SEPARATION OF POWER AND LEGISLATIVE INSTITUTIONS: THE
CONSTITUTIONAL THEORY OF LEGISLATIVE ORGANIZATION
If a tree falls on the President, do members of the House get hurt?
Chapter 3
A Test of the Constitutional Theory of Legislative Organization
Gisela Sin
University of Michigan
I claim that movements in the preferences of the Senate and president that change the
identity of the “representative bill”1 that will be approved by either a coalition of a House
majority, Senate majority and the president or a House and Senate supermajority, can induce
House members to alter the balance of power in the House. In this chapter I examine that result
by analyzing institutional changes adopted by the U.S. House from 1879 until 2001. Utilizing an
extensive data set and simple statistical models, I show that when House members face a new
bargaining situation caused by changes in the Senate and/or the president, they are far more likely
to modify their organizational arrangements.
Throughout congressional history, changes in the preferences of the president and/or the
composition of the Senate have regularly been followed by significant alterations in the
distribution of power in the House—even during times of stability within the House itself. These
alterations occur in ways that the Constitutional Theory of Legislative Organization (CTLO)
predicts, but past scholarship does not. In the first section of this chapter I delineate the
predictions developed from the Constitutional Theory and from Traditional Theories,2
emphasizing their differences, especially in situations in which the membership of the House
remains constant across terms. While Traditional Theories predict no influence of ideological
changes in the Senate and the president on decisions regarding the internal distribution of power
in the House, CTLO predicts that preferences of the Senate and the President have a large impact
on the balance of power in the House. Even when we hold constant the preferences of members
of the House on substantial issues, the House may still undergo institutional change. More
1
A representative bill embodies the joint expectation of all the members of the House concerning the issues
that will be decided on the next legislative term and aggregates the set of initial bargaining positions that
the House presents to the Senate in negotiations over the ensuing legislative term.
2
By Traditional Theories, I mean previous scholarly work that has focused on the internal organization of
the House and have claimed how well the internal dynamics of the House serve different objectives: (1)
make gains from exchange possible (Shepsle 1979, Shepsle &Weingast 1981, 1987, 1995; Weingast &
Marshall 1988), (2) advance the interests of the median member of the House (Krehbiel and Gilligan
1989,1990; Krehbiel 1991) or (3) ensure the advantages of the majority party (Cox and McCubbins 1994,
2005; Rohde 1991; Aldrich and Rohde 1997, 1998, 2000, 2001). Although different in their specific
arguments, they all claim that institutional design is a consequence of House members’ preferences. For a
more detailed explanation of their arguments and results, see previous chapter.
1
specifically, CTLO predicts that even when House members’ preferences over substantive issues
do not change, changes in the partisan balance of the Senate and/or the identity of the president
that affect the set of achievable legislative outcomes induce important changes in the in the
preferences of House members over the distribution of power in the House. Extant theories
consider that the preferences of exogenous institutions are irrelevant when House members make
decisions regarding the organization of the House.
In the second section I present the data, formulate the concepts and develop indicators of
changes in the distribution of power in the House and of the variables that help me understand
these changes: constant Houses across congressional terms and modifications in the partisan
balance of the Senate and the identity of the president. In the third section, I analyze the data
using exact tests where the repeated sampling assumptions of standard techniques like  2 tests
are difficult to believe. I specifically test the claim that changes in the Senate and/or president
have no effect on the decision to alter rules and procedures of the House. The results allow me to
reject such claim. Furthermore, the combination of results shown in this chapter provides strong
support for the argument that changes in the majority of the Senate and/or identity of the
president significantly affect decisions to alter the distribution of power in the House.
After presenting the main results, I check for the existence of overt and hidden bias
resulting from a missing, yet theoretically relevant, variable. Bias is present when there are
systematic differences between congresses that experience or do not experience changes in the
Senate and/or president. To control for overt bias, I include variables that the existing literature
suggests are correlated with the institutional outcomes. The results show that the effect of
changes in the Senate and /or the president remain strong even when depedent upon alternative
explanations. To address hidden bias, I conduct a sensitivity analysis to determine how large the
effect of unobserved variables would need to be in order to qualitatively alter the results. The
analysis shows that attributing responsibility for institutional change to an unobserved covariate
2
rather than to an effect of changes in the Senate and/or the president would require a covariate
four times as powerful as changes in those exogenous actors.
