Interbranch Bargaining and Discretionary Appropriations
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Interbranch Bargaining and
Discretionary Appropriations∗
Ben Hammond†
September 3, 2021
Abstract
The federal power of the purse is constitutionally vested in the legislative branch, yet
Congress delegates agenda-setting power over the budget to the executive. I develop
a new theory of public budgeting to explain Congress’s continuing rationale for doing
so. In the model, the President has more information than Congress about the im-
plications of budgetary decisions. Congress may base its appropriation on either the
presidential budget request or the previous year’s appropriation, but movements away
from either policy entail greater uncertainty about the ultimate policy outcome. The
model identifies the political determinants of presidential budget requests and congres-
sional appropriations, including the conditions under which legislators revise requests
or, in some cases, strategically accommodate them. I test the theory on a granular and
comprehensive panel of discretionary appropriations. The analysis shows that, contrary
to the findings of the extant literature, legislators regularly accommodate presidential
proposals and that these accommodations are more likely as preference divergence be-
tween the branches decreases. Moreover, when legislators choose to revise presidential
proposals, their revisions are increasing in both preference divergence and legislative
capacity. The study has important implications about the nature of budgetary politics
and the role of executive influence over legislative policy making.
1 Introduction
In the years following the American invasion of Iraq and Afghanistan after 9/11, the De-
partment of Veterans Affairs (VA) experienced a significant rise in demand for mental health
∗
I thank Brandice Canes-Wrone, Julian Dean, Dan Gibbs, Nathan Gibson, Gleason Judd, Patricia Kirk-
land, Michael Kistner, Frances Lee, Nolan McCarty, Julia Payson, Michael Pomirchy, and Leah Rosenstiel for
their invaluable feedback. I am also grateful to the Center for the Study of Democratic Politics at Princeton
University for their financial support of this project. All errors are my own.
†
Ph.D. Candidate, Department of Politics, Princeton University, blh2@princeton.edu
1services among returning veterans. This increased demand stemmed from conflict-related
post-traumatic stress, which VA health administrators somewhat anticipated, and from trau-
matic brain injuries resulting from repeated exposure to improvised explosive devices, which
the administrators did not. The VA responded by developing a long-term mental health
treatment plan, which called for a substantial expansion of VA mental health treatment
centers across the country and for all first-time patients requesting mental health services to
receive an initial evaluation within 24 hours and a comprehensive evaluation within 14 days.
President George W. Bush then incorporated these recommendations into his annual budget
request to Congress. Upon receiving the President’s proposal, Congress then faced a choice
about whether to fund the plan, modify it in various ways, or develop a new proposal from
scratch. However, because of legislators’ limited experience with healthcare administration,
revising the plan could lead to unintended consequences for veterans’ health outcomes. In
turn, anticipating Congress’s reaction may have influenced the President’s proposal. Too
high a request might cause legislators to modify the plan further or scrap it altogether, while
too low a request may not produce the desired outcome.1
Such observations are not new. The process of interbranch bargaining over policy out-
comes is a regular feature of America’s separation-of-powers system of government, and
nowhere is it more evident than in the annual appropriations process. Each year Congress
and the President actively bargain over thousands of programs, projects, and activities that
comprise the $1.4 trillion U.S. discretionary budget. The outcomes of this process are sub-
stantively important. Funding decisions for federal programs set national priorities in the
coming year, and taken as a whole, they represent the role of governmental activity in Amer-
ican society, a point of serious contention among policy makers and voters alike.
Who was to make these funding decisions was a question of central importance to the
framers of the Constitution, and during debate over ratification, they highlighted the vesting
1. Congress agreed to fund President Bush’s plan, which President Obama expanded upon, and the VA’s
annual budget for mental health services increased from $2.2 billion in fiscal year 2003 to $6.5 billion in fiscal
year 2013, nearly a threefold increase over 10 years. This represents one of the largest expansions of any
government service during this time period.
2of appropriations power in the legislative branch. Addressing Anti-Federalists’ fears of a
tyrannical president, James Madison reminded Patrick Henry that “[t]he purse is in the
hands of the representatives of the people. They have the appropriation of all moneys” (qtd.
in Chafetz 2017, 57). Likewise, Alexander Hamilton told the New York ratifying convention
that “where the purse is lodged in one branch, and the sword in another, there can be no
danger” (qtd. in Chafetz 2017, 57).
The framers of the Constitution might be surprised to discover the role that presidents
play in the appropriations process today. The modern budget process has its roots in the
Budget and Accounting Act of 1921, which established the Bureau of the Budget to centralize
budgetary functions within the executive branch and granted presidents the authority to
make nonbinding budget requests to Congress. Political scientists widely regard this act
as a seminal moment in the institutionalized Presidency (e.g., Chafetz 2017; Fenno 1966;
Kiewiet and McCubbins 1991; Pfiffner 1979; Whittington and Carpenter 2003), and scholars
have long observed that presidential budget requests play an influential role in congressional
deliberations (e.g., Fenno 1966; Wildavsky 1964). But if legislators are constitutionally
vested with the power of the purse, which James Madison describes in Federalist 58 “as the
most complete and effectual weapon with which any constitution can arm the immediate
representatives of the people,” then why would they delegate agenda-setting power over the
purse to the President? Moreover, how does this delegation of authority shape the policy
decisions generated by the annual appropriations process?
To examine these questions, I develop a new model of public budgeting. In the model,
legislators are unable to establish sufficient autonomous sources of expertise over the federal
budget. Instead, knowledge of the implications of budgetary decisions is concentrated in the
executive. Unless legislators are willing to commit significant resources to build their own
expertise, they only learn about the potential implications of their appropriations decisions
from either the presidential budget request or the previous year’s appropriation. Move-
ments away from either policy entail greater uncertainty about the ultimate policy outcome.
