Estimating the Risk–Propensities Underlying Agency Budgetary Decisions:
An Empirical Analysis of SEC Budget Requests, 1949–1997*
George A. Krause†
University of South Carolina
Draft as of
February 6, 2002
*
An earlier version of this paper was presented at the annual meetings of the 1999 Public Choice Society. March
12!14, 1999. Hotel Monteleone. New Orleans, Louisiana. The author wishes to thank Janice Boucher Breuer,
Brandice Canes–Wrone, Dan Carpenter, Stephen Dilworth, David Lewis, Ken Meier, Tom Romer, Andy Whitford,
B. Dan Wood, and LeeAnne Krause for their helpful suggestions and comments along various stages of this project.
I thank Tim Groseclose and Dan Ponder for providing some of the ADA voting score data used in this paper. This
paper is dedicated to the memory of Sherry Ann Krause. Any errors that remain are my own.
†
Associate Professor of Political Science, Department of Government and International Studies, University of South
Carolina, Columbia, SC 29208. [email protected] (e–mail).
Abstract
Students of organizations accept the notion that administrative agencies make decisions
in an uncertain policy environment. Existing systematic research on public bureaucracy either
makes simplifying a priori assumptions about such behavior, or ignores this fundamental issue
altogether. This study proposes an empirical test of risk–bearing behavior in order to shed light
on how bureaucratic organizations make decisions under conditions of uncertainty. This test is
theoretically grounded in formally–derived conditions of risk–aversion, risk–neutrality, and risk–
seeking behaviors. In addition, hypotheses concerning heterogeneous agency response based on
type of uncertainty (external versus internal) as well as the context by which it is experienced
(divided verus unified government) are proposed and empirically tested utilizing annual data on
Securities and Exchange Commission (SEC) agency budget requests for the 1949–1997 period.
The regression analysis yields risk coefficients that are compatible with analytically derived
results. The empirical findings suggest that administrative agencies exhibit relatively more
concern about organizational maintenance through their budgetary decision making behavior
when the type of uncertainty is outside the decision making purview of the organization.
However, mixed statistical evidence is obtained that agencies place comparatively greater value
on organizational maintenance, via their budget requests, during an era of greater political
instability induced by divided party government than under unified government.
1. Introduction
A major tenet of public bureaucracy is that administrative agencies possess incomplete and
imperfect information when having to make decisions. A diverse array of seminal scholarly works in the
field ranging from Herbert A. Simon’s theory of bounded rationality (1976) to William Niskanen’s
budget maximizing bureaucrat (1971), to in–depth theoretical characterizations of administrative
organizations by Anthony Downs (1967), James Q. Wilson (1989), and Arthur Stinchcombe (1990) treats
this as a fundamental issue when trying to understand how bureaucratic agencies function. In the classic
treatise Organizations in Action, James D. Thompson (1967: 159) claims that uncertainty is the
fundamental problem for complex organizations, and coping with this uncertainty are the essence of the
administrative process (see also, Crozier 1964). Thus, one can conclude that analyzing the uncertainty
that the agency experiences are vital for understanding the how bureaucratic organizations make
decisions in a democracy.
Unfortunately, the nature of agency response to uncertainty that underlies our modern
analysis of administrative organizations is either assumed away (i.e., ignored) or assumed fixed a priori
(e.g., risk–neutrality) in existing scholarship (see Carpenter 2000 for an exception). Notwithstanding
research on parallel systems and redundancy in bureaucratic organizations (Bendor 1985; Heimann
1997), scholars have yet to provide a systematic way to accurately assess how administrative agencies
respond to uncertainty. In order to understand agency decision making under uncertainty, theory–laden
criteria must be advanced that can enable scholars to empirically demarcate between risk–aversion,
risk–neutrality, and risk–seeking behaviors.
Estimating agency risk propensities underlying their decisions will uncover the extent to which
bureaucratic agencies value organizational maintenance in an uncertain world. An agency that behaves
in a more (less) risk–averse (risk–seeking) manner places a greater premium on their own organizational
maintenance, all else being equal. This perspective is consistent with the view that bureaucratic agencies
1
wish to buffer themselves from the uncertainty (Crozier 1964; Downs 1967; Thompson 1967).
Conversely, an agency behaving in a more (less) risk–seeking (risk–averse) manner places less emphasis
on their own organizational maintenance when confronting uncertainty, ceteris paribus. Such behavior
might reflect an agency’s greater willingness to act in a risk–acceptance manner either because they are
performing beyond their own expectations (March 1999: 20), or to appease political principals wishing to
limit the supply of administrative resources (e.g., Banks 1989; Miller and Moe 1983).
The main contribution of this study is twofold. First and foremost, I provide a direct statistical
test of agency risk–bearing behavior that is based on a simple comparative–static theoretical analysis of
bureaucratic budgetary decision making under uncertainty developed elsewhere (Krause 2001). This
formal–theoretic analysis is predicated on the utility that the agency receives from seeking greater
funding1 vis–a–vis the utility derived from the uncertainty (i.e., volatility) that they experience.2 This
perspective suggests that variations do occur regarding bureaucratic organizations’ desire to protect their
technological cores from environmental influences, by buffering them from fluctuations involving the
latter (Thompson 1967: 67).3 Rather than treating budgetary resources as an “ends” to be maximized for
purposes of creating organizational wealth (Niskanen 1971; Tullock 1965; but see Blais and Dion 1991
1 As a general rule, this implicitly presumes that an agency’s actual funding will be a monotonically increasing
function of their own budget requests that are distinct from the OMB/presidential figures. In other words, the agency
will always obtain positive marginal utility from BR since it is presumed that these organizations will prefer more
money as opposed to less, all else being equal, because what the agency does not ask for in terms of budgetary
resources, it will not likely acquire from political principals. This is because it is generally rare for agencies to
receive greater appropriations than that which they request for. In other words, an agency will typically get some
fraction (however large or small) of the additional funds that they seek to obtain. This point is supported with
Securities and Exchange Commission (SEC) agency budget request and final appropriations data for the 1949–1997
sample period covered in this study.
2 Uncertainty is viewed as a continuous phenomenon that is captured by the variance or the volatility that the agency
is experiencing in terms of their budgetary resources or task demands. This view of uncertainty is compatible with
scholarship on decision making by administrative organizations (March 1999: 182–183; Thompson 1967).
3 Thompson specifically uses the concept of buffering applied to boundary spanning of organizational subunits;
whereas, in this study it pertains to agency resource preferences. Both usages treat buffering as a means of coping
with uncertainty present in administrative organizations.
2
for a critique), agencies instead view them as a “means” by which to cope with uncertainty that is
omnipresent (Cyert and March 1963; Downs 1967: 138–139; March 1999; Pfeffer and Salancik 1978;
Thompson 1967; Wilson 1989).
In addition, I maintain that bureaucratic agencies formulate budget requests in a heterogeneous
fashion in response to uncertainty. This differential response manifests itself in two distinct ways. First,
I hypothesize that agencies will behave in a relatively more risk–averse fashion in response to uncertainty
that is determined outside of their decision making locus vis–a–vis that which occurs within the rubric of
the bureaucratic organization. This is because the agency will possess relatively less information and
ability to handle external uncertainty that is determined by elected officials outside of their purview
compared to internal uncertainty which occurs to some extent within the realm of the administrative
organization. Thus, agencies are hypothesized as exhibiting relatively greater uncertainty avoidance
(March 1999: 229) for that which they possess less formal authority or control over (Thompson 1967:
12).
I also assert that agency budgetary behavior should be comparatively more risk–averse during
times of divided government than periods of unified government for a given type of risk–bearing
behavior, all else being equal. Agencies will be relatively more concerned with organizational
maintenance during divided government versus unified government because of the weaker policy
coherence and less stable policy expectations in the political environment associated with the former era,
all else being equal. Next, I propose a statistical test that allows one to delineate among risk–averse,
risk– neutral, and risk–seeking agency budgetary decision making under divided and unified government
political contexts.
2. Linking Theory to Empirics: Deriving a Statistical Test of Agency Budgetary Risk–Bearing Behavior
An agency’s funding request serves as a choice variable that is distinct from (and also precedes)
3
the OMB/presidential request solicited to Congress on their behalf, thus allowing one to analyze how
bureaucratic agencies respond to uncertainty through their budgetary decision making.4 Agency budget
requests represent one of the few mechanisms, whereby, bureaucratic preferences are actually revealed.
Thus, agency budget requests provide an appropriate vehicle for analyzing the risk–bearing proclivities
of administrative organizations. Simply, agency budget requests yield insight into how bureaucratic
organizations seek fiscal resources with the purposes of creating slack resources for their organizational
and policy missions (Cyert and March 1963; Downs 1967; Thompson 1967; Wilson 1989). The
perspective advanced in this study is different from the budgetary aggrandizement/maximization view
advanced by Niskanen (1971) by instead viewing these resources as a hedge against uncertainty (Downs
1967: 138–139). Specifically, an agency preoccupied with uncertainty avoidance will seek resources to
remove dependence upon other institutional actors (Pfeffer and Salancik 1978).
In constructing an empirical test of agency budgetary decision making that gauges risk–bearing
propensities, it is desirable to link the theoretical microfoundations of such behavior with its statistical
modeling. I argue elsewhere (Krause 2001), agency utility is assumed to be a function of two variables –
their budgetary residual or BR (i.e., the difference between their request in the current year and actual
