Delegation or Unilateral Action?

KENNETH LOWANDE∗

March 20th, 2015†

Studies of unilateral action typically conceive of presidential directives as movement of an existing status quo policy.Yet, bureaucratic cooperation is often necessary if presidential action is to be truly “unilateral.” In this vein, I arguethat such directives can be more fruitfully studied as instances of delegation. I develop a theory of delegation within theexecutive branch, modeling the conditions under which the president is likely to delegate—and provide discretion—toadministrative subordinates outside the Executive Office. I test this theory by analyzing executive orders for contentcontaining authority delegated to subordinates. I show that, contrary to the dominant paradigm of presidential studies,the politics of direction action is mitigated by the necessity for bureaucratic cooperation.

∗Ph.D. Candidate, University of Virginia, lowande@virginia.edu†Paper prepared for presentation at the 2015 Southern California Law and Social Science Forum. I thank Jeff Jenkins and Craig

Volden for helpful comments and suggestions, Matt Dickinson, Will Howell, and Rob McGrath for sharing their data, as well asThomas Gray and Nick Jacobs for volunteering their time in service of coding validation.

On April 8th, 1952, Harry Truman issued an executive order that seized the property of dozens of American

firms (among them, the Youngstown Sheet & Tube Company) whose output was “indispensable" to the

Korean War effort. Among notable cases cited by scholars of presidential power, E.O. 10340 is second in

importance only to 9066, issued by FDR, which authorized Japanese interment during World War II. If one

looked to dominant theories of “unilateral action” for admittedly stylized explanations of this particular case,

we would have to conclude that Truman singlehandedly altered the status quo.1 Yet, a few basic features

of the order and the case itself render this explanation unsatisfying. First, E.O. 10340 did not order agents

from within the Executive Office of the President to begin seizing steel mills—it delegated authority to the

Secretary of Commerce, Charles Sawyer. Second, the order did not prescribe in legal minutiae how the

Secretary was to seize and manage the mills, it gave him substantial discretion. The Secretary was permitted

to “act through or with the aid of such public or private instrumentalities or persons as he may designate,”

to “determine and prescribe terms and conditions of employment under which the plants, facilities, and

other properties possession of which is taken pursuant to this order shall be operated,” to issue regulations

he deemed necessary, and even to return possession of the mills to their owners if he judged it appropriate.

This implies E.O. 10340 should be deemed an instance of delegation to bureaucratic agents, as opposed to a

change in policy “with the stroke of a pen”(Mayer, 1999).

But these facts are not sufficient to establish that presidential orders ought to be looked at as instances

of delegation. For instance, if it were the case that Truman pre-negotiated the actions of the Secretary, and

that perfect compliance was virtually guaranteed by top-down control, the principal-agent relationship may

be insignificant or uninteresting. However, that is not the case in either the example provided or the larger

universe of presidential actions. According to archival material and interviews after the fact, Sawyer was a

“reluctant” participant, who cooperated on the condition that he would be provided a “‘free hand”’ in carrying

out the order (Marcus, 1977). According to Neustadt (1960), even Truman’s unambiguous intent did not

compel Sawyer to act in a predictably compliant way. He initially refused to put union wage demands in place,

and only did so after they were invalidated by the Supreme Court (21-22). These historical accounts suggest

that without discretion, the Secretary would have refused to comply—and that even then, full compliance did

not occur. More broadly, in nearly every instance of presidents “acting alone,” the president must rely on

agents within the bureaucracy who have some degree of institutional independence, such that studies (most

notably, Neustadt and Nathan [1985]) frequently cite the importance of executive management and agency

1Though it would later be restored following the Supreme Court’s ruling in Youngstown Sheet & Tube v. Sawyer (1952)

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compliance in discussions of presidential success.

In this paper, I analyze presidential directives as delegation. I present a model of delegation within the

executive branch, designed to approximate the presidential policymaking and bureaucratic implementation.

Placed in the context of the existing paradigm, this theoretical framework suggests that bureaucratic agency

is a mechanism by which members of Congress secure better policy outcomes when the President acts

alone. This is because presidential directives often delegate to subordinates, who may be influenced by the

threat of sanctioning from Congress and relevant committees. I support this perspective with an analysis

of important executive orders issued between 1947-2001. First, I demonstrate that a substantial majority

of presidential actions involve delegation to bureaucratic agents. I find, in-keeping with expectations, that

Presidents provide less discretion to bureaucratic agents as the policy disagreement between the President

and relevant congressional committees increases. Moreover, I show that agents receive more discretion when

the President is faced with a large congressional majority capable of imposing substantial costs on those who

implement the president’s policies. These findings suggest that, beyond the influence of Congress and the

Judiciary, presidential policymaking is contingent on bureaucratic agency.

I. Unilateral Action, Agency Problems, and the Executive Branch

In his seminal book, Howell (2003) writes that “modern presidents often exert power by setting public

policy on their own and preventing Congress and the courts—and anyone else for that matter—from doing

much about it”(14). This perspective, elaborated in Moe & Howell (1999), has influenced a generation

of quantitative research on the presidency, which attempts to uncover the precise political circumstances

that enable the President to act alone. Empirically, much of this research has focused on the study of

presidential directives: executive orders (Mayer, 1999; Fine and Warber, 2012; Chiou and Rothenberg, 2014),

proclamations (Rottinghaus and Maier, 2007; Rottinghaus and Lim, 2009), signing statements (Kelley and

Marshall, 2010; Ostrander and Sievert, 2012), and memoranda (Cooper, 2002; Lowande, 2014). More

recently, the conceptual focus on what presidents can accomplish alone has informed investigations of the

president’s role in the distribution of federal spending (Mccarty, 2000; Berry et al., 2010; Hudak, 2014;

Reeves and Kriner, 2015). This work has furthered our understanding of the president’s influence over

policymaking. However, there are several reasons to believe that as a theoretical base, it remains inadequate.

First, as the introduction suggests, the stylized depiction of a status quo movement fails to capture the

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essence of presidential directives themselves. Second, as Mayer (2009) points out, taking the unilateral action

paradigm to its logical end results in a conclusion which may be normatively unappealing.

If the ambiguities of presidential authority mean that the boundaries of presidential power aredetermined by precedent [...] if presidents have incentives to act first [...] if Congress and thecourts face hurdles [...] presidential power becomes uncontrollable and sinister. (443)

Even if value judgements are resisted, an important question remains: in an era of congressional polarization,

what is to prevent the president from becoming “all powerful”? If we take into account that acting independent

of Congress requires cooperation from bureaucratic agents, then the gradual implications unilateral action

become less “sinister" and more contingent. Presidential history is replete with cases of agency heads

reinterpreting presidential directives, or refusing to comply outright.2 This is not to say that “unilateral action”

is a complete misnomer, or that the existing paradigm is wholly incorrect. However, it is important to build on

a central point: that when Congress passes a law or the President issues and executive order, both actors are

relying on bureaucrats for policy change. Absent that recognition, theories risk aggrandizing the capabilities

of the president and downplaying the agency of the bureaucrat. The appropriate research question, then,

is: how does bureaucratic agency influence policy outcomes when the president attempts to “go it alone”?

Reframing the issuance of presidential orders as instances of delegation sheds light on the content, rather

than the frequency of unilateral action—and, by implication, provides more information about the degree to

which action is unilateral, and the potential for deviation from the president’s preferred outcomes.

Importantly, benchmark studies of legislative delegation of policymaking authority to bureaucratic

agents provide a conceptual way forward. They have highlighted the critical trade-offs for members of

Congress looking to delegate power: between policy outcomes and discretion (Epstein and O’Halloran,

1994, 1996), between outcomes and executive aggrandizement (Volden, 2002a), and between informed and

ideological personnel (Gailmard and Patty, 2007, 2013). Additionally, some theoretical work on delegation

and information sharing conceives of the key principal as either a legislature or an executive (Patty, 2009;

Ting, 2009). This suggests the basic framework of this study may shed light on a principal beyond the median

voter in Congress. Given this, presidential directives offer a rare opportunity for verification of proposed

theory. Systematic empirical support for delegation models rests on a few essential studies (Epstein and

2For instance, Secretary of the Treasury Louis McLane under Andrew Jackson (during the Bank of the US controversy). A morerecent example is Melissa Hathaway, former Cyber Security Czar under Barack Obama, who inside sources said resigned after“spinning her wheels” under the administration—after serving under in the Bush White House (Gorman 2009). Many other cases areoutlined by Neustadt (1960).