3Furthermore, regardless of how closely Congress adheres to either policy, this learning is im-
perfect, and the information obtained declines in the complexity of the policy environment.
As a result, the players face a trade-off between improving the utility from the budgetary
outcome and reducing the cost of uncertainty associated with turning appropriations into
the downstream social benefits they ultimately provide.
The model identifies the political determinants of presidential budget requests and con-
gressional appropriations, including the conditions under which legislators revise requests or,
in some cases, strategically accommodate them. The model predicts that Congress’s decision
to accommodate the request is decreasing in preference divergence between the branches and
increasing in the cost of uncertainty. Moreover, when legislators choose to revise the request,
their revisions are increasing in preference divergence and decreasing in the cost of uncer-
tainty. Consistent with the comparative statics of previous bargaining models (e.g., Romer
and Rosenthal 1978), policy change is increasing in the distance between a representative
legislator’s ideal point and the status quo.
I test predictions from the model on a novel data set of account-level appropriations
from FY1960 to FY2021. Crucially, this data set reflects the individual policies over which
Congress and the President bargain in the annual appropriations process, and it constitutes
the most comprehensive panel of the U.S. discretionary budget yet assembled. Using these
data, I find that, contrary to the findings of the extant literature, legislators regularly accom-
modate presidential proposals and that these accommodations are more likely as preference
divergence between the branches decreases. Moreover, when legislators choose to revise the
request, their revisions are increasing in both preference divergence and legislative capacity.
Using an instrumental variables design, I also recover a plausibly causal estimate of the effect
of presidential budget requests on congressional appropriations. This design also allows me
to recover estimates of institutional preferences on enacted appropriations.
The findings have important implications about the nature of budgetary politics and the
role of executive influence over legislative policy making. For scholars and pundits alike, it
4may be tempting to view presidents as the dominant player in the annual appropriations
process. After all, they are the leaders of their political party, and their annual budget re-
quests tend to set the agenda for the congressional budget process. However, an important
implication of the theory is that Congress still maintains substantial advantages over the
President in the annual appropriations process. Although legislators may opt to delegate
some of this authority to the executive, they do so in order to adopt higher quality policies for
which they better understand the ultimate policy outcomes associated with their decisions.
To be sure, presidents still wield considerable influence over congressional policy making.
But another important implication of the theory is that presidential power in the annual
appropriations process is neither consistent with the idea of presidents as clerks (Neustadt
1960) nor presidents as unilateral actors (Howell 2003). Instead, presidential power stems
from their informational advantage over legislators (Gailmard 2013), and this power is con-
strained in important ways, most notably by Congress’s ability to rely on the previous year’s
appropriation to inform its policy choice. As a result, since both Congress and the Presi-
dent share the cost of uncertainty associated with Congress’s appropriations decisions, the
branches tend to be incentivized to cooperate with one another in the policy-making process.
2 Theories of Budgetary Politics
Over the last 60 years, an extensive literature has sought to explain the policy outcomes
generated by the annual appropriations process. Work in this area tends to examine the
conditions under which budgetary changes are incremental or not.2 The two most widely
cited theories of public budgeting are incrementalism (Wildavsky 1964) and punctuated equi-
librium theory (Jones, Baumgartner, and True 1998). The key assumption of both theories is
2. There is also a rich literature on distributive politics. Work in this area tends to examine whether
certain political actors direct a disproportionate share of benefits to their constituencies, including the effect
of committee position (Berry and Fowler 2016; Hammond and Rosenstiel 2020), gender (Anzia and Berry
2011), ideology (Alexander, Berry, and Howell 2015), majority party status (Albouy 2013; Carroll and
Kim 2010), and presidential copartisanship (Berry, Burden, and Howell 2010) on federal funding or federal
spending. However, Congress provides most funding by program, not by location. Hence a separate literature
investigates the programmatic outcomes generated by the annual appropriations process.
5bounded rationality; that is, policy makers are not capable of comprehensively collecting and
processing all the information necessary to make rational calculations about funding levels.
Building on this assumption, incrementalist theory predicts that policy makers will adopt
small changes at the margins of the previous year’s appropriation to see if anything good or
bad happens (Wildavsky 1964, 17). Punctuated equilibrium theory predicts that, although
incremental outcomes may predominate, non-incremental changes are possible under certain
conditions.
While providing many useful insights, both theories of budgetary politics have significant
limitations. First, they leave mostly unaddressed how the legislative and executive branches
interact over the budget and the policy-making trade-offs these interactions induce. As
Howell, Jackman, and Rogowski (2013) note, the tools available to one set of political actors
may not be available to others, and when this occurs, political disputes extend beyond which
individual policies are adopted to include how political actors bargain among one another
in the policy making process. Second, the causal mechanisms underlying these theories
are not precisely defined, and empirical researchers have long raised concerns about testing
key predictions derived from each theory. For example, many scholars have noted that an
incremental policy outcome can occur in the absence of an incremental process, and an
incremental process can result in non-incremental outcomes (Anderson and Harbridge 2010;
Bailey and O’Connor 1975; Wanat 1974). Likewise, Anderson and Harbridge (2010) point
out that predictions stemming from punctuated equilibrium theory are difficult to disprove,
since only frequent non-incremental changes or the absence of non-incremental changes are
inconsistent with the theory.