appropriation from the prior year) and uncertainty that it experiences (F). By holding agency utility at
some fixed positive level ( U ), one can isolate the theoretical relationship between agency budgetary
decisions reflected in BR and uncertainty (F). The aim of this exercise is not to maximize agency
utility, nor to set forth a full–blown model of the budgetary process explaining budgetary outcomes.
Rather the focus of this investigation is on how an agency determines their budget requests in response to
uncertainty for a given fixed level of utility consistent with logic of standard research on portfolio theory
in financial economics (e.g., Tobin 1958; Hirshleifer and Riley 1992). Thus, comparative–static analysis
4 The focus of this study is on an agency’s own resource preferences, not the ensuing political bargaining that takes
place between the president and Congress well after an agency’s preferences are revealed via their request submitted
to OMB.
4
yield results consistent with the theoretical conditions of budgetary risk–aversion, risk–neutrality, and
risk–seeking behavior within the context of divided and unified party government (Krause 2001).
The basic foundations of this analysis can be characterized by the power function5:
U = c + BR α − σ β
(1)
where agency utility is an additive function of the agency’s budgetary residual6 (BR) that represents the
difference between their request for the current year and what they received last year in congressional
appropriations – i.e., BRt = [(FRt) !(FAt!1)], and also the level of uncertainty that they confront (F). It is
assumed that the agency seeks a level of funding that is greater than what they received from the previous
year – i.e., BR > 0.7 The agency will always obtain positive marginal utility from BR since it is safe to
presume that they will prefer more funding as opposed to less, all else being equal.8 This is because
budgetary resources that the agency does not ask for will not likely be provided by political principals, all
5 The power function is employed since its interpretation is intuitively straightforward and can be readily linked to
empirically testable propositions. The mathematical results for the general solution for each type of risk–bearing
agency budgetary behavior are robust to alternative functional forms (see Krause 2001, Appendix).
6 Budgetary residual refers to the marginal increment being sought by the agency compared to what it obtained in
appropriations for the previous year. This concept differs from economic rents that remain once an organization’s
functional needs are met (Niskanen 1971). The budgetary residual is premised on a linear decision rule that is
commonplace in budgetary decision making models (c.f. Davis, Dempster, and Wildavsky 1966; Padgett 1980: 355)
– i.e., FRt = FAt!1 + >t where >t is defined as the budgetary residual sought by the agency. Rearranging terms yields:
BR/ >t = FRt – FAt-1. This is a plausible assumption consistent with the adaptive nature of administrative behavior.
7 This simplifying assumption is for purposes of mathematical tractability in the analytic derivations since a negative
root might exist under certain circumstances when using a power functional form. However, an actual budget
appropriation reduction from one year to the next is not precluded since the current year budgetary appropriation is
not germane for this assumption to hold. Moreover, relaxing this assumption would not alter the theoretical
predictions generated from this comparative–static analysis (See Note 5).
8 The positive marginal utility assertion does not mean to imply that agencies will pad budgets in a conspicuous
manner since there are other factors that will constrain them to do so such as the ideological orientation of political
institutions, macro–level budgetary constraints induced by macroeconomic performance, and agency enforcement
workloads. The present theoretical analysis treats these items and other strategic considerations as being fixed in
order to have a tractable problem that can be analytically solved. This a reasonable way to handle this issue since the
purpose of the study is to focus on agency decision making under uncertainty, as opposed to strategic interaction
with political institutions. At any rate, these political factors are accounted for in the empirical analysis to ensure the
risk coefficients calculated serve as valid statistical estimates of agency budgetary risk–bearing behavior.
5
else being equal.9 This proposition is assumed to be true, irrespective of the type of agency budgetary
risk–bearing behavior. Risk–averse agencies are assumed to obtain increasing positive marginal utility
from BR since they place the greater emphasis on budgetary resources, and hence, organizational
maintenance than do those acting in accordance with risk–neutrality and risk–acceptance. Likewise, a
risk–neutral agency is assumed to exhibit constant positive marginal utility from BR, while a
risk–seeking agency is assumed to exhibit decreasing positive marginal utility from BR – i.e., each
successive increment in BR brings them smaller utility gains.
For the purposes of this study, uncertainty is captured by the degree of volatility or variance
experienced by the agency (March 1999:182–183; Thompson 1967); whether it is in the form of
budgetary appropriations or enforcement workload outputs. Therefore, uncertainty can be viewed as the
degree of ambiguity faced by the bureaucratic organization that is germane to their operation (Downs
1967; March and Olsen 1976; Wilson 1989). Uncertainty is presumed to have a negative effect on
agency utility under risk–aversion, be orthogonal with respect to agency utility under risk–neutrality, and
exert a positive effect on agency utility under risk–seeking behavior.
Instead of listing separate utility functions for each type of risk–bearing behavior, the base risk–
averse utility function is employed for expositional purposes.10 These three types of behavior are
captured by subsequently allowing the theoretical model’s parameters to be either negative, zero, or
positive. Solving for BR while also holding agency utility equal to a fixed positive level (U = U )
yields:
9 This means that agency utility rises as BR increases, all else being equal. In other words, an agency will prefer
more funding to less funding, all else being equal. This current discussion only pertains to the impact of the
budgetary residual on agency utility.
10 Please note the subsequent differences in signs that occur when analyzing risk–seeking behavior in this study
compared to that which appears in Krause (2001), where he does present the form of the utility function for each
type of risk–bearing behavior.
6
1
BR = (U − c + σ β ) α
(2)
where: 0 < c < U ,
thus U , serves as a supremum for c by definition since BR > 0 by assumption. Dropping out the fixed
terms ( U , c) in (2) for simplicity sake, without any loss of generality, the simple deterministic nonlinear
relationship between BR and F is obtained:
BR = σ
β
α
.
(3)
Taking the natural logarithm of (3) linearize this functional relationship as well as provide a
fixed–elasticity interpretation to
β
α
:
ln BR =
A one percentage change in uncertainty results in a
β
ln σ .
α
(4)
β
β
percentage change in BR. The
parameter term
α
α
is a risk coefficient that captures the ratio impact on agency utility attributable to uncertainty ($) and the
agency budgetary residual ("), respectively. The value associated with the risk coefficient captures the
agency’s propensity for organizational maintenance in response to uncertainty.
The possibility that the agency’s budgetary risk–propensity might uniquely vary between divided
and unified government regimes are also considered since these particular states are assumed to infer
differential effects that uncertainty has on agency utility. This is because the agency has less stable
expectations about their political environment attributable to the lower level of institutional stability
reflected by government fragmentation (Davis, Dempster, and Wildavsky 1966), and also supplied
with less policymaking flexibility via the contents of enacting legislation under a divided government
regime (Epstein and O’Halloran 1999: 77–81). This, in turn, makes it more difficult for administrative
7
units to construct coherent public policies during such periods.11 This qualitative distinction in agency
risk–bearing behavior between eras of divided and unified government is consistent with the admonition
that risk preferences are supplemented with context–dependent considerations, as opposed to being
treated fixed (March 1999: 244–245).
Summary information on the comparative–static results analytically derived in Krause (2001),
and their implications for empirical hypothesis testing appear in Table 1. In the risk–averse case,
agencies must seek greater increases in BR when F rises – i.e.,
β
> 0 . Risk–averse behavior implies
α
that administrative agencies place a greater relative emphasis on organizational maintenance than
otherwise. This is because organizational slack as a cushion to absorb uncertainty, is a rational response
for obtaining bureaucratic flexibility (Cyert and March 1963; Downs 1967: 138–139; Thompson 1967;
Wilson 1989). However, the nature of risk–averse agency budgetary behavior will vary depending upon
divided or unified party government. This is because changes in uncertainty during a divided
government regime will have a relatively more adverse impact on the rate of an agency’s marginal utility
than during an era of unified government since the stability of their institutional environment, and hence,
agency expectations are comparatively weakened under the former regime. Under divided government,
risk– averse agency budgetary decision making can be
β
β
β
> 1, = 1, or 0 < < 1 . The risk
α
α
α
coefficient value depends upon whether the relative amount of utility accrued to the agency by seeking
additional funds outweigh the disutility from rising uncertainty. When uncertainty has a greater (lesser)
11. This view differs from a conventional power accrual story predicting that agencies will enjoy greater discretion
under divided government, all else being equal (e.g., Bryner 1987; Dahl and Lindblom 1953; Hammond and Knott
1996). Administrative agencies are not presumed to care so much about playing political principals off of one
another to accrue power as an ends as these accounts typically infer because the extent to which the agency wishes to
protect themselves from political and policy uncertainty is what is central to coping with this phenomenon. This
alternative perspective is valid given that the purpose of this study is to analyze the risk–bearing behavior of
administrative agencies. Moreover, divided government results in greater uncertainty (variance) than unified
government since conflict among political principals rises during such eras. Thus, agencies receive stronger mixed
signals (i.e., greater variance) concerning the direction of policy administration during the former regime than the
latter. This is true even if the typical adopted policy is of a more moderate ideological nature as a result of political
compromises.
8
impact on agency utility relative to that of the budgetary residual being sought by the agency, then the
risk–averse agency will seek a larger (smaller) than proportional rise in BR compared to the rise in F –
β
β 

