Testing Rival Decision-Making Theories
on Budget Outputs: Theories and
Comparative Evidence
CHRISTOPHER G. REDDICK
This study demonstrates the link between the degree of economic rationality and budgetary decision-making outputs for Canada, the United Kingdom, and the United
States. Three empirical models are derived with low, intermediate, and high degrees
of economic rationality, namely, “garbage can,” incrementalism, and rational choice
budgeting, respectively. The methods used are time series analyses on real disaggregated national government budget outputs for the post–World War II period for Canada, the United States, and the United Kingdom. There was some support found for
budgetary incrementalism, and the most consistent support for rational choice budgeting. There was no support for garbage can budgeting.
INTRODUCTION
A core explanation of the budgetary process in the twentieth century was incrementalism.
In this approach, changes in the allocation of resources are decided at the margin. However, many challenging decision-making models have appeared in the literature that attempted to refute the claims made by the incrementalist model. One such approach is the
“garbage can” theory of decision making that was originally used to describe the anarchical features of university decisions. Other challengers to incrementalism have been more
rational and comprehensive approaches—such as program budgeting—that provide specific links between resource inputs (dollars) and outputs (results).
This study explores three rival models of decision making to arrive at some conclusion
regarding the degree of economic rationality in budget outputs. We can argue that the
Christopher G. Reddick is an Assistant Professor of Public Administration at the University of Texas at
San Antonio. His research and teaching interests are in public budgeting and finance. He can be reached
at the University of Texas at San Antonio—Downtown Campus, Department of Public Administration,
501 West Durango Blvd., San Antonio, TX 78207.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
1
garbage can theory is the least rational of the three due to its inherent randomness in decision outputs, while the rational choice model is the most rational, and incrementalism is
somewhere in between these models. We examine all three models of rationality and
measure their performance in three advanced industrial democracies.
The purpose of this article is not to provide a definitive answer to the question of what
theory best predicts budget outputs: garbage can, incremental, or rational choice. It is exploratory in nature and argues against traditional approaches in the literature that have
usually modeled one type of budgeting, the most common being budgetary incrementalism. This article does this differently by focusing on not one but three countries, which
have varying institutional arrangements (i.e., parliamentary, presidential, and federal).
This article first outlines the theory for each of the models and derives its empirical
specifications. The second section will demonstrate the data and methods used to measure the three models. The last two sections will present the results of the tests and conclude by demonstrating their significance to modern public budgeting.
THEORY AND EMPIRICAL SPECIFICATIONS
There are three rival budget models that illustrate the link between public spending outputs and public policy. These models do not take into account systemwide exogenous factors, such as macroeconomic and political impacts. At its simplest level the fundamental
assumptions and differences among the three models are that garbage can budgeting is a
random process (random expectations); budgetary incrementalism is a backward-looking
process (adaptive expectations); and rational choice budgeting is a forward-looking process (rational expectations).
In the article, several assumptions regarding the three models should be clarified: the
garbage can process is random, incremental decision making focuses on past events, and
the rational choice approach focuses on the future. (Later in this article, we will look at
the assumptions of how these theories are empirically specified.)
First, the original garbage can model as outlined by Cohen et al. explicitly endorses a
model that has four independent streams1 (problems, solutions, participants, and choice
opportunities), and uses computer simulation to test randomness in decision-making outputs.2 Takahashi’s study extends the seminal work done by Cohen et al. and models a
cross-sectional “garbage can” of Japanese manufacturing firms.3 In our study we use the
1. M. Cohen, J. March, and J. Olsen, “A Garbage Can Model of Organizational Choice,” Administrative Science Quarterly 17 (1972): 1–25.
2. There is a growing tendency to argue that the garbage can theory can be used to describe decision
making in the public sector. For example, see Jonathan Bendor, Terry M. Moe, and Kenneth W. Shotts,
“Recycling the Garbage Can: An Assessment of the Research Program,” American Political Science Review 95 (2001): 169–190. This theory has escaped much of the criticism that other decision-making models, such as rational choice, have endured.
3. Nobuo Takahashi, “A Single Garbage Can Model and the Degree of Anarchy in Japanese Firms,”
Human Relations 50 (1997): 91–108.
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existing framework and assumptions of the original Cohen et al. article and translate it into
a time series analysis. Takahashi’s randomness assumption that we endorse states (1) at
each time period, the entrances and exits of budgetary participants are respectively characterized by positive and negative potential energy (to be defined later), a uniform random number; and (2) at each time period, the entrances and exits of budgetary problems
are respectively characterized by positive and negative potential energy requirements, a
uniform random number. Potential energy is defined as the amount of commitment participants put toward a budgetary policy goal. Some participants have positive potential
energy and wholeheartedly endorse a spending proposal, and vice versa for negative potential energy.
A brief example of how a budget process would fit this type of model is the case of a
decisionmaker having to choose from many policy options, all with similar outcomes and
consequences, and having limited resources and time to make a decision (a more detailed
discussion and example will be provided later). The options presented to the analyst will
depend upon the current problem faced, the possible solutions considered, the mix of
people in the consultation process, and the choice opportunities on the table. Under these
circumstances there may be randomness on average in outcomes. Each budgetary option
will have an equal probability of being chosen. Therefore, the assumption of randomness
implemented in Cohen et al.’s and Takahashi’s studies using cross-sectional data is applied to time series data. Both of these authors essentially test the assumption that there is
randomness; we extend their work and apply it explicitly to time series data. This model
does not imply that every output will be random; however, this model indicates that over
an extended period there will be randomness in outputs.
Second, there is an assumption in this study that incremental decisionmakers focus primarily on past events. This resides under the circumstances decisionmakers face in the
public sector—the lack of time and resources needed to make rational decisions. Therefore, decisionmakers would simplify the process by examining the past as a way of moving
forward. This approach becomes a heuristic device that decisionmakers can use in a democracy with multiple interests and centers of power. During periods of normalcy or stability making marginal changes from last year’s budget output seems plausible. This may
equally apply to the concept of decremental budgeting.4 In this model decisionmakers,
when faced with a reduction in organization slack, make marginal reductions instead of
additions (i.e., traditional incrementalism).
Finally, rational choice budgeting resides under the assumption that decisionmakers
operate under constraints in the short run as a result of two key uncontrollable factors: the
poor performance of the macroeconomy and the lack of public acceptance of tax increases that could balance the budget in the short run.5 However, rational choice budget4. Allen Schick, “Incremental Budgeting in a Decremental Age,” Policy Sciences (1983): 1–25.
5. The concept of rational choice budgeting discussed in this study is different from what is normally
labeled rational budgeting in the literature; examples of these budget techniques include performance
budgeting, zero-based budgeting, and program budgeting, to name a few.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
3
ing implies that even though knowing decisionmakers operate under constraints in the
short run, they are still forward thinking. If they just considered the past as a way of moving forward, they would not worry about large debts and persistent deficits.