I also assess whether my results are robust across a variety of definitions of congressional
stability. The results show that my conclusions are robust to different measurements of this
variable. In the last section, I conclude by drawing some implications of my result.
1. Hypothesis and Predictions
Traditional Theories of legislative organization assume that House members do not take
the president and the Senate into account when organizing internally. They claim that when
rational and strategic legislators decide on internal organizational matters, they only take into
account internal House politics. Therefore, institutional design is a consequence of House
members’ preferences. Two observable implication of this claim are that we should observe
changes in the rules of the House only when its membership changes significantly and not when
its membership remains constant, and that those changes in rules and procedures should not be
associated with changes in the preferences of the Senate and/or president.
An alternative point of view, offered by the Constitutional Theory, addresses the
following question: assuming that House members do take into account the president and the
Senate when organizing internally, what observable consequences should follow? This
perspective claims that when changes in the partisan balance of the Senate and/or the identity of
the president modify the characteristics of the ‘representative’ bill that can be approved by a
coalition among the Senate, House and the president or a supermajority from the House and
Senate, we can expect modifications in the internal distribution of power in the House.
The following table compares the predictions of both theories in more detail.
3
TABLE 1. Predictions of Traditional Theories of Legislative Organization and the Constitutional Theory
of Legislative Organization
 in Senate and /or
president.3
~  in House
 in rules/
procedures
of the
House
~  in Senate and/or
president
~  in House
 in Senate and /or
president.3
 in House
~  in Senate and/or
president
 in House
CTLO: YES
CTLO: NO
CTLO: YES
CTLO: YES
Traditional
Theories: NO
Traditional
Theories: NO
Traditional
Theories: YES
Traditional
Theories: YES
The table represents all possible combinations of existence or absence of changes in the
membership of the House, the partisan balance of the Senate and the identity of the president.4
The cells show the predictions of the Constitutional Theory and the Traditional Theories with
respect to whether changes in the rules and procedures of the House should be observed.
First, consider the cases where the preferences of members of the House change (last two
columns). In these cases the CTLO and Traditional Theories are observationally equivalent.
Both theories, in both cases, predict changes in rules and procedures. For Traditional Theories,
changes in the membership of the House are a sufficient condition for producing an internal
redistribution of power. For CTLO, changes in the preferences of members of the House, Senate
and president that change the “representative bill” that can be approved by a coalition among the
Senate, House and the president or a supermajority from the House and Senate, induce House
members to alter the distribution of power in the House.
These changes in the Senate and/or the president need to alter the identity of the ‘representative’ bill that
can be approved by a coalition among the Senate, House and the president or a supermajority from the
House and Senate.
3
4
A more precise and specific definition of these concepts is developed in the next section.
4
Second, consider those cases where the identity of the majority party in the House and the
relative size of its factions are held constant (first two columns). Traditional Theories and the
Constitutional Theory disagree in their predictions.
Traditional Theories predict there will be no observable changes in the organization of
the House, regardless of changes in the president and/or the Senate. According to these theories,
if neither the median of the floor nor the party composition changes dramatically, a redistribution
of power should not occur. More specifically, Traditional Theories forecast no differences
between the first two columns. Even if we consider that some changes in rules and procedures of
the House could occur due to factors other than changes in the preferences of House, Senate or
president, Traditional Theories would predict that the distribution of those cases across the first
two columns should be equal. As a result, Traditional Theories have very distinct and clear
predictions with respect to the probabilities of changes in rules and procedures of the House when
the preferences of House members do not experience changes: such changes should either not
occur, or if they do occur, they should be independent of changes in the Senate and/or Presidency.