> 1  0 < < 1 . When uncertainty and the agency budgetary residual each has the same

α
α 
β
magnitude of impact on agency utility, then the BR will rise proportionally with F – i.e.,
= 1 . During
α
i.e.,
unified government, the risk–averse agency will seek increased budgetary funding that is less
proportional than changes in uncertainty – i.e., 0 <
β
< 1 . This is because uncertainty will have a less
α
deleterious impact on agency utility relative to the positive utility garnered from BR. If, however,
β
= 1 under unified government, then uncertainty will not have such a differential effect on agency
α
utility, and that this distinction is not empirically valid in such instances.12 To summarize, budgetary
risk–aversion means that agency requests will rise less than proportional to changes in uncertainty during
times of unified government; whereas, it may do likewise, rise proportional, or greater than proportional
under a divided government regime.
[Insert Table 1 Here]
Under risk–neutrality, the agency’s budgetary decision making will not be responsive to
variations in uncertainty, irrespective of the divided/unified government distinction – i.e.,
β
= 0 . Risk–
α
neutral budgetary behavior indicates that administrative agencies will obtain increases in utility at a
constant rate from trying to obtain additional funding, yet will neither gain nor lose utility from variations
involving uncertainty. This type of risk–bearing behavior implies that administrative agencies are
indifferent with respect to uncertainty, and hence, place no greater or lower premium on organizational
maintenance as uncertainty changes. Risk–neutral bureaucratic agencies will neither seek more resources
as an organizational response to absorb greater uncertainty (budgetary risk–aversion), nor seek a smaller
budgetary residual as uncertainty rises (budgetary risk–seeking).
12 It cannot be insinuated, however, that this particular relationship lacks theoretical merit. Rather, this is an issue
that can be empirically substantiated or refuted in other bureaucratic agency settings.
9
Risk–seeking agency budgetary behavior will be reflected by an inverse relationship between the
agency’s budgetary residual and the uncertainty it experiences – i.e.,
β
< 0 ; whereby, they receive
α
positive utility from the budgetary residual that they request and also from greater uncertainty.13
Substantively, risk–seeking budgetary behavior exhibited by administrative agencies implies that they
de–emphasize organizational maintenance in attempting to deal with uncertainty by seeking a smaller
budgetary residual in response to rising uncertainty, all else being equal.14 An agency’s budgetary
risk–seeking behavior might be indicative of the stronger preference they hold for meeting politicians’
desire to limit funding growth, due to the latter’s concern over inefficiency and undesirable policy effects
that risky budgetary investments can bring about, relative to their preference for creating slack resources
as a means to help the agency cope with a rising tide of uncertainty. As with risk–aversion, risk–seeking
behavior will also vary depending upon the divided versus unified party government distinction. Once
again, an era of divided government is assumed to have a more adverse impact on the rate of marginal
(agency) utility with respect to uncertainty than during an era of unified party government for the same
reasons discussed earlier in this section.
As a result, risk–seeking agency budgetary decision making will reflect less than proportional
changes in the budgetary residual sought by the agency in response to a change in uncertainty during the
divided government regime – i.e., − 1 <
β
< 0 . This is because uncertainty will have a comparatively
α
greater adverse impact on agency utility during periods of divided government vis–a–vis unified
government. In an era of unified government, risk–seeking agency budgetary behavior should be
13 One must recall that this will be the opposite sign from equation (1) since U= c1 + BR" + c2 + F $ (i.e., utility has a
positive relationship with agency utility), and hence in shorthand notation, BR = – F $/".
14 Although risk–seeking behavior by administrative agencies is typically an uncommon occurrence (Thompson
1967), I do not falsely preclude this possibility on a theoretical level. Research has documented certain situations
where administrative agencies will seek smaller gains in budgetary resources as a response to the conditions of their
policy environment relating to uncertainty or other factors affecting agency budgetary decision making (Johnson
1992; Khademian 1992). Thus one cannot preclude the theoretical possibility that administrative agencies may
request relatively smaller increases in budgetary resources in response to rising uncertainty, and hence, behave
consistent with budgetary risk–seeking behavior.
10
β
< −1 by definition. However, the second derivative (shape) of the budgetary residual with respect to
α
uncertainty is indeterminate, and can be either positive, zero, or negative. The shape of this relationship,
however, depends upon the absolute level of uncertainty (F) in relation to the relative balance between
the rate of positive marginal utility attributable to uncertainty to the ratio of positive marginal utility with
respect to both uncertainty and the agency’s budgetary residual.15 The agency’s utility accrued from
uncertainty will exceed their utility from their proposed budgetary residual when
β
< − 1 . This makes
α
logical sense since when one considers that uncertainty will have a less detrimental (or more favorable)
impact on agency utility under unified government relative to divided government, all else being equal.
If
β
= −1 either under divided or unified government, then uncertainty does not have such a differential
α
effect on agency utility.
 β
 is valid, the
α
In order to ensure that the ensuing statistical estimate of the risk–coefficient 
deterministic bivariate relationship captured in (4) can be viewed within the context of a statistical model
by including an intercept parameter ((), a k–dimensional column vector of control variables (X k) with
corresponding parameter vector (B k), and a random disturbance (,)
ln BR = γ +
β
ln σ + π k X k + ε .
α
(5)
Equation (5) represents a log–log econometric specification with respect to the relationship between BR
and F, yet takes on a semi–logarithmic form for the vector of control variables that are assumed, at least
initially, not to require a logarithmic transformation for data or substantive reasons.16 Next, I discuss the
nature of heterogeneous agency budgetary decision making under uncertainty.
15 Both the logic and interpretation of these theoretical results are fully elaborated upon in Krause (2001: 23–24).
16 This issue will be explicitly considered in the next section when the vector of control variables are introduced.
11
3. Heterogeneity of Agency Budgetary Decision Making under Uncertainty
The risk–bearing nature associated with agency budgetary decision making can vary in relation
to the source of the uncertainty and also the context in which they experience (March 1999: 244–245).
Specifically, an agency’s desire for organizational maintenance will vary based on the distinctions
between external and internal uncertainty as well as divided and unified party government. Regarding
the former, uncertainty can either be completely outside of the agency via decisions made by political
principals (i.e., external uncertainty), or occur within the administrative organization, yet is beyond their
full control (i.e., internal uncertainty). For instance, appropriations volatility is one example of external
uncertainty since congressional appropriation decisions are not made by the agency, but instead are
legally determined by the President and Congress (Kiewiet and McCubbins 1988, 1991). Task demand
volatility, measured in the form of enforcement workload volatility, captures the internal uncertainty that
occurs within the sphere of the agency’s organization in terms of their own output. Although one might
be skeptical of enforcement workload volatility as a valid measure of agency uncertainty, this is not the
case because agencies do not control the volume of regulatory enforcement violations that occur in a
given year because these activities are largely conditioned as a response to their policy environment.
Further, the budget request partly pertains to what the agency anticipates their workload to be for the
upcoming year since it does know what it will be given their lack of perfect and complete information.
These measures satisfy the general criterion advanced in seminal scholarship in organizations that view
administrative agencies as part of a larger environment in which it has imperfect control over (Barnard
1938; Selznick 1949).
Not only should administrative agencies feel comparatively weaker in attempting to cope with
external uncertainty relative to internal uncertainty, but it should also translate into an inferior
understanding of the former type of uncertainty compared to the latter for these organizations. This leads
to my first hypothesis concerning heterogeneity in agency budgetary decision making:
12
Hypothesis 1: An administrative agency will behave in a relatively more risk-averse
manner in response to appropriations (external) uncertainty vis-a-vis enforcement
workload (internal) uncertainty, ceteris paribus.
The logical basis to this hypothesis is simple – a bureaucratic agency will be relatively less concerned
with organizational maintenance when dealing with uncertainty concerning items that it possesses greater
information and decision making influence over, such as bureaucratic enforcement outputs, compared to
those decisions which are made explicitly outside of the organization, such as budgetary appropriation
decisions. This hypothesis is grounded in the notion that handling uncertainty in which the agency has
no formal authority or control over is a more daunting challenge than when it has some limited control
over as an organization (Thompson 1967: 12). As a result, one should observe an agency placing greater
emphasis on organizational maintenance with respect to appropriations uncertainty relative to
enforcement workload uncertainty. In other words, an agency will place a relatively stronger premium
on the creation of slack resources as a means to cushion the agency from dealing with budgetary
(external) uncertainty compared to task demand (internal) uncertainty, all else being equal.
Shifting budgetary decision rules reflect the contextual nature by which such decisions are made
(Wildavsky 1988; Kiewiet and McCubbins 1988, 1991). As I maintain elsewhere (Krause 2001), the
contextual distinction between divided versus unified party government in the area of agency budgetary
decision making that is assumed to manifest itself through the rate of change in marginal utility with
respect to movements in uncertainty. Specifically, uncertainty should have a more deleterious impact on
agency utility under periods of divided government than under unified government, all else being equal.17
Therefore, agency budgetary risk–aversion reflects marginally greater agency disutility from uncertainty
under divided government compared to unified government, while agency budgetary risk–seeking
17 Risk-neutral behavior is unaffected by this particular effect attributable to the divided/unified government
distinction because it is perfectly inelastic (unresponsive) to movements involving uncertainty by definition.
13
behavior exhibits marginally smaller gains in positive agency utility from increases in uncertainty during
a divided government era relative to a unified government era.
Why should one expect relatively greater risk–averse behavior to be displayed by administrative
agencies under divided government compared to unified government? Institutional stability will be
comparatively strengthened under unified government since it will generate more stable policy
expectations, and thus produce better informed agency decision making under conditions of uncertainty
(Davis, Dempster, and Wildavsky 1966). Thus divided government will create a situation where
institutional stability is eroded and agency expectations concerning political institutions become clouded
relative to times of unified government. While it is true that conflict among political principals present
under divided government might induce ideologically moderate policymaking (Alesina and Rosenthal
1995; Fiorina 1996), the degree of uncertainty or variance concerning these outcomes should be higher
than under unified government. The higher level of conflict among political principals under a divided
government regime will typically produce noisier political signals to administrative agencies than those
emitted during a unified government regime. Furthermore, bureaucratic agencies will have a more
difficult time administering public policies in a coherent manner because they are afforded less
discretionary authority by political institutions who prefer the use of legislation to policy delegation
under these circumstances (Epstein and O’Halloran 1999: 80–81). Under such circumstances,
administrative agencies will become more protective of their organizational interests and mission, and
thus act accordingly. This means that bureaucratic agencies will seek comparatively greater slack
resources as a rational means to obtain administrative and policy flexibility (Downs 1967), and make
their organizational mission more coherent (Wilson 1989) under divided government vis–a –vis unified
government, all else being equal.
Therefore, an administrative agency should exhibit a greater preference for organizational slack,
or minimize the reduction of this particular commodity, during periods of divided government, all else
14
being equal. This leads to my second hypothesis:
Hypothesis 2: An administrative agency will behave in a relatively more risk–averse
manner under divided government compared to unified government, ceteris paribus.
Although administrative agencies may exhibit budgetary risk–aversion, risk–neutrality, or risk–seeking
behavior, these entities will act in a comparatively more risk–averse manner under divided government
than under unified government. Hypothesis 2 suggests that bureaucratic agencies will place a relatively
greater emphasis on organizational maintenance during times of divided government compared to unified
government, all else being equal. This is because these organizations will be operating in both a less
stable and more constrained decision making environment in the former era relative to the latter era.
Next, I present the case study selected for empirical analysis.
4. Empirical Laboratory: The Securities and Exchange Commission (SEC)
The Securities and Exchange Commission (SEC) is the empirical vehicle used to test these two
hypotheses, and more generally determine the nature of agency risk–bearing budgetary decision making.
The SEC was established as an independent regulatory commission in 1933 by Congress. This
bureaucratic agency grew out of the public demand for increased economic regulation following the
