Divided Government and the Fragmentation of American Law
Sean Farhang†
Miranda Yaver‡
We investigate institutional explanations for Congress’s choice to fragment statutory
frameworks for policy implementation. We argue that divided party government, which fuels
legislative-executive conflict over control of the bureaucracy, motivates Congress to fragment
implementation power as a strategy to enhance its control over implementation. We develop
a novel measure of fragmentation in policy implementation, collect data on it over the period
1947 to 2008, and test hypotheses linking separation of powers structures to legislative
design of fragmented implementation power. We find that divided party government is
powerfully associated with fragmentation in policy implementation, and that this association
contributed to the long-run growth of fragmentation in the post-war U.S. We further find
that legislative coalitions are more likely to fragment implementation power in the face of
greater uncertainty about remaining in the majority.
†
Associate Professor of Political Science and Public Policy at the University of California, Berkeley.
Charles and Louise Travers Department of Political Science. 210 Barrows Hall #1950 Berkeley, CA 947201950 ([email protected]).
‡
Ph.D. Candidate in Political Science at Columbia University. Department of Political Science. 420
West 118th Street, 7th Floor, New York, NY 10027 ([email protected]).
We thank Tom Clark, Marc Meredith, and Scott Ainsworth for their helpful comments and suggestions.
We gratefully acknowledge the support of the National Science Foundation, Law and Social Science Program,
Grant No. 1024466. Data and replication code are available through the American Journal of Political
Science dataverse: http://dx.doi.org/10.7910/DVN/29129.
1
In his classic work on American bureaucracy, James Q. Wilson (1989: 297-301) characterized American policy implementation as a “barroom brawl” with “many participants” and
“no referee.” He contrasted this fragmented and decentralized American style of policy implementation with a European model in which, according to Wilson, policy implementation
is carried out by only a few key authoritative actors in a more centralized and hierarchical
system (see also Moe 1989, 1990; Kagan 1999, 2001). This notion that American policy
implementation is “fragmented” has become something of a commonplace among students
of policy implementation in the American state. While the concept of American state fragmentation has been given a wide range of meanings by scholars, one key idea—Wilson’s
many brawling participants—is simply this: implementation authority is splintered and distributed over a larger number of discrete actors and divided over more separate institutions,
thereby increasing challenges of coordination and multiplying opportunities for conflict.
While this idea of fragmentation has been in circulation for more than a quarter century,
in recent years there has been a surge in scholarly interest in its causes and consequences
(Freeman and Rossi 2012; Biber 2012; Stephenson 2011; Bradley 2011; Marisam 2011; Gersen
2006; Buzbee 2005; Ting 2003; Kagan 2001). In the domain of statutory policy, this work
recognizes that fragmented legal frameworks for policy implementation are a function of
legislative design, with legislators assigning roles and powers to actors and institutions that
carry a law into effect. One broad question this work has taken up, at a theoretical level, is
why legislators would choose fragmented implementation designs.
However, despite the level of scholarly interest in the causes of fragmentation, systematic
empirical work on the subject is extremely thin. We are aware of no published work that
has sought to measure the number of discrete actors, separate institutions, or episodes of
overlapping jurisdiction incorporated into policy implementation designs. We argue that
important empirical work on legislative designs that constrain administrative or presidential
authority in implementation (Epstein and OHalloran 1999; Lewis 2003; Huber and Shipan
2
2002) measures different concepts than fragmentation, and that no inferences about fragmentation (as we define it) can be drawn from that work. Absent such a measure, we lack
systematic information about basic questions such as whether and how this dimension of
fragmentation in the American state has changed over time, and what factors explain its
presence, persistence, and patterns of development. Those questions motivate this study.
Focusing on regulatory legislation, we develop a novel measure of fragmentation in policy
implementation, collect data on it from 1947 to 2008, and test hypotheses linking separation
of powers structures to legislative design of fragmented implementation power. We are
centrally interested in the effect of divided party government on fragmentation, and the
theory that Congress uses fragmentation to inhibit presidential subversion of congressional
preferences in implementation. We demonstrate that divided party government is powerfully
associated with fragmentation, and that this association contributed to the long-run growth
of fragmentation in the post-war U.S. We further find that legislative coalitions are more
likely to fragment implementation power amid greater uncertainty about remaining in the
majority.
Fragmenting Implementation
We first advance a conceptually clear definition of fragmentation. We discern in the
literature a number of distinct, while clearly interrelated, dimensions of the concept of fragmentation that we wish to integrate. First, more fragmented policy implementation designs
rely upon a larger number of distinct actors and entities—such as boards, commissions,
secretaries, separate administrative officers, judges, and litigants—to carry a law into effect
(Wilson 1989; Kagan 1999, 2001; Jordana and Sancho 2004). On this view, the sheer volume of actors and entities playing a role in implementation contributes to its fragmented
character.
Second, power can be fragmented by dividing it over multiple distinctive sources of institutional authority, each of which has a significant measure of autonomy and independence.
3
This vein of work, as applied to the United States, has largely focused on the distribution
of implementation power across separate administrative agencies (Freeman and Rossi 2012;
Biber 2012; Bradley 2011; Gersen 2006; Buzbee 2005). This concept is distinct from the
number of separate actors and entities since the sheer number does not register whether
such actors and entities are located within a single agency or across multiple agencies. The
literature suggests, and it is sensible to expect, that dividing power across multiple agencies
raises distinct challenges of coordination because separate agencies can have different—even
conflicting—policy goals, priorities, and ideologies (Clinton and Lewis 2008).
Third, a distinctive form of fragmentation arises when legislators not only empower multiple actors and/or agencies to implement policy, but also give them authority and responsibility to perform the same functions with respect to the same statutory provisions, such
as empowering two different administrators to make rules on the same subject (Freeman
and Rossi 2012; Bradley 2011; Marisam 2011; Gersen 2006; Ting 2003). Scholars have
characterized this form of fragmentation as “redundant,” “duplicative,” or “overlapping” by
design, potentially instigating “turf wars.” This scenario is distinct from assigning multiple
actors and/or agencies separate functions to be performed in concert, each with exclusive
jurisdiction over its delegated function. Drawing these threads together, we characterize a
design as highly fragmented if it relies upon many actors, numerous agencies, and contains
frequent episodes of overlapping jurisdiction. Below, we propose measures of each of these
three forms of fragmentation, and a composite measure that taps the underlying concept
that unites them.
Why Fragmentation Matters
Scholars working from multiple perspectives have long regarded fragmentation’s consequences as vitally important, though they have reached radically different conclusions about
its effects. One line of analysis emphasizes that fragmentation imposes great costs on American society by producing a structurally ramshackle and operationally dysfunctional state.
4
It makes government inefficient, ineffective, unresponsive, and incoherent, and produces legal uncertainty, indeterminacy, and contradiction (Moe 1989, 1990; Schuck 1992; Kagan
1999; 2001; Lewis 2003; Marisam 2011). Because fragmentation weakens control over policymaking by elected officials, it erodes the democratic accountability of policymakers (Moe
1989, 1990; Lewis 2003, 3; O’Connell and Farber 2014). It renders law and policymaking
excessively complex and opaque, biasing outcomes in favor of elites endowed with legal and
political resources needed to navigate it, thus delegitimizing the state in the eyes of the wider
public (Schuck 1992).
However, a different theoretical account of fragmentation has grown in recent years,
emphasizing its potential virtues. On this view, delegation to more actors and institutions
can produce competition and innovation that advances state efficiency and effectiveness;
cultivate the creation and use of valuable information and expertise; productively leverage
distinctive forms of institutional capacity; and foster the representation of a wider range of
groups and interests in the policymaking process (Landau 1969; Busch, Kirp, and Schoenholz
1999; Ting 2003; Gersen 2006; Stephenson 2011; Biber 2012). It can also insulate agencies
from capture because of the added difficulty associated with capturing many implementers
(Bradley 2011: 778). It can aid congressional monitoring of implementers by creating a
system of inter-agency “fire alarms,” with some implementers incentivized to surveil and
report on others to Congress, thus enhancing democratic control and accountability (Freeman
and Rossi 2012: 1138).