This model seems consistent with the neo-conservative agendas of the 1980s in Canada, the United Kingdom, and the United States. Mulroney, Thatcher, and Reagan in
these countries proposed policies to especially reduce domestic spending and the level of
employment in the bureaucracy. For example, in the United States Reagan’s policies
were a test case of supply-side economics—cutting government taxes, cutting domestic
spending, and not worrying about the deficit. In the rational choice approach governments do not have to worry about deficits in the short-term, because cuts in taxes would
prop up an ailing economy and bring in additional revenues to balance the budget over
the long run (i.e., Laffer Curve). The assumption in this approach is that decisionmakers
are constrained in the short run, but think rationally, in an economic sense, and plan to
balance the budget over the long run.
In addition to the policies of the three leaders, there were pressures on the U.S.
Congress, for example, to take action on mounting deficits; therefore, Congress passed
Gramm-Rudman-Hollings (GRH) Deficit Reduction Act of 1985. This act called for automatic cuts in discretionary spending if Congress did not make time-constrained budget reductions. Although this approach did not accomplish its original objectives, the role of the
Comptroller-General was declared unconstitutional; the mood this act fostered forced
decisionmakers to be cognizant of tough choices when it came to the budget.
An essential distinction among the rational, incremental, and “garbage can” models
can be demonstrated by placing them on a continuum. If we placed all three models side
by side with respect to their degree of economic rationality, the “garbage can” theory
would be the least rational of the three, rational choice budgeting would be the most rational, and incrementalism would be somewhere in between these two models. That the
“garbage can” model is the least rational seems an obvious conclusion. Outputs in this
model are random and chaotic and there is no weighing of policy choices against one another. At the opposite extreme, in rational choice budgeting budgetmakers plan and look
into the future by weighing the costs and benefits of policy actions. In between is incremental budgeting, a method that is used to simplify the decision-making process and that
rejects the notion that decisionmakers have a desire to plan ahead in a political process.
Therefore, incremental budgeting has an element of rationality when compared to the extreme end of the spectrum, “garbage can” budgeting.
Garbage Can Budgeting
To reiterate, the garbage can model of organizational choice was attributed to the seminal
article by Cohen et al. In the garbage can model, a decision is an outcome of four relatively independent streams within an organization: problems, solutions, participants, and
choice opportunities. Therefore, this theory demonstrates randomness in budget outputs,
which is essentially what we are modeling.
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With this randomness assumption, garbage can budgeting is well represented by a simple random walk process. More formally, garbage can budgeting can be estimated by letting the current value of public spending be equal to last year’s expenditure value plus a
random error:
SPENDt ⫽ SPENDt⫺1 ⫹ et, (or DSPENDt ⫽ et)
(1)
In this equation, SPEND represents real government spending. The random walk model
is a special case of the AR(1) process6 where SPENDt = a 0 + a 1 SPENDt − 1 + ε t , when
a 0 = 0 and a 1 = 1. Therefore, the garbage can model is estimated by expression (1).
Spending in this model is completely random and can fly off in any direction. Randomness
in outputs explicitly indicates no relationship among problems, solutions, participants,
and choice opportunities. Unlike the following budgetary incrementalism model, there is
no lag structure in this model since evidence of a relationship between the current year’s
spending and the previous year’s spending is not a strict random walk. We must also derive weaker versions of the garbage can model, taking into account some element of economic rationality by participants in the process.
Three models can be used to illustrate weaker versions of garbage can budgeting:
DSPENDt ⫽ cSPENDt⫺1 ⫹ et,
(2)
DSPENDt ⫽ a0 ⫹ cSPENDt⫺1 ⫹ et,
(3)
DSPENDt ⫽ a0 ⫹ cSPENDt⫺1 ⫹ a2t ⫹ et.
(4)
Model (2) has already been derived, and as we know it is a pure random walk model,
while (3) adds an intercept, a0, or drift term, and (4) includes both a drift, a0, and linear
time trend, a2t. Theoretically with the weaker versions of the random walk, we should notice the following: in terms of budgetary decision making, a random walk with a trend may
have spending heading in a particular direction for an extended period. A trend can also
have a permanent change in the direction of budgetary outputs from a shock, such as a
displacement of government spending (e.g., OPEC oil shocks of the mid-1970s).
A weaker version of the garbage can model with a trend and/or drift is essentially evidence of Wagner’s Law occurring.7 This theory suggests that the process of industrialization leads to an expansion of the public sector. The most commonly cited mechanism in
Wagner’s Law is related to increased economic affluence; the government’s share of the
economy increases as gross domestic product (GDP) rises because the elasticity of public
expenditures is assumed to be greater than one, the idea being that as GDP rises there
6. There is a lengthy discussion of an AR(1) model’s implications to public budgeting in the forthcoming section.
7. A. Wagner, Finanzwissenschaft pt. I. (Leipzig: C.F. Winter, 1877).
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
5
would be a greater role for government to do such things as building infrastructure, and
enforcing laws to accommodate the more affluent population. This can be demonstrated
through a time series analysis as a linear randomness of budget outputs occurring at a
fixed percentage. A trend in the data does affect the character of decision-making processes. It means that decisions are being made around a constant trend, suggesting significant elements of Wagner’s law in the process. This does not affect the character of the
garbage can decision making but it imparts an upward (or possible downward)8 slope. As
a result, the random walk with a trend would demonstrate that changes in real public
spending are responding to changes in GDP or to the economic affluence of a nation over
the history of the series.
How can these three simple time series models be applied to public-sector budgeting?
As an illustration, consider a gambler who bets on a flip of a fair coin. When a coin flip results in heads, the gambler wins one dollar; for tails the gambler loses one dollar. Since the
flip of a fair coin results in heads as often as tails in the long run, the gambler expects to
break even, winning exactly as much money as he or she has lost. Successive flips of the
coin are expected to have independent outcomes, that is, heads and tails are unrelated. A
random expectations process would be the total amount of money won or lost in the coin
flipping example. As the payoff from each experiment is expected to be zero, the sum of
the payoffs is also expected to be zero. However, the gambler could get a run of heads or
tails and so the sum may drift upward or downward in practice (hence, this is a random
walk). For example, if two gamblers each have a finite amount of cash, one of the gamblers
is likely to win all the other’s money before the nth coin flip. This phenomenon, “gambler
ruin,” is an interesting property of the random expectations process.9 This wandering
movement in the time series output is a possible indication of garbage can budgeting.
The garbage can model can be illustrated by the following simple budget example.
Consider an emergency meeting between, for example, the U.S. Secretary of Education
and six departmental managers, about the upcoming budget due for submission the following morning. In the meeting, each of these six departmental managers is requesting
money to fund a proposed project. However, the secretary has enough money to fund
only one of the proposals. Perhaps the secretary has many proposals to consider and very
little time; therefore, he or she will have to rely solely upon the information that the departmental managers provide. Furthermore, each of the proposals is similar and will solve
the problem with which the department must contend. As a result of this situation, the
secretary uses a random guess to find the most suitable proposal. Therefore, with six departmental managers requesting money from one secretary, for one project, the probability of a manager’s proposal being chosen is approximately one-sixth. Budgetmakers as a
result would use random guesses when making decisions under these circumstances.
8. If the spending category is downward sloping this indicates evidence of a counter Wagner’s Law as
demonstrated by Aaron B. Wildavsky, Budgeting: A Comparative Theory of Budgetary Processes (Boston: Little, Brown and Company, 1975).