Stock Market crash of 1929 and the subsequent dilemmas associated with the Great Depression in the
U.S. during the 1930's. The SEC’s policy mandate is to enforce the securities and commodities laws
under the Securities Act of 1933 and the Securities and Exchange Act of 1934 and subsequent
amendments to it (Khademian 1992).18
The SEC is a very suitable choice for an empirical laboratory in studying the risk–bearing nature
of agency budgetary decision making for three reasons. First, an analysis of the SEC provides a more
18 Please see Khademian (1992) for an excellent treatment of the political history of U.S. securities regulation.
15
conservative test of Hypothesis 2 since this agency has typically received strong bipartisan political
support (Khademian 1992), thus attenuating partisan differences that may exist among electoral
institutions over such policies. Any notable distinction in agency budgetary risk–bearing behavior
between eras of divided and unified government will be more difficult to discern than if an empirical
investigation of an agency whose policy tasks elicit strong partisan cleavages from political institutions
were undertaken. Another advantage of empirically analyzing the SEC is that it is an independent
regulatory commission. Thus, the budgetary requests that they submit to political overseers represent a
more accurate measure of this agency’s true budgetary preferences, compared to executive bureaus which
might internally adjust their funding requests within the larger executive department where it is housed
before it is eventually submitted to OMB. The final reason for studying the SEC pertains to data
availability. Agency budgetary request data emanating from the agency is rather difficult to obtain for a
lengthy period of time, and other data sources such as congressional hearings do not contain this
information on a consistent basis, nor is it included in the annual Budget of the U.S. Government.19 The
SEC has published their own budgetary requests since 1949 in their Annual Report of the (SEC)
Commission.
5. Data and Methods
To reiterate, the budgetary residual (BR) variable captures agency budgetary decision making by
measuring the first–difference between their current real request for the current year (FRt) and their actual
congressional real appropriation from the previous year (FAt!1). The actual dependent variable analyzed
in this study involves transforming BR into logged first–differences such that ln BRt = [ln(FRt) !ln(FAt!1)]
× 100 for the purposes of linearizing the relationship with uncertainty consistent with (4). Multiplying
19 The Budget of the U.S. Government contains data on the presidential budgetary request via OMB and actual
congressional appropriations, but does not include data on budgetary requests made of the agency’s own volition.
16
this expression by a constant of 100 has the added advantage of providing an annual percentage change
interpretation to this variable.
The external uncertainty measure that is determined outside the agency is operationalized as the
unconditional standard deviation of the first–difference in actual SEC budget appropriations based on a
moving average from the previous i years. Internal uncertainty is measured as the unconditional standard
deviation of the first-difference in actual SEC enforcement workload (defined as the sum of
administrative proceedings, investigations, and injunctive actions) based on a moving average from the
previous i years. Both uncertainty measures are also transformed in the same logarithmic fashion as the
budgetary residual dependent variable in order to facilitate meaningful comparisons when assessing the
budgetary residual– uncertainty relationship consistent with (4). The presumption that agencies respond
in a retrospective manner to uncertainty is consistent with commonly accepted models of budgetary
processes grounded in naive or sophisticated adaptive behavior that reflects limited cognitive and
information processing capabilities (Bendor and Moe 1985, 1986; Carpenter 1996; Davis, Dempster, and
Wildavsky 1966; March and Simon 1958; Padgett 1980; Simon 1976; Wood and Waterman 1993). An
agency formulates their budget request for the current year (t) by observing the volatility of actual real
budget appropriations (external uncertainty) and/or agency aggregate enforcement workload (internal
uncertainty) from the previous i years (t – 1, t – 2, ........., t – i). A three year moving average of the
annual growth rate for each time series is chosen on substantive grounds since it is rather common to
observe agencies mention the three year change in their budgets in their annual reports (Carpenter
1992).20 This three year window serves as the basis for agency’s adaptive budgetary responses to the
uncertainty that they experience. In the regression specifications testing for varying risk propensities
under unified and divided party government, the uncertainty variable(s) in question are interacted with a
20 Carpenter (1992: footnote 19) notes that presidents are cognizant of this three-year window for administrative
agencies is evident, for example, in Eisenhower’s statements when submitting his final budget to Congress (Budget
of the United States Government, FY 1962: M53–M54).
17
divided government dummy that equals 1 when agency budgetary decisions are made under divided
government and zero otherwise. This dummy is also incorporated in a separate manner in these
specifications, for the purposes of assessing deviations in agency risk–bearing behavior under these
different circumstances.
A vector of control variables are also included in these model specifications to ensure reliable
risk–coefficient estimates that accurately capture agency budgetary risk-bearing behavior. Task demands
placed on the agency include a three-year moving average of the annual percentage growth rate (in logfirst differences) of the SEC’s enforcement workload outputs that represent the positive impact of recent
past workload activity on current budget requests submitted by the agency21; and also the (natural log)
number of individuals employed in the securities and commodities industry at year t–1 captures the effect
industry size has on SEC budget requests attributable to the administrative demands placed on it from a
growing industry. The three-year moving average of the annual percentage growth rate (in log-first
differences) of the SEC’s real budgetary appropriations that represent the positive impact of recent past
budgetary resources on current budget requests submitted by the agency. This variable ensures that a
potential spurious relationship between BR and F is avoided in the case where uncertainty is amplified
due to a period of sustained robust annual budget growth experienced by the agency.22 In addition, the
SEC experienced a notable rise in administrative responsibility, and hence, rising budgetary pressures,
21 Given that budget requests made on behalf of the agency for fiscal year t actually occur in year t!1, these agency
performance measures reflect observable information at time t!1 from the time at when the actual decision was made
since it was not made available until the end of the relevant annual period. This same logic was also the basis for
using GNP growth at time t!2. For the industry size and political variables, it is reasonable to presume that this
effect is contemporaneously observed in the period for which the actual budget request is being submitted in year t!1
given that both the ideological behavior of elected officials and industry size is known at the time of the request.
22 Supplemental SEC agency appropriations are not considered in this study because they pertain to statutory
mandated pay increases for existing agency personnel that are of a nondiscretionary nature which are not subject to
agency choice. Furthermore, these ancillary resources do not affect the production process of the agency through
either increasing staff personnel or non–human (e.g., capital) inputs that are vital to organizational maintenance in
the face of uncertainty.
18
beginning in 1987 when the agency was required by Congress to create a separate office within the
agency for the Electronic Data and Gathering Retrieval (EDGAR) Management system instead of
remaining in the Executive Director’ Office (Khademian 1992: 190–196). This required the SEC to seek
additional resources in order not to shortchange funding on their other agency activities (Khademian
1992). This variable is lagged by one year, and is measured as a dummy variable that equals one in the
1988–1997 period, and zero otherwise. This variable should possess a positive coefficient reflecting the
rise in the SEC budgetary residual with the advent of a separate office and set of operations within the
agency for the EDGAR system.
The ideological preferences of elected officials are also important for understanding
administrative behavior (e.g., Scholz, Headrick, and Twombly 1991; Wood and Waterman 1993). This is
measured by five separate variables representing the real Americans for Democratic Action (ADA)
scores for the president, median House (sub)committee member score pertaining to both oversight and
appropriations separately, and median Senate (sub)committee member score also for both oversight and
appropriations in year t–1 when the request is being submitted by the agency.23 The hypothesized
relationship is unclear. A positive relationship between these variables and agency budget requests are
indicative of more positive (liberal) scores resulting in higher budget requests since these institutions
may be more receptive to greater agency funding requests. Conversely, the SEC may seek higher budget
requests as electoral institutions become more conservative as a way of offsetting the latter’s relative
23 The Senate Banking and House of Representatives Energy and Commerce committees are used for the 1948–
1954 period, and the Senate Securities subcommittee and House Oversight subcommittee of these committees are
employed for all subsequent years in the sample period. The appropriations (sub)committee data for the 1948–1954
period comes from the entire House and Senate Appropriations Committees, and for subsequent years comes from
the subcommittees (House: Independent Offices, 1955–1966; Independent Offices and/or HUD, 1967–1972; HUD,
Space, and Science, 1973–1974; Commerce, Justice, State, and Judiciary, 1975–1996; Senate: Independent Offices,
1955–1970; Housing & Urban Development, Space, and Science, 1971-1974; Commerce, Justice, State, and
Judiciary, 1975–1997). The real ADA scores for Congress come from Groseclose, Levitt, and Snyder (1999), and
the real presidential ADA scores for the 1948–1994 period are from Krause (2000: footnote 12). The presidential
ADA scores for 1995 and 1996 were generously provided to me by Dan Ponder (the 1996 measure is the inverse of
the ACU presidential score since the presidential ADA score for that year is unavailable).
19
preference for lower government spending (and support) for administrative agencies. Finally, the state of
the economy, measured as real economic (GNP) growth from year t–2 to t–1, accounts for the impact of
the macroeconomy on agency budgetary decision making. One expects that a burgeoning economy will
provide the agency an incentive to seek greater budgetary resources than otherwise regimes that agencies
encounter by allowing for varying structural relationships involving agency budgetary decisions and their
response to task demand, political, and environmental factors attributable to changing decision rules
(Wildavsky 1988).
6. Statistical Findings
Univariate Results
Figure 1 graphically plots the time series for the log ratio of BR to F – i.e.,
ln BR
ln σ
– with
respect to budgetary appropriations and enforcement workload volatility during the 1949– 1997 annual
period. Two features are most striking about these graphs. First, even allowing for the presence of a
large spike in the log ratio of BR to FEW for 1963, this measure still has a smaller median value than the
corresponding log ratio of BR to FApp appearing in Table 2 (MedianRB1 = 2.34, MedianRB2 = .79 –
Mann–Whitney U-test statistic = 4.16, p ..00).24 This is indicative of support for Hypothesis 1 that
proposes that the relationship between BR and appropriations (external) uncertainty is greater compared
to that of BR and enforcement workload (internal) uncertainty. Furthermore, visual inspection of these
graphs provide complementary indirect evidence that corroborates this point as the log ratio of BR to FApp
appears noticeably less smooth compared to the log ratio of BR to FEW.25
24 This variable operationalization is consistent with the treatment of these variables in (4) that serves as the basis
for the regression analysis performed in the next section. The difference in medians Mann–Whitney U test is
preferred over the less conservative difference in means t–test since these variables are heavily skewed based on the
Jarque–Bera normality test results performed in preliminary data analysis.
25 If one drops the overly influential observation in 1963, a Brown–Forsythe equality of variance test supports this
conjecture (Mean Absolute Median Difference for the log ratio of BR to FApp = 1.43, Mean Absolute Median
20
Table 2 shows further empirical support for Hypothesis 1 is obtained for a given political regime.
The log ratio of budgetary residual to appropriations uncertainty under divided (unified) government is
higher than this same measure for enforcement workload uncertainty under divided (unified) government
(MedianRB3 = 2.51, MedianRB5 = .91 – U-test statistic = 3.33, p ..001; MedianRB4 = 1.71, MedianRB6 = .45
– U-test statistic = 2.56, p ..011). Also, the log ratio of the SEC’s budgetary residual– appropriations
volatility is noticeably higher under divided government compared to unified government consistent with
Hypothesis 2 (MedianRB3 = 2.51, MedianRB4 = 1.71 – U–test statistic = 2.13, p ..034). This hypothesis is
also supported in terms of the log ratio of the SEC budgetary residual to their enforcement workload
volatility (MedianRB5 = .93, MedianRB6 = .45 – U–test statistic = 2.41, p ..016).
[Insert Table 2 About Here]
In addition, a nonparametric median–based statistical test among k ordered alternatives termed
the J-test (Jonckheere 1954)26 is performed on this data to determine whether or not agency budgetary
decision making is typically most risk–averse in response to appropriations uncertainty under divided
government, followed by appropriations uncertainty under unified government, then by enforcement
workload uncertainty under divided government, and with enforcement workload uncertainty under
Difference for the log ratio of BR to FEW = .90; Brown–Forsythe test statistic = 4.93 [p ..03]).
k −1
ni
26 The J–test statistic is: J = ∑ U ij = ∑ ∑ U ij , where the Mann–Whitney count equals: U ij = ∑ # ( X hi , j ) and
k
i< j
k
h =1
i =1 j = i + 1
# (Xhi, j) equals the number of times the data Xhi precedes data in sample j, where i < j. As the sample size becomes
large, the sampling distribution of J is approximately standard normal based on the following formula:
J* =
(J − µ )
J
σJ
, where the mean is given by
k
 2