Debates over the consequences of fragmentation are empirically and normatively complex.
They are also unsettled. These debates are all the more salient amid increasingly fragmented
regulatory designs in the contemporary era, which we document below, and which may
explain the proliferation of work on fragmentation in recent years. A paper seeking to shed
new light on the causes of fragmentation cannot adjudicate among contending perspectives
on its consequences. However, the rich debate over the consequences of fragmentation for
5
American governance and democracy highlights the importance of understanding where it
comes from and how it has changed over time.
Fragmentation, Divided Government, and Policy Control
Divided government creates incentives for Congress to fragment implementation power,
and the postwar growth in the conditions of divided government has importantly contributed
to the material increase of fragmentation in federal regulatory policymaking. Divided party
government contributes to fragmentation through the mechanism of legislative-executive conflict over control of bureaucracy. Legislators and the interest groups that influence them are
aware that presidents possess considerable capacity to influence agency behavior, and they
design laws to guard against that influence (Moe 1989, 1990; Epstein and O’Halloran 1999;
Huber and Shipan 2002; Lewis 2003). Divided government greatly increases an enacting
coalition’s attention to designing frameworks of policy implementation meant to achieve its
own policy goals while insulating from executive subversion.
Huber and Shipan (2002) find that divided government leads to more detailed laws, with
detail measured by a law’s word count. Epstein and O’Halloran (1999) find that divided
government leads Congress to delegate less discretion to the bureaucracy, with lower degrees
of discretion measured by higher levels of formal structural constraints on administrative action (e.g., appropriations limits, reporting requirements, or sunset provisions) relative to the
magnitude of the tasks delegated. Lewis (2003) finds that when creating new agencies under
divided government, Congress is more likely to structurally insulate the agency from presidential influence through mechanisms such as imposing qualifications on who the president
can appoint, fixing the duration of their service, and placing agencies at a greater remove
from presidential control. This body of work amply demonstrates that legislative coalitions
facing an ideologically opposed executive branch strategically design legislation with the goal
of guarding against policy shifting away from the enacting legislative coalition’s preferences.
We stress that none of the foregoing studies examined whether divided government is
6
associated with fragmentation as we have defined it: division of implementation authority
over a larger number of distinct actors, over a larger number of different agencies, and giving multiple actors the authority to perform the same function with respect to the same
statutory provisions. Nothing can be inferred about fragmentation, so defined, from the dependent variables in this earlier work. As a logical matter, it is possible that laws can be very
detailed as measured by Huber and Shipan, delegate little discretion as measured by Epstein
and O’Halloran, and create highly insulating agency structures as measured by Lewis, while
lodging all power to implement a law in a single institutional actor (such as a Secretary
or a Board), sitting within a single agency, with no episodes of overlapping jurisdiction.
Conversely, a vague statute, with few express constraints on bureaucratic discretion, and
few attributes of structural agency insulation, could spread implementation over numerous
institutional actors and numerous agencies, and contain many episodes of overlapping jurisdiction. Below, we compare our measure of fragmentation to Lewis’s measure of insulation
and find that the two are unrelated as an empirical matter.1
Fragmentation and Constraint. Fragmentation of an implementation framework
can serve the enacting coalition’s goal of constraining implementers from subverting its
preferences, contributing to the “stickiness” of the status quo. This feature of fragmentation
will be more attractive to coalitions under divided government. Increasing the number of
actors and agencies that must be coordinated to accomplish decisive action can, on balance,
1
We note that while Epstein and O’Halloran (1998) and Huber and Shipan (2002) do not discuss “fragmentation,” Lewis (2003) does. However, Lewis is referring to a phenomenon quite different and very much
broader than our concept of fragmentation. While Lewis’s concept includes placing multiple agencies in
competition and creating overlapping jurisdictions (8, 27-28), it further includes all things that “significantly
constrain the president’s ability to manage the bureaucracy” (4), or that limit “political control” of agencies
(10). Thus, Lewis considers allowing a single independent regulatory commission to dominate policymaking on an issue—despite its highly unitary and concentrated power—to be an example of fragmentation
because it limits presidential influence relative to an agency headed by a cabinet secretary (22). Likewise,
because more specific and detailed statutes are associated with lesser presidential discretion—with Congress
determining more policy in the statute—Lewis characterizes them as fragmenting (10). His concept of “fragmentation” thus embraces all that limits presidential discretion. We use the same word to denote a much
narrower concept: the splintering of power over a larger number of distinct actors and different agencies,
and more episodes of overlapping jurisdiction among them.
7
make significant departures from the policy status quo more difficult. It creates “coordination
challenges” (Freeman and Rossi 2012: 1138) and “a system of checks and balances” (Jordana
and Sancho 2004: 302) that will limit presidential influence on implementation of the policy
in question (see also Moe 1989; Williamson 1993; Lewis 2003). For this reason, presidents
have consistently favored more unified, centralized, hierarchical administrative designs, which
give them more power over implementation (Moe 1989; O’Connell 2006: 1704; Lewis 2003).
Of course, a fragmented implementation design will also constrain the enacting coalition
itself if it seeks to manage “bureaucratic drift” arising from bureaucrats’ own preferences,
interests, and objectives, which can be quite different from their legislative or executive
principals (McNollgast 1987; Moe 1989). However, despite this reduction in control by the
enacting coalition, there are several reasons why the calculus will still often favor fragmentation under divided government. First, ideologically distant presidents with hierarchical
control pose a greater threat to the enacting coalition’s preferences than bureaucratic drift
arising from other sources (Moe 1989; Moe and Howell 1999; Lewis 2003). Presidents in
American government are held distinctively responsible by their support coalition for the
output of federal policymaking institutions. They also possess unique institutional capacity
for unilateral action that Congress has difficulty countering. Thus, when presidents have distant policy preferences and are armed with robust, hierarchical instruments of control, they
will have both strong political incentives and potent institutional capacity to depart from
the enacting coalition’s preferences. Under these conditions, hostile presidents are rightly
regarded by enacting coalitions as the most “fearsome” threat of subversion on the American
political landscape (Moe 1989: 281).
Second, when Congress uses fragmentation to guard against subversion by distant presidents, it is not left without means to manage bureaucratic drift arising from bureaucrats’
own preferences. It can use administrative procedures and constraints to control bureaucracy, such as spending limits, time limits, public hearing requirements, legislative vetoes,
8
and appeals procedures (Epstein and O’Halloran 1999: 99-101; Wilson 1989: 243; McCubbins
and Schwartz 1984). Decisions about agency funding through appropriations bills provide
another particularly potent mechanism of controlling bureaucratic drift arising from bureaucrats’ own preferences (Davidson and Oleszek 2006: 357-58; Eskridge et al. 2014: 1009-10;
Headrick et al. 2002). The use of public hearings and investigations can also “subject recalcitrant bureaucrats to public humiliation that devastates their careers” (McNollgast 1987:
249; Davidson and Oleszek 2006: 355). Thus, while it is surely true that fragmentation,
ceteris paribus, can limit the enacting coalition’s ability to use hierarchical controls to manage bureaucratic drift, it can still be a sensible strategy under divided government. This is
because: (1) ideologically distant presidents with strong instruments of hierarchical control
pose a distinctively worrisome threat, and (2) the enacting coalition has other means at its
disposal to address the problem, albeit imperfectly.