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O’Halloran, 1999; Volden, 2002b; Huber and Shipan, 2002). Since then, as Moe (2012) notes, formal work

on delegation has largely outpaced empirical testing.3

I.1 The “Presidential” Branch

Importantly, the theoretical framework in this paper builds on the basic point that agency problems are perva-

sive in the executive branch. That is, when presidents want a policy changed, they must rely on subordinates

with agency. Their wishes and directives are not self-executing. Perfect monitoring is implausible. The

multiplicity of policy areas and the limitations of a single office generate an asymmetry of time, energy,

and knowledge. This fundamental feature of the president’s position has been the basis for the study of

presidential management of the bureaucracy and influence over policy outcomes. Among scholarship on

“politicization”, for example, the notion that presidential and bureaucratic preferences often diverge is at the

core of most explanations for the politics of appointments (Lewis, 2008; Hollibaugh et al., 2014). Moreover,

bureau responsiveness, even after “politicization”, is not guaranteed (Dickinson and Rudalevige, 2004).

Agency problems are a well-known, systemic part of presidential administrations. Consider, for instance, the

Nixon Administration’s adversarial relationship with the executive bureaucracy (Aberbach and Rockman,

1976). Conceiving of presidential directives as acts of delegation, then, incorporates much of what is already

assumed (explicitly or implicitly) in scholarship on the president’s relationship with the executive branch.

However, this literature also highlights an important challenge. That is, under the broadest definition,

nearly every action the president takes can be considered an act of delegation. A “delegation-all-the-way

down” perspective risks returning to an understanding of the President as overwhelmed and ultimately

incapable of seriously influencing policy (e.g. Lowi 1985). Thus, it is essential to acknowledge that when

delegating, the President faces a range of potential agents, who vary in terms of ideological disposition

(Clinton and Lewis, 2008; Chen and Johnson, 2014), institutional independence (Selin, 2015), and ultimately,

monitoring cost. While precise specification of the institutional cost to monitoring the President’s agents may

by worthwhile, I show that analytical and empirical leverage can be gained through a necessary simplification.

That is, I conceive of the president’s decision to delegate as a dichotmous choice: delegate to “external”

agents—those in government corporations, independent agencies, and cabinet departments; or, delegate to

actors within what has been called the “presidential” branch—the White House (WHO) and Executive Office

3For key examples of theoretical development in this area, see: Huber & McCarty (2004), Callander (2008), Wiseman (2009),Fox & Jordan (2011), Callander & Krehbiel (2014).

4

(EOP). The growth of the president’s immediate institutional apparatus has been well documented and studied

(Dickinson, 1996; Rudalevige, 2002; Dickinson and Lebo, 2007). President’s have the capacity to develop

policy in a wide variety of issue areas with the resources at their disposal within the WHO and EOP. Thus,

this simplification nonetheless preserves an essential point, made first by Moe’s (1985, 1993) perspective on

presidential politicization and centralization: that within the WHO and EOP, presidents face fewer collective

action problems (compared with Congress) and lower risks of policy drift (compared with the rest of the

executive bureaucracy). Building on these premises, I present a model of presidential delegation and leverage

a data set of discretion in executive orders to verify its central intuition.

II. A Theory of Presidential Delegation

Given the motivating conceptual shift, a theory of delegation from the Oval Office should answer two

questions. First, when (and why) will the president delegate to agents outside the relative arm’s reach of the

White House and Executive Office? Second, and relatedly, what conditions the level of discretion provided

to these agents? I present a straightforward spatial model of delegation within the executive branch with

three players: the President (P), a congressional committee (C), and an external agent (A). The congressional

committee may be thought of as the committee with oversight jurisdiction over the corresponding agency.4 In

the model, the President attempts to implement a policy outcome (x ∈R) nearest to their preferred (xP), while

minimizing the resource cost (τ) associated with developing policy in the White House and EOP. To avoid

that cost, the President may delegate via directive to an external agent (e ∈ {0,1}) and provide them with a

level of discretion (D ∈ [0,R+]).5 As the elected heads of the federal bureaucracy, presidents have acquired

institutional resources designed to centralize design-making in the Executive Office of the President (EOP).

Nonetheless, complete centralization is impossible. Presidents must set priorities, engaging in a key trade-off

when selecting bureaucratic agents. Thus, a core assumption of the argument is that presidents have limited

resources to formulate policy within their immediate domain.6 In the context of the model, this means that

4This is particularly important, given the broad understanding of sanctioning behavior I put forth. Drafting punitive legislationand holding hearings occur at the committee level, such that assuming C to be the congressional floor median may inappropriatelylimit the threshold of political support that determines whether Congress engages in sanctioning activity.

5I define “discretion” (like Epstein & O’Halloran 1999) as delegated authority, together with the severity of procedural andoversight constraints placed on that authority.

6It may be useful to think of the analogous legislative environment: A “make-or-buy” framework views Congressional committeesas appendages of floor majorities (the “make” option) and bureaucratic agencies as contractors (the “buy" option; Epstein andO’Halloran 1999). In the Presidential context, the EOP would be the equivalent “in-house” producer, whereas agents in Cabinetdepartments, independent agencies, and commissions would play the role of contractors.

5

President’s must pay some exogenous cost if they forgo external delegation.

The congressional committee looks to obtain a policy closest to its bliss point (xC) through the lever

of agent sanctioning (s ∈ {0,1}). Here, they engage in ex post actions in response to the President. That

is, Congress could enact a law that directly punishes the agent, subject to well-known coalition thresholds

for lawmaking (filibuster and veto-override pivots). These coalition thresholds are what provide existing

theories of unilateral action with their implications. In absence of large Congressional majorities, it is

difficult for Congress to marshall the political resources to enact a law intended to supersede a presidential

directive. However, in absence of a super-majority, there are still sanctions Congress can impose on the

agents. Limitations riders can be inserted into appropriations bills (MacDonald 2010).7 Bureaucrats can be

called to testify on Capitol Hill. They can be held in contempt of Congress. This suggests that Congress

can impose targeted sanctions on executive branch agents that do not require the political capital demanded

by lawmaking. This secondary action imposes costs directly on the agents implementing the president’s

program.

Another important feature of the model is that the agent, if chosen to make policy, can opt out by refusing

to comply (v ∈ {0,1}) with the president’s directive. If it opts in, it selects a policy conditional on the level of

discretion supplied by the President. While outright non-compliance is not a typical feature of delegation

models, there is reason to believe that this activity influences policy-making.8 Deviation and resignation is a

regular part of presidential administrations. For example, in 1933, Treasury Under Secretary Dean Acheson

resigned rather than implement FDR’s Executive Order 6102, which required all newly mined gold bullion

to be delivered to the federal government. To pick a more contemporary example, in 2004, then-acting

Attorney General James Comey refused to reauthorize a wiretapping program under George W. Bush. More

generally, since the Clinton Administration, presidents have used directives to set (or reset) rule making

deadlines—which are frequently broken or ignored entirely. Moreover, this feature may render the model

more generalizable beyond the American context. Bureaucratic noncompliance is a pervasive issue which

underlies many comparative studies on corruption. Thus, assuming that bureaucrats have the capacity to

disobey provides a more accurate, general depiction of executive politics. Ultimately, the model suggests

that in equilibrium, observations of bureaucratic non-compliance should be low (or nonexistent). Thus, as

7Because members of Congress imbed this instrument within “must pass” legislation, it arguably requires a lower politicalthreshold to enact.

8Ting (2015) explores legislative strategies for achieving agency compliance in terms of policy selection—a substantively differentform of non-compliance than agencies “opting out.”

6

with other political phenomenon with equilibrium effects, like the presidential veto (?), observed cases may

understate their overall impact on the broader process.