N
2
N
3
n 2j (2n j + 3)
+
−
(
)
∑

j =1

σ J2 = 
72
k
 2

N
n 2j 
−

∑


j =1
µJ =
4
and the variance is
. These J–test statistics are more preferred than the more common
nonparametric Kruskal-Wallis difference in medians test since the former test has greater statistical power due to the
more specific nature of the hypothesis being tested based on a priori theory (Siegel and Castellan 1988: 218).
21
unified government exhibiting the least risk–averse behavior. The J and J* test results provide strong
inferential evidence for this hypothesized sequence (J–statistic = 971, p < .001; J*–statistic = – 5.157,
p < .001). While these tests support the two hypotheses set forth about heterogeneous agency response
to uncertainty through their budgetary choices, additional tests are in order that not only control for other
exogenous variables that may influence SEC budgetary decisions, but also directly generates risk–
coefficient estimates of agency budgetary behavior in response to uncertainty. An empirical assessment
of agency budgetary decision making under uncertainty within a multivariate context follows.
Multivariate Results
The regression results and corresponding Wald coefficient restriction tests appear in Tables 3 –
5.27 The first set of findings displayed in Table 3 considers an external source of uncertainty to the
agency in the form of appropriations uncertainty.28 In every instance, the risk coefficient capturing the
relationship between agency budgetary residual and appropriations uncertainty is positive – i.e.,
β
> 0,
α
thus implying that the SEC seeks additional funding when uncertainty rises, ceteris paribus. However,
unequivocal statistical evidence of budgetary risk-aversion is found in the Baseline models (Models 1
and 2) that do not demarcate between divided and unified government regimes, and also during times of
unified government (Models 3 and 4) – i.e., 0 <
βUnified
α Unified
≤ 1 . Specifically, the SEC’s budgetary risk–
aversion exhibits unit elasticity in an inferential sense and inelastic responsiveness to uncertainty, in
27 In those instances when at least one of the lags involved in computing the Ljung–Box Q–statistic is significant at
p # .10, then the covariance matrix of the standard errors are corrected with the Newey–West (1987) approach with
2
 T 9
R = 3, where R is the truncation lag parameter chosen based on their analytical suggestion that l = 4 ⋅ 
 .
 100 
28 One might claim that the uncertainty variables employed in the regression analysis are a byproduct of spurious
long–term trends in these moving average variables, as opposed to capturing true volatility that they agency is
experiencing. Augmented Dickey–Fuller unit root tests performed on the moving average of SEC real
appropriations and agency enforcement workload growth refute this claim – Appropriations measure: ADF(2) =
– 3.78 (p < .01); Workload measure: ADF(5) = – 4.22 (p < .01).
22
terms of their point estimates, during periods of unified party government. Stated in terms of the
agency’s utility function, the SEC’s dispreference for appropriations uncertainty ($) is less than their
preference for obtaining additional funding (") in these particular instances. Furthermore, these
parameter estimates are consistent with the hypothetical parameter values based on the theoretical
predictions laid out in Table 1. The SEC’s budgetary behavior during divided government inconclusively
falls somewhere between no effect (i.e., risk– neutrality) and unit elastic responsiveness to uncertainty
(i.e., risk–aversion) – i.e., 0 ≤
β Unified
α Unified
≤ 1 . While these tests do not provide support for Hypothesis 2
consistent with the univariate results, neither do they suggest that the SEC is more budgetary risk–averse
during periods of unified government, as displayed by the Wald coefficient restriction test results
appearing at the bottom of Table 3.
The other exogenous variables provide insight into what information is being employed by the
SEC in their budgetary decisions. The findings from the Baseline–Full model (Model 1) reveal that
agency budget requests are sensitive in an inelastic manner to variations in their enforcement workload
over the preceding three year cycle. For instance, each one percent rise in this task demand variable in
the full model produces a 0.23% rise in the SEC’s budgetary residual per annum during the sample
period. Economic growth has a positive impact on agency budgetary seeking with each one percent
increase in GNP growth resulting in a 0.70% rise in BR. In addition, the movement of EDGAR system
from the Executive Director’s Office to its own special unit caused on average a 12.92% per annual rise
in the agency budgetary residual since the late 1980's in Model 1. Moreover, the ideological preferences
of the president and the legislative branch’s oversight (sub)committees also shape SEC budgetary
decisions. In those models differentiating between unified and divided party government in agency risk
propensities (Models 3 & 4), each one point rise (i.e., liberal direction) in Senate oversight ideology
results in slightly less than a one percent increase in SEC budgetary residual sought by the agency (.073
× 11.863 = .87). This relationship, however, is negative concerning both House oversight and
23
presidential ideological preferences. For instance, as House oversight (sub) committee ideology becomes
more conservative by one ratings point in the Baseline–Full Model (Model 1), the SEC’s budgetary
residual falls by almost an average of two percent (– 0.16 × 11.863 = – 1.90) per annum.29 Likewise, as
presidential ideology becomes more conservative by one ratings unit, the SEC’s budgetary residual
increases by a little more than one percent (– 0.10 × 11.863 = – 1.19) per annum in this same model.
The nature of the elastic responsiveness of SEC budgetary decision making to political institutions’
ideology has two interpretations. It may mean that the SEC will seek additional funding because
conservative elected officials, such as Republican presidents, will support stricter enforcement of
securities laws and regulations relative to more liberal counterparts such as Democratic presidents (Moe
1982; but see Kohlmeier 1969, Khademian 1992 for contrasting perspectives). An alternative, yet more
plausible interpretation of these findings is that the political component of SEC’s budgetary decisions is
made in a countercyclical fashion with respect to their political environment. Thus the SEC engages in a
form of “ideological counterbalancing”, analogous to that which voters employ (Alesina and Rosenthal
1995; Fiorina 1996), having to deal with a more conservative House or president by seeking additional
slack resources than it would do so otherwise. The basis for such strategic agency behavior is suggestive
of the SEC’s desire to compensate for the ideological composition of political institutions that they do
not determine, yet must seek resources from in order to attempt to smooth its funding stream through
time. Finally, the ideological composition of the Senate oversight (sub)committee and both House and
Senate Appropriations (sub)committees each fail to influence SEC budgetary decision making in these
instances.30
29 The political ideology coefficients have a semilogarithmic interpretation. The average slope coefficient for these
variables are computed as β ⋅Y , where $ is unstandardized regression coefficient appearing in the table and Y is
the average value of the variable in question (Gujarati 1995: 178).
30 Besides the nonsignificant divided government dummy in each model specification, I also tested for political–
institutional ideological fragmentation effects on SEC budgetary decisions in preliminary analysis not reported here.
24
[Insert Tables 3 – 5 About Here]
Table 4 contains the regression results when internal uncertainty, in terms of the three year
moving standard deviation in SEC’s enforcement workload, is posited to affect this agency’s budgetary
decision making. Both the conventional and Wald coefficient restriction significance tests of the risk
coefficient lead one to infer that the SEC exhibits budgetary risk–neutrality with respect to task demand
uncertainty – i.e.,
β
= 0 . In other words, the SEC desire to seek budgetary resources is indifferent with
α
respect to changes involving uncertainty coming from their enforcement activities. This result provides
additional support for Hypothesis 1 by showing that the SEC’s response to appropriations volatility
(i.e., external uncertainty) is of a noticeably more risk–averse nature compared to task demand volatility
(i.e., internal uncertainty).31 As with the previous set of models dealing with appropriations uncertainty,
these results conclusively show that the risk coefficients fail to differ from one another not just based on
the coefficient equality tests, but also due to the results of the conventional hypothesis tests where the
risk coefficient equals zero and the maintained (null) hypothesis of the Wald test equal to positive unity.
The other exogenous variables produce results that are generally consistent with the previous set
of empirical results. SEC budget requests are influenced by the enforcement workload of the agency, the
ideological preferences of presidents and congressional oversight committees, the size of the industry
that they are responsible for regulating, economic growth, and policy innovation emanating from within
the agency. Once again, neither congressional appropriation committees’ ideology nor the moving
average growth in agency real appropriations affect SEC budgetary decisions. These results are
consistent with existing scholarship on the politics of SEC regulation (Khademian 1992; Kohlmeier
Variables consisting of median ideological difference between presidents and each congressional oversight
(sub)committee, and also for presidents and appropriations (sub)committee were shown to have no independent
effect on the dependent variable, nor alter the risk coefficients in any statistically meaningful way in all cases except
in a revised version of Regime – Model 3 (Full) where its p-value rises from .066 to .199. However, the overall fit
of this revised specification is slightly inferior to the original one reported in Table 3.
31 This finding is corroborated by the univariate results presented in the previous subsection.
25
1969; Moe 1982; Weingast 1984).
The final set of regression analyses that appear in Table 5 incorporate both appropriations and
enforcement workload uncertainty measures into the same model specification.32 The risk coefficients
for appropriations uncertainty are generally greater than the corresponding ones for enforcement
workload uncertainty, except in Model 11 where this difference is not statistically discernible in either
times of unified or divided party government. In these typical cases, the SEC displays budgetary
risk–aversion with respect to appropriations uncertainty, yet behaves in a manner consistent with
budgetary risk–neutrality when confronting enforcement workload uncertainty. This means that the
SEC’s disutility obtained from uncertainty ($) vis–a–vis their utility received from budgetary resources
that they seek (") is comparatively greater for appropriations volatility than it is for task demand
volatility. These findings corroborate the previous sets of empirical results that show the SEC is
indifferent to variations involving uncertainty under their administrative jurisdiction involving task
demands, while their distaste for external uncertainty is at most equal to, if not less than, their preference
for obtaining additional budgetary resources. Once again, the multivariate based empirical evidence fails
to uncover support for Hypothesis 2 that a differential response occurs between times of divided and
unified party government in each specification. Therefore, the SEC’s less stable policy expectations, and
hence, eroding institutional stability under divided government does not translate into relatively
budgetary greater risk–aversion in such periods compared to unified government. One additional
interesting result of note pertains to the inelastic risk–seeking behavior in SEC budgetary decisions under