Fragmentation and Inducement. Moreover, an old but recently burgeoning line of
work emphasizes that, in addition to simply hobbling presidential control of the bureaucracy,
fragmentation may serve a number of additional legislative goals. As discussed above, this
work argues that placing multiple actors in the same policy space can engender productive
competition and innovation; produce better information; and harness distinctive forms of
institutional capacity (Landau 1969; Busch, Kirp, and Schoenholz 1999; Ting 2003; Gersen
2006; Stephenson 2011; Biber 2012). It can mitigate the risk of agency capture by interest
groups hostile to the enacting coalition’s preferences because it is more difficult to capture
many implementers (Bradley 2011: 778). It can contribute to congressional capacity to monitor implementers by creating interagency “fire alarms,” with some implementers monitoring
and reporting upon others to Congress (Freeman and Rossi 2012: 1138). The use of such
institutional design strategies to induce bureaucratic behavior consistent with the enacting
coalitions’ preferences will be more attractive when it sees the executive as an enemy, prone
to lead the bureaucracy in the wrong direction. Moreover, this point is related to the cost
9
that Congress faces when it fragments, thereby weakening its own capacity for hierarchical
control. The notion that fragmentation may induce desired bureaucratic behavior suggests
that it may thereby reduce the need for congressional supervision through hierarchical control.
In sum, fragmentation imposes added burdens of inter- and intra-agency coordination on
a president from the opposing party seeking to control policy implementation. It can also
affirmatively induce implementation behavior desired by Congress when a distant president
threatens to subvert. Thus, we predict that divided government will be associated with
higher levels of fragmentation.
Divided Government Hypothesis: Under conditions of divided government, Congress
will be more likely to fragment implementation power.
Other Institutional Sources of Fragmented Design of Implementation Power
Existing literature suggests a number of other political-institutional theories to explain
fragmentation. An adequate empirical model of the relationship between divided government
and fragmentation must incorporate these theories, and given that they have not previously
been tested, they are of considerable interest in themselves.
Legislative Bargaining and Fragmentation. It has been argued that fragmentation in implementation arises from political compromise, with fragmentation being used to
buy the support of political opposition (Moe 1989; King 1997; Ting 2003; Lewis 2003; see
also Maltzman and Shipan 2008). To achieve passage of legislation amid partisan conflict,
winning legislative coalitions must usually compromise with the losing group when making decisions about bureaucratic structure, and the losing group is dedicated to curtailing
agency powers and gaining influence over agency decisions in whatever ways it can. Thus,
the losing group will pressure for fragmented authority with the goal of constraining administrators and hampering vigorous implementation by the opposing party (Moe 1989: 326).
This institutional bargaining logic suggests two testable hypotheses. First, the greater the
10
divisiveness and opposition in the legislative politics surrounding a bill, the more likely that
fragmentation will be used as a strategy to buy the support of political opposition.
Divisiveness Hypothesis: As the legislative divisiveness surrounding a legislative proposal increases, it will be more likely to fragment implementation power.
Second, legislative majorities governing by thin margins of control will be more likely to
fragment implementation power than will majorities who govern by wide margins of control.
The thinner a majority’s margin of control, the greater will be its need to compromise in
implementation design in order to achieve passage.
Margin of Control Hypothesis: As the majority’s margin of control becomes thinner,
it will be more likely to fragment implementation power.
Electoral Uncertainty, Coalition Drift, and the Stickiness of the Status Quo.
Moe (1989) argues that current legislative coalitions are mindful of the prospects of electoral
defeat and the possibility that future legislative coalitions will seek to guide implementation in an undesirable direction. This is the problem of “coalition drift.” Congress thus
enacts formal rules and structures calculated to limit bureaucratic discretion with the goal
of insulating policy from future legislative coalitions. This strategy of insulation can be
effective in the American separation of powers system because the many impediments that
the system presents to enacting laws (particularly its many veto points) often apply with
even greater force to repealing an existing law, around which vested interests may already
have formed (Moe 1989). This institutional logic for constraining bureaucracy operates as an
incentive to fragment implementation power. As Oliver Williamson characterized this cause
of fragmented bureaucratic design, “Incumbent politicians who create and design bureaus
are aware that the opposition can be expected to win and take control in the future,” and
thus “a farsighted majority party will...design some degree of (apparent) inefficiency into the
agency at the outset...to frustrate the efforts of successors...to reshape the purpose served
by the agency” (Williamson 1993: 107). According to this logic, greater concern about loss
11
of power by the majority party will make an enacting coalition more likely to fragment.
Electoral Uncertainty Hypothesis: As the risk of losing power through elections
increases for the majority party, it will be more likely to fragment implementation power.
The Data and Model
The Body of Laws
In order to build a dataset of laws to investigate the fragmentation of implementation
power, we start with the 366 federal statutes, passed between 1947 and 2008, that were
identified by David Mayhew (1991) as highly significant.2 We study implementation power
in the context of domestic regulation. Coders read all of the Mayhew laws in order to
identify those containing domestic regulatory commands. “Regulatory,” as used here, refers
to “any governmental effort to control behavior by other entities, including...business firms,
subordinate levels of government, or individuals” (Foreman 2001: 12982). A regulatory
“command” is a mandatory proscription of actions that the legislation seeks to prevent (e.g.,
an employer must not discriminate based upon race), or a mandatory requirement that
the regulated population engage in required conduct (e.g., a lender must disclose required
information to a borrower). Using this broad conceptualization of regulatory commands, we
included all laws that contained any civil regulatory commands directed at behavior within
the United States.
We emphasize the breath of our concept of “regulatory” policy. In addition to traditional
regulatory policy, our data includes commands that are a part of welfare state policy, as with,
for example, commands meant to regulate Medicaid fraud and the use of food stamps. It also
includes commands aimed at controlling the expenditure of federal funds for wide-ranging
purposes, including transfer programs, infrastructure building, and economic development.
Our choice to limit the study to regulatory policy, so defined, was guided by the judgment
that it would allow us to achieve a consistent measure of fragmentation. Theoretically, any
2
Mayhew has provided updates to his identification of significant legislation over time.
12
mandatory command implicates a comparable range of implementation options, and thus is
susceptible to the same menu of types of delegations. Further, and critically to our design,
this limitation allows a consistent measure of the magnitude of the intervention, which we
regard as a critical control. We address this further below when we describe the data and
how it was coded. In the conclusion, we consider the prospects for extending the paper’s
insights into other broad policy types.
Of Mayhew’s 366 significant statutes, 218 contained domestic regulatory commands. The
average page length of the 218 laws was 110 pages, and thus the federal statutory law that
is the basis of the analysis that follows spanned approximately 23,980 pages of the Statutes
at Large. Each law was read in full in order to code the variables described below, which
demanded identification of detailed substantive information about the policy content of each
statute, as well as detailed information about the provisions governing implementation of
the statute’s regulatory commands.
Measuring Fragmentation: The Dependent Variable
Actors Count. We counted each discrete named actor/entity in each law that was
empowered to execute one or more core regulatory functions to implement a law’s regulatory
commands, which we define as (1) holding adjudications, (2) making administrative rules,
(3) imposing some type of administrative sanction or order, or (4) prosecuting enforcement
actions. Common actors delegated administrative implementation authority are Secretaries,
Administrators, Boards, Offices, Commissions, Commissioners, or Directors working within
a federal administrative body. In addition to administrative actors, we counted judges as
actors when the law relied in part on direct enforcement through lawsuits, and we counted
private prosecutors as actors if the law contained a private right of action. Federal courts
and private litigants are fundamentally important parts of the implementation of federal
statutes (Farhang 2010), and they have been incorporated into important conceptualizations
of fragmentation in American policy implementation (Wilson 1989: 299-300; Moe 1989: 276;
13
Kagan 2001).
Agencies Count. We counted the number of different federal agencies delegated some
authority to carry out a core regulatory function to implement regulatory commands in the
law. Included in the agencies count are executive branch agencies, independent administrative agencies, agencies of the legislative branch, and federally-created for-profit corporations
that served the role of implementing the act’s regulatory commands.
Overlapping Functions Count. We counted the number of times that multiple actors
were simultaneously given the authority to perform the same regulatory implementation
function in order to implement the same regulatory commands of a law, such as where two
administrators are empowered to make rules on the meaning of the same commands, or to
prosecute lawsuits alleging violation of the same commands. This applies narrowly to actors
implementing the same regulatory commands of a law, and not simply to actors given the
same type of functional authority to be exercised over different regulatory commands. We
provide examples of overlapping functions from our data in the Appendix.