The utility of the President is given by

UP =−|xP− x|e− (1− e)τ (1)

so that they attempt to minimize the disutility associated with distant policy and the cost of developing policy

in-house. The committee’s utility is governed solely by the policy outcome, such that

UC =−|xC− x| (2)

Finally, the Agent’s utility is governed by policy outcomes and (if incurred) the cost associated with a

congressional sanction (c ∈ [0,R+]).

UA =−|xA− x|− sc (3)

I present the complete game sequence below, but before moving on, it is important to highlight ways in which

the model differs from relevant landmark work. First, in contrast to Howell (2003), presidential directives

are seen as the beginning of process by which the president resets the status quo. Notably absent are critical

pivots in Congress, the Judiciary, or the lawmaking process, more generally. This reflects a key distinction:

whereas the unilateral politics model outlines conditions that lead to presidential action, the model below

attempts to explain how the agency of bureaucrats influences the final outcomes—after the decision to act

alone has been made.

Second, in contrast to landmark models of delegation (Bawn, 1995; Epstein and O’Halloran, 1999;

Volden, 2002a; Gailmard and Patty, 2007), the principal’s key trade-off is not motivated by gains in expertise.

Instead, delegation is attractive because it allows the President to dedicate limited resources to other policy

initiatives. I take this to be a more accurate characterization of the principal-agent scenario encountered by

the President—particularly when compared to the substantive justifications for the information asymmetry

between Congress and the bureaucracy. Members of Congress are said to have limited time, staff, and

knowledge that renders them less able to match the expertise of agents. But the President is surrounded

by a full-time staff of experts, many of whom, are employed at will and possess specialized knowledge in

substantive areas. This is not to say that all expertise needed to produce policy could be contained within the

7

WHO or EOP—quite explicitly, its limitations will be captured by τ . But I argue those limitations should be

characterized as a shortage of resources, not a shortage of potential expertise.

II.1 Sequence of Play

1. Nature selects the status quo policy, q.

2. The President chooses whether to delegate or develop policy within the White House and EOP(e ∈ {0,1}).

3. (a) If e = 0, then the EOP develops the President’s preferred policy (x = xp) and the President pays aresource cost, τ .

(b) If e = 1, then the President selects a level of discretion, D.

i. The Committee chooses a sanction rule, σ : x→ s.ii. The Agent chooses whether or not to comply (v ∈ {0,1}).

iii. A. If the Agent refuses to comply, then the policy reverts to the status quo (x = q).B. If the Agent complies, then the Agent selects a policy, x ∈ [xP−D,xP +D].

(c) The Committee, according to its prior rule (σ ) chooses whether to sanction the Agent (s).

(d) Play ends and payoffs are distributed.

A few simple examples underscore the applicability of this setup to the political process in question.

Presidents, in pursuit of their goals, observe some existing policy that they would desire to move. Faced

with an initial choice of whether to use resources within their immediate domain, or delegate to external

agents. In 1976, for instance, Gerald Ford chose to delegate policymaking functions to the Federal Energy

Office within the Executive Office, rather than vest those functions in the newly created Federal Energy

Administration (predecessor to the Department of Energy).9 If a President delegates, they are then faced

with the task of formulating the agent’s mandate to make policy. Their directive can be limited, or it can

provide the agent policy latitude. The President could, for example, require that the agent consult with other

agencies or departments, as Harry Truman did when he redelegated wartime employment functions to the

Department of Labor in 1945.10 Relatedly, the President could simply specify the new policy in great detail,

to limit the range of policies the agent could implement.11 Next, Congress, observing the President’s directive,

makes its preferences known to the agent. This kind of Congressional posturing has become particularly

salient recently, as members of Congress reacted to Barack Obama’s series of immigration-related directives

9E.O. 11930 - “Performance by the Federal Energy Office of Energy Functions of the Federal Energy Administration” (July 30,1976)

10“E.O.9617 - Transfer of Certain Agencies and Functions to the Department of Labor,” (September 19, 1945)11Indicators of constraints on discretion are discussed in greater detail in the measurement section of this paper.

8

in November 2014. Senator Jeff Sessions (R-AL), for example, reminded the applicable departments that

“Congress has the power and every right to deny funding for unworthy activities.”12 This sequence appears to

reflect another recent presidential initiative, Barack Obama’s proposed ban on armor-piercing ammunition.

In response, Rep. Jim Sensenbrenner (R-WI) introduced legislation to abolish the agency responsible for

the president’s initiative, the Bureau of Alcohol, Tobacco, and Firearms (ATF).13 Though the President pays

political costs for overturned or impeded policies, bureaucrats endure punishments which very in severity.

Thus, appropriately, the Committee has the ability to sanction them directly, which influences the agent’s

actions. The final outcome is a function of the agent’s decision to opt-in to the President’s directive, and the

policy the agent implements.

II.2 Solutions

Given the number of possible policy orientations and status quo, the inclusion of multiple principals increases

the number of scenarios exponentially, compared with other benchmark models of delegation. Since the basic

intuition of the model can be gleaned without specifying every possible scenario, I focus my analysis on

the situation depicted in Figure 2. Without loss of generality, I assume the ideal points of the President and

the Committee are 0 and 1 respectively, and that 0 < xA < 1. Though this is a useful simplification, it also

approximates a typical scenario in policymaking among separated powers. Agency missions, appointees, and

policy tasks are often the explicit result of inter-branch bargaining. Consequently, I argue it is reasonable to

assume that their ideal outcome lies somewhere between their elected principals.

Figure 1

xP

0

xA xC

1

Working backwards, consider the committee’s choice of sanction. Recall, this is a deterministic function

of the policy selected by x—however, there are an infinite number of arbitrary decision rules (σ ) that the

committee could specify, some of which, will be more useful than others. For simplicity, consider two

possible sanction rules: Request (σ1, s = 1 iff |xC− x| > |xC−q|) and Demand (σ2, s = 0 iff x = xC). The

12November 20, 2014. Quoted in Shabad (2014).13Marcos, Cristina. 2015. “Republican proposes abolishing the ATF amid bullet ban controversy,” The Hill March 5th. The ATF

has since withdrawn the proposal—preserving the status quo.

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intuition behind each is straightforward. The committee “demands” when it sanctions the agent unless it

secures its ideal point, and it “requests” when it only sanctions if the new policy makes it worse off. Given

this set of sanction rules, we can specify the agent’s optimal policy choice (x∗) for every possible q. For

Demanding, the optimal policy choice can be expressed as:

x∗σ2=

D if D < xA

xA if xA < D < xC

if |xA− xC| ≥ c and xC < D

xC if |xA− xC|< c and xC < D

(4)

Here, demanding only induces a behavior change when the President has provided a sufficient level of

discretion and the sanction cost is sufficiently high. When the committee adopts a sanction rule of Request

(σ1), the number of optimal strategies expands considerably, given that x∗ becomes a function of the status

quo, the sanction rule, its associated cost, and how much discretion it has been provided. The agent’s optimal

policy selection for any given status quo, level of discretion, and cost of sanction—conditional on σ1 can be

written as:

x∗σ1=

D if D < xA ∀ q

if D≥ xC−|q− xC|, |xA−D|< c and xA < D < xC, xC < q

xA if D > xA and xA > q

if xA < D < q and xA < q < xC

if |xA−q| ≥ c and xA < q < xC, D > q

if xA > q− xC and xC < q, D > q

if xA < q− xC, |xA− (q− xC)| ≥ c and xC < q, D > q

if D < xC−|q− xC| and xA < D < xC, xC < q

if D≥ xC−|q− xC|, |xA−D|> c and xA < D < xC, xC < q

2xC−q if xA < q− xC, |xA− (q− xC)|< c and xC < q, D > q

q if |xA−q|< c and xA < q < xC, D > q

(5)

Given this characterization of x∗, we can illustrate the effect of non-compliance (v = 0) on the universe of

possible policy selections. Because Congress can only apply negative pressure (sanctioning imposes a cost),

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the movement of policy becomes the chief determinant of whether the agent will opt-in. This rule is presented

in Lemma 1. According to this condition, for the agent to comply with the president’s directive, the new

policy must—at a minimum—make the agent indifferent between x∗ and the status quo. Put simply, the

President cannot force the agent to implement an x that makes the agent worse off in terms of policy. In

Figure 2 and 3, which depict x∗ given q, this means that the region between xP and xA represents a kind of

“bureaucratic” gridlock interval, wherein delegation always results in the status quo (Brady & Volden 2006,

Krehbiel 1998).14

Lemma 1. Given xP < xA < xC, v∗ = 1 only if |xA−q| ≥ |xA−x∗| ∀ q. Proof: By definition, s = 0

when v = 0, so, given UA, v∗ = 1 when −|xA− x∗|− s∗c≥−|xA−q|. Since c ∈ R+, −s∗c≤ 0,

and the sanction becomes irrelevant for satisfying the inequality.