In contrast, the Constitutional Theory predicts that holding constant the identity of the
majority party in the House and the relative size of its factions, modifications in the partisan
balance of the Senate and/or the identity of the president should produce, under certain
conditions, observable changes in the rules and procedures of the House. That is, when changes
in the president and/or Senate alter the set of bills that would be accepted by either a majority of
the House, Senate and president or a supermajority in the House and Senate, then House
legislators have an incentive to incorporate those changes into their decisions regarding
distribution of power.5 If the Constitutional Theory is correct, we should observe that most of the
Houses where preferences are held constant across two consecutive congressional terms and that
have modified their internal distribution of power should have experienced a change in the
partisan balance of the Senate and/or the identity of the president. The Constitutional Theory
5
For a formal statement of the predictions, see Sin and Lupia 2005.
5
would predict clear differences between the first two columns. We should discern a noticeable
pattern, where constant Houses that changed their rules and procedures are situated
predominantly in the first column.
In what follows I focus on the distinct observable implications produced by Traditional
Theories and the CTLO. Both theories predict new power arrangements when House
membership changes, however only the Constitutional Theory predicts a change in the
distribution of power in the House when the preferences of House members remain constant from
one congressional period to the next. These pivotal events provide a litmus test for both the
CTLO and Traditional Theories, allowing one or both to be refuted.
If CTLO is right, then the new insights regarding the timing and causes of legislative
organization can change our understanding of why the House decides to distribute power among
its members as it does. Furthermore, CTLO would explain a whole range of institutional changes
that existing theories of legislative organization do not. More generally, if we find that the
proportion of Houses that initiate changes is far greater after a change in the Senate and/or
president than when such changes do not occur, then CTLO could provide evidence for an
endogenous theory of institutional choice.
To test whether or not changes in the Senate and/or president influence House members’
decisions regarding distribution of power in the House, I examine major rule changes in House
procedures over several decades. More specifically, I study all Congresses from 1879 until 2001
in which the composition of the House remained constant across two consecutive terms. I
compare situations where the House, Senate and president remained constant to situations were
either the Senate or the president or both changed. These comparisons allow me to establish
whether the partisan composition of the Senate and the identity of the president cause changes in
the power sharing arrangements of the House. In what follows I present the data, describe the
nature of the empirical study, test my hypothesis and explain the implications of my findings. I
6
also test for overt and hidden bias. In the last section I assess the robustness of the results to
different operationalization of the variables.
2. Data
Dependent Variable: Changes in the distribution of power in the House.
Changes in the distribution of power in the House refer to changes in the organization of
the House that entail a modification in the balance of power among its members. For example,
decisions that affect committee staffing and funding in turn affect what committee chairs and
members can do; organizational decisions that reshape committee boundaries entail a
redistribution of power from one committee to another. A rule change in House procedures
means that the power different House members had prior to the change no longer exists. The
changes in rules produce a new configuration of the power in the House.
To operationalize this concept, I use Cox and McCubbins’ (2005) list of rules and
organizational changes in the House from the 46th Congress until the 100th Congress.6
First, Cox and McCubbins combed through “standard online databases [they used
Voteview 2.9] for all recorded votes pertinent to rule and organizational changes” (2005:109).
That is, they counted “each resolution (or amendment to a resolution) that changed House rules or
organization and got one or more roll call votes.”(2005:110). This method for counting changes
could have missed
any instances where the Speaker makes a ruling that changes the rules of the House but
that is never challenged in a roll call vote. Similarly, we miss instances where rules
change as a result of a proposal that is adopted without a roll call vote at any stage of its
consideration. Despite these potential omissions, we are satisfied that our method
captured all of the major rules changes in the House over the time period we investigate;
neither Schickler nor Binder, nor any of the other standard secondary sources, report any
rule changes that we miss. (Cox and McCubbins 2005:153)
Second, after building the full list they excluded some resolutions that were in effect for less than
six months as well as other that had no discernible partisan consequences. In particular, they
6
For the 101st-106th Congress I followed their same procedures.
7
“considered rule changed resolutions as having partisan consequences if at least one of the roll
call votes on the resolution was a party vote (pitting over 50% of Democrats against 50% of
Republicans)” (2005:154)7.