Table 1 contains examples of the application of our coding protocol to portions of laws
in our data. The implementation provisions appear on the left side of the table, and the
corresponding counts for actors, agencies, and overlapping functions appear on the
right side of the table. In the Appendix, we provide further discussion of how we coded these
examples.
Fragmentation. Each of the above measures taps a different dimension of fragmentation. A variable that combines information from each is superior to any one measure standing
alone. The dependent variable of fragmentation is, for each law, the mean of the standardized measures for actors, agencies, and overlapping functions. To test the validity of
this scale, we first computed Cronbach’s alpha, which measures the internal consistency of a
scale, or the extent to which multiple items in a scale measure the same underlying concept
or construct. The Cronbach’s alpha for these variables was .9, which is considered to be
14
excellent validity (DeVillis 2012; Kline 2000).
We also used principal components factor analysis with orthogonal rotation to compute
a factor score for each law based upon the three measures. The variables loaded heavily
on one factor, rendering an eigenvalue of 2.5 for the first factor. The factor loadings on
the first factor for the three items range between .85 and .96, showing that all three items
are strongly associated with the underlying factor, and at a roughly comparable level. The
second largest eigenvalue was only .4. The very strong single-factor outcome was clearly
confirmed by examining a scree plot. The fact that all three items load heavily on one factor
again shows, consistent with the Cronbach’s alpha, that the three items comprising our
fragmentation scale are measuring the same underlying construct in an internally consistent
and reliable fashion (DeVellis 2012).
The factor score generated was correlated with the unweighted mean of the standardized
variables at .999, and performed virtually identically in the models presented below, with no
meaningful differences in statistical or substantive significance. Consequently, in our main
analysis we display and discuss the simpler and more transparent approach of the unweighted
mean of the standardized variables. We display parallel models using the factor score as our
dependent variable in Table A1 of the Appendix.
Figure 1 displays lowess plots of the composite measure of fragmentation from 1947 to
2008, as well as for each of the three underlying fragmentation measures. The composite fragmentation measure reveals that fragmentation in significant federal statutes was relatively
flat during the first two decades of the series; grew significantly beginning in the 1960s;
roughly plateaued in the mid-1980s; and experienced further growth over the last decade
of the series. All three underlying fragmentation measures followed a similar developmental
path. This clear commonality over time is consistent with the excellent Cochran’s alpha
score and the heavy loading on a single factor, further reinforcing the internal consistency
of the composite measure. Actors, agencies, and overlapping functions all cohere in
15
tapping a single underlying dimension of fragmentation in legislative design.
In order to compare our fragmentation variable to important existing work on legislative
strategies of agency constraint, we examine Lewis’s (2003) agency insulation data.3 We
constructed an additive index from his six measures of agency design attributes calculated
to insulate from presidential control. The correlation of the average annual values of our
fragmentation index and the index of Lewis’s insulation measures is .012; the index of Lewis’s
insulation measures does not have a positive (or negative) time trend, and the movement
of the two measures over time appears unrelated; and there is no statistically significant
relationship between them. Details of the comparison are provided in the Appendix. The
comparison makes clear that our fragmentation measure taps something different than agency
designs using express structural limits on presidential influence.
Political-Institutional Variables
Divided government is coded 0 when the president’s party controls both chambers
of Congress, and coded 1 otherwise. Figure 2 shows differences in the mean values of the
composite fragmentation scale, and the three underlying measures, under divided versus
unified government. With respect to all four, there is a clearly higher mean value under
divided government. Relative to its value under unified government, the fragmentation scale
increases by 38 percent under divided government. To test the theory that more divided
Congresses will be more likely to engage in bargaining that leads to fragmentation, we
include both law-level and Congress-level measures of congressional divisions. To test the
divisiveness hypothesis, we include a law-level partisan divisiveness variable. Following
Harbridge’s (2015) approach to measuring partisanship in rollcall voting, we subtracted the
percentage of Republicans voting yea (0-100) from the percentage of Democrats voting yea
(0-100). This yields a variable ranging from -100 to 100, with -100 indicating a vote on which
no Democrats voted yea and all Republicans voted yea, and 100 indicating a vote on which
3
Epstein and OHalloran’s data are not publicly available, and Huber and Shipan did not examine federal
legislation.
16
all Democrats voted yea and no Republicans voted yea. We then took the absolute value.
Thus, the variable is 0 where the identical percentage of Democrats and Republicans voted
yea, indicating perfect bipartisanship. It is 100 when there is perfect party line voting, or
the greatest degree of partisanship in voting. From 0 to 100 there is a continuous movement
from complete bipartisanship to complete partisanship.4
At the Congress level, to test the margin of control hypothesis we include margin of
control , which is defined in each chamber as the number of seats held by the majority
party minus the number of seats held by the minority party, divided by the total number
of seats, averaged across the two chambers. To facilitate an interaction discussed below,
we invert the variable’s direction by subtracting it from 1. Thus, higher values reflect more
narrowly-divided Congresses, which theory predicts will be associated with higher levels of
fragmentation.
To test the hypothesis that Congress will be more likely to fragment implementation
power in the face of electoral uncertainty, we measure close races in the last round of
elections, defined as the percent of total seats in the last national election that were won by
five percentage points or fewer. We believe that having had more tight races in the prior
election cycle, as compared to fewer, is associated with higher levels of perceived electoral
vulnerability. Because uncertainty is difficult to measure, as a robustness check we also utilize
an alternative variable measuring actual seat share losses in the next election, defined as
4
An alternative view may be that more consensual votes, rather than more conflictual votes, will be
associated with greater fragmentation because consensual votes occur as a result of accommodation of the
minority through fragmentation. However, we believe that the divisiveness hypothesis predicts a positive
association between divisive votes and fragmentation for the following reason. Our data are limited to laws
that passed. We anticipate that where accommodation of minority preferences was necessary to passage,
the majority would accommodate sufficiently to achieve passage, but not more. In this scenario, within the
set of laws that passed, the most divisive votes (those that achieve just enough consensus to pass) would
be associated with fragmentation, and the least divisive votes (unanimous and near unanimous ones) would
occur in scenarios in which fragmentation to achieve passage was either not necessary, or less necessary. We
discuss the issue further in the interpretation section. In alternative specifications, we also substituted a
measure of divisiveness in the vote that did not account for party: the percentage of all legislators voting
against each law within each chamber, averaged across the two (see Maltzman and Shipan 2008: 260). This
variable provided insignificant, as did partisan divisiveness. Like Maltzman and Shipan, we treat voice votes
as having no votes against the law (2008: 260, n. 18).
17
the percentage of seats in each chamber, relative to total seats, lost by the majority party,
averaged across the two chambers. Seat share losses for the majority party takes on
positive values when the majority loses seats, and negative values when it gains seats.
Further, we model the possibility that the effects of electoral uncertainty on fragmentation
will be conditional upon margin of control . Uncertainty among the majority coalition
about how it will fare in the next election may be especially worrisome to a party holding
on to power by a thin majority, when the loss of only a small number of seats would move
them to minority status, and this will heighten their incentive to insure against future loss
of power by inducing status quo bias. On the other hand, when holding power by a wide
margin of control, signs of electoral uncertainty may be, by comparison, less likely to induce
efforts to insure against loss of power. Thus, the interaction of the electoral uncertainty
variables defined above and margin of control captures uncertainty about remaining in
the majority. The electoral uncertainty variables and margin of control were centered on
their mean to facilitate interpretation of the interaction.
We also account for the possibility that partisan preferences are associated with fragmentation. It is commonly believed that liberalism in American politics is, other things equal,
associated with greater support for centralized state control, while conservatism is associated
with greater skepticism of a centralized, hierarchically organized national government. To
control for the possibility that Republican Congresses will be more likely to fragment implementation power than Democratic Congresses, we include partisan seat share, which
is positive when Democrats control Congress and reflects their margin of total seats over
Republicans in each chamber, divided by total seats in each chamber, averaged across the
chambers. It is negative when Republicans control Congress and reflects their margin over
Democrats.