To address some of these gaps, I develop an informational model of interbranch bargaining
over annual appropriations. To be sure, this study is hardly the first to do so. A rich theoret-
ical and empirical literature on the presidency examines how presidents shift congressional
appropriations in their favor, including through the use of public appeals (Canes-Wrone
2006), in areas of foreign policy (Canes-Wrone, Howell, and Lewis 2008), and during times
6of war (Howell, Jackman, and Rogowski 2013). While these important studies examine the
tools available to presidents, I focus on the tools available to legislators, namely the institu-
tional design of the annual appropriations process. As far as I am aware, it is the first study
since Kiewiet and McCubbins’s (1991) seminal book on delegation to do so. While their work
emphasizes the role of political parties to explain congressional delegation to the President,
this article focuses on the cost of uncertainty associated with congressional appropriations
decisions.
My theory of public budgeting is closely related to work by Wildavsky (1964) and incorpo-
rates many of the themes he highlights, namely the complexity of budgeting, the importance
of the previous year’s appropriation, and legislators’ reliance on presidential budget requests.
However, there are also key differences between my approach and Wildavsky’s. The first re-
lates to strategic behavior. As Kiewiet and McCubbins (1988) point out, empirical tests
of incrementalism (e.g., Davis, Dempster, and Wildavsky 1966) tend to assume that presi-
dents do not act strategically when proposing requests. The model here endogenizes both
presidential agenda setting and congressional policy making to account for the possibility of
strategic behavior by both players. A second key difference relates to the role of information.
Wildavsky’s work assumes that budgeting is too complex to make rational calculations. In
this view, policymakers tinker at the margins of an agency’s base budget, and once an ac-
tivity is incorporated, it is never re-examined. I assume there is nothing sacrosanct about
the base budget, only that legislators do not possess sufficient expertise to initiate a spe-
cific outcome on their own. Hence Congress is highly dependent on the presidential budget
request and the previous year’s appropriation to obtain information about the implications
of their budgetary decisions, and the information obtained declines as legislators revise the
base policy.
The model fits within a class of models of agenda setting where a proposer makes a
“take it or change it” offer to a principal, who may amend the offer, but this typically
reduces its quality. Such models have been applied to opinion writing on the Supreme Court
7(Lax and Cameron 2007), presidential bargaining with Congress (Howell, Jackman, and
Rogowski 2013), and regulatory capacity and capture (McCarty 2013). In particular, the
model builds upon and extends the framework developed in McCarty (2017) on regulation
and self-regulation in complex policy domains. In that work, the principal only learns about
the implications of her policy choice from an expert recommendation, whereas I extend the
work to include the equivalent of a status quo. Hence my argument that the principal may
learn from multiple base policies is new.
3 Information and Budgeting Model
In the model, Congress C and the President P bargain over annual appropriations. It
may be useful to think of the President as the entire executive branch. Actions available
to the players follow those of the annual appropriations process. That is, the President
submits a nonbinding budget request to Congress, denoted as αp , and Congress makes its
appropriations decision, denoted as αc .
Consistent with standards models of delegation, the actors have preferences over out-
comes, but they only control policy. Outcomes here can be interpreted as the downstream
levels of social benefit for a given appropriation. To clarify this notion, consider an example
from veterans’ medical care. A budgetary outcome might represent the standard of care per
patient provided to the nation’s veterans for their service-connected injuries. Let Y ⊂ R
denote the set of outcomes with c and p the ideal outcomes for Congress and the President,
respectively. Each player has quadratic preferences over outcomes, so the utility of y for
player i is −(y − i)2 . Without loss of generality, I set c = 0 and p > c.
Furthermore, there exists uncertainty ω between outcome y and the policy choice αc
such that y = αc − ω. The policy choice αc can be interpreted as the enacted appropriation
for an individual account within the U.S. discretionary budget, while uncertainty ω can be
interpreted as how the levels of social benefit vary for a given appropriation. Building on the
8previous example, suppose Congress and the President bargain over the annual appropriation
αc for veterans’ medical care, but both players are uncertain about the standard of care per
patient resulting from αc . This may be due to a myriad of factors: user demand may
be difficult to predict; the cost of prescription drugs may rise or fall unexpectedly; and
hospitals may be in unknown states of disrepair. Too low an appropriation may not produce
the desired standard of care per patient, while too high an appropriation may unnecessarily
divert resources away from other important priorities, such as providing for the nation’s
defense or preserving public health.
Congress seeks to enact appropriations that generate its desired outcome, but the Pres-
ident is the more informed agent about the implications of Congress’s appropriations de-
cisions. Following the literature on delegation, I model expertise as knowledge about how
to obtain specific outcomes. The model further adopts the approach in McCarty (2017)
by endogenizing the variance of the uncertainty term ω. In particular, ω has mean 0 and
θg(d)
variance 1+e
.
There are two types of uncertainty captured in the ω term. The first is incorporated in
the parameter θ, which represents a baseline level of uncertainty. It may be helpful to think
of θ as the uncertainty that bureaucrats have about the mapping of a given appropriation
onto the downstream social benefits it ultimately provides, despite the substantial informa-
tional advantages bureaucrats possess over legislators. Returning to the example of veterans’
medical care, although the VA uses actuarial models to predict user demand at veterans’
hospitals in the upcoming year, θ might represent the confidence levels VA health adminis-
trators have in these detailed forecasts, as user demand influences the standard of care per
patient for a given appropriation. The second is incorporated in the parameter g(d), which
reflects the extent to which this baseline level of uncertainty increases as legislators revise a
base policy. Formally, g(d) = (d+κ)2 +1−κ2 , where κ denotes the rate at which uncertainty
increases as legislators revise the base policy and d denotes the magnitude of these revisions.