Next, consider the task of satisfying this condition in the context of the level of discretion provided by the

President. Since D defines the set of policies the agent may select, it figures prominently into whether the

agent will comply with the directive. Lemma 2 presents this relationship. Importantly, for the agent to comply

the President must provide a level of discretion greater than or equal to the policy that would render the agent

indifferent between it and the status quo:

Lemma 2. Given xP < xA < xC, v∗ = 1 only if D ≥ xA−|q− xA| ∀ q. Proof: Implicit, given

Lemma 1 and x∗ ∈ [−D,D].

Lemma 1 and 2 specify minimum conditions for the agent to opt-in, however, this choice is further constrained

by the committee’s ability to sanction. Even when both conditions are satisfied, the committee can prevent

the agent from opting in if the sanction is costly enough. For example, when q < xC (so that the committee

prefers to maintain the status quo) and c > |xA−q|, no level of discretion can induce the agent to comply

with the president’s request. When this cost is less than the potential policy gains on the part of the agent,

the President must provide a level of discretion which compensates for enduring a sanction (xA−D ≤ c).

Figure 2 illustrates this basic point. Larger values of c necessarily expand the gridlock region, and require the

President to provide more discretion than would otherwise be necessary without the threat of sanction.

14Note that, though Lemma 1 and 2 are qualified in terms of the spatial orientation xP < xA < xC, they hold for any configurationof the players. Since sanctioning only applies negative pressure after a policy has been selected, the President and the committee cannever induce the agency to implement a policy that makes it worse off.

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Figure 2: Discretion Region under Variable Sanction Cost (τ = 0)

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Figure 3: Discretion Region under Variable Sanction Cost (τ = .5)

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12

Turning to the committee’s choice of sanction rule, recall that, intuitively, only conditional strategies have

the potential to induce a positive behavior change (from the committee’s perspective), so it is left to specify

the conditions in which Request and Demand (and all those in between) are optimal. Given v∗ and x∗, we

can express the optimal sanction rule in the following way:

σ∗ =

σ1 if q < xC

σD⇒ s = 1 iff x∗ 6= D if xC < q, 2xC−q≤ D≤ xC

σ2 if xC < q, xC < D

(6)

When the status quo pits the committee and the President’s ideal policies directly at odds, it pronounces that

it will sanction if the policy is shifted further away from their ideal point. For distant status quos, in which all

players benefit, their optimal strategy is to demand the closest available policy, conditional on the level of

discretion provided by the President. Next, before presenting the President’s optimal choice of D, it is useful

to introduce some additional notation. Let c denote the sanction cost which satisfies the condition

|xA− (q− xC)|< c (7)

and conversely, let c denote the cost that does not. That is, the sanction cost is “high” when it is greater than

the policy gain between the agent’s ideal policy and the point which would allow them to avoid sanctioning

altogether. In other words, this is the value of c which induces the agent to condition its choice on the

committee’s preferred outcome. With this in hand, we can characterize the equilibrium selection of D:

Proposition 1. Given xP < xA < xC, the President’s optimal choice of discretion can be written

as

D∗ =

0 if 2xC ≤ q < xA

2xA−q+ c if xA < q < 2xC and c = c

2xC−q if xA < q < 2xC and c = c

(8)

Proof: In turn, P provides no discretion when q < xA, given Lemma 1 and 2, which imply

that for these status quos, A will either opt out, or opt in and select x∗P. P provides discretion

commensurate with A’s policy gains and c, given c. When c, P must provide discretion to satisfy

C’s demand. P provides no discretion when the status quo is sufficiently distant from all, since

all benefit from the move to xP.

13

Though the implications of the proposition will be discussed in following section, a few points are worth

noting. For instance, holding fixed the policy positions of the actors, discretion is increasing in c, for

xA < q < 2xC. Intuitively, the more trouble the committee is capable of giving the agency, the more discretion

(and by extension, policy concessions) the president will have to permit. Another important point is that if

the cost reaches the threshold of c, discretion contracts and expands with the ideological distance between the

President and the Committee. For a subset of q, when the potential cost is sufficiently high, the optimal level

of discretion is a direct result of the agent’s desire to avoid congressional ire. Finally, we can characterize

the circumstances in which the President will delegate to external agents (e = 1). Given UP, this becomes a

straightforward comparison of the resource cost paid to develop policy “in-house” and the policy loss the

president would incur by delegating. In this case, that cost is equivalent to the level of discretion the president

must provide in equilibrium. This basic trade-off is presented in Equation 9.

e∗ =

0 if |D∗|> τ

1 if |D∗| ≤ τ

(9)

Moreover, the knowledge of τ also changes the distribution of x∗, as the committee’s optimal sanction rule

under some q changes to Demand. In Figure 3, the increase in τ produces a region in which the committee

can obtain it’s ideal point—given knowledge of the costliness of developing policy in house and the conditions

for bureaucratic compliance.

II.3 Implications

The model depicts clear trade-offs for a president with the intent to “go it alone.” The first, and most critical

component of the model is that when the president signs a directive, a new status quo is not final. The

policymaking process continues as the agency—charged with carrying out the directive—and the appropriate

congressional committee—charged with monitoring the agency—attempt to influence the outcome. These

additional considerations lead to three hypotheses about the nature of unilateral presidential action, which all

suggest that such action is contingent on bureaucratic agency—even in the absence of uncertainty. The first

and second hypotheses illustrate the trade-off between White House centralization and policy. Mathematically,

Hypothesis 1 emerges from Equation 9, but it supports much of what has been written about questions of

centralization. Rudalevige (2002), for example, argues that presidents centralize legislative policy formulation

14

when the institutional resources within the White House and Executive Office are available. In the context

of unilateral presidential policymaking, the model suggests that dwindling resources within the President’s

immediate control make external agents—and the less-than-ideal policies they would select—more attractive

options.

Hypothesis 1. All else equal, as the cost of developing policy within the Executive Officeincreases, the president delegates to external subordinates more often.

Next, consider the influence of the congressional committee’s power to sanction. Here, the risk of bureaucratic

non-compliance importantly constrains the President’s ability to enact xP. The president must “buy-off” the

agent with increased discretion (up to and including the ability to select its own ideal point) as the cost

of incurring a congressional sanction increases. Together with Hypothesis 1, this suggests that delegation

necessarily involves trading away policy gains—a relationship which is compounded when congressional

committees can impose greater costs on executive agencies.

Hypothesis 2. All else equal, as the cost of congressional sanction increases, the presidentprovides more discretion to external subordinates.

Finally, though the precise interpretation is more contingent, the model sheds light on the relationship between

the latitude afforded bureaucratic agents, and policy disagreement between the President and Congress. As in

Howell (2003), since the model begins with the generation of some exogenous q, the precise relationship

depends on the distribution of status quo policies. However, a few stylized facts illustrate what the model

suggests we would observe empirically. Recall that, according to σ∗, the committee sanctions for all status

quo policies moved within [xP,xC]. Thus, as this region expands, the threat of sanction necessarily expands.

This directly impacts the level of discretion the president is willing to provide. As the sanction region expands,

the President must provide more discretion to “buy-off” agents closer to the committee (either to avoid

sanction or select their preferred policy). This implies the following hypothesis:

Hypothesis 3. All else equal, as the policy disagreement between the President and Congressincreases, the president will provide less discretion to external subordinates.

When inter-branch conflict is high, the president’s best option will often be to delegate to an external agent

with similar preferences. This agent is provided less discretion, not because the president disfavors the agency

or worries about deviation—but because the agency itself does not require much discretion to implement a

policy which would render it better off. Agencies closer to their committee, on the other hand, will necessarily

15

require more discretion (both to satisfy Lemma 1 and to compensate for any sanction incurred), and as a

result, paying the presidential resource cost will become more attractive.