9. Richard McCleary and Richard A. Hay, Applied Time Series Analysis for the Social Sciences (London: Sage Publications, 1980).
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If we apply this scenario to all government budgetary decision-making proposals, a startling implication occurs. Governments, which have to rely on policy entrepreneurs for advice, as we demonstrated above, will have budgetary policy outcomes that resemble a
random process in which the current outcome has no relationship to the previous outcome.
In this process, budgetary decision making is obviously not rational since outcomes are not
considered by weighing each of the proposal’s merits against one another. Furthermore, it
is not incremental since there is no reliance on the previous spending decisions.
This illustration of the garbage can model demonstrates its fundamental premise that
oftentimes ideas are polished up and claimed to be new. Budgetary decisions in this
approach are very much dependent on the current problems the organization faces, on solutions that are feasible to implement, on participants in the process, and on choice opportunities available. This theory clearly does not provide for rational-ordered outcomes.
In summary, the expectations of decisionmakers in this model are essentially random
and depend on the particular combination of problems, solutions, participants, and choice
opportunities of a decision. A weaker version of this model as demonstrated by Equations
(3) and (4) argues that decision making is not totally random and chaotic and contains an
element of rationality. However, it is not systematically weighing all the costs and benefits
of particular policy options. This is not a definitive test for the garbage can model since we
can say only that outputs are or are not random at the macro level. An extension of the
work done here might include and examine other factors that may create random occurrences in outputs.10 The following model possesses a greater degree of rationality than the
garbage can model.
Budgetary Incrementalism
The seminal work of testing budgetary incrementalism was done by Davis et al.11 This
budgetary incremental model isolates the change dynamic itself, predicting that allocation
changes are characterized by the same proportionate increase or decrease from year to
year. Hence, it is a relatively constant percentage change model.12 In its strongest form,
budgetary incrementalism implies that external variables have no impact on budgetary
outputs.13 As noted with budgetary incrementalism, there is a unique relationship between previous years’ and current year’s expenditures. The close relationship indicates a
10. Perhaps a pooled time series analysis would be an appropriate extension of Cohen et al.’s work.
However, we use this type of analysis as a starting point for this theory with budget data.
11. Otto A. Davis, M. A. Dempster, and A. Wildavsky, “A Theory of the Budgetary Process,” American Political Science Review 60 (1966): 529–547.
12. James N. Danziger, “Assessing Incrementalism in British Municipal Budgeting,” British Journal
of Political Science 6 (1976): 335–350; and James N. Danziger, Making Budgets: Public Resource Allocation (London: Sage Publications, 1978).
13. William D. Berry, “The Confusing Case of Budgetary Incrementalism: Too Many Meanings for a
Single Concept,” Journal of Politics 52 (1990): 167–196.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
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relatively narrow range open to the influence of economic or political factors after previous expenditures are taken into account. The model is internal because it assesses the extent to which budget choices in one period are predictable, relying exclusively on budget
choices in the previous period. We can formally demonstrate that the adaptive expectations mechanism would be the most suitable representation for this model.
The adaptive expectations hypothesis can, for example, have the following form in (5)
when it is known that SPENDt (government public spending) follows a first-order autoregressive process (i.e., AR(1)) as the budgetary incrementalism model implies:
SPENDt ⫽ a0 ⫹ a1SPENDt⫺1 ⫹et
(5)
The expression (5) can be explicitly derived if we know that the optimal forecast of
SPENDt formed at t⫺1—which we denote by SPEND*t —will be (in the sense of yielding
minimum mean squared forecast errors)
SPEND*t ⫽ E(SPENDt /SPENDt⫺1, SPENDt⫺2...)
⫽ a0 ⫹ a1SPENDt⫺1
(6)
The optimal method of updating expectations in this case will be
SPEND*t ⫺ SPEND*t⫺1 ⫽ k(SPENDt⫺1 ⫺ SPENDt⫺2 )
(7)14
where k is an adjustment coefficient and SPENDt⫺1 and SPEND*t⫺1 represent the actual
and the expected rates of spending in the previous period, respectively. This simply states
that expectations should be revised only if the rate of expenditure in the previous period
has been accelerating or decelerating. We shall refer to (5) as the empirical specification
for budgetary incrementalism and (7) as the theoretical specification for deriving this empirical specification.
As demonstrated in (5), budgetary incrementalism can be well represented by an
AR(1). The literature in this area indicates one common theme of incremental budgeting:
the present is an extrapolation of the past. An AR(1) model essentially captures the past
as a predictor of the future.15 It is important to note how this model differs from the random walk model. The random walk model is nonstationary. This simply means that
spending wanders all over the place. However, unlike the random walk model, an AR(1)
model is stationary (does not wander) and has a pattern in spending that is determined by
last year’s spending. However, the budgetary incrementalism model should also be tested
14. This is essentially the same as the adaptive expectations model in P. Cagan, “The Monetary Dynamics of Hyperinflation,” in Studies in Quantity Theory of Money, ed. M. Friedman (Chicago: University
of Chicago Press, 1956), except this model has a finite distributive lag.
15. McCleary and Hay. Walter Enders, Applied Econometric Time Series (New York: John Wiley &
Sons, 1995).
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with a constraint, the idea being that internal “top-down” pressures—from within government—may affect the incremental decisionmakers’ choice, especially during periods of
fiscal restraint, as evident in these countries since the mid-1970s crisis of control.
A revenue-constrained version of budgetary incrementalism can also be formally
stated. Top-down pressures will force the government to cut back on spending as demonstrated by the following budgetary incrementalism model:
SPENDt ⫽ a0 ⫹ a1SPENDt⫺1 ⫹ a2REVENUEt⫺1 ⫹ et
(8)
We expect that all the coefficients in (8) for budgetary incrementalism should be positive.
With these two models we hope to detect any evidence of budgetary incrementalism, taking into account both “bottom-up” and “top-down” pressures.
In this study, we are not saying that the incremental model does not possess any economic rationality. However, comparing the rational choice approach to incrementalism
shows that the latter does not look into the future and weigh policy choices against one
another. The last type of model considered in this article is rational choice budgeting,
which is the most rational and systematic of the three approaches.
Rational Choice Budgeting
The literature on the rational expectations intertemporal budget constraint for the public
sector was introduced by the seminal article of Hamilton and Flavin.16 The basic idea of
such a constraint is that when the government runs a deficit, it is implicitly promising to
run sufficient surpluses in the future in order to repay the accumulated debt and interest.
The most startling implication of the rational expectations school to this study is the
Ricardian equivalence theorem. This states that the issue of public debt or deficits in the
current period is always accompanied by a planned increase in future taxes needed to service the higher level of debt.17 The Ricardian equivalence theorem is based on the notion
that governments plan ahead. This is theoretically possible with their relatively easy access to otherwise costly information. Unlike in the adaptive expectations model, decisionmakers make mistakes but they tend not to persist in them, by making systematic errors.
Decisionmakers in the rational choice model can take advantage of advances in the
processing and collection of information as a result of information technology. This is especially apparent for the national governments of Canada, the United Kingdom, and the
United States that have highly specialized statistical agencies such as the U.S. Treasury
Department and Census Bureau, the U.K. Office for National Statistics (ONS), and Statistics Canada. This approach argues that it makes a great deal of sense for decisionmakers to use information if it is relatively inexpensive and can be quickly processed.