Substantively, it may be helpful to conceptualize κ as legislative capacity. Programs with
9high legislative capacity might consist of those with Members especially knowledgeable about
the issues, those with a large number of staffers conducting oversight, or those with staffers
who can leverage their relationships with an agency, industry, or interest group to obtain
information outside of the formal budget request. Programs with high legislative capacity
have a low κ, resulting in a smaller variance of ω. Returning to the example of veterans’
medical care, κ might represent legislators’ knowledge about how appointment waiting times
increase as legislators reduce funding below the budget request. Finally, the term e denotes
costly effort the President may exert to reduce the variability of budgetary outcomes at a
cost of γe. It may be helpful to think of e as presidential investment in quality by providing
recourses into an improved budgetary forecast for that particular year.
Conceptually, these technical assumptions about the preferences of the players reveal
several important features of the model. First, the uncertainty between a policy and an
outcome (in terms of θ and κ) varies by program. In simple program areas, the enacted
appropriation and the desired budgetary outcome are often close, while in complex program
areas, they may differ considerably. Second, the uncertainty term ω is increasing in d,
signifying that the policy on which Congress bases its appropriations decision reveals only
local information.3 That is, Congress may learn a great deal about the implications of its
policy choice for appropriations that are similar to the one recommended by the President
but little about the effects of those that are different. Third, even when Congress accepts
the base policy without revision (i.e., d = 0), there still exists a baseline level of uncertainty
θ.
The model further assumes that legislators may learn about the relationship between a
policy and an outcome from either the presidential budget request αp or the previous year’s
3. Standard delegation models typically assume that outcomes consist of a policy choice and a random
shock. Nonexperts know the relationship between the policy and the shock but do not learn the shock’s
exact value, while experts observe its exact value and are therefore able to choose a target policy to obtain
a specific outcome. Callander (2008) identifies certain limitations of the standard model, namely that the
nonexpert need only observe one policy and one outcome to become an expert, which induces an ex post
incentive by the principal to renege on her delegation of authority and instead move policy to her preferred
outcome.
10appropriation φ. It may be helpful to conceptualize φ as the status quo. That is, φ denotes
the implications of continuing funding at the existing level based on legislators observing
outcomes generated by the previous year’s appropriation. This approach is consistent with
seminal models of policy making that emphasize the role of the status quo in the adoption of
new policies (Krehbiel 1998; Romer and Rosenthal 1978).4 I further assume that presidential
investment in quality does not reduce the uncertainty variance the following year (i.e., e = 0
when Congress uses φ to make its policy decision).5
Figure 1: Game Sequence
Congress
President President Congress Outcome
chooses base
chooses e proposes αp chooses αc ω realized
policy {αp , φ}
The sequence of the game is as follows. Given an appropriation φ enacted the previous
year, which both players observe, the President first chooses a level of investment e and then
a budget request αp to submit to Congress. Congress observes e and αp , decides whether
to base its policy decision on either the presidential budget request or the previous year’s
appropriation, and chooses an appropriation αc .6 All players know all ideal points, utilities,
and outcomes.
4. Modeling the status quo as exogenous may seem unrealistic, since it is chosen by Congress. There are
two interpretations for this assumption. The first is that elections act as a lottery over which congressional
preferences are chosen, and it is not in the current Congress’s interest to maximize the utility of future
Congresses that may have divergent preferences. The second is that the distribution of ω changes from year
to year.
5. In spatial models of policy making, scholars disagree on the reversion point in the annual appropriations
process. Krehbiel (1998) and Canes-Wrone (2006) argue that absent the enactment of an appropriation, the
government shuts down; hence the reversion point is 0. Kiewiet and McCubbins (1988) argue that legislators
typically adopt a continuing resolution that provides funding at existing levels; hence the reversion point
should be the previous year’s appropriation. Spatial models of policy making are often sensitive to how the
reversion point is modeled, and the former assumption should result in a larger set of policies that could
be enacted than the latter. The model here makes no assumption about a reversion point, since it assumes
only that legislators obtain information about the implications of their policy choices by observing outcomes
generated by the status quo.
6. For simplicity, note that the President has no option to veto αc in the model. I expect the presidential
veto to increase the effect of executive influence over legislative deliberations; hence predictions from the
model likely underestimate executive bias in legislative-executive bargaining.
11Optimal Appropriations Decisions for Congress
In this take-it-or-change-it model of the annual appropriations process, Congress faces a
trade-off between increasing the utility of the budgetary outcome, represented by the first
term in the utility function below, and reducing the cost of uncertainty associated with
turning appropriations into social benefits, represented by the second term. Beginning in
the last stage, Congress observes φ, e, and αp and chooses αc to maximize
h θ i
EUc = − (αc − c)2 + [(d + κ)2 + 1 − κ2 ] .
1+e
Since Congress chooses whether to base its appropriations decision on the presidential budget
request or the previous year’s appropriation, it has a best response function conditional on
each option. Lemma 1 characterizes Congress’s conditional best response functions.7
Lemma 1. Congress’s conditional best response functions to {φ, αp , e} are given by
Congress’s Best Response Using Budget Request Congress’s Best Response Using Previous Year
(1+e)c+θ(αp −κ) θκ
c+θ(φ−κ)
1+e+θ if αp < c − 1+e
if φ < c − θκ
1+θ
αc (αp , e) = (1+e)c+θ(α +κ)
p θκ (1) αc (φ) = c+θ(φ+κ) (2)
1+e+θ if αp > c + 1+e 1+θ if φ > c + θκ
αp otherwise.
φ otherwise.