Law Characteristics
Policy Domain. The nature of policy areas regulated in a statute may be associated
18
with our fragmentation measures given, for example, their degree of complexity. We assign
each law a policy code and included a set of fourteen policy area dummy variables.
Scope. We control for the magnitude or scope of the regulatory interventions undertaken
in a law since more extensive regulatory interventions may require more actors and agencies,
and present more opportunities for overlapping functions. To measure the extensiveness of
a statute’s intervention, scholars have developed a protocol for counting a statute’s “major
provisions” (Franchino 2007: 109; see also Epstein and O’Halloran 1999: 275). We adapt
this approach to the task of counting regulatory commands in each law. The core idea is to
count each separate regulatory command, resulting in a sum of the number of regulatory
commands, or the requirements and prohibitions imposed upon regulated entities. The
regulatory commands measure does not count material amplifying upon the meaning of
the command, such as definitions, exceptions, rules of application, or rules of construction.
Such information concerns a law’s level of detail, not the scope of its intervention. The
regulatory commands measure also does not count portions of the statute allocating
and defining authority delegated to implementing actors. Such information importantly
affects our dependent variable, which is based upon counts of delegated authority. Rather,
based upon content analysis of each law, regulatory commands isolates and measures the
number of discrete commands of substantive regulatory policy undertaken in the law.
Complexity. Growth in policy complexity has been identified by some as an explanation
for the growth in fragmentation of administrative power (Schuck 1992; Barnes 2004: 35).
That is, the intrinsic complexity of some policy issues leads to more fragmented implementation designs. One approach in the literature to measuring statutory policy complexity is to
use the length of statutes in pages or words (Franchino 2000; Maltzman and Shipan 2008).
Maltzman and Shipan theorize that policy complexity is associated with laws touching upon
a larger number of issues, and they use page length as a proxy for this. To a considerable
extent, this idea of complexity overlaps our regulatory commands variable, which is a
19
more direct and targeted measure of the number of issues regulated. However, there may
be aspects of the complexity of a regulatory law’s intervention other than number of issues
regulated, and thus we incorporate page length as well.
An additional approach to measuring policy complexity focuses on the nature of the issues regulated rather than the scope or number of issues regulated. Franchino (2007: 145)
measures complexity of legislative subject matter based upon whether legislation contains
a rulemaking delegation, arguing that rulemaking delegations are associated with the legislature wishing to avoid the investment of time necessary to make policy in complex areas,
instead leveraging administrative capacity. With respect to each regulatory command , we
recorded whether an administrator was delegated substantive rulemaking authority covering
that part of the law. Generally, such rulemaking authority exists only if it is specifically
delegated (Pierce 2010: 408-09). We then constructed rulemaking ratio, which is the
number of regulatory commands in each law governed by delegated substantive rulemaking
authority, divided by the total number of regulatory commands in each law. Higher values
are associated with greater reliance on agency rulemaking.
Time Trends
Time trends, and the potentially dynamic nature of fragmentation in the postwar U.S.,
must be addressed for several reasons. Figure 1 reflects that fragmentation grew strongly in
the 1960s, roughly plateaued in the mid-1980s, and grew again at the end of the series. Over
the same period, divided government became more frequent. Thus, we must account in the
model for the broad growth trend in fragmentation in order to confidently isolate the effects of
divided government. Further, as a theoretical matter, it is possible that choices by Congress
concerning fragmentation at a given point in time will be affected by fragmentation levels in
earlier periods. Past levels of fragmentation may encourage choices to fragment in subsequent
periods by increasing the number of already existing actors who may receive delegations.
Fragmentation may also beget fragmentation to the extent that it reflects changing norms of
20
regulating in Congress. When coalitions draft laws, they may be influenced by recent models
of implementation designs, whether unitary or fragmented.
We incorporate a measure of general growth in the administrative state over time. The
sheer growth in executive branch size may be associated with growth in the number of potential recipients of delegations, and thus opportunities to fragment implementation. Executive
branch size reflects total executive branch civilian employment.5
In one set of model specifications, we include year , which is a simple linear time trend
to model the growth of fragmentation over time. In alternative specifications, we model the
nonlinear growth and development of fragmentation using standard cubic spline functions
with knots to fit the shape of changes in fragmentation over time.6 When using non-linear
splines to model a time trend, it is appropriate to place knots in the series at points of
inflection (Eubank 1999: 297-98), and we rely upon the representation of the data in Figure
1 to identify them. The points of inflection are at 1960, 1985, and 1998. The temporal
function created by the procedure is defined as a continuous smooth function that is linear
before the first knot, is a piecewise cubic polynomial between successive knots, and is again
linear after the last knot. The procedure always creates one fewer variables than knots
(Calcagno, Hu, and Kanigel 2013). This estimation strategy allows us to explicitly model
the pattern of growth of our composite measure of fragmentation over time.
Findings
Descriptive statistics are presented in Table 2. Our dependent variable measuring fragmentation is continuous and thus we analyze it using ordinary least squares regression. In
each of the models below, we cluster standard errors by Congress to allow for correlation
within Congresses in our data. Model A in Table 3 presents a sparse model with only our
independent variables testing the institutional hypotheses, along with the linear time trend.
5
6
The data come from Historical Federal Workforce Tables, Office of Personnel Management.
We use Stata’s rc spline command to create the spline variables.
21
Model B adds other control variables. Model C substitutes the cubic spline for the linear
time trend. The key results are consistent across all three models.
Divided government is significant with the expected sign in all three models, and
the magnitude of the effect is somewhat reduced by inclusion of the controls. To put the
magnitude of the effect in perspective, consider that the mean value of our fragmentation
scale under unified government is 1.07. The coefficient on divided government in the full
model indicates that conditions of divided government are associated with an increase in the
fragmentation scale of .38, or 36 percent over its mean under unified government. In Model
C, the increase is 33 percent over the mean under unified government.
Partisan divisiveness is insignificant, suggesting that the extent of partisan divisiveness over a law is not associated with higher levels of fragmentation. We also considered,
as an alternative operationalization of divisiveness, the share of total votes that were nay,
averaged across the chambers. This proved insignificant as well. It is possible that this lack
of a divisiveness effect arises from the fact that concessions making a law more fragmented
also have the intended effect of achieving a more consensual vote, whereas divisive votes
occur where the majority coalition made the fewest concessions to the minority.7
Even if this dynamic were at play in rollcall behavior, the margin of control hypothesis
would still predict that Congresses with majority parties governing with thinner margins of
control would pass laws with more fragmented implementation. While the evidence would not
exist at the individual rollcall level, the fragmentation-enlarging concessions made to achieve
more consensual votes would be increasingly necessary in more closely divided Congresses.
However, the main effect of margin of control is also insignificant in all three models,
indicating that the margin by which the majority party controls Congress is not associated
with higher levels of fragmentation. The main effect of close races is insignificant as well,
indicating that the percent of close races in the last round of elections was not associated
7
See note 4 for a brief discussion of why we nevertheless think that it is theoretically sensible to predict
a positive relationship between divisive votes and fragmentation.
22
with fragmentation. Because margin of control and close races are centered on 0 and
interacted, the coefficient and standard error on each variable reflects the results only when
the other variable is at its mean.8
However, the interaction of margin of control and close races is significant in all
three models, with the magnitude of the effect somewhat reduced by inclusion of the controls.
This variable captures electoral uncertainty not just in terms of the number of competitive
races in the last round of elections, but also conditional upon the majority party’s margin
of control, with low values associated with wide margins of control and few competitive
races, and high values capturing thin margins of control and many competitive races. In
the full model with a linear time trend (Model B), an increase of one standard deviation in
the interaction is associated with a .16 unit increase in fragmentation. This is an increase
of 12 percent over the mean value of the fragmentation index in the data (which is 1.3).