β
divided government – i.e., − 1 <  Divided 
α

 Divided  EW
< 0
– that is compatible with the analytically derived
32 Preliminary analysis indicated that the estimates of appropriations and workload uncertainty suffer from
multicollinearity problems because of their joint inclusion in these model specifications. This is hardly surprising
given the modest bivariate correlation coefficient between these variables (r = .33).
26
comparative–static results.
7. Conclusion
The manner in which bureaucratic agencies make decisions in response to uncertainty, is
perhaps, the most fundamental act bureaucratic agencies undertake in their operations (Crozier 1964;
Downs 1967; March 1999; Niskanen 1971; Simon 1976; Stinchcombe 1990; Thompson 1967; Wilson
1989). Direct examination of this topic, however, has been overlooked in existing systematic treatments
of public bureaucracy. In the few studies that do explicitly consider bureaucratic responses to
uncertainty, agency risk–bearing behavior is fixed a priori as a simplifying assumption (Bendor and
Moe 1985; Bendor, Taylor, and van Gaalen 1985, 1987), or it is derived in a manner where the agency’s
range of responses to this uncertainty is restricted only to the risk–neutral case (Carpenter 2000). As a
result, little is actually known on both theoretical and empirical levels about the how bureaucratic
agencies make decisions under conditions of uncertainty.
It is essential that the nature of their risk–bearing behavior be directly analyzed if we wish to
understand how public bureaucracies respond to uncertainty. This study’s treatment of budget requests
serving as a means to attain slack resources for the purposes of organizational maintenance, is consistent
with seminal research on administrative organizations (Cyert and March 1963; Downs 1967; Thompson
1967; Wilson 1989). These resources create bureaucratic flexibility by allowing them to hedge against
the uncertainty that they experience. The willingness of a bureaucratic agency to extract additional (or
fewer) slack budgetary resources in response to uncertainty cannot be determined unless their risk–
bearing behavior can be theoretically articulated and subsequent empirical tests are linked accordingly.
In this study, I proposed a general empirical test of agency choice involving budgetary
risk–bearing behavior. This test is general in the sense that it not only allows for risk–averse,
risk–neutral, or risk–seeking budgetary decision making by administrative agencies, but also can be
27
applied to other types of bureaucratic decisions with appropriate modification. This statistical test is
grounded in conditions derived from a formal–deductive model of agency budgetary decision making
under conditions of uncertainty developed elsewhere (Krause 2001). The agency’s utility is a function of
their desire to obtain budgetary resources and also the uncertainty that they experience, while allowing
for a qualitative distinction to be made by the agency between divided and unified party government
regimes. In doing so, this allows for heterogeneous agency budgetary decision making by treating their
risk preferences as varying in relation to context in which they confront uncertainty (March 1999:
244–245).
The empirical analysis of SEC budgetary decision making yields statistical estimates of risk
coefficients that are consistent with the theoretical conditions of agency budgetary risk-bearing behavior
developed in Krause (2001). Evidence of heterogeneity in SEC budgetary decision making in response
to changes in uncertainty is obtained on several dimensions. For instance, the SEC responds in a risk–
averse manner to appropriations uncertainty, while it behaves in a manner consistent with risk–neutrality
with regards to movements in enforcement workload uncertainty. This finding is consistent with
Hypothesis 1 laid out in this study, which states that administrative agencies will be relatively more
willing to seek slack budgetary resources when the uncertainty that they experience is external to the
bureaucratic organization. Mixed evidence of SEC budgetary decision making structurally altering their
response to changes in uncertainty based on the divided and unified party government regime distinction
do appear. While the univariate inferential results show clear direct evidence of stronger budgetary risk–
aversion in periods of divided government compared to that of unified government for a given type of
uncertainty, the results of the multivariate analysis revealed no such differences.
This study has important implications for understanding bureaucratic decision making.
Administrative agencies will place a relatively greater premium on organizational maintenance by trying
to obtain slack budgetary resources in the face of external uncertainty vis-a-vis internal uncertainty, all
28
else being equal. Although administrative agencies might wish to balance their organizational mission
with a desire for maintaining a positive reputation with their political principals (Kaufman 1981; Wilson
1989), the former goal takes precedence with respect to external uncertainty. Moreover, the fact that the
SEC’s risk coefficient point estimates are consistently less than positive unity but greater than zero with
respect to appropriations uncertainty indicates that such inelastic budgetary risk–aversion is due less to a
distaste for uncertainty than it is for a preference for obtaining additional funding in the presence of
rising uncertainty. Under such circumstances, administrative agencies will employ a budget strategy that
serves as a rational response for obtaining bureaucratic flexibility in times where they are most
constrained by political institutions in some manner. This is achieved by seeking organizational slack as
a cushion to absorb uncertainty (Downs 1967: 138–139). While uncovering this heterogeneity in agency
budgetary decision making is noteworthy, the larger contribution of this study has been to provide an
empirical test of agency risk propensities based on a formal theory of bureaucratic decision making that
is contingent on the relative change in the rate of utility accrued from uncertainty vis–a–vis the budgetary
resources being sought. This type of analysis can be applied to research that analyzes how bureaucratic
agencies arrive at various types of decisions, such as their preferences for administrative discretion
(Krause N.d.) or policy actions, when utilizing varying decision rules within an uncertain policymaking
environment.
29
TABLE 1
Comparative–Statics Corresponding to Agency Budgetary Risk– Coefficients
Risk Bearing Condition
&
Political Regime
Theoretical
Relationship
(Direction)
Theoretical
Relationship
(Shape)
Theoretical
Prediction
(Parameter Value)
Risk-Averse Agency Budgetary Behavior:
(General)
BR′ (σ ) > 0
BR′′ (σ ) = 0
β
>0
α
Risk-Averse Agency Budgetary Behavior:
(Divided Government)
BR′ (σ ) > 0
BR′′ (σ ) > 0
β
>1
α
BR′ (σ ) > 0
BR′′ (σ ) = 0
β
=1
α
>
<
BR′ (σ ) > 0
BR′′ (σ ) < 0
0<
β
<1
α
Risk-Averse Agency Budgetary Behavior:
(Unified Government)
BR′ (σ ) > 0
BR′′ (σ ) < 0
0<
β
<1
α
Risk-Neutral Agency Budgetary Behavior:
(General, Divided, and Unified Government)
BR′ (σ ) = 0
BR′′ (σ ) = 0
Risk-Seeking Agency Budgetary Behavior:
(General)
BR′ (σ ) < 0
BR′′ (σ ) = 0
Risk-Seeking Agency Budgetary Behavior:
(Divided Government)
BR′ (σ ) < 0
BR′′ (σ ) > 0