III. Reanalyzing Executive Orders, 1947-2001

Since the proposed model aims to explain variation in the character of executive unilateralism, it is appropriate

to reanalyze data which has been the basis for the unilateral politics model’s empirical support. Specifically, I

model delegation and discretion within “significant” executive orders from 1947-2001, collected by Howell

(2005). The effort to distill the policy significance of legal documents has been a substantial component

of work on lawmaking (Mayhew 1991, Clinton & Lapinski 2006) and unilateral action (Mayer & Price

2002, Chiou & Rothenberg 2014), so it is important to underscore the appropriateness of this case selection.

Like legislation, it is essential to separate the important orders from the unimportant as a matter of interest.

Presidency scholars want to develop theories which explain variation in important—rather than mundane—

actions. For studies of unilateral policymaking, however, there is an added threshold—that presidential

directives satisfy the basic stylized picture of presidential policymaking. For instance, can exempting an

official from mandatory retirement be considered a movement from q to xP? Do these “housekeeping” orders

represent policy selections from a left-right continuum of options? Executive orders, in particular, perform a

variety of functions, so that only subset will be of theoretical interest. The 290 non-ceremonial executive

orders culled by Howell were selected on the basis of being mentioned in the New York Times.15 In this

case, the profit-motive of the Times usefully helps to satisfy both conditions—it serves as a consistent,

contemporaneous rater that shies away from reporting the mundane, and thus, produces a set of observations

that (for the most part) are worth explaining. As a final step, I omitted 20 orders which establish short term

boards of inquiry meant to adjudicate labor-management disputes. Many of these boards were created under

the authority of the Labor Management Relations Act of 1947, prescribe no policy movement, and thus, fall

outside the scope conditions of either the unilateral politics or presidential delegation models.

15More recent work by Chiou & Rothenberg (2014) uses multiple raters to model significance as a latent variable. I have limitedthe analysis presented in this paper for an important reason. As a social scientific enterprise, the perspective I advance speaks directlyto the unilateral politics model, so reanalyzing the data that provided its original support is appropriate.

16

III.1 Measuring Delegation and Discretion in Executive Orders

The presidential delegation model and its implications suggest the empirical analysis must include measures

of several key parameters: delegation to external subordinates (e), discretion (D), the president’s resource

cost (τ), the cost of congressional sanction (c), and the policy disagreement between the president and the

congressional committee (|xP− xC|). The endogenous parameters (e, D) were produced by hand-coding each

order in the sample. The complete coding procedure (reproduced in the Appendix) is adapted from Epstein &

O’Halloran’s (1999) coding of summaries of landmark legislation. It follows a basic logic—that presidential

directives can be parsed into substantively distinct sections, and that the content of those sections can be used

discern the degree of latitude the agent has in implementing policy.

First, the number of policy sections in the order is recorded—which generally followed the document’s

section and subsection formatting. Sections which delegate authority are then logged, along with the actors to

whom the authority was delegated. Examples of delegated authority vary in degree and cover a wide range of

policy areas. They include: orders to carry out a general policy without constraining details,16 the creation of

new agencies,17 or a directive to develop programs and policies in pursuit of some broader goal.18 Next, the

delegated authority’s institutional location is recorded—the specific agent(s) referenced by the order. This

coding procedure provides two potential measures of External (e). The first is a simple dichotomous indicator

variable, coded “1” if the majority of the delegated sections in the order were assigned to external agents.

This provides a useful approximation of the decision as outlined in the model presented earlier. However,

since some executive orders delegate authority to both the WHO/EOP apparatus and external agents, I also

provide a continuous specification—the ratio of authority provided to external agents.

For the purposes of measuring discretion tracking delegated authority is not sufficient. Recall that the

key feature of E.O. 10340 (Truman’s steel mill seizure order) is not simply that it gave the Secretary of

Commerce authority, but that authority came with strikingly few explicit restrictions. In other words, it placed

few constraints on delegated powers. To account for this, the presence or absence of several constraint types

were recorded. Specifically, constraints on delegated authority included: requiring the actor to consult other

16For example, E.O. 11118, issued by John F. Kennedy, which directed the Secretary of Defense to “take all necessary steps” tointervene for the integration of Alabama’s schools.

17See E.O. 10934, which established the Administrative Conference of the United States.18See, for example, Richard Nixon’s E.O. 11625, which provided the Secretary of Commerce with promoting equal opportunity in

business ownership.

17

actors prior to implementation,19 requiring the policy be implemented within a time limit,20 providing explicit

details of policy,21 requiring the actor to submit a report or otherwise notify the President or Congress of its

actions,22 and including a section of definitions within the order.23 Though extensive coding procedures were

outlined, generation of the above variable is inherently subjective. For validation purposes, two researchers

with no knowledge of the theory or hypotheses coded a random sample of 40 orders (≈ 15 percent of the

total sample). Despite the complexity of the procedure, inter-coder reliability rates were high, with an ICC

of 0.87 for the number of sections and 0.80 for discretion, the dependent variable used in the analysis. A

complete description of this validation is included in the Appendix.

It may be useful to work through a typical example. Executive Order 12129, which was issued by

Jimmy Carter in 1979, contains 10 substantive sections describing the creation of the Critical Energy Facility

Program—a “unilateral" predecessor to energy policy reform in Congress which would come in 1980.24

Four of those sections explicitly delegate authority to the Director of the Office of Management and Budget

to institute the program—which meant selecting energy producing facilities to receive special permits for

construction. However, the order also requires the OMB to consult with other agencies, notify the President

and the agencies of their decision, and adhere to procedures and guiding policy principles produced by

a previous interagency council. So, though the order clearly delegates power to the Director, it provides

boundaries wherein the OMB may operate. In other words, the order delegates, but it does not provide

substantial discretion.

Combining the above information into a useful index necessarily involves imposing some functional form

on the latent outcome of discretion. Epstein and O’Halloran (1999) developed a discretion index by counting

delegated provisions and major provisions of legislation. Recall that the level of discretion (D) allows the

agent to select a policy, x, which is, by definition any point between [xP−D,xP +D]. In order to normalize

the index, suppose further, that Discretion ∈ [0,1]. That is, discretion is a bounded ratio where 1 denotes

complete discretion over some delegated power, and 0 denotes no delegated authority. To produce values of

19Though E.O. 11507 gave applicable departments the authority to develop their own pollution-prevention standards for publicfacilities, it required they consult with the Secretary of Health, Education and Welfare before putting the policies in place.

20For example, E.O. 11365 required the National Commission on the Civil Disorders set an explicit deadline for the Commissionto submit final recommendations and disband.

21Though Lyndon Johnson gave the Civil Service Commission to promulgate regulations (E.O. 11222) relating to ethicalconduction of public personnel, he also included an itemized list of what constituted unethical conduct, limiting the authority of theCommission to deviate from those standards.

22For example, Kennedy’s E.O. 10934 required the ACUS to submit reports to the President on a recurring basis.23E.O. 13089, which prescribed policies for coral reef protection, defined “coral reef ecosystems” at the outset, which constrained

the application of the order by the Secretaries of Interior and Commerce.24Carter’s plan was rejected by Congress from 1977-1978 by the Senate.

18

discretion within those bounds, I impose the function in Equation 1. Here, the first term ( dp ) is the number of

delegated sections (d) over the total number of sections (p), and represents the amount of delegated authority.

The second term is a constraint penalty: the number of constraints in the order (c) divided by the total number

of constraint types, multiplied by the amount of delegated authority. To use the previous example—Carter’s

Executive Order 12129—the delegation term would be 0.4, and the constraint penalty would be 0.24, for a

discretion index of 0.16.