Congress’s conditional best response functions reveal several features of the model. First,
when either the budget request or the previous year’s appropriation is close enough to
Congress’s ideal outcome, Congress will strategically defer to either option without revising
it. Second, since the two budgets may differ in quality, this critical distance may differ. For
θκ
the President’s budget request, the critical distance is 1+e
, which is increasing in uncertainty
(in terms of θ and κ) and decreasing in quality e. For the previous year’s appropriation, the
critical distance is θκ. Third, if Congress revises either option, it chooses an appropriation
αc that is a weighted average of its ideal point c, the base policy (αp or φ), and the level of
7. See Appendix A.1.1 for proof.
12uncertainty (in terms of θ and κ). If Congress works off the budget request, the weight on
1+e
Congress’s ideal point is 1+e+θ
, which is increasing in quality, and the weight on the budget
θ
request is 1+e+θ
, which is proportional to the baseline level of uncertainty. If Congress works
off the previous year’s appropriation, the weights on Congress’s ideal point and the previous
1 θ
year’s appropriation are 1+θ
and 1+θ
, respectively.
Congress’s Choice of Base Policy
Figure 2 illustrates the decision that Congress faces when choosing whether to base its ap-
propriation on either the presidential budget request αp or the previous year’s appropriation
φ. The x-axis charts the distance between Congress’s ideal point c and φ, and the y-axis
charts the distance between c and αp . In each panel, the region shaded in blue depicts the
conditions under which Congress prefers the request as a base policy, and the region shaded
in red depicts the conditions under which Congress prefers the previous year as a base policy.
The dashed line between the two regions signifies the point at which Congress is indifferent
between the two policies, which I denote as the function f (φ, c, θ, κ, e). It may be helpful
to conceive of f (·) as the inflection points in the classic policy making game of Romer and
Rosenthal (1978).
The left panel of Figure 2 illustrates Congress’s choice without presidential effort. When
e = 0, the policies are the same quality, and Congress can only gain utility by choosing
the base policy that results in an appropriation closer to its ideal outcome. Hence Congress
chooses whichever policy is closer to its ideal point, and f (·) = φ. Note the critical dis-
tance c + θκ for each base policy. When the policy nearer Congress’s ideal point is less
than this threshold, Congress prefers to accommodate that base policy, since any marginal
gain in utility from improving the budgetary outcome does not offset the associated cost of
uncertainty. When the policy nearer Congress’s ideal point is greater than this threshold,
Congress prefers to revise it.
The right panel of Figure 2 illustrates Congress’s choice with presidential effort. When
13Figure 2: Congress’s Choice of Base Policy
Without Presidential Effort (e = 0) With Presidential Effort (e > 0)
Presidential Budget Request (αp )
Presidential Budget Request (αp )
C revises φ
C revises φ
C accepts φ
C revises αp C accepts φ C revises αp
c + θκ
f
θκ
C accepts αp c+ 1+e
f
C accepts αp
c c + θκ c c + θκ
Previous Year’s Appropriation (φ) Previous Year’s Appropriation (φ)
e > 0, the policies are different qualities, and the trade-off between improving the utility of
the budgetary outcome and reducing the cost of uncertainty differs between the two base
policies. Observe that Congress strictly prefers the request when it is closer to Congress’s
ideal point than the previous year, represented by the area below the 45-degree line. The
intuition behind this assertion is that Congress improves its budgetary outcome by playing
off the request in this region, and since the request is of higher quality than the previous
year when e > 0, the cost of uncertainty associated with it is also lower. Hence Congress is
always better off using the request as its base policy in this region.8
The more interesting cases are the ones in which Congress prefers the request to the
previous year’s appropriation even though it is farther from Congress’s ideal point. This is
represented by the blue-shaded area above the 45-degree line. The function f (·) characterizes
the budget request for which Congress is indifferent between the two policies, with f (·) a
piecewise function that incorporates three indifference subcases: (1) when Congress would
accommodate either the request or the previous year, (2) when Congress would revise the
8. To see this formally, consider the case when Congress would accommodate either policy. Comparing
θ
Congress’s expected utilities reduces to (αp −c)2 + 1+e ≤ (φ−c)2 +θ, which is always true when αp −c ≤ φ−c
and e > 0. The two other cases reduce to similar conditions.
14request or accommodate the previous year, and (3) when Congress would revise either the
request or the previous year. The solution to f (·) can be found in the appendix, but it
is weakly increasing in the distance between Congress’s ideal point and the previous year’s
appropriation, in uncertainty (in terms of θ and κ), and in quality. Also note that the critical
distances differ for the base policies. When αp ≤ f (·), Congress accommodates the request
θκ
when it is below the threshold c + 1+e
and revises it above this threshold. When αp > f (·),
Congress accommodates the previous year when it is below the threshold c + θκ and revises
it above this threshold.
Lemma 2: Congress’s Best Response. Congress bases its appropriations decision on
the presidential budget request and chooses αc consistent with Equation (1) if and only if
αp ≤ f (·). Otherwise, Congress bases its appropriations decision on the previous year’s
appropriation and chooses αc consistent with Equation (2).9
Optimal Budget Requests for the President
Knowing Congress’s best response, the President chooses to maximize her expected utility
with her budget request αp and effort e. There are three cases to consider: the accommoda-
tion case, the revision case, and the indifference case.
First, the President can send a budget request that Congress accepts without revision.
This is the accommodation case. The optimal budget request in the accommodation case is
either the President’s ideal point p or the corner solution
θκ
αp∗ = c + .
1+e
Recall that P ’s choice of e is endogenous but, since it is chosen before αp , it serves as a
heuristic to more clearly convey the optimal budget request for the President. Congress’s
appropriations decision is increasing in uncertainty (in terms of θ and κ) and decreasing
9. See Appendix A.1.2 for proof.
15in quality e. The accommodation case occurs when (1) preference divergence between the
(1+e+θ)κ
branches is sufficiently small such that p − c ≤ 1+e
and (2) the status quo is sufficiently
extreme such that αp∗ ≤ f (·).
Second, the President can send a request that triggers a response from Congress. This is
the revision case. The President’s optimal budget request in the revision case is αp∗ = p − κ.