− 1<
β
<0
α
Risk-Seeking Agency Budgetary Behavior:
(Unified Government)
BR′ (σ ) < 0
BR′′ (σ ) > 0
−1<
β
<0
α
BR′ (σ ) < 0
BR′′ (σ ) = 0
β
= −1
α
BR′ (σ ) < 0
BR′′ (σ ) < 0
β
< −1
α
β
=0
α
β
<0
α
>
<
Note: If political regimes have a meaningful impact on agency utility through uncertainty, three outcomes exist that are inconsistent
with the theoretical results:
− 1<
β
β
≤ −1 (risk-seeking: divided government), ≥ 1 (risk-averse: unified government), and
α
α
β
< 0 (risk-seeking: unified government).
α
Only the last outcome (which appears in Table 1) is not analytically ruled out for
reasons noted on page 10. Risk-averse behavior under unified government is not distinguishable from the baseline empirical model
β
= 1 . Risk-seeking behavior under divided government is not
α
β
distinguishable from the baseline empirical model when
= −1 . See Krause (2001) for the analytical derivations.
α
that does not differentiate between political regimes if
FIGURE 1
Univariate Time Series Plots: Log Ratio of SEC Budgetary Residual -- Uncertainty, 1949-1997
(Appropriations and Enforcement Workload Uncertainty)
20
15
10
5
0
-5
50 55 60 65 70 75 80 85 90 95
BR/Appropriations Uncertainty
20
15
10
5
0
-5
50 55 60 65 70 75 80 85 90 95
BR/Workload Uncertainty
TABLE 2
Simple Inferential Tests of the Heterogeneous Nature of SEC Budgetary Risk-Bearing Behavior (1949-1997)
Nonparametric Difference in Medians Tests Based on Log Ratio of Budgetary Residual–Uncertainty Measures
Variable
Median Value
Hypothesis Test
U-test Statistic
Probability Value
2.34
___________
__________
_________
.79
RB1 úRB2
4.16***
.000
 ln BR 
RB3 : Median 

 ln σ App  Divided
2.51
RB3 úRB4
2.13**
.034
 ln BR 
RB4 : Median 

 ln σ App  Unified
1.71
RB3 úRB5
3.33***
.001
 ln BR 
RB5 : Median 

 ln σ EW  Divided
.93
RB4 úRB6
2.56**
.011
 ln BR 
RB6 : Median 

 ln σ EW  Unified
.45
RB5 úRB6
2.41**
.016
 ln BR 
RB1 : Median 

 ln σ App  Overall
 ln BR 
RB2 : Median 

 ln σ EW  Overall
 ln BR 
 ln BR 
 ln BR 
 ln BR 
H A : Median 
< Median 
< Median 
< Median 




 ln σ App  Divided
 ln σ EW  Divided
 ln σ EW  Unified
 ln σ App  Unified
J Test Statistic: 971*** (p < .001)
J* Test Statistic: – 5.157*** (p < .001)
Note: All numerical figures are rounded to the nearest hundredth decimal place except probability values which are
rounded off to the nearest thousandth decimal place.
*
p < .10
**
p < .05
***
p < .01.
TABLE 3
Baseline and Regime–Based Statistical Models of SEC Budgetary Decision Making Behavior, 1949 -1997
(Appropriations Volatility Empirical Model Specifications)
Model 1
Baseline
(Full)
Model 2
Baseline
(Reduced)
Model 3
Regime
(Full)
Model 4
Regime
(Reduced)
Constant
78.62*
(44.18)
[.083]
70.33***
(24.54)
[.007]
83.56*
(49.23)
[.099]
65.83***
(22.71)
[.006]
Appropriations Uncertaintyt
.60*
(.35)
[.090]
.63*
(.33)
[.060]
.86*
(.45)
[.066]
.88*
(.44)
[.051]
___________
___________
–.62
(.69)
[.375]
–.45
(.56)
[.428]
Moving Average of % )
Real Appropriationst
.21
(.18)
[.244]
.25
(.16)
[.136]
.18
(.18)
[.319]
.20
(.16)
[.220]
Moving Average of % )
Enforcement Workload t
.23**
(.11)
[.040]
.22**
(.10)
[.027]
.28**
(.13)
[.036]
.26**
(.12)
[.042]
% ) GNPt!2
.70**
(.29)
[.022]
.70**
(.29)
[.021]
.62**
(.30)
[.047]
.59**
(.29)
[.050]
Log(Industry Size)t!1
–5.75
(3.99)
[.159]
–4.97**
(2.08)
[.022]
–6.05
(4.60)
[.197]
– 4.48**
(1.93)
[.026]
EDGARt!1
12.92***
(3.40)
[.001]
12.70***
(3.32)
[.001]
13.74***
(3.75)
[.001]
13.09***
(3.49)
[.001]
Median House
Oversight (Sub)Committee Ideologyt–1
–.16
(.093)
[.105]
–.13**
(.05)
[.016]
–.18*
(.10)
[.067]
–.15**
(.06)
[.018]
Median Senate
Oversight (Sub)Committee Ideologyt!1
.05
(.04)
[.222]
.05
(.04)
[.196]
.06
(.04)
[.104]
.06*
(.04)
[.089]
Presidential Ideologyt!1
–.10***
(.03)
[.003]
–.10***
(.03)
[.002]
–.12**
(.05)
[.017]
– .12***
(.04)
[.003]
Divided Government t–1
_________
__________
.78
(5.45)
[.887]
.12
(4.54)
[.979]
Median House
Appropriations (Sub)Committee Ideologyt!1
.02
(.10)
[.805]
_________
.01
(.11)
[.967]
__________
Independent
Variable
Appropriations Uncertaintyt
×
Divided Government t–1
Median Senate
Appropriations (Sub)Committee Ideologyt!1
.03
(.14)
[.821]
_________
.07
(.15)
[.658]
__________
1.31
[.252]
1.27
[.260]
________
_________
_________
__________
.09
[.762]
.08
[.782]
β
Ho : Divided = 0
α Divided
_________
_________
.16
[.688]
.90
[.342]
β Divided
=1
α Divided
__________
_________
1.55
[.214]
1.60
[.206]
Adjusted R2
.42
.45
.41
.43
Standard Error of Estimate
6.14
5.99
6.20
6.06
Ljung–Box Q-Statistic:
P2 ~ (7)
11.75
[.109]
11.08
[.135]
8.82
[.266]
8.86
[.263]
Jarque–Bera Test for Normality
1.04
[.596]
1.06
[.588]
1.20
[.55]
1.26
[.532]
Ho :
Ho :
Ho :
β
=1
α
βUnified
α Unified
=1
White Heteroskedasticity Test
17.36
12.00
22.74
19.83
[.689]
[.800]
[.535]
[.469]
Notes: All coefficients and corresponding standard errors are rounded out to nearest hundredth, while all probability values are
rounded out to the thousandth decimal place. The numbers in parentheses are Newey–West standard errors. The numbers
inside brackets are probability values.
*
p < .10
**
p < .05
***
p < .01 (Two–tailed tests).
TABLE 4
Baseline and Regime–Based Statistical Models of SEC Budgetary Decision Making Behavior, 1949 -1997
(Enforcement Workload Volatility Empirical Model Specifications)
Model 5
Baseline
(Full)
Model 6
Baseline
(Reduced)
Model 7
Regime
(Full)
Constant
108.64**
(52.80)
[.047]
87.54**
(43.06)
[.049]
120.26**
(44.34)
[.010]
88.85**
{39.09}
[.029]
Enforcement Workload Uncertaintyt
–.01
(.25)
[.964]
.02
(.23)
[.928]
.13
(.25)
[.606]
.16
{.19}
[.415]
___________
___________
–.43
(.29)
[.143]
–.35
{.30}
[.251]
Moving Average of % )
Real Appropriationst
.26
(.21)
[.232]
.28
(.19)
[.145]
.36
(.26)
[.180]
.34
{.23}
[.160]
Moving Average of % )
Enforcement Workload t
.24*
(.12)
[.056]
.23*
(.12)
[.051]
.25*
(.14)
[.086]
.23*
{.14}
[.096]
% ) GNPt!2
.75**
(.32)
[.024]
.69**
(.33)
[.045]
.56*
(.33)
[.095]
.49
{.41}
[.235]
Log(Industry Size)t!1
–7.98*
(4.49)
[.084]
–6.12
(3.44)
[.083]
–9.05**
(3.73)
[.021]
–6.34*
{3.46}
[.075]
EDGARt!1
15.41***
(3.43)
[.001]
15.23***
(3.40)
[.000]
14.90***
(3.26)
[.000]
14.80***
{3.69}
[.000]
Median House
Oversight (Sub)Committee Ideologyt–1
–.19**
(.090)
[.047]
–.16***
(.05)
[.006]
–.20**
(.09)
[.031]
–.16**
{.07}
[.020]
Median Senate
Oversight (Sub)Committee Ideologyt!1
.05
(.04)
[.245]
.05
(.04)
[.185]
.07*
(.04)
[.090]
.07
{.06}
[.198]
Presidential Ideologyt!1
–.10***
(.03)
[.003]
–.10***
(.03)
[.001]
–.11**
(.05)
[.022]
– .11**
{.04}
[.013]
Divided Government t–1
_________
__________
4.26
(6.24)
[.500]
3.72
{6.46}
[.568]
Median House
Appropriations (Sub)Committee Ideologyt!1
.01
(.10)
[.933]
_________
–.01
(.11)
[.909]
__________
Independent
Variable
Enforcement Workload Uncertaintyt
×
Divided Government t–1
Model 8
Regime
(Reduced)
Median Senate
Appropriations (Sub)Committee Ideologyt!1
.08
(.13)
[.559]
_________
.11
(.11)
[.340]
__________
16.06***
[.000]
17.64***
[.000]
________
_________
_________
__________
11.82***
[.001]
19.45***
[.000]
β
Ho : Divided = 0
α Divided
_________
_________
1.52
[.218]
.56
[.456]
β
H o : Divided = − 1
α Divided
__________
_________
8.70***
[.003]
9.93***
[.002]
Adjusted R2
.39
.41
.40
.41
Standard Error of Estimate
6.31
6.20
6.25
6.20
Ljung–Box Q-Statistic:
P2 ~ (7)
10.30
[.172]
9.41
[.225]
9.15
[.242]
7.59
[.371]
Jarque–Bera Test for Normality
1.54
[.464]
1.77
[.412]
1.20
[.548]
1.23
[.540]
Ho :
β