D =dp−[

dp∗ c

t

](10)

In this way, this discretion index is the proportion of subsections which contain delegated authority,

weighted downward by the number of constraints placed on executive agents. The index itself is somewhat

arbitrary, in that it weighs each constraint equally. Moreover, each delegated section is also given equal

weight—despite variance in substantive significance. However, alternative measurement strategies necessitate

costly trade-offs. The ratio can be deconstructed and understood in terms of its constitutive parts, such that

the point estimates presented later retain some degree of interpretability. Though adopting a hierarchical

measurement model (a la Johnson & Lambert 1999, Clinton & Lapinski 2006, Chiou & Rothenberg 2014)

may be a useful validation of the dependent variable, the estimates generated would be less meaningful. On

the other hand, requiring some rating procedure to weigh the significance of individual provisions would

introduce more researcher bias, and render the results far less replicable. The measurement strategy employed

here, then, provides an appropriate middle ground.

The validity of this measurement strategy must be judged, in part, by a qualitative assessment of the

values it produces. To aid in that validation, I have reproduced the tails of the discretion distribution in

Appendix C. These tables show the ten orders with the highest and lowest (non-zero) levels of discretion, as

determined by the measurement strategy above. Overall, the examples seem to be accurate representations of

the key construct. The low discretion orders are all near examples of direct policymaking. They prescribe

changes to government policies explicitly, and when they delegate, they place considerable constraints on

the actors to whom authority was delegated. Notable examples include changes to regulations governing the

secrecy of documents and information. The high discretion tail, on the other hand, shows Presidents vesting

considerable, significant authority in a single agency without much detail or explicit limitation. Several of

these orders came as Presidents gave the Secretary of Defense extraordinary authority to use military force

19

within the United States. However, the list also includes orders affecting other areas of policy policy—law

and crime, food distribution, and age discrimination.

III.2 Modeling Executive Orders

This leaves the presidential resource cost (τ), the cost of congressional sanction (c), the policy disagreement

(|xP− xC|) to consider. In terms of measurement, τ presents a non-trivial challenge because many factors

may lead to low or high levels of τ . This cost will certainly vary by policy area. Though the WHO/EOP

apparatus mimics many of the functions and policy areas performed by the executive branch, certain areas

of policy may be too costly to confine to its purview. Moreover, the cost may be a function of available

resources within the “presidential branch”—the personnel and appropriations at the president’s direct disposal.

However, a high τ might also be the result of these resources being taxed by other initiatives and priorities.

Presidential administrations may have high capacity for developing and implementing policy, but if all

available personal and offices are dedicated to other task, the cost of additional assignments may be high.

This leaves three potential indicators of τ : WHO/EOP personnel, the year in administration, and policy area.

The WHO/EOP employment figures come from Dickinson & Lebo (2007). The year in administration is a

simple running count (beginning at 0), that indicates the number of years the president who issued the order

had been in office. Policy area is an unordered categorical variable based on the classification scheme of the

Policy Agendas Project. Overall, delegation to external agents should be positively related to increased τ ,

operationalized as these three covariates. That is, delegation to external agents should be negatively correlated

with WHO/EOP personnel and positively correlated with year in administration, while we should observe

clear differences between topics based on the a priori assumption that the resource cost varies based upon

policy area. Delegation to external agents is modeled as a function of these proxies and several controls:

e = α +β1Personnel +β2YearInAdmin+β3PolicyArea+β4Redelegation+β5Year+(ε)

Next, to test Hypothesis 2, I use a measure of strength of the majority party in Congress as a proxy for c.

When Congress punishes an agency for implementing some policy, it has a wide variety of options—which

impose variable costs on the agency itself. The coalition threshold for calling agency officials to testify is low,

compared with passing a piece a legislation which abolishes an agency entirely. The strength of the majority

20

party, then, is an appropriate proxy for Congress’ capacity to impose costs within the presidential delegation

model. Ma jStrength indicates the percentage of seats held by the majority party above the majority threshold,

averaged over both chambers. According to Hypothesis 2, this should be positively correlated with discretion.

Finally, to test Hypothesis 3, I must include a measure of |xP− xC|. This is operationalized as the spatial

distance (DW-NOMINATE; Poole & Rosenthal 2007) between the President and the relevant committees in

the House and Senate (McGrath 2013). Relevant committees were identified by matches based upon policy

areas. An alternative strategy is to match committees based upon oversight jurisdiction. However, in many

cases, oversight jurisdiction overlaps, and frequently changes as agency and committee structure evolves.

While committee jurisdiction based on policy area has changed somewhat over time, it is comparatively

stable. Finally, because Hypothesis 3 intimately depends on the spatial orientation outlined in the model, it is

necessary to provide an approximation of xP < xA < xC. For the postwar era, divided government helpfully

identifies this scope condition. It follows the logic behind the basis for Hypothesis 3 as an implication—that

bureaucratic actors occupy a middle ground between elected institutions. I therefore interact ideological

distance with divided government, with the expectation that β2 < 0.

D = α +β1Distance+β2Distance∗DivGov+β3Ma jStrength+β4Redelegation+

β5Year+β6Personnel +(ε)

To test Hypotheses 1-3, it is also necessary to include several variables, not explicitly part of the theoretical

model, so that the underlying relationships are not obscured. First, I include a simple time trend, to control

for the possibility that the dependent variables are systematic functions changes in the characteristics of the

instrument. That is, though presidents have always issued executive orders for policy purposes, the gradual

accumulation of precedents and increased presidential incentives to claim credit for policy change may render

an order issued in 1950 comparable to one issued in the late 1990s. That is, what constitutes an observation of

potential unilateral action may have changed. Second, I include a indicator variable for whether the executive

order contains delegations based on 3 USC § 301, which gives the President the authority to re-delegate any

power originally delegated by Congress. This variable is particularly important, given the process which

generates potential status quos (which I have not modeled). For example, Presidents may delegate to external

agents more often during unified government simply because Congress tends to provide them with more

discretionary authority when inter-branch disagreement is low. Finally, to model discretion, it is necessary to

21

include a measure of τ (WHO/EOP personnel) to account for the possibility that (according to the model)

variation in this parameter may also drive Discretion.

IV. Results

Initial descriptive information about the orders themselves provides strong support for viewing unilateral

policymaking as an act of delegation. Of the 270 orders, 81.2 percent contain some authority delegated to

subordinates. Note that, as a matter of comparison, Epstein & O’Halloran found that 90.3 percent of their

257 landmark laws contained some delegation to the executive branch (1999, 94). Importantly, instances

of “direct legislation” and “direct presidential policymaking” among these dataset are roughly comparable.

This underscores the basic point made at the outset: that when either institution initiates some policy change,

they most often rely on bureaucrats for the details and implementation of their broader goals. Moreover, the

proposed dichotomy between the WHO/EOP and external actors (cabinet departments, independent agencies,

and government corporations) bears out in the sample. Among the orders, 61.4 percent delegate to some

external subordinate, and 54.3 percent delegate more than half of their authority to these agents. So though

presidential directives most often delegate to those outside the president’s immediate reach, there is still

meaningful variation to explain.

These findings also bear on an important question relating to the creation of the orders themselves. Recent

work by Rudalevige (2012, 2014) has shown that the formulation executive orders, like other presidential

directives, are most often part of an internal bargaining process between the President and the relevant agency.

In some cases, orders are drafted by the agencies themselves. In others, executive orders are never signed

because agencies disagree with the policy in question. Thus, it is reasonable to ask how this process bears

on the assumptions contained in the presidential delegation model. I argue these features largely support

the perspective advanced, because they are mechanisms by which the agency informs the President of its

policy preferences. In other words, a game of perfect information (in terms of spatial distance) is not far

from reality. Observed variation in the outcomes of interest (external delegation and discretion) bolster the

argument that the content of the orders reflects this process. If the formulation of the orders themselves

reflected the selection of the precise policy agreed to by the President and the agency, variation in constraints

and the level of ambiguity in orders should not be present. Note, as well, this underscores another similarity

between unilateral action and lawmaking—since bureaucrats are often an essential part of drafting legislation.

22

IV.1 Delegation Beyond the Executive Office

While the results of the first models suggest that the proposed processes operate, they stop well short of

providing support. Recall that delegation to external agents should be negatively associated with personnel

and positively associated with year in administration, there should be clear differences between policy topics.

There is some clear variation by policy areas. However, while interpretation of this variation is largely

post-hoc, the areas that do emerge as significantly different from macroeconomics (the base category) do not

match up with prior expectations about the executive branch. Much of the president’s WHO/EOP apparatus,

for instance, is dedicated to international affairs, yet it emerges as significantly different. Additionally, neither

personnel or year in administration are statistically distinguishable from zero.