16. J. D. Hamilton and M. A. Flavin, “On the Limitations of Government Borrowing: A Framework
for Empirical Testing,” American Economic Review 76 (1986): 808–819.
17. Robert J. Barro, “Are Government Bonds Net Wealth?” Journal of Political Economy 82 (1974):
1095–1117.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
9
The application of the rational expectations permanent income hypothesis, first noted
by Hall, can help to show that the government borrowing requirement is relatively
straightforward.18 Hall stated that previous decisions about consumption had no bearing
on present consumption. This hypothesis can be applied to budgetary decision making. It
is easily demonstrated that government surplus (deficit) evolves from the strict random
walk model, as the government would use all available information:
DSURPLUSt ⫽ DSURPLUSt⫺1 ⫹ et
(9)
where SURPLUS reflects the real government surplus (deficit) as a percentage of real
GDP, D is the first difference operator, and et is the error term. However, with allowance
for expenditure elements, vt , possibly in response to unforeseeable and random macroeconomic or political shocks, the innovation of the et due to new information, wt, is augmented by a moving average in vt according to
et ⫽ wt ⫹ vt⫺vt⫺1
(10)
If we follow Harvey, assuming w and v are independent white-noise errors, (10) collapses
to the first-order moving average (MA) process:19
et ⫽ et ⫹ fet⫺1
(11)20
In Equation (11) both et and et⫺1 are random errors with means of zero, and neither can be
partly predicted on any information available at the end of period t⫺1. It follows that the
forecasting error has a mean of zero and cannot be predicted based on any information
available at the end of t⫺1. The basic idea is that the surplus (deficit) or the balance of
budgetary inflows and outflows can be influenced by surprise shocks as measured by an
MA(1) process, but only in the short run. Therefore, in the long run there will be sufficient budgetary surpluses to counteract the deficits of the past (i.e., intertemporal budget
constraint). This decision-making framework fits with the concept of rational choice budgeting.
The rational choice theory can be represented by an MA(1) process as demonstrated in
18. Robert Hall, “Stochastic Implications of the Life Cycle–Permanent Income Hypothesis: Theory
and Evidence,” Journal of Political Economy 86 (1978): 971–988.
19. A. C. Harvey, Time Series Models (New York: John Wiley & Sons, 1981).
20. The moving average parameter f is constrained by the “bounds of invertibility”; see McCleary and
Hay. For this model the MA(1) process constraints are –1 < f ⫹1. In other words the parameter f must be
smaller than unity in absolute value. This parameter estimate is the standard representation for an MA
process as demonstrated in McCleary and Hay and is derived empirically from Ronald MacDonald and
Alan E. Speight, “Does the Public Sector Obey the Rational Expectations–Permanent Income Hypothesis? A Multi-country Study of the Time Series Properties of Government Expenditures,” Applied Economics 21 (1989): 1257–1266.
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(11). Moving average processes are characterized by a finite influence (in this case the
present year). The current surplus (deficit) position is only considered by the forecast for
future change in this variable. In (11) the spending change persists for exactly one observation and then is gone from the budgetary system. This is a fundamental difference from
the adaptive expectations budgetary incrementalism model in which there is a lingering
impact of the present being weighted on the past outputs. By contrast, in the MA(1) all information for the forecast has already been incorporated in the model. There is no need to
look at the past since this information would be redundant.
In Equation (11) we have used only the surplus (deficit) to test rational choice budgeting. This variable essentially represents the balancing function of the budget. We are arguing that governments are aware of the costs of having unbalanced budgets. Therefore,
they operate their policies over the short run under constraints in anticipation of balancing their budget over the long run. There is a similar analogy when applied to the individual who borrows money in the short run, knowing full well that he or she will eventually
have to pay off the debt.
In the rational choice model, running deficits in the past in hopes of covering them in
the future is a rational element of the process. Decisionmakers when determining next
year’s budget operate under constraints in the short run, such as slow economic growth,
entitlement spending dominating the majority of spending (two-thirds in the United
States and Canada), and lack of public acceptance of tax increases. However, this does not
prevent policymakers from thinking rationally and planning for the future. An easy escape for politicians is to think only in the short term and let deficits accumulate. However,
in the rational choice approach politicians are rational enough to look into the future and
plan to pay off the accumulated debt and deficits.
The version of rational choice budgeting discussed here argues that the surplus (deficit) of a country is representative of a general equilibrium. There is a drive to balance the
budget, but there are constraints that decisionmakers must operate under in the short run
that do not facilitate this equilibrium condition (budget balance). The MA process, as opposed to the AR process, does not consider past values since under this assumption
decisionmakers have already incorporated that information into their present decision. It
would not be rational for them to use only past information when they clearly know that
the present situation has changed, or is anticipated to change. The incremental model in
the traditional sense is a naïve forecast that does not incorporate new information when
making a decision; it will simply extrapolate the past.
Differences between Adaptive and Rational Expectations and the Budget
There are three fundamental differences between the rational expectations approach and
the adaptive expectations type of mechanism. First, the emphasis in rational expectations
is on expectations being forward looking, rather than simply being extrapolations of past
trends (backward-looking adaptive expectations). Second, agents are acting in an optimizing manner by processing all the relevant information. Third, the rational expectations
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
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approach provides a central role for budgetary decision-making theory in determining expectations. The theoretical models—with the exception of rational expectations—are essentially backward-looking descriptions, in that the past is extrapolated in some way to
predict the future. With rational expectations, the emphasis switches to decision-making
theory, making efficient use of the available information to predict the future. Adaptive
expectations might be characterized as using a rule of thumb and in fact may be viewed as
a description of the process, while rational expectations requires the specification and estimation of a full empirical model.
Consider a simple hypothetical budget example to help differentiate between adaptive
and rational expectations. Assume that the political economy behaves in such a way that
the level of spending is always a simple function of an incremental decision-making rule:
SPENDt ⫽ 2Rt
(12)
where SPEND is real public spending, R is the change rule, and the value of 2 represents
the inflation rate as a percentage per annum. Furthermore, assume that the government is
able to determine the change rule, Rt, and that it follows this rule:
Rt ⫽ 20 ⫹ 3(t⫺1964)
This simply states that the rule has increased by three units each year (that is, Rt ⫽ 20 or
20 million pounds or dollars in 1964, 23 in 1965, and so forth).21 This rule is assumed to be
known and believed by all agents in the government. Finally, make the assumption that
expectations are formed adaptively with the coefficient of expectation equal to 0.5:
SPEND*t ⫽ SPEND*t⫺1 ⫹ 0.5(SPENDt⫺1⫺ SPEND*t⫺1 )
and that for 1964 expectations are correct:
SPEND1964 ⫽ SPEND*1964 ⫽ 40
Therefore, the behavior of the budget over the succeeding four years is depicted in Table
1. Under these particular circumstances, the adaptive expectations mechanism performs
rather poorly. Rather than converging to zero, the expectations errors increase from year
to year.
