indiana. To advance the
argument for a new research emphasis on trust and accuracy, the
next section examines the performance of the actual Indiana consen-
sus forecasts against the competing naive models. is comparison
to naive models renders a clear image of why such models would
produce revenue baselines that would go unaccepted by state actors,
especially during business cycle peaks or troughs, which inevitably
produce signifi cant forecast errors. e following section further
demonstrates the importance of forecast acceptance by following
the political history of the Indiana Revenue Forecast Technical
Committee. is political history begins with the controversies that
led to the inception of the committee, which was designed to garner
political acceptance, and further allowed it to weather several politi-
cal challenges levied through the Great Recession. In the conclusion,
we suggest several important research questions for a new literature
on forecast adoption into the budget as a trusted constraint, which
is a topic that the fi eld of public administration is particularly well
suited to address.
Previous Literature: Revenue Forecast Methods
and Errors
e academic literature on state revenue forecasting has largely
focused on an ex post analysis of forecast errors and the relationship
is article is the fi rst to bring
the concern for the political
acceptance of revenue forecast-
ing to the forefront.
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