Figure 4 presents a plot of this interaction term’s effect on fragmentation with 95 percent
confidence bands. The figure reveals a clear upward trend, consistent with the theory that
under higher levels of uncertainty about remaining in power, Congress will be more likely
to rely upon fragmented design for policy implementation. We note that above the value
of about .0025 on the interaction, denoted by the vertical line, the lower confidence band
reflects that the upward slope cannot be distinguished from a zero slope at the 95 percent
confidence level. However, 90 percent of our data falls below that level. The portion of the
figure above .0025 is very sparsely populated with data, resulting in uncertain estimates. In
the region below .0025, where 90 percent of our data lie, the effect is clearly positive and
statistically significant. In the spline model, the effect of a one standard deviation increase
in the interaction is also associated with an increase of 12 percent in fragmentation over the
mean value of the fragmentation index.
In order to test the robustness of the electoral uncertainty finding, we substitute seat
8
Margin of control and close races remain insignificant even if their interaction is dropped from the
model.
23
share losses for close races and rerun all three models. The results are presented in
Table 4. The main effect of seat share losses is significant in every model with the
expected sign. An increase of one standard deviation in seat share losses is associated
with a .09 increase in fragmentation, or an increase of seven percent over the mean value of
the fragmentation index, in both the full model with the linear time trend (Model B), and
the spline specification (Model C). The interaction of seat share losses and margin of
control is insignificant in all three models. Despite this inconsistency in the conditionality
of the alternative measures of electoral uncertainty upon margin of control , in both cases
we observed quite differently constructed measures of electoral uncertainty to be significantly
and positively associated with fragmentation. With the alternative uncertainty measure in
the linear model (Model B), divided government is associated with a 26 percent increase
in fragmentation relative to its mean value under unified government. The increase is 24
percent in the spline model (Model C).
Partisan seat share is insignificant. Higher levels of Republican control of Congress
are not associated with more fragmented implementation powers. Executive branch size
is also insignificant, such that the sheer size of the executive branch is not associated with the
legislative choice to fragment power. Regulatory commands, measuring the scope of the
regulatory intervention, is highly significant with a large effect. Across all models with this
control in Tables 3 and 4, a one standard deviation increase is associated with an increase
of .32 in fragmentation, or 25 percent over its mean. Page length and rulemaking ratio
are insignificant.
Finally, in the Appendix we present models with individual counts of actors, agencies,
and overlapping functions as dependent variables. With respect to all three individual
dependent variables, the results on the divided government, divisiveness, margin of control, and electoral uncertainty hypotheses are consistent with those in the composite model.
While we view the composite measure as clearly superior in characterizing fragmented leg-
24
islative design, the findings are robust to specifications with the individual components of
fragmentation.
Conclusion
A generation of scholars has debated the causes and consequences of the fragmented
character of the American state’s infrastructure for implementing public policy, with its
many participants in competition and conflict. This rich and important literature has lacked
an adequate empirical measure of fragmentation to subject theories to systematic evaluation,
or even to map its development over time. In this paper, we have advanced a conceptually
clear definition of fragmentation; developed a novel empirical measure of this facet of the
American state spanning the postwar period; and applied it to a body of important regulatory
laws, requiring detailed content analysis of about 24,000 pages of the United States Statutes
at Large.
Our analysis of this measure reveals robust, consistent evidence that under conditions of
divided government, Congress is more likely to fragment regulatory implementation power.
The barroom brawl of American policy implementation has grown larger and more intense
since the 1960s, and the persistent condition of divided party government has been a critical
variable contributing to this escalating fragmentation. American presidents wishing to pursue their own policy missions have inherited structures that are, from their point of view,
more and more cumbersome, unwieldy, and hard to manage. They were, in part, intended to
be so. Congresses wishing to check subversion of legislative preferences by hostile executives
have denied them effective and efficient means to go their own way. Moreover, there is every
reason to believe that the fragmented structures thereby created are highly durable. Political
scientists have emphasized that legislation can create benefits that mobilize beneficiaries in
favor of maintaining a policy in the face of attempts to change it (Pierson 1994; Weir and
Skocpol 1985). The multiplicity of newly powerful secretaries, boards, committees, interest
groups, lawyers, and litigants can be expected to defend their piece of implementation power
25
from consolidation under centralized control.
Our results also shed light on the character of the American state in cross-national
perspective. In describing American policy implementation, lamentably, as a barroom brawl,
Wilson (1989) contrasted it with the relatively more centralized and hierarchal structures
typical of other economically developed democratic states, which he thought were more
effective. Partly in response to Wilson, Moe (1990: 240-41) suggested that this facet of the
American state was a function of the American separation of powers system, as contrasted
with parliamentary arrangements. Owing to the much closer identity of legislative and
executive preferences and interests in parliamentary regimes, legislators are less prone to
design implementation structures with the goal of constraining executive power, and thus
they are less likely to fragment. Consistent with Moe’s insights, this paper’s main result
ties American constitutional separation of powers structures to the distinctively fragmented
character of American policy implementation in cross-national context.
Our data and models also provided the opportunity for the first systematic tests of
some longstanding theories about fragmentation. Using two quite different measurement
approaches, we find that fragmentation is associated with higher uncertainty about remaining in power, with fragmentation used to insulate against coalition drift. We did not find
support for the theory, which appears repeatedly in the literature, that divisiveness over the
passage of laws incentivizes Congress to fragment implementation power as a strategy to
buy support. Nor did we find support for the related hypothesis that thin margins of control
by the majority party, by necessitating bargaining to achieve passage, independently lead to
fragmentation.
Finally, our novel measure of regulatory fragmentation points the way toward studying fragmentation in the implementation of other types of policy. Another major policy
type ripe for inquiry is spending laws, in which the federal government appropriates large
amounts of funds to be administered and distributed by governmental and private non-
26
governmental entities to implement programs to deliver social benefits, economic development, infrastructure-building, and scientific and technological innovation. We believe that
something like our measurement strategy could be ported to this policy context, though
it would require clearly conceptualizing and operationalizing the menu of delegations from
which Congress draws when it constructs an implementation scheme to distribute funds.
This would be a productive avenue for future research.