Rather than make fewer concessions when revising the request is costly, the President de-
viates more from her ideal point since it is difficult for Congress to expropriate information
from the request. By accommodating sufficiently, congressional revisions – and the subse-
quent increase in the cost of uncertainty associated with those revisions – are small. Further
note that the baseline level of uncertainty θ plays no role in the President’s optimal policy
target. The enacted appropriation under the revision case is
(1 + e)c + θp
αc∗ = .
1+e+θ
Congress chooses an appropriation that is a weighted average of its ideal point and the
President’s. The President’s efforts to produce a high quality request demonstrate a trade-
off between reducing the cost of uncertainty and revealing expertise that Congress may use
to shift outcomes in its favor. Conversely, as the baseline level of uncertainty θ increases,
enacted appropriations are increasingly biased toward the President. The revision case occurs
(1+e+θ)κ
when (1) preference divergence is sufficiently large such that p − c > 1+e
and (2) the
status quo is sufficiently extreme such that αp∗ ≤ f (·).
Third, the President can send a budget request to preempt Congress from basing its
appropriation on the previous year, in which case she proposes the request that makes
Congress indifferent between the options. This is the indifference case. The President’s
optimal budget request in the indifference case is αp∗ = f (·), which is weakly increasing in
the distance between Congress’s ideal point and the previous year, increasing in uncertainty
(in terms of θ and κ), and increasing in quality e. When the previous year’s appropriation
16is sufficiently close to Congress’s ideal point such that Congress prefers the previous year
to either the accommodation or revision cases, the President deviates more from her ideal
point to make her request more attractive to legislators. This result is consistent with the
comparative statics of previous bargaining models in which policy change is increasing in the
distance between the status quo and a representative legislator’s ideal point. When preference
(1+e+θ)κ
divergence is sufficiently small such that p − c ≤ 1+e
, the enacted appropriation in the
indifference case is αc∗ = f (·). When preference divergence is sufficiently large such that
(1+e+θ)κ θ[f (·)−c+κ]
p−c > 1+e
, the enacted appropriation in the indifference case is αc∗ = c + 1+e+θ
.
(1+e+θ)κ θκ
The indifference case occurs when (1) p − c ≤ 1+e
and either f (·) < p or f (·) < c + 1+e ,
(1+e+θ)κ
depending on whether the interior or corner solution is played, and (2) p − c > 1+e
and
f (·) < p − κ.10
Proposition 1: Optimal Budget Requests and Enacted Appropriations. The Pres-
ident’s optimal budget request and Congress’s optimal appropriations decision are charac-
terized by three cases. In the accommodation case, the President proposes her ideal outcome
θκ
p or the corner solution c + 1+e
, and Congress adopts the request without revision. In the
c(1+e)+θp
revision case, the President proposes p − κ, and Congress adopts the appropriation 1+e+θ
.
In the indifference case, the President proposes f (·), and Congress adopts the appropria-
θ[f (·)−c+κ]
tion f (·) when preference divergence is sufficiently small or c + 1+e+θ
when preference
divergence is sufficiently large.11
Optimal Effort for the President
Next consider the President’s optimal investment e∗ to produce a high quality budget request.
In the accommodation subcase in which the President proposes her ideal policy and Congress
10. The equilibrium outcomes described in the information and budgeting model are unique, but it is
worth noting that other equilibria can also be supported when presidents submit budget requests that are
uninformative to Congress. I discuss this further in the appendix.
11. See Appendix A.1.3 for proof.
17accepts it, the President’s expected utility can be rewritten as
θ
EUp = −[ + γe].
1+e
q
The solution is e∗ = max{0, θ
γ
−1}. Presidential investment is high when policy uncertainty
is high and low when the marginal cost of effort is high.
Now consider the revision case. Given Congress’s best response and the President’s
optimal budget request, the President’s expected utility function can be rewritten as
h 1+e θ i
EUp = − (p − c)2 + (1 − κ2 ) + γe .
1+e+θ 1+e
The President’s expected utility function has three components. The first is related to
preference divergence between the President and Congress. Presidential investment in quality
1+e
increases the coefficient on the term 1+e+θ
and thus lowers the President’s utility. This
negative incentive causes the President to underinvest in quality when crafting her annual
budget request, since it is directly attributable to Congress’s ability to expropriate this
information to shift the enacted appropriation toward c and away from p. The second term
reflects the cost of uncertainty. Presidential investment in quality reduces uncertainty and
thus increases utility. The third term simply reflects the cost of the President’s informational
investment, which is increasing in marginal cost.
Finally, consider the indifference case. When Congress prefers to revise the indifference
solution f (·), the President’s optimal effort in quality is e∗ = 0. The intuition behind this
assertion is that the threat of Congress expropriating that information to shift outcomes
away from the President overwhelms any reduction to the cost of uncertainty, which the
players share. However, when Congress prefers to accommodate the indifference solution
f (·), the President faces no threat of expropriation, and investment in quality e > 0 may
allow the President to further shift outcomes in her favor, similar to e∗ in the accommodation
case. In the appendix, I discuss the President’s optimal effort e∗ in further detail.
18Comparative Statics for Congressional Revisions
Although it is useful to sort through the strategic considerations of presidential investments
in quality, my primary interest resides in deriving the comparative statics of interbranch
influence over annual appropriations. Of particular interest are the political determinants
of congressional revisions, which equate to the absolute difference between the congressional
appropriation and the presidential budget request. I denote this as ∆∗ , with ∆∗ = |αc − αp |.
Taking the partial derivative of ∆∗ with respect to the parameters of interest, I find that
congressional revisions are increasing in preference divergence and decreasing in uncertainty.