Null findings such as these pose a particular problem, because they provide little information about which

component of the research process failed. It can be said that Hypothesis 1 is unsupported by the analysis

in Table 1, but list of potential reasons is necessarily speculative. However, as noted at the outset, of key

parameters in the presidential delegation model, τ posed the most serious challenge. Ultimately, for the

purposes of this analysis, I settled on three variables which arguably contribute to the president’s resource

cost within the WHO/EOP, but could be considered a direct measurement of the variable itself.25 The raw

count of personnel in the WHO/EOP obscures variation in the distribution of that personnel by office and

policy area. The running tally of years in administration provides some general indicator of tasks accruing

presidential administrations, but no indication of which tasks or to what degree they limit the President’s

capacity for policy implementation. In other words, they are blunt proxies which may still obscure the

underlying relationship proposed by the model. This possibility is further bolstered by the descriptive findings

previously presented and the results in the following section, which strongly support Hypotheses 2 and

3. These expectations and underlying assumptions of the model are well supported, suggesting that the

estimates in Table 1 are more likely the result of deficient measurement. Ultimately, this suggests that further

work to identify the costs associated with Presidential policy-making is necessary if the implications of the

presidential delegation model are to be adequately tested.

25Note, I find no evidence that the null findings in Table 1 are the result of multicollinearity. Though each has some relationshipwith the same underlying variable, they are not highly associated with one another.

23

Table 1: Modeling Delegation Outside the WHO/EOP

Variable Logit OLS(1) (2)

Personnel 0.0002 0.00004(0.0001) (0.00002)

Year in Administration -0.051 -0.010(0.070) (0.015)

Policy Area Macroeconomics (Base) – –Civil Rights 1.799∗∗ 0.390∗

(0.805) (0.171)Health 17.616 0.707

(2,399) (0.504)Agriculture 16.748 0.542

(1,196) (0.278)Labor and Employment 0.956 0.191

(1.011) (0.224)Education -16.181 -0.423

(2,399) (0.497)Environment 2.270 0.432

(1.265) (0.224)Energy 0.755 0.153

(0.818) (0.179)Immigration 0.914 0.187

(1.207) (0.275)Transportation 1.112 0.233

(1.040) (0.234)Law and Crime -0.469 -0.117

(1.039) (0.221)Social Welfare 0.243 0.049

(1.601) (0.363)Housing and Community Development 0.657 0.136

(1.182) (0.271)Banking and Finance -1.110 -0.222

(1.347) (0.270)Defense 0.849 0.174

(0.655) (0.138)Science, Technology and Communications 1.139 0.233

(1.193) (0.275)Foreign Trade -0.094 -0.037

(0.979) (0.209)International Affairs 1.897∗ 0.383∗

(0.755) (0.154)Government Operations 0.826 0.168

(0.703) (0.150)Public Lands Management 1.546 0.330

(1.049) (0.221)§301 Redelegation 1.164∗∗ 0.241∗∗

(0.355) (0.074)Year 0.013 0.003

(0.010) (0.002)Constant -27.195 -5.800

(19.523) (4.320)

N 267 267R2 0.157Log Likelihood -160.419

∗p<0.05; ∗∗p<0.01

IV.2 Discretion Beyond the Executive Office

Modeling the content of significant executive orders, I find strong support for Hypotheses 2 and 3. Recall

that, the cost of a congressional sanction (c), operationalized as the strength of the majority party, should

be positively associated with discretion. The underlying explanation is that in equilibrium, presidents must

sacrifice policy gains to prevent bureaucrats from opting out in the face of the punishment from Congress. The

ideological distance between the President and the relevant committee under divided government, however,

should be negatively associated with discretion—with the explanation that the expansion of this region

increases the likelihood that the president will delegate to ideologically proximate agents and provide them

with minimal discretion.

Since the hypothesis pertains to external agents, I subset the data to those orders which delegate to more

than half of their authority to external subordinates. The unit of analysis then, is a significant executive

order which delegates outside the WHO/EOP. These results, and the results of several robustness checks, are

presented in Table 2. The baseline (1) model supports Hypothesis 2, in that a standard deviation increase

(+0.12) in the strength of the majority party results in a 4.6 percentage point increase in the the level of

discretion. In terms of seats, this means an additional 6 Senators and 26 House representatives (or another

combination which would average to 0.12). In support of Hypothesis 3, in the baseline (1) model, a standard

deviation movement in ideological distance during divided government is associated with a 4.7 percentage

point reduction in the discretion ratio. To illustrate what these relationships mean substantively, consider

a simple example. Suppose an executive order of moderate length—20 substantive sections—delegates

authority to the head of a cabinet department and that five of these sections explicitly direct the Secretary

to develop or implement some policy. A standard deviation increase in the level of disagreement between

the president and the committee with oversight jurisdiction could manifest itself in either the imposition

a additional constraint or the reduction in delegated authority. For instance, the President could require

that the Secretary consult with the OMB prior to making final decisions, or put a deadline in place for the

Secretary’s actions. Alternatively, it could mean that, instead of delegating the fifth section, the president

merely describes the precise regulation to be followed. In the context of majority party strength, this means

that additional seats translate into additional delegated authority or fewer constraints, on average.

Importantly, majority party strength does not seem to be driven by a supposed increase in delegation

through lawmaking. The findings hold up when §301 re-delegations are controlled for—a key proxy for

25

the increase in discretionary authority granted to the President. Moreover, these results are robust to variety

of model specifications and estimation routines. Standard OLS regression fails to account for the fact that

Discretion ∈ [0,1], which may produce heteroskedastic results for variables strongly associated with the

dependent variation (Paolino 2001). I provide three alternative specifications (Models 2-4), which may be

more appropriate. Column 2 presents the results of a Tobit regression with bounds at 0 and 1. Model 3 uses

Ferrari & Cribari-Neto’s (2004) parameterization of a zero-one inflated beta distribution. Finally, Model 4

presents the results of a fractional logit, as described by Papke & Wooldrige (1996). Each is designed to

account for limited dependent variables, so not surprisingly, they are generally more efficient than Model 1.

Though the coefficients themselves are not comparable, the T-statistics for key parameters demonstrate the

findings described earlier are not sensitive to more appropriate model specifications.

Table 2: Modeling Discretion in Executive Orders

OLS Tobit Inflated FractionalBeta Logit

(1) (2) (3) (4)

Ideological Distance 0.184 0.189 1.09∗ 0.877(0.15) (0.15) (0.55) (0.67)

Ideological Distance * −0.226∗ −0.21∗ −0.77∗ −1.14∗∗

Divided Government (0.090) (0.09) (0.33) (0.42)

Strength of Majority 0.388∗ 0.395∗ 1.55∗ 1.78∗

(0.184) (0.19) (0.68) (0.82)

Year −0.006∗∗ −0.005∗∗ −0.018∗∗ −0.029∗∗

(0.002) (0.001) (0.006) (0.007)

§301 Redelegation 0.024 0.025 0.096 0.138(0.047) (0.04) (0.18) (0.21)

WHO/EOP Personell 0.00001 0.00001 -0.00004 0.00006(0.00002) (0.00001) (0.00006) (0.00007)

Constant 11.144∗∗ 11.6∗∗ 0.341∗∗ 0.547∗∗

(3.116) (3.29) (0.12) (0.15)

N 132 132 132 132

Note: (∗) p < .05; (∗∗) p < 0.01, fractional logit estimatedwith quasi-binomial family and logit link function.

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V. Conclusion

The preceding analysis helps to adjudicate between existing, longstanding perspectives on the presidency.

While Neustadt’s (1960) perspective highlights the inherent limitations of a president who must bargain their

way to policy change, the wave of research dedicated to unilateral action shifted focus to the President’s

capacity for direct policy change. The model presented herein takes into account that the President is (1)

often reliant on bureaucratic agents for implementing policy and (2) often has the motive and opportunity to

act independent of Congress. Importantly, this perspective demonstrates that bureaucratic agency is often a

means by which members of Congress secure better outcomes when the President acts alone. In short, the

politics of direct action is mitigated by the necessity for bureaucratic cooperation.