In summary, adaptive expectations are effective when the variable being forecast is reasonably stable but adaptive expectations perform poorly when forecasting trends. This is
21. We use the 1960s as an example since it was assumed to be a period most commonly associated
with incremental budgetary change. See Aaron B. Wildavsky, The Politics of the Budgetary Process
(Boston: Little, Brown and Company 1964).
12
Public Budgeting & Finance / Fall 2002
TABLE 1
Adaptive Expectations with a Budgetary Incrementalism Model, k ⫽ 0.5
Year
Rt
SPENDt
SPEND*t
Error
1964
1965
1966
1967
1968
20
23
26
29
32
40
46
52
58
64
0
40
43
48
53
6
9
10
11
why adaptive expectations and budgetary incrementalism were so popular in the 1950s and
1960s when budgetary growth rates were relatively stable and predictable during that
period’s preoccupation with Keynesian economics and planning. When spending accelerated, as it did after the mid-1970s as a result of stagflation, or the simultaneous occurrence
of high inflation, high employment, and low economic growth, adaptive and budgetary
incrementalism performed less satisfactorily. Therefore, stability within government
spending would make the adaptive expectations model of budgetary decision making a
very useful forecasting tool.
However with rational expectations, the government follows a fixed rule that is known
and since the economy follows the reduced form in (12), budgetary agents can use this information to form accurate forecasts of future spending levels. The optimal rational expectations forecasting rule is given by (13):
SPEND*t ⫽ 2rt*
⫽ 40 ⫹ 6(t⫺1964)
(13)
We suggest that the reader verify the behavior of the department using this forecasting
rule demonstrating that expectations errors are indeed zero.
Consider now a slightly different case in which the government does not follow any fixed
rule of determining the level of spending, but for any period it is publicly announced in the
preceding period. We continue to assume that the behavior of the budget is described in
Equation (12). Budgetary agents can again make accurate forecasts of the future spending
level by utilizing information they can easily access according to (13). In the real world, the
government cannot exactly determine the spending level. They do provide spending targets in their budget announcements, if the behavior of the economy is reasonably predictable. Therefore, with rational expectations this information will be used efficiently by
budgetary decision makers, a fundamental difference from adaptive expectations since
(13) will be utilized instead of (12). The following section demonstrates the dataset used to
test the three models.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
13
DATA
The database used for these models is composed of annual disaggregated data from 1950
to 2000 for the United States and the United Kingdom, and from 1961 to 2000 for Canada
(data are only available in this country for this time period). The database for Canada was
taken from the Statistics Canada (CANSIM).22 The United States data were taken from
Standard and Poors’s DRI.23 Both databases have been used by economists and to a lesser
degree political scientists to especially measure government growth and budgetary incrementalism.24 This United Kingdom database was taken from the Office for National
Statistics (ONS).25 The disaggregated series we have extracted from these databases are
identified in Table 2. We have used disaggregated data at the departmental level. We
would expect that budget outputs would be the most incremental at this level, as opposed
to the bureau or lower levels.
There is mounting evidence to suggest that different spending categories are influenced by varying political and macroeconomic factors.26 Real public spending (deflated
by its GDP price deflator) was sorted into its discretionary and mandatory components.
Discretionary spending comes closest to actual budgetary decision making. When compared to mandatory spending, discretionary spending is more readily reviewed each year
and is not as directly related to the performance of the macroeconomy. This is not saying
that discretionary spending is reviewed each year from the ground up, but budgetmakers
can more directly have an influence over this category than over mandatory spending. By
contrast, mandatory spending is partly controlled exogenously by the performance of the
macroeconomy. This type of spending essentially acts as an automatic stabilizer, increasing when the economy is in recession and decreasing when the economy recovers. Therefore, all the mandatory disaggregated spending has been taken as a percentage of real
GDP. However, since the discretionary spending categories come closest to actual budgetary decision making, these categories have been translated into percentage changes27
for garbage can budgeting and budgetary incrementalism. We have done this to see the
relative change between the discretionary spending processes.
All disaggregated budget outputs in this study have been transformed into real terms
22. Canadian Socio-Economic Information Management System (CANSIM) (Ottawa: Minister of
Supply and Services, 2001).
23. DRI Basic Economics Quarterly Subscription: Citibase (Toronto: CHASS Datacentre University
of Toronto, 2001).
24. The most notable incidence of Citibase’s use in political science, however, was demonstrated by a
study of government growth by William D. Berry and David Lowery, Understanding United States Government Growth: An Empirical Analysis of the Postwar Era (New York: Praeger, 1987).
25. ONS Databank (Colchester, Essex: The Data Archive, 2001).
26. Tsai-Tsu Su, M. S. Kamlet, and D. C. Mowery, “Modeling U.S. Budgetary and Fiscal Policy Outputs: A Disaggregated Systemwide Perspective,” American Journal of Political Science 37 (1993): 213–
245; and Frank R. Baumgartner, Bryan D. Jones, and James L. True, “Policy Punctuations: U.S. Budget
Authority, 1947–1995,” The Journal of Politics 60 (1998): 1–33.
27. Percentage changes are measured as [t ⫺ (t ⫺ 1)] / [(t ⫺ 1)] in decimal form.
14
Public Budgeting & Finance / Fall 2002
by their GDP price deflator for garbage can and rational choice budgeting, and both real
and nominal values are compared for budgetary incrementalism. We assume that for all
the models tested in this study, decisionmakers are rational enough to discount or compensate for changes in the price level.28 This is not an unreasonable assumption since
incrementalists still possess rationality but are constrained in their budget choices. It is
also assumed that the inflation rate is clearly known by all participants and is without cost
for them to process. Technically, since inflation is inherently incremental if we did not deflate inflows and outflows, these models would give a privileged position to budgetary
incrementalism. In addition, there is the issue of multicollinearity. Without controlling for
inflation, the variables may be related as a result of changes in the price level, not from
changes in the variables we are measuring. The solution that we proposed for the budgetary incrementalism models and others, such as ones Fischer and Kamlet have done, was to
obtain two sets of parameter estimates, one based on real measures and the other based
on nominal data.29 This leads us to the results of the test of the three rival models.
RESULTS
To reiterate, the theory of garbage can budgeting states that decisions are made with no
relationship to what was decided in the past or what may be decided in the future. It is a
random (expectations) process. The garbage can theory is tested by a pure random walk
model. Alternative versions of this model as demonstrated in Equations (3) and (4) include a random walk with drift, and a random walk with drift and a trend, which are
weaker versions of the random walk model, implying that something in the data generating process is not wholly random.30
The best way to test for this is to see if the data-generating process of public spending is
stationary or not. Remember that a stationary process implies that spending heads in a
28. Gregory W. Fischer and Mark S. Kamlet, “Explaining Presidential Priorities: The Competing Aspiration Levels Model of Macrobudgetary Decision Making,” American Political Science Review 78
(1984): 356–370.