For ease of exposition, I assume e = 0 in these calculations. In the accommodation case,
Congress strategically defers to the presidential budget request; hence congressional revisions
(1+e+θ)κ
are 0. However, the condition for the accommodation case p − c ≤ 1+e
is more likely to
be satisfied as preference divergence p − c decreases and as uncertainty (in terms of θ and
p−c
κ) increases. In the revision case, congressional revisions can be rewritten as ∆∗ = 1+θ
− κ.
Clearly ∆∗ is increasing in preference divergence between the branches and decreasing in
uncertainty (in terms of θ and κ). In the indifference case when Congress chooses to revise
φ−c
f (·), congressional revisions can be rewritten as ∆∗ = 1+θ
. Here ∆∗ is increasing in the
distance to the status quo and decreasing in bureaucratic uncertainty.
Proposition 2: Congress’s Choice to Accommodate or to Revise. Congress’s choice
to accommodate the request is decreasing in preference divergence and increasing in uncer-
tainty (in terms of θ and κ).
Proposition 3: Congressional Revisions. Conditional on Congress revising the request,
its revisions are increasing in preference divergence and decreasing in uncertainty (in terms
of θ and κ).
Figure 3 illustrates the effect of preference divergence on congressional revisions.12 Hold-
ing the other parameters fixed, when preference divergence is sufficiently small, the President
is in the accommodation case. Any policy gains resulting from Congress moving the policy
12. Figure 3 sets c = 0, φ = 4, κ = 1, and e = 0.
19Figure 3: Preference Divergence and Congressional Revisions
Congressional Revisions (αp − αc ) (1+e+θ)κ
1+e
φ−c+κ
Preference Divergence (p − c)
target closer to its ideal point are insufficient to offset the associated cost of uncertainty.
Knowing this, the President submits a budget request she knows Congress will accommo-
(1+e+θ)κ
date. When preference divergence exceeds the critical distance 1+e
, the policy gains are
sufficient to offset the associated cost of uncertainty, and the President submits a proposal
she knows Congress will revise further, with the magnitude of these revisions increasing in
preference divergence. When preference divergence exceeds φ − c + κ, policy change is con-
strained by the status quo. In this region, presidential budget requests are constant, as are
congressional revisions.
Figure 4: Uncertainty and Congressional Revisions
Congressional Revisions (αp − αc )
Congressional Revisions (αp − αc )
p p
p−κ φ
Bureaucratic Uncertainty (θ) Legislative Uncertainty (κ)
Figure 4 illustrates the effects of uncertainty on congressional revisions. Recall that
20outcomes consist of a policy choice and a random shock, with the variance of the random
shock increasing in terms of the baseline level of uncertainty θ and legislative uncertainty κ.
As a result, an increase in uncertainty corresponds to an increase in the cost of revising the
policy target for both players. The effect of the baseline level of uncertainty θ is represented
in the left panel.13 When θ is sufficiently small, the President is in the revision case. Since
the cost of uncertainty is small, revising the policy target is cheap, and Congress makes large
changes to the request. As θ increases, the cost of revising the policy becomes more expensive,
and legislators make smaller changes to the request. When θ is sufficiently large, revising
the policy target becomes too expensive to offset any policy gains. As a result, Congress
strategically accommodates the request without revising it. In the right panel, the effect of
κ follows a similar logic. When κ is sufficiently small, the players are in the indifference case,
and revising the policy target is cheap for Congress. Knowing this, the President makes
fewer concessions in her proposal. As κ increases, the cost of revising the policy grows.
Since the players share the cost of uncertainty, the President benefits from making greater
concessions in her proposal, thereby lessening the need for subsequent revisions. When κ is
sufficiently large, revising the policy target becomes too expensive to offset any policy gains,
and Congress strategically accommodates the request without revising it.
4 Empirical Tests
In this section, I test several of the theory’s predictions on an original data set of discre-
tionary appropriations. After describing the data and their potential contribution to the
scholarship on budgetary politics, I conduct two sets of empirical tests. First, using an
instrumental variables design, I estimate a plausibly causal effect of presidential budget re-
quests on congressional appropriations. This design also allows me to recover estimates of
institutional preferences on policy making. Second, I examine whether preference divergence
and legislative capacity are associated with Congress’s decision to accommodate or to revise
13. Figure 4 sets c = 0, p = 5, φ = 4, and e = 0 (with κ = 1 in the left panel and θ = 1 in the right panel).
21the budget request, as well as the magnitude of congressional revisions.
4.1 Compiling Original Data on Account-Level Appropriations
Discretionary appropriations provide a unique tool for social scientists to conduct empirical
tests on public policy by mitigating many of the endogeneity concerns related to the study
of interbranch bargaining. Most notably, presidents are required by law to submit an annual
budget request to Congress. As several scholars have pointed out, this avoids much of
the selection bias associated with presidential position taking (Canes-Wrone 2006; Howell,
Jackman, and Rogowski 2013). Unlike the traditional legislative process in which presidents
may take public positions on certain issues and remain silent on others, appropriations require
administrations to take public positions on every line-item budget every year. Moreover,
appropriations provide a continuous measure that can be easily compared across programs
and across years.
Ideally, empirical tests of interbranch bargaining over annual appropriations require data
that reflect the individual policies over which Congress and the President bargain. In the
appropriations context, this is by appropriations account. An appropriations account, ac-
cording to the Government Accountability Office, serves as the “basic unit of an appropriation
generally reflecting each unnumbered paragraph in an appropriation[s] act.” When Congress
provides funding within an appropriations account, it authorizes the Department of the
Treasury to transfer funding to that agency to carry out the specific programs, projects, and
activities within that account’s purview. The annual budget of a cabinet-level department
typically consists of dozens of appropriations accounts, and agencies are prohibited from
shifting funds from one account to another unless given that authority by Congress.14
However, collecting data by appropriations account is prohibitive. As far as I am aware,
no government agency systematically tracks and publishes presidential budget requests and
14. The unauthorized transfer of funds between appropriations accounts would trigger an Antideficiency
Act (ADA) violation. The ADA prohibits federal agencies from obligating funds in excess or in advance of
an appropriation, and federal employees who violate the ADA may be subject to administrative and penal
sanctions.