In particular, direct action necessitates important trade-offs between limited institutional resources and

potential policy gains. Delegation to subordinates engenders the risk of Congressional influence on final

outcomes, as members of Congress threaten to punish agents who would implement the President’s orders.

In the course of the post-war era, unilateral directives provided wide variation in delegation and discretion to

public employees. When policy disagreement between the President and the relevant oversight committees

increased, the President tended to provide less discretion. When Congressional majorities grew in strength,

Presidents tended to provide more discretion. In the case of the former, delegation to distant subordinates

(and the cost in terms of policy gain) may have outweighed that of developing policy “in-house.” In the case

of the latter, increased Congressional power necessitates policy compromise on the part of the President. This

compromise occurs because all government policy cannot be developed and implemented within the confines

of the “presidential branch.”

Finally, this study tempers the more normatively unappealing aspects of unilateral politics—while raising

some additional challenges. Though the frequency and intensity of direct presidential action appears to

follow an upward trajectory over time, outcomes are ultimately contingent on bureaucratic cooperation.

Presidential power is often the power to delegate, and as a result, the expansion of executive power through

accruing precedent is not necessarily guaranteed. There are limits to what agents will do when faced with

multiple principals. But this raises additional questions about bureaucratic discretion. Specifically, persistent

incentives for both Congress and the President to delegate imply that regulation and rule-making will become

increasingly central to politicized, largely partisan struggles among separated powers.

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Appendix

A. Coding Discretion in Executive Orders

The author and supplementary coders followed the coding instructions described below to produce uniformityin coding executive orders.

1. Record the directive’s meta-data according to the following guidelines:

(a) Record any referenced legislation, not only what is referenced in the preamble.

(b) Record any referenced US Code, not only what is referenced in the preamble.

(c) The preamble, containing “whereas” statements, does not qualify as a section and is not includedin the analysis.

2. Record the number of sections in the directive, according to the following guidelines:

(a) The text must be formatted or indented in such a way that it is separate from the previous text.

(b) To be considered a separate section, the text must differ in substantive content from the previoustext.

(c) Sections which are followed by a enumerated list of functions do not qualify as sections, but eachlisted item does.

(d) Sections labeled “General provisions” count as a single section, regardless of number of thesubsections which follow.

(e) Sections labeled “Policy” or “purpose” count as a single section.

(f) Sections labeled “Definitions” (with a series of subsections following) count as a single section.

(g) Lists which elaborate a policy in a previous section (such as lists of agencies or composition of acommittee) are not considered sections.

(h) In borderline cases, record two sets of section counts - to be compared upon further review fordifference in resulting dependent variable.

3. Record any delegated provisions and the actor to whom the authority was delegated, according to thefollowing guidelines. Delegate authority may involve:

(a) The creation of a task force, advisory board, or agency with policy-making functions.

(b) The authority to promulgate rules.

(c) The authority to redelegate authority to unspecified actors.

(d) Orders to develop policies, plans, or recommendations.

(e) Authority to perform functions independent of presidential approval.

(f) The authority to hold hearings or compel testimony in pursuance of delegated authority.

(g) The president requiring all departments and executive agencies to furnish the delegatee withinformation.

4. Record whether each constraint is present, defined in the following way:

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(a) Consultation: order divides delegated authority among multiple actors, or explicitly requiresan agent consult with another actor in developing policy. Example: “The Administrator shallperform his duties subject to the direction, control, and coordination of the Director of DefenseMobilization.” This includes formal requirements for consultation of public (e.g. notice andcomment procedures or others specified in the order).

(b) Time: order explicitly sets a time limit on how long the authority is to be exercised. Note,provisions specifying the “effective date” of an order are not considered time limits.

(c) Notification/Reporting: order requires agent to notify Congress, the President, or other interestedparties at the close of or during it’s policymaking activities. This includes provisions that requirethe agent to “make reports from time to time” and those that require it to notify the public of itsactions.

(d) Detail: order follows delegated authority with an enumerated list of guidelines by which the agentis to exercise its authority, or otherwise specifies a policy the agent is to carry out in detail at theoutset of the directive.

(e) Definitions: provides a list of definitions (i.e. “ ‘agency’ refers to ... ”).

5. Record the agent to which the constraints apply, using the following guidelines:

(a) Powers delegated to the heads of cabinet departments or administrative agencies are recorded asdelegated to the agency itself. For example, “Attorney General” will be coded as “Justice.”

(b) If the same section is delegated to multiple actors, include all actors in the entry.

(c) For advisory committees and inter-agency task forces, record the name of the task force with theagency or department of the Chairman in parentheses.

(d) For powers delegated to “all department and agency heads,” label the entry “General.”

6. Record the descriptive traits of the directive, according to the following guidelines:

(a) Redelegation: order explicitly delegates authority vested by Congress to the agent. Note, this isalmost always cited directly in the section itself.

(b) General Orders: directive contains general orders to all executive departments and agencies.

(c) Agency Creation: the department, agency, office or bureau to whom authority was delegateddid not exist prior to the order. Note: though advisory committees or ad hoc task forces are notconsidered newly created agencies, sub-units within larger departments do qualify.

(d) Convening: the order convenes private citizens/experts for advisory or decision-making purposes.

(e) Revocation: the order revokes, amends, or supersedes an existing directive (executive order,proclamation, reorganization plan, or memorandum).

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B. Intercoder Reliability

Given that the paper’s empirical results rest on the accuracy of the coding procedure, I undertook a conven-tional validation step for content analysis of this kind. Two researchers followed the above procedure to codea random sample of 40 executive orders. The chief concern—beyond “agreement”—was that discrepanciesin coding were not systematic, such that they reflected the author’s own bias toward expected findings. Ireport the results for key variables in Table 3. Notably, the functional form in Equation 10 seems to coercethe items, such that the DV is more reliable than the coding of the variables that make up the index itself. Tosome extent, this mitigates concern that the findings reported in Table 2 could be the result of error generatedby the coding procedure.

Table 3: Interrater Coefficients

Coefficient Delegation? Sections Del. Ratio Cons. Ratio Discretion RedelegationInterclass Correlation 0.79 0.87 0.66 0.65 0.80 0.61Pearson’s r – 0.90 0.66 0.66 0.80 –Maxwell’s RE 0.86 – – – – 0.77

C. Examples of Discretion in Executive Orders

Table 4: Orders with Lowest Discretion

President Title Number DiscretionTruman Prescribing Regulations [for Information Classification] 10290 0.017Eisenhower Safeguarding Official Information in the Interests of the Defense of the US 10501 0.018Truman Prescribing Portions of the Selective Service Regulations [...] 9979 0.026Nixon Prevention, Control, and Abatement of [...] Pollution at Federal Facilities 11507 0.031Carter Floodplain Management 11988 0.032Eisenhower Prescribing Regulations Governing the Selection [of] the Armed Forces 10650 0.032Clinton Setting Customer Service Standards 12862 0.033Johnson Amending Executive Order No. 10647 10647 0.038Clinton Federal Plan To Break the Cycle of Homelessness 12848 0.04Clinton Prohibiting Certain Transactions With Respect to Iran 12959 0.04

Table 5: Orders with Highest Discretion

President Title Number DiscretionJohnson Declaring a Public Policy Against Discrimination on the Basis of Age 11141 0.80Johnson Providing Federal Assistance in the State of Alabama 11207 0.80Johnson Providing for the Coordination [...] of [...] Law Enforcement [...] Programs 11396 0.80Truman Possession, Control, and Operation of Certain Railroads 9957 0.91Truman Regulations for Carrying Out the Provisions of the Joint Resolution 9864 1.00Eisenhower Providing Assistance for the Removal of an Obstruction of Justice [in AR] 10730 1.00Kennedy [...] Expanded Program of Food Distribution to Needy Families 10914 1.00Kennedy [...] Assistance for Removal of Unlawful Obstructions of Justice in [AL] 11118 1.00Johnson [...] Restoration of Law and Order in the State of Michigan 11364 1.00Johnson [...] Restoration of Law and Order in the Washington [...] 11403 1.00

33