29. Ibid., 356–370.
30. There are several well-written books and articles that should be consulted by anyone wishing to
pursue time series analysis. Enders’ Applied Econometric Time Series provides an excellent introduction
to time series analysis and covers the advanced techniques used in this study. Harold D. Clarke, Helmut
Norpoth, and Paul Whiteley, “It’s About Time: Modelling Political and Social Dynamics,” in Research
Strategies in the Social Sciences: A Guide to New Approaches, ed. Elinor Scarbrough and Eric Tanenbaum
(Oxford: Oxford University Press, 1998), provides a highly readable applied introduction to time series
analysis with direct application to political science. However, the two classics for beginners of time series
analysis are by McCleary and Hay and by David McDowall, Richard McCleary, Errol Meidinger, and
Richard Hay, Interrupted Time Series Analysis (Thousand Oaks, Calif.: Sage Publications, 1980). These
authors essentially translated the work of George Box and Gwilym Jenkins, Time Series Analysis, Forecasting, and Control (San Francisco: Holden Day, 1976) and made it readily accessible to social scientists.
Therefore, any reading list should start with McCleary and Hay and with McDowall et al., followed by
Clarke et al., and then Enders should be consulted for more advanced time series modeling techniques.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
15
16
TABLE 2
Garbage Can Budgeting Tested by Dickey-Fuller Unit Root Tests
Canada
United Kingdom
Weaker GC
Models
GCa
Pure
Drift
Public Budgeting & Finance / Fall 2002
Variable and
MacKinnon
(1991)
Critical Value
⫺1.95
⫺2.92
Discretionary
Aboriginal
Agriculture
Post Office
Council
Defence
Expenditure
Goods
⫺2.50
⫺5.17
⫺2.72
⫺3.91
⫺3.55
⫺2.06
⫺2.27
⫺5.85
⫺5.76
⫺3.01
⫺3.84
⫺3.55
⫺3.31
⫺3.61
United States
Weaker GC
Models
Drift
&
Trend
GC
Pure
Drift
Weaker GC
Models
Drift
&
Trend
Pure
Drift
Drift
&
Trend
⫺1.95
⫺2.92
⫺3.50
GC
⫺3.50
Variable and
MacKinnon
(1991)
Critical Value
⫺1.95
⫺2.92
⫺3.50
Variable and
MacKinnon
(1991)
Critical Value
⫺7.92
⫺5.63
⫺3.85
⫺3.96
⫺3.50
⫺4.66
⫺3.87
Discretionary
Consumption
Culture
Defence
Education
Environment
Expenditures
Health
⫺4.09
⫺2.82
⫺7.25
⫺4.46
⫺3.10
⫺2.41
⫺3.15
⫺5.43
⫺3.04
⫺7.08
⫺4.56
⫺2.95
⫺3.60
⫺3.22
⫺5.38
⫺4.96
⫺6.76
⫺4.16
⫺3.61
⫺3.61
⫺7.10
Discretionary
Agriculture
Commerce
Community
Defense
Education
Energy
Environment
⫺5.40 ⫺5.71 ⫺6.27
⫺4.69 ⫺4.64 ⫺4.59
⫺3.51 ⫺3.89 ⫺4.33
⫺14.31 ⫺13.99 ⫺13.15
⫺3.86 ⫺4.47 ⫺4.99
⫺2.07 ⫺3.22 ⫺3.67
⫺3.61 ⫺3.92 ⫺3.97
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
Health
⫺4.09
Hospital
⫺4.76
Housing
⫺4.27
Language
⫺5.55
Local
⫺4.43
Postsecondary ⫺3.80
Provincial
⫺3.68
Railway
⫺6.45
Research
⫺11.52
Scholarship
⫺2.53
Territorial
⫺4.16
Universities
⫺3.97
⫺4.35
⫺4.92
⫺4.97
⫺5.92
⫺4.64
⫺3.70
⫺4.62
⫺6.15
⫺9.41
⫺3.97
⫺5.14
⫺4.56
⫺5.11
⫺5.31
⫺5.97
⫺5.94
⫺4.80
⫺3.72
⫺6.42
⫺5.23
⫺6.73
⫺4.20
⫺5.56
⫺4.50
Mandatory
Family
Medicare
Old Age
Veterans
⫺4.89
⫺3.19
⫺4.09
⫺4.70
⫺4.82
⫺3.50
⫺4.79
⫺5.14
⫺4.59
⫺3.23
⫺3.92
⫺4.74
Housing
Local
Miscellaneous
NHSb
Other
Public Order
Services
Mandatory
Social
Subsidies
⫺6.54
⫺3.34
⫺4.14
⫺2.45
⫺2.14
⫺2.50
⫺6.83
⫺5.46
⫺4.58
⫺5.46
⫺4.53
⫺3.51
⫺3.26
⫺6.57
⫺6.17
⫺4.59
⫺5.67
⫺4.37
⫺4.96
⫺3.55
⫺4.53
⫺3.43
⫺5.38
⫺3.53
⫺5.41
⫺3.59
⫺5.31
Expenditures
Government
GSSTc
Health
International
Justice
On-Budget
Outlays
Transportation
⫺3.73
⫺5.57
⫺5.03
⫺2.03
⫺5.23
⫺2.48
⫺6.43
⫺5.76
⫺4.97
⫺3.23
⫺5.23
⫺3.49
⫺6.68
⫺5.77
⫺4.93
⫺4.36
⫺5.17
⫺3.51
⫺7.68 ⫺10.03
⫺4.15 ⫺4.78
⫺9.66
⫺5.25
Mandatory
Income
Medicare
SocialSecurity
Veterans
⫺5.28
⫺2.85
⫺2.54
⫺7.49
⫺5.38
⫺3.87
⫺4.04
⫺7.33
⫺5.32
⫺3.20
⫺3.86
⫺7.66
Notes: The MacKinnon (1991) critical value at the 5 percent level with the pure random walk (no intercept and trend) is –1.95, the random walk with drift is –2.92,
and the random walk with drift and trend is –3.50. aGC is short form for Garbage Can. bGSST is General Science Space & Technology. cNHS is the National
Health Service.
17
particular direction. It does not have the wandering quality of the nonstationary random
walk. A stationary process simply implies that spending does not wander and reverts to a
constant mean and variance. Thus if budgetary outputs are stationary we can conclude
that there is no evidence of a garbage can model of budgetary decision making at least as
measured by this set of time series data. The easiest way to test for the garbage can model
is to use a Dickey-Fuller test for unit roots. A unit root in the data-generating process provides evidence of a nonstationary process. If there is evidence of nonstationary budget
outputs, then outputs are random, which is essentially what the random walk predicts.
More specifically, Table 2 demonstrates the rejection of a pure random walk model in
Canada. Expenditure has a t-statistic of ⫺2.06, and a MacKinnon critical value31 of ⫺1.95
at the 0.05 significance level32—a rejection of a unit root and consequently garbage can
budgeting. This process shows similar results for expenditures in the United Kingdom,
with a t-statistic of ⫺2.41; the United States, with total expenditures, received a t-value of
⫺3.73. Both consequently exceed the critical value, thereby rejecting a pure random walk
model at the 0.05 significance level. There was no support found for the pure random walk
model. Is there any support for weaker versions of this model, such as a random walk with
drift and/or a random walk with drift and a trend? The weaker versions represent an element of determinism representing a Wagner’s Law description of budgeting.
The weaker versions of the garbage can model are tested by a random walk with an intercept and by a random walk with an intercept and a trend. The results in Table 2 further
demonstrate that for Canada, the United Kingdom, and the United States there is no evidence of a weaker version of a random walk model. For all these cases the calculated values exceed all the critical values at the .05 significance level. Therefore, there is no
evidence of a Wagner’s Law–type spending process in this model. The forthcoming model
that is considered differs from the random walk model by having the present year’s budget output heavily influenced by the immediate past.