22congressional appropriations at such a granular level of detail over time, and previous em-
pirical studies of interbranch bargaining over discretionary appropriations aggregate funding
at the agency or bureau level. The underlying source for much of this work is an impressive
data set compiled by Kiewiet and McCubbins (1991) and later extended by Canes-Wrone
(2006) and Howell, Jackman, and Rogowski (2013). These data track agency- or bureau-
level presidential budget requests and congressional appropriations for a portion of the U.S.
discretionary budget from FY1933 to FY2006.
To test empirical predictions derived from the information and budgeting model, I compile
a new data set of the U.S. discretionary budget by appropriations account from FY1960 to
FY2021. In total, the 62-year panel covers $59.7 trillion and has 49,200 unique account-year
observations for an average annual appropriation of $1.2 billion per account, adjusted for
inflation. To build this data set, I collect the universe of annual appropriations bills during
this time period and reconstruct by hand the comparative statements of new budget authority
(CSBAs) contained in the committee reports and the conference report (or its equivalent).
Each committee CSBA contains account-level data that reflect the Congressional Budget
Office’s (CBO) score of that chamber’s committee mark and of the presidential budget
request, and the conference table typically contains the CBO score of the floor-passed bills
in each chamber, as well as the final bill ultimately enacted into law.15 I describe the data
more fully in the appendix.
This new data set contributes to the scholarship on budgetary politics in four ways.
First, whereas previous work on interbranch bargaining tracks appropriations data for select
subagencies, I comprehensively track appropriations data for the entire U.S. discretionary
budget. Second, I track funding levels at a more granular level than these previous studies by
capturing data at the account level rather than at the agency or bureau level.16 Third, I track
15. The Congressional Budget Office was established in 1974, during the middle of the panel. Before CBO
was established, the tables reflect the relevant Appropriations Committee’s internal score of a bill.
16. In their study on budgetary incrementalism, Anderson and Harbridge (2010) also track discretionary
appropriations at the account level. However, as far as I am aware, they do not track funding levels through-
out the policymaking process, only enacted appropriations.
23these funding levels throughout the policymaking process. That is, I capture funding levels
for presidential budget requests, for committee-reported and floor-passed bills in the House
and Senate, and for public laws, although I use only presidential budget requests and public
laws in the empirical analyses that follow. Fourth, I review each account-year observation to
determine comparability to the previous year’s appropriation and to the presidential budget
request. That is, I remove account-year observations in which legislators or presidents change
the accounting structure from one year to the next and hence are not suitable to compare.
As far as I am aware, this data set constitutes the most granular and comprehensive panel
of the U.S. discretionary budget available.
4.2 The Political Determinants of Congressional Appropriations
The information and budgeting model makes several predictions about the relationship be-
tween presidential agenda setting and congressional policy making. First, we should expect
to find evidence that legislators strategically accommodate presidential proposals, consis-
tent with the accommodation case. That is, we should expect to find a disproportionate
share of account-year observations in which congressional revisions are 0. Second, the model
predicts that, holding all other parameters fixed, presidential budget requests should have
an independent effect on congressional appropriations, as noted in Congress’s conditional
best response function in Equation (1). Third, a parsimonious explication of the model
predicts that legislative preferences should influence both presidential budget requests and
congressional appropriations, while presidential preferences should influence congressional
deliberations only through the request.
Figure 5 provides evidence in support of the first hypothesis. The left panel of the
figure plots the presidentially-requested change from the previous year on the x-axis and
the congressionally-enacted change from the previous year on the y-axis, aggregated at the
agency level comparable to previous studies. The right panel plots the same, disaggregated
at the account level. If Congress enacts the exact amount requested by the President,
24Figure 5: Discretionary Appropriations, FY1960-2021
Agency−Level Account−Level
100% 100%
Congress's Enacted Appropriation
Congress's Enacted Appropriation
% Change from Previous Year
% Change from Previous Year
50% 50%
0% 0%
−50% −50%
−100% −100%
−100% −50% 0% 50% 100% −100% −50% 0% 50% 100%
% Change from Previous Year % Change from Previous Year
President's Budget Request President's Budget Request
then the observation will fall on the 45-degree line. If legislators enact more (less) than
requested, then the observation will appear above (below) the 45-degree line. Two points
bear mentioning. First, there is a clear positive correlation between budget requests and
enacted appropriations, as has long been observed by previous scholars (e.g., Fenno 1966;
Wildavsky 1964). Second, Congress regularly enacts the exact amount requested by the
President, as evidenced by the 45-degree line in the raw data in the right panel. As far
as I am aware, this finding is new to the literature, as previous scholarship observes that
“Congress rarely appropriates the same amount of money to an agency that the president
requested” (Kiewiet and McCubbins 1991, 178). To be sure, this previous observation is not
an inaccurate interpretation of the data, as displayed in the left panel; however, it appears
to be an artifact of aggregating funding levels at the agency or bureau level. Using the more
granular data, legislators enact the exact amount requested in 25 percent of all account-year
observations, which comprises 6 percent of total funding during this period.
Empirical Strategy
Testing the second and third hypotheses requires a more systematic examination of the data.
To identify the political determinants of congressional appropriations, the dependent vari-
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