β
= 1  = −1

α
α
Ho :
βUnified
α Unified
=1
White Heteroskedasticity Test
18.72
12.60
15.78
12.02
[.603]
[.763]
[.896]
[.915]
Notes: All coefficients and corresponding standard errors are rounded out to nearest hundredth, while all probability values are
rounded out to the thousandth decimal place. The numbers in parentheses and curly brackets are Newey–West and OLS
standard errors, respectively. The numbers inside brackets are probability values.
*
p < .10
**
p < .05
***
p < .01
(Two–tailed tests).
TABLE 5
Baseline and Regime–Based Statistical Models of SEC Budgetary Decision Making Behavior, 1949 -1997
(Appropriations and Enforcement Workload Volatility Empirical Model Specifications)
Model 9
Baseline
(Full)
Model 10
Baseline
(Reduced)
Model 11
Regime
(Full)
Model 12
Regime
(Reduced)
Constant
88.06
(58.12)
[.139]
77.90*
(42.76)
[.076]
110.32*
(55.09)
[.054]
81.18**
(36.78)
[.034]
Appropriations Uncertaintyt
.64**
(.28)
[.030]
.68***
(.25)
[.009]
.86*
(.44)
[.061]
.88**
(.43)
[.050]
___________
___________
–.51
(.72)
[.486]
–.28
(.55)
[.610]
–.07
(.23)
[.767]
–.06
(.21)
[.766]
.05
(.25)
[.850]
.06
(.23)
[.781]
___________
___________
–.40
(.27)
[.147]
–.36
(.27)
[.192]
Moving Average of % )
Real Appropriationst
.21
(.18)
[.243]
.24
(.16)
[.135]
.30
(.24)
[.219]
.30
(.19)
[.129]
Moving Average of % )
Enforcement Workload t
.22*
(.12)
[.074]
.21*
(.11)
[.061]
.22
(.13)
[.105]
.21
(.13)
[.123]
% ) GNPt!2
.74**
(.28)
[.012]
.74**
(.28)
[.013]
.57*
(.33)
[.099]
.53*
(.30)
[.092]
Log(Industry Size)t!1
–6.47
(4.84)
[.190]
–5.56
(3.36)
[.106]
–8.66*
(4.71)
[.075]
–6.08**
(2.97)
[.048]
EDGARt!1
13.01***
(3.49)
[.001]
12.75***
(3.36)
[.001]
13.37***
(3.79)
[.001]
12.50***
(3.44)
[.001]
Median House
Oversight (Sub)Committee Ideologyt–1
–.15
(.09)
[.111]
–.13**
(.05)
[.015]
–.17*
(.10)
[.099]
–.13**
(.06)
[.037]
Median Senate
Oversight (Sub)Committee Ideologyt!1
.05
(.04)
[.231]
.05
(.04)
[.197]
.08*
(.04)
[.060]
.08*
(.04)
[.058]
Presidential Ideologyt!1
–.10***
(.03)
[.003]
–.10***
(.03)
[.001]
–.10*
(.05)
[.051]
– .11**
(.04)
[.012]
Divided Government t–1
_________
__________
7.30
(7.76)
[.354]
5.69
(7.05)
[.425]
Independent
Variable
Appropriations Uncertaintyt
×
Divided Government t–1
Enforcement Workload Uncertaintyt
Enforcement Workload Uncertaintyt
×
Divided Government t–1
Median House
Appropriations (Sub)Committee Ideologyt!1
.02
(.10)
[.854]
_________
.00
(.11)
[.983]
__________
Median Senate
Appropriations (Sub)Committee Ideologyt!1
.04
(.14)
[.790]
_________
.09
(.14)
[.508]
__________
β
Ho :  
=1
 α  APP
1.58
[.214]
1.69
[.194]
________
_________
β
= −1
Ho :  
 α  EW
16.27***
[.000]
19.41***
[.000]
5.94**
[.015]
7.59***
[.006]
β
β
=  
Ho :  
 α  EW
 α  APP
 βUnified
Ho : 
α
 Unified


= 1

 APP
_________
__________
.11
[.746]
.11
[.746]
β
Ho :  Divided
 α Divided

=0

 APP
_________
_________
.29
[.593]
1.69
[.194]
β
Ho :  Divided
 α Divided

= 1

 APP
_________
_________
.99
[.319]
.88
[.350]
_________
_________
14.44***
[.000]
16.74***
[.000]
 βUnified
Ho : 
α
 Unified


= 1

 EW
β
Ho :  Divided
 α Divided

= −1

 EW
_________
_________
9.53***
[.002]
12.96***
[.000]
β
Ho :  Divided
 α Divided


 EW
_________
_________
2.71*
[.100]
2.23
[.136]
_________
_________
2.41
[.121]
2.64
[.105]
__________
_________
1.26
[.262]
3.62*
[.057]
Adjusted R2
.41
.44
.41
.43
Standard Error of Estimate
6.21
6.06
6.21
6.11
Ljung–Box Q-Statistic:
P2 ~ (7)
13.49*
[.061]
12.58
[.083]
12.37*
[.089]
11.28
[.127]
Jarque–Bera Test for Normality
1.21
[.545]
1.25
[.536]
1.01
[.604]
1.18
[.555]
=0
 βUnified
Ho : 
α
 Unified


=

 APP
 βUnified

α
 Unified
β
Ho :  Divided
 α Divided

=

 APP
 β Divided

 α Divided
White Heteroskedasticity Test



 EW


 EW
19.29
14.16
22.75
19.93
[.684]
[.774]
[.746]
[.70]
Notes: : All coefficients and corresponding standard errors are rounded out to nearest hundredth, while all probability values are
rounded out to the thousandth decimal place. The numbers in parentheses are Newey–West standard errors. The numbers
inside brackets are probability values.
*
**
***
p < .10
p < .05
p < .01 (Two–tailed tests).
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