In our previous derivation of budgetary incrementalism, spending decisions at the
present are entirely driven by the spending decisions made in the past (last year’s spend31. J. G. MacKinnon, “Critical Values for Cointegration Tests,” in Long-Run Economic Relationships:
Readings in Cointegration, ed. R. F. Engle and C. W. J. Granger (Oxford: Oxford University Press, 1991).
32. D. A. Dickey and W. A. Fuller, “Distribution of the Estimates for Autoregressive Series with a
Unit Root,” Journal of the American Statistical Association 74 (1979): 427–431; and D. A. Dickey and
W. A. Fuller “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica 49 (1981): 1057–1072. They note that for the detection of a unit root the distributions of the appropriate test statistics are nonstandard and cannot be analytically evaluated using traditional t-tests or
F-tests. Under the null hypothesis, it is inappropriate to use classical statistical methods to estimate and
perform significance tests on the coefficients a1. The OLS estimate of the random walk model will yield a
biased estimate of a1. Hence, the usual t-test cannot be used to test the hypothesis a1 ⫽ 1. As a result, we
have used Dickey-Fuller critical values since t-tests are inappropriate under the null of a unit root. The
reader should note that Dickey-Fuller critical values are counterintuitive in this model compared to traditional t-values. For instance, a Dickey-Fuller calculated value greater than the critical value provides a rejection of garbage can budgeting. By contrast, t-values state that if the calculated value is larger than the
critical value then the parameter estimate should be accepted at conventional significance levels.
18
Public Budgeting & Finance / Fall 2002
ing). Therefore, spending follows an adaptive expectations process. This is simply tested
by measuring the spending series to see if it follows an AR(1).
Tables 3, 4, and 5 demonstrate all the disaggregated categories found for incrementalism measured in both real and nominal values for the three countries.33 The nominal and
real measures of expenditures and revenues have some dissimilar results. Inflation has
some impact in the incrementalist models. This may be contrary to some of the existing
work in the field. If we examine the parameter estimates in these tables we notice that almost all the coefficient estimates for these models are positively signed, which is what we
were expecting. The evidence in Table 3 shows that for Canada we found only 5 out of the
total of 23 categories (or 22 percent) supporting budgetary incrementalism. For example,
in Canada, total expenditure, hospital spending, postsecondary education spending, and
spending on Medicare all had incremental outputs. In addition, spending on languages at
the federal level had a decremental outcome. In Table 4 we looked at the United Kingdom and found 3 out of 16 categories (or 19 percent). The categories found in this country
are total government consumption spending, spending on defense and total expenditures.
Therefore, if we average the number of categories found in both Canada and the United
Kingdom, approximately 20 percent show evidence of incremental outputs. In Table 5 the
United States has the most categories with 9 out of 19, almost 50 percent of the total
disaggregated categories. The categories of spending that were not incremental, and are
not reported in Table 5, are very interesting. Notably, on-budget outlays, which don’t include transfers and are the most discretionary portion of the budget, were not found to be
incremental. There were no revenue-constrained incremental models found in this study,
so we have not reported any results for this in Tables 3–5. The most interesting finding is
that in all three countries, budgetary incrementalism occurred at the total expenditure
level, but the evidence is less clear at the dissagregated level. This similar result was found
in all three countries. The last model tested is rational choice budgeting, which possesses
the most economic rationality of the three models tested in this study.
Rational choice budgeting has a specific relationship to rational expectations theory.
The intertemporal budget constraint is predicated upon a government that is implicitly
promising to run sufficient surpluses in the future in order to repay the accumulated debt
and interest when it runs sustained budgetary deficits.
We must see if the surplus (deficit) series is stationary.34 Visually, if we glance at Figure
1 for Canadian, British, and American real surplus (deficit) as a percentage of real GDP,
it appears that over the long term they are heading toward surplus. These countries were
running deficits for a substantial portion of the 1970s until late 1990s, especially Canada
and the United States. However, we must also test the series for the presence of a unit
33. In order to conserve space in Tables 3–5 we have only reported the statistically significant results
that supported incrementalism.
34. A time series is weakly stationary if its mean and variance are time invariant: E(SPENDt) ⫽ e and
E(SPEND2t ) ⫽ e 2 for all t, and its autocovariances E(SPENDt⫺i ⫺ e,SPENDt⫺j ⫺ e)(i ⫽ j) depend only on
the length of the time lag, k, separating i and j.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
19
20
TABLE 3
Five Categories Found for Canadian Budgetary Incrementalism Tested by an AR(1) Model,
Comparing Nominal and Real Values
Expenditure
Constant
Public Budgeting & Finance / Fall 2002
AR(1)
Hospital
Language
Postsecondary
N
R
N
R
N
R
N
0.09
(3.37)*
0.68
(5.58)*
0.04
(3.47)*
0.41
(2.69)*
⫺0.16
(⫺0.67)
0.75
(6.20)*
⫺0.20
(⫺0.90)
0.74
(6.00)*
0.13
(2.70)*
⫺0.45
(⫺2.60)*
0.07
(1.65)
⫺0.47
(⫺2.81)*
⫺0.33
(⫺1.08)
0.79
(7.56)*
R
⫺0.35
(⫺1.27)
0.77
(7.24)*
Medicare
N
⫺0.13
(⫺0.64)
0.48
(2.76)*
R
⫺0.21
(⫺1.05)
0.49
(2.94)*
Notes: Categories of spending detected are 5 out of 23 (total) tested, which is 22 percent. Categories that were not statistically significant such as Provincial,
Defence, and so forth are not reported to conserve space; N and R are nominal and real values; ⫺t-statistics are in parentheses; and *p ⱖ 0.05 with a t-statistic
critical value of 1.96.
TABLE 4
Three Categories Found for United Kingdom Budgetary Incrementalism Tested by an
AR(1) Model, Comparing Nominal and Real Values
Consumption
Model Variables
Constant
AR(1)
Defense
Expenditures
N
R
N
R
N
R
0.09
(3.61)*
0.66
(5.86)*
0.02
(3.30)*
0.37
(2.82)*
0.06
(2.57)*
0.60
(5.69)*
0.00
(0.13)
0.44
(4.02)*
0.09
(3.65)*
0.73
(7.34)*
0.03
(4.48)*
0.43
(3.23)*
Notes: Categories of spending detected are 3 out of 16 (total) tested, which is 19 percent. Categories that were not
statistically significant, such as Education, Environment, and so forth, are not reported to conserve space. N and R
are nominal and real values; t-statistics are in parentheses; and *p ⱖ 0.05 with a t-statistic critical value of 1.96.
root, since we know that rational choice intertemporal budget balance requires that the
budget be mean stationary. That is, it should exhibit a tendency to revert to a constant
mean. The long-run mean is expected to be zero since the budget should be in balance in
general equilibrium. This is done by testing for unit roots. If the series is not stationary, it
is a random walk and by default cannot be an MA(1) process.