27
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30
Table 1: Counting Fragmentation
Implementation Provisions
Fragmentation Counts
Scenario 1: Voting Rights Act
Attorney General
Prosecute lawsuits
Actor Count
Two actors: (1) Attorney General (2) federal judges
Agency Count
One agency: Department of Justice
Overlapping Functions
Zero
Scenario 2: Telecommunications Act
Federal Communications Commissioner
Prosecute lawsuits
Promulgate rules
Actor Count
Three actors: (1) FCC, (2) private prosecutors,
(3) federal judges
Private Party
Prosecute lawsuits
Agency Count
One agency: FCC
Overlapping Functions
One overlapping function: FCC and private
prosecutors both given authority to prosecute lawsuits
Scenario 3: Federal Water Pollution Control Act
Secretary of Labor
Hold administrative adjudication
Promulgate rules
Actor Count
Four actors: (1) Secretary of Labor, (2)
EPA Administrator, (3) Attorney General, and (4)
federal judges
Administrator of EPA
Prosecute Lawsuits
Promulgate Rules
Agency Count
Three agencies: (1) Department of Labor, (2)
EPA, (3) Department of Justice
Attorney General
Prosecute lawsuits
Overlapping Functions
Two overlapping functions: (1) Secretary of
Labor and Administrator of the EPA can
promulgate rules, and (2) Secretary of Labor and
Administrator of the EPA can prosecute lawsuits
31
Table 2: Descriptive Statistics
Mean
Fragmentation Variables
Composite Fragmentation Measure
Number of Actors
Number of Agencies
Overlap
Factor Score
Political Environment Variables
Divided Government
Margin of Control
Partisan Seat Share
Electoral Uncertainty
Spline 1
Spline 2
Executive Branch Size
1.297
4.784
2.890
2.445
0
SD
Min
Max
0.911
0
2.916
0
1.905
0
3.371
0
1 -1.000
5.628
18
13
26
5.793
0.558
0.498
0
1
0
0.103 -0.205 0.153
0.131
0.130 -0.098 0.358
0.134
0.034 0.088 0.241
1978.165 15.757
1947
2008
11.848 15.930
0 53.289
2026.113 324.475
699
3370
Law Characteristics
Rulemaking Ratio
Regulatory Commands
Page Length
Partisan Divisiveness
0.704
0.363
22.415 32.968
110.737 162.824
0.166
0.209
32
0
1
2
0
1
218
1054
0.863
Table 3: OLS with Composite Fragmentation DV, Std. Errors Clustered by Congress
Divided Government
Partisan Divisiveness
Margin of Control
Close Races
Margin of Control x Close Races
Year
A
0.499**
(0.172)
0.306
(0.301)
-1.581
(0.821)
-1.534
(1.892)
54.886**
(15.153)
0.020***
(0.004)
Spline 1
Spline 2
Partisan Seat Share
Executive Branch Size
Regulatory Commands
Page Length
Rulemaking Ratio
Policy FE
R2
N
∗∗∗
p < .001,∗∗ p < .01,∗ p < .05
0.166
218.000
33
B
C
0.385** 0.367*
(0.120)
(0.137)
-0.068
-0.060
(0.315)
(0.316)
-1.150
-1.085
(1.127)
(1.177)
-1.564
-1.668
(1.360)
(1.371)
38.765* 37.241*
(14.955) (15.140)
0.019***
(0.004)
0.024
(0.012)
-0.005
(0.014)
0.442
0.388
(0.857)
(0.960)
-0.000
-0.001
(0.000)
(0.000)
0.010** 0.010**
(0.003)
(0.004)
-0.000
-0.000
(0.001)
(0.001)
0.106
0.105
(0.127)
(0.127)
X
X
0.459
0.460
218.000 218.000
Table 4: OLS with Seat Share Losses Variable
A
0.412*
(0.167)
0.048
(0.300)
0.003
(0.920)
0.881*
(0.383)
-0.312
(5.734)
0.016**
(0.005)
Divided Government
Partisan Divisiveness
Margin of Control
Seat Share Losses
Margin of Control x Losses
Year
B
0.283**
(0.097)
-0.260
(0.291)
0.902
(1.016)
0.800*
(0.305)
-0.266
(4.631)
0.015**
(0.004)
Spline 1
Spline 2
Partisan Seat Share
Executive Branch Size
Regulatory Commands
Page Length
Rulemaking Ratio
Policy FE
R2 0.136
N
∗∗∗
p < .001,∗∗ p < .01,∗ p < .05
34
1.041
(0.837)
-0.000
(0.000)
0.010*
(0.004)
0.000
(0.001)
0.146
(0.128)
X
0.452
0.454
218.000 218.000
C
0.255*
(0.114)
-0.231
(0.286)
0.877
(1.097)
0.794*
(0.310)
-0.455
(4.424)
0.027
(0.015)
-0.013
(0.017)
0.859
(1.029)
-0.000
(0.000)
0.010*
(0.004)
0.000
(0.001)
0.144
(0.128)
X
218.000
2
3
4
2010
1
2005
5
2010
2000
4
2005
1945
1950
1955
1960
1965
1970
1975
1980
1985
1995
Overlap
2000
Agencies
1990
Year
1995
Year
1990
1985
1980
3
1
2
2.5
6 7 8
1.5
3 4 5
.5
Actors
Fragmentation
1975
1970
1965
1960
1955
1950
1945
2010
2005
35
2010
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
Year
Year
2005
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
2
Figure 1: Fragmentation Over Time
Figure 2: Difference-in-Means of Fragmentation by Divided Government
Fragmentation
Actors
Unified
Divided
0
0
.2
2
.4
.6
4
.8
6
Divided
1
Unified
Agencies
Overlap
Unified
3
2
1
0
0
1
2
3
4
Divided
4
Unified
36
Divided
37
Appendix of Supplementary Information
In this Appendix, we (1) give examples of overlapping jurisdiction; (2) illustrate our coding protocol with examples; (3) compare our fragmentation measure to David Lewis’s (2003)
insulation measure; (4) present models with our fragmentation factor score as the dependent variable; and (5) present models with the individual actors, agencies, and overlapping
jurisdiction dependent variables.
Overlapping Jurisdiction
Examples of overlapping jurisdiction are as follows:
• Overlapping rules: In the Federal Coal Mine Health and Safety Act of 1969,9 the
Secretary of the Interior and the Secretary of Health, Education, and Welfare were simultaneously authorized to promulgate rules to implement some of the same provisions
of the act.
• Overlapping prosecutions: In the Civil Rights Act of 1964,10 the Department of Justice
and aggrieved individuals were both authorized to prosecute federal job discrimination
lawsuits for some of the same violations.
• Overlapping adjudications: In the Taft-Hartley Act of 1947,11 some violations could
be adjudicated either before the National Labor Relations Board or in a federal court.
• Overlapping administrative sanctions: In the Garn-St. Germain Depository Institutions Act of 1982,12 both the Federal Banking Agencies and the Director of the Office
of Thrift Supervision were authorized to assess civil penalties for the same violations
of the law.
Discussion of Coding Protocol
9
83
78
11
61
12
96
10
Stat.
Stat.
Stat.
Stat.
803.
241.
136.
1469.
38
In Table 1, we apply our coding protocol to parts of three laws in our data. The left
column of the table lists enforcement powers assigned to particular actors. The right column
reflects each of our three fragmentation counts applied to the corresponding portions of each
law. Of course, in our actual data we apply the protocol to the full laws and not only portions
of them. We provide simplified versions of our coding protocol here for the sake of brevity.
• The Voting Rights Act of 196513 (Scenario 1 in the table) empowers the Attorney
General to prosecute lawsuits to enforce the act.
– Actors Counts: (1) The Attorney General and (2) federal judges (who will preside
over enforcement actions), are delegated authority, rendering a count of two for
actors.
– Agency Counts: The Department of justice is the only agency delegated authority,
rendering a count of one for agencies.
– Overlapping Functions: There are no episodes of overlapping functions. The Attorney General is in sole possession of the prosecutorial function, and courts are
the exclusive venue for adjudication. This renders a count of zero for overlapping functions.
• The Telecommunications Act of 199614 (Scenario 2 in the table) empowers the
Federal Communications Commission to make rules and prosecute lawsuits, and it
also contains a private right of action authorizing private suits.
– Actor Counts: (1) The Federal Communications Commission, (2) private prosecutors, and (3) federal judges (who will preside over enforcement actions) are
delegated authority, rendering a count of three for actors.
13
14
79 Stat. 437
110 Stat. 56
39
– Agency Counts: The Federal Communications Commission is the only agency
delegated authority, rendering a count of one for agencies.
– Overlapping Functions: The Federal Communications Commission and private
prosecutors are both given authority to prosecute lawsuits over violations of the
same provisions of the act, rendering a count of one for overlapping functions.
• The Federal Water Pollution Control Act of 1972 15 (Scenario 3 in the table)
empowers the Secretary of Labor to hold administrative adjudications and promulgate
rules; empowers the Administrator of the Environmental Protection Agency to prosecute lawsuits and promulgate rules; and empowers the Attorney General to prosecute
lawsuits.
– Actor Counts: (1) The Secretary of Labor, (2) the Administrator of the EPA, (3)
the Attorney General, and (4) federal judges (who will preside over enforcement
actions) are delegated authority, rendering a count of four for actors.
– Agency Counts: (1) The Department of Labor, (2) EPA, and (3) the Department
of Justice are delegated authority, rendering a count of three for agencies.
– Overlapping Functions: (1) The Secretary of Labor and the Administrator of the
EPA can promulgate rules to implement some of the same parts of the act, and (2)
the Secretary of Labor and the Administrator of the EPA can prosecute lawsuits
arising from some of the same violations of the act, rendering a count of two for
overlapping functions.16
Comparing Our Fragmentation Measure to Lewis’s Insulation Measure
15
79 Stat. 437
While Table 1 reflects that the law contains both administrative adjudications and court adjudications,
we did not add this to the overlapping functions count. This is because the two types of adjudications
never apply to the same provisions of this law. An addition to the overlapping functions count occurs
only when the same functional authority is assigned to multiple actors as applied to the same provisions of
a law such that there is actual functional overlap.