There is indeed evidence in Table 6 that the budgetary outputs for the surplus (deficit)
variable in Canada, the United Kingdom, and the United States follow an MA(1) process.
The rational expectations MA(1) process shows statistically significant coefficients at the
0.05 level for the surplus (deficit) variable. The results demonstrate that budgetary
decisionmakers look forward one period into the future when making decisions. In addition to the MA(1) tests, unit root tests were also performed, and reinforce these findings.
Decision outputs are considered by only using all available information at the present to
make a forecast, unlike incrementalism, which relies exclusively on past outcomes.
Another test for rational choice budgeting, a Phillips-Perron test for unit roots, was
conducted. A unit root found in the data-generating process indicates a nonstationary
process, and consequently a rejection of rational choice budgeting. However, the test results demonstrated in Table 6 provide strong support that there is not a unit root found in
the surplus (deficit) variable. In Canada, the Phillips-Perron test statistic was ⫺5.38; in the
United Kingdom, it was ⫺4.46; and in the United States, it was ⫺5.09—all of which are
well above the MacKinnon critical value at the 0.05 significance level of –2.92. This provided an acceptance of rational choice budgeting and a rejection of a unit root in the
data-generating process. The results found here for rational choice budgeting are reinforced by not finding any evidence of garbage can budgeting. If we found evidence of a
random walk model or garbage can outputs, at this (or micro) level of public spending,
then this would not be consistent with support for rational choice budgeting at the aggre-
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
21
TABLE 5
Nine Categories Found for United States Budgetary Incrementalism Tested by an
AR(1) Model, Comparing Nominal and Real Values
Defense
Model Variables
Constant
AR(1)
Education
N
R
N
0.05
(1.22)
0.54
(5.65)*
0.01
(0.30)
0.53
(5.19)*
0.13
(3.37)*
0.35
(2.54)*
Expenditures
Model Variables
Constant
AR(1)
N
0.07
(6.16)*
0.53
(4.94)*
R
0.04
(4.84)*
0.40
(3.31)*
Constant
AR(1)
N
0.09
(3.62)*
0.41
(3.16)*
R
0.05
(2.11)*
0.41
(3.17)*
0.09
(2.38)*
0.36
(2.67)*
N
N
0.10
(4.08)*
0.45
(3.41)*
Medicare
N
R
0.08
(5.92)*
0.11
(6.02)*
0.03
(1.42)
0.37
(2.73)*
Justice
R
0.14
(5.81)*
0.41
(3.11)*
R
0.06
(2.87)*
0.41
(3.09)*
Health
Transportation
Model Variables
R
Environment
0.04
(3.63)*
0.11
(7.39)*
N
R
0.11
(5.72)*
0.39
(2.87)*
0.07
(4.24)*
0.32
(2.28)*
Social Security
N
R
0.07
(4.42)*
0.51
(7.47)*
0.03
(1.97)*
0.56
(8.66)*
Notes: Categories of spending detected are 9 out of 19 (total) tested, which is 47 percent. Categories that were
not statistically significant, such as Agriculture, Energy, and so forth, are not reported to conserve space. N and
R are nominal and real values; t-statistics are in parentheses; and *p ⱖ 0.05 with a t-statistic critical value of 1.96.
gate (or macro) level. Enders recommends that both the Phillips-Perron and an Augmented Dickey-Fuller unit root test be conducted to determine the stationarity of a time
series.35 Again, we found no evidence of a unit root in the data-generating process. Therefore, all the values from both an Augmented Dickey-Fuller and a Phillips-Perron test provided a rejection of a unit root in the data-generating process, further bolstering the
35. Enders, Applied Econometric Time Series; see footnote 15.
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Public Budgeting & Finance / Fall 2002
FIGURE 1
TABLE 6
Rational Choice Budgeting Tested by MA(1), Phillips-Perron, and Augmented
Dickey-Fuller (ADF) Tests
Phillips-Perrona
ADFa
MA(1)
Canada
United Kingdom
United States
Surplus (Deficit)
Surplus (Deficit)
Surplus (Deficit)
⫺5.38a
⫺3.88a
0.30
(1.96)*
⫺4.46a
⫺4.50a
0.46
(3.61)*
⫺5.09a
⫺3.46a
0.27
(2.10)*
Notes: t-statistics are in parentheses; and *p ⱖ 0.05 with a t-statistic critical value of 1.96. a the critical value for
the Phillips-Perron and ADF tests at the 0.05% level is ⫺2.92.
Reddick / Testing Rival Decision-Making Theories on Budget Outputs
23
presence of rational choice budgeting. What is the significance of these findings for public
budgeting?
CONCLUSIONS
This article has examined three rival budgetary decision-making theories in differing political and institutional settings. Each of these theories implies varying degrees of economic rationality. The garbage can model is the least rational among the three: decisions
can be somewhat chaotic and very dependent on the problems at hand, on participants in
the decision-making process, on solutions proposed by participants, and on the choice opportunities open to decisionmakers. There was no evidence found for this type of model.
Another model tested was budgetary incrementalism, which has a higher degree of economic rationality than the previous mentioned model.
In this study the evidence found for budgetary incrementalism demonstrated no overwhelming support. Interestingly, there were around 20 percent of the disaggregated categories found for Canada and the United Kingdom and around 50 percent for the United
States. This evidence may be attributed to the different institutions of the budget process
in a parliamentary system compared to a presidential system. In the American system the
budget is essentially determined by the legislative branch, while in a parliamentary system
it is almost entirely determined by the executive branch. The parliamentary system allows
the governing party to have a stronger hold on priorities compared with a presidential system. The devolution of power in the United States may have something to do with the necessity of making incremental choices compared with other more rational methods.
However, the model that garners the most consistent support across the three countries is the rational choice model of budgeting. It argues that decisionmakers have the
ability to think using a long-term perspective when making decisions. It follows that
decisionmakers use rational expectations correcting for systematic errors occurring in the
decision-making process. This rational choice model of budgeting seems very relevant
and plausible in contemporary public budgeting. Decisionmakers with their access to
large amounts of data and inexpensive processing power are more inclined in the current
environment to make rational calculations. Advances in information technology throughout the 1980s and 1990s and the government’s increasing use of statistical agencies that
collect and analyze data demonstrate this. This evidence is supported by the increased use
of normative rational decision-making techniques, such as program- and performancebased budgeting in modern governments.36 In this model in the short term, as a result of
macroeconomic (e.g., recessions) and political constraints (taxpayer fatigue—e.g., Proposition 13 in California) it is very difficult for a government to be able to balance its budget
on a yearly basis. Policymakers in budgeting are trying to achieve some long-term equilib36. Irene S. Rubin, “Budget Theory and Budget Practice: How Good the Fit?” Public Administration
Review 50 (1990): 179–189.
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Public Budgeting & Finance / Fall 2002
rium in which a balanced budget is a major goal. As a result the evidence in this study
shows that the economic rationality of decision making in fiscal policy making is greater
than many of the alternative perspectives admit. Thus, this work may imply a rethinking
of the simple incrementalist models of decision making, as well as of the garbage can
model, which implies that the policy-making process can be irrational and chaotic in many
respects.
NOTE
This author would like to thank the anonymous referees for their thorough and helpful
comments.
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25