16
40
In order to compare our fragmentation index to important existing work on legislative
strategies of agency constraint, we examine Lewis’s (2003) agency insulation data. For 182
new agencies created by Congress between 1946 and 1997, Lewis coded whether each agency
contained one of five characteristics that insulated from presidential control: (1) statutory
limits on who could be appointed; (2) statutory limits on the length of their terms; (3)
whether the agency was independent, defined as being free of a layer of bureaucratic authority
above it; (4) whether it was located outside the cabinet; and (5) whether it had a commission.
We created an additive index for each agency ranging from 0 to 5, from lowest to highest
insulation. Figure 1A plots a lowess curve through the annual average agency insulation level.
The temporal pattern bears little resemblance to the fragmentation measures in Figure 1. It
reflects more consistent and sharp oscillation with no long-run growth pattern.
The correlation of the average annual values of our fragmentation index and the index
of Lewis’s insulation measures is -.012, and the correlation is .15 when they are averaged at
the Congress level. When we regress 41 years of the annual average fragmentation index on
the average annual index of Lewis’s insulation measures, there is no statistically significant
relationship (p=.94), and this is true if we do the same for 24 Congress-level averages rather
than year-level averages (p=.5).17 Finally, if we regress the index of Lewis’s insulation
measures for the 182 agency observations on a year counter, the time variable is insignificant
(p=.87) and the coefficient rounds to zero. When we do the same with our 218 observations
of the fragmentation index, time is significant (p=.000) and positive with a large effect. The
two measures do not move in tandem over time.
With respect to the quite different long-run time trends, we note that several other
measures of legislative efforts to limit presidential subversion trend upward, as ours does.
This is true of Epstein and O’Halloran’s measure of constraints upon bureaucratic discretion
(1999, 119). With respect to Huber and Shipan’s (2002) use of word counts as a measure
17
The reason the year observations are less than double the Congress observations is that there was no
data in some years for the indexes.
41
of constraint, scholars have long noted growth in the length of federal statutes since about
the 1960s (Kagan 2001; Melnick 1994). The three measures exhibiting long-run growth are
focused generally on statutes addressing particular policy issues, rather than more narrowly
on legislation designing agencies, which is Lewis’s focus. Given the more general purpose of
agency design as compared to statutes solving more particular policy problems, it may be
that Congress is comparatively less reactive to the current occupant of the White House.
Further, Lewis’s data covers all acts of agency creation, whereas our data and Epstein and
O’Halloran’s (1999) contain only highly significant legislation, which may be more prone to
engender partisan conflict. These factors may explain why our fragmentation measure is not
associated with Lewis’s agency insulation.
Using a Factor Score Dependent Variable
We also used principal components factor analysis with orthogonal rotation to compute a
factor score for each law based upon the three measures of fragmentation. Using this factor
score as an alternative dependent variable to the unweighted mean of the three standardized
fragmentation measures, in Table A1 below we replicate the our primary models (displayed in
Table 3 of the paper). The results are unchanged in terms of both statistical and substantive
significance.
Disaggregating the Composite Measure
In Table A2, we present count models using the three raw count variables measuring
different aspects of fragmentation: actors, agencies, and overlapping functions. We
present the full model specifications with a linear time trend. In specifications substituting
the spline for the linear time trend, the results are not materially different, as was true with
the composite dependent variable. A poisson model is estimated for actors and agencies
because the dispersion parameter, alpha, is not significantly greater than zero, indicating
that the data are not overdispersed and therefore are appropriately estimated using a poisson
42
model.18 A negative binomial model is estimated for overlapping functions because the
dispersion parameter is significantly greater than zero. The coefficients of count models are
not directly interpretable. For purposes of interpretation, exponentiating the coefficients
renders the factor change in the dependent variable associated with a unit increase in an
independent variable.
These models are similar to the composite model (Table 3) with respect to which variables are significant and which are not. We focus here on the key variables of theoretical
interest. Divided government is significant in all three models. The move from unified
to divided government is associated with a 19 percent increase in the predicted number of
actors, a 25 percent increase in the number of agencies, and a 102 percent increase in the
number of episodes of overlapping jurisdiction. Thus, when our fragmentation scale is
disaggregated into its constituent parts, the magnitude of the effect of divided government
is materially larger for overlapping functions—though all are statistically significant and
substantively important. Overlapping functions is arguably the most explicit strategy
of control among the three measures, and it seems the closest to the idea of “redundancy,”
“duplication,” and “overlap” emphasized in recent work on fragmentation as a strategy of
control.
In all three models, margin of control and close races remain insignificant, and their
interaction is significant and positive. The coefficients in both the actors and agencies
models reflect that an increase of one standard deviation in close races∗margin of control
is associated with an increase of 11 percent in the predicted count of actors and agencies
relied upon for implementation. For the overlapping functions model, the increase is 23
percent in the number of episodes of giving different actors simultaneous authority to perform
the same function. As with divided government, the effect of close races∗margin of
control on the disaggregated dependent variables is largest with respect to overlapping
18
We obtain the same results if running a negative binomial model instead.
43
functions, though the effect is substantial in all three models.
44
Table A1: OLS with Factor Score Fragmentation DV, Std. Errors Clustered by Congress
Divided Government
Partisan Divisiveness
Margin of Control
Close Races
Margin of Control x Close Races
Year
A
0.544**
(0.190)
0.339
(0.332)
-1.746
(0.904)
-1.632
(2.085)
60.655**
(16.748)
0.022***
(0.005)
Spline 1
Spline 2
Partisan Seat Share
Executive Branch Size
Regulatory Commands
Page Length
Rulemaking Ratio
Policy FE
R2
N
∗∗∗
p < .001,∗∗ p < .01,∗ p < .05
0.166
218
45
B
C
0.418** 0.400*
(0.133)
(0.152)
-0.081
-0.072
(0.346)
(0.346)
-1.266
-1.203
(1.263)
(1.310)
-1.690
-1.792
(1.515)
(1.525)
42.830* 41.338*
(16.616) (16.859)
0.021***
(0.004)
0.025
(0.013)
-0.005
(0.016)
0.489
0.437
(0.963)
(1.073)
-0.000
-0.001
(0.000)
(0.000)
0.011** 0.011**
(0.004)
(0.004)
-0.000
-0.000
(0.001)
(0.001)
0.116
0.114
(0.141)
(0.142)
X
X
0.461
0.461
218
218
Table A2: Individual Count Models, Std. Errors Clustered by Congress
Divided Government
Partisan Divisiveness
Margin of Control
Close Races
Margin of Control x Close Races
Year
Partisan Seat Share
Executive Branch Size
Regulatory Commands
Page Length
Rulemaking Ratio
Policy FE
N
∗∗∗
p < .001,∗∗ p < .01,∗ p < .05
46
Actors
0.170*
(0.076)
-0.127
(0.185)
-0.603
(0.751)
-1.111
(1.125)
24.229**
(9.708)
0.013***
(0.002)
0.161
(0.535)
0.000
(0.000)
0.003**
(0.001)
0.000
(0.000)
0.028
(0.101)
X
218
Agencies Overlap
0.226**
0.703***
(0.076)
(0.139)
-0.028
0.337
(0.194)
(0.376)
-1.390
-2.289
(0.839)
(1.348)
-0.136
-3.559
(0.773)
(2.073)
24.144** 48.326**
(8.545)
(17.588)
0.012*** 0.023***
(0.003)
(0.005)
-0.218
-1.271
(0.659)
(1.083)
-0.000
0.000
(0.000)
(0.000)
0.004*** 0.010***
(0.001)
(0.003)
-0.000
-0.001
(0.000)
(0.001)
0.171
0.334*
(0.118)
(0.156)
X
X
218
218
3
2
1
0
Agency Insulation
4
5
Figure 1A: Lewis Agency Insulation Over Time
2000
1995
1990
1985
47
1980
1975
1970
1965
1960
1955
1950
1945
Year