Cox and McCubbins’ method yielded a larger number of rule changes than the methods
of both Binder (1997)8 and Schickler (2001)9. However, a few of the changes included by those
two scholars would have failed one of Cox and McCubbins’ two selection criteria (partisan effect
or short duration). For completeness, however, they included all rules cited by Binder or
Schickler in the data set. (Cox and McCubbins 2005:111 footnote 64).
Cox and McCubbins’ final list is composed of “124 resolutions with rule or
organizational changes in the period 1880-1988 that had non-trivial partisan effects”
(2005:111)10.
Explanatory Variables
Changes in the Senate. We know there exists a change in the Senate when the:
- Majority party in the Senate changes.
- Majority party in the Senate gains or losses the ability to invoke cloture11.
7
Thanks to the generosity of Gary Cox, I was able to examine those excluded rules and procedures. None
of those rules changes affect my results.
8
Binder’s (1997) selection criterion for changes in rules that affect minority rights is the following: “Under
the identification standard, any rule identified by the minority party as a minority right is counted as
such…Under the effect standard, a rule qualifies as a minority right if over time its effects redound to the
advantage of the minority party –allowing it to challenge or influence majority control of the
agenda…These two criteria offer a set of decision rules for evaluating whether or not a rule counts as a
minority right” (Binder, 1997:23-24).
9
Shickler’s selection criterion for organizational changes is the following: “I define an “important”
institutional change as one that historians or congressional specialists perceive to have had substantial
effects on congressional operations. I operationalize this definition by counting a change as important if
five sources each suggest that the institutional change in question had such effect” (Schickler, 2001:277).
10
For more information on their method, selection criteria as well as for a list of changes, see Cox and
McCubbins (2005) Chapter 4 and its appendix.
11
The number of Senators required to invoke the cloture rule has varied through history: “Since 1975,
three-fifths of the membership has been required to invoke cloture…With some variations, two-thirds
majorities were required from 1917 to 1975. Before 1917 the Senate had no formal rule for ending debate
and moving the previous question.” (Wawro and Schickler, 2003:1).
8
Why should this latter condition be considered a change that modifies the partisan
balance of the Senate? “Even though the Constitution implies that only a majority of the US
Senate is required to pass legislation, the rules of the modern Senate concerning debate
effectively mean that supermajorities are necessary for passage” (Wawro and Schickler, 2003:1).
In another paper, Wawro and Schickler claim that
cloture reform could have been meaningful in the sense that it reduced the uncertainty
that legislative entrepreneurs faced when trying to push legislation and how it might have
increased the efficiency of passing legislation in the Senate by providing a clear threshold
for cutting off debate and bringing legislation to a final vote…[T]he adoption of the
cloture rule had a substantive impact on the operation of the Senate, contrary to what the
conventional wisdom would have us believe. (Schickler and Wawro, 2004:32)
Therefore, whether a party has enough votes to invoke cloture affects its bargaining
position with respect to the minority party, the House and the president. Thus I consider the
Senate’s partisan balance altered when a party reaches or loses the filibuster pivot point.
Changes in the president: There exists a change in the president when a new president
inaugurates a term.
Changes in the House
Changes in the House refer to alterations in the membership of the House. That is,
whenever the median member of the floor, the median member of the party or the level of intraparty homogeneity and inter-party heterogeneity changes considerably, a change in the House has
occurred.
The operationalization of this variable involves two indicators:
- First, the replacement of the majority party by another party.
- Second, a change in the relative size of the factions of the majority party.
9
This means I consider the House to be constant when the identity of the majority party
across consecutive congressional terms is the same and the relative sizes of the factions within the
majority party are equivalent.12
In order to identify majority party factions and their relative sizes for each Congress, I
rely on secondary sources whose focus is party factions in presidential primaries and in Congress.
In general, these studies place legislators into factions based on their party ideology, the region
they come from and their home state. Or this latter point, scholars agree that party ideology has
been unified at the state and local levels, which means that a state’s party delegation to Congress
is considered ideologically cohesive. Scholars classify party factions in the House of
Representatives in the following way:

Democrats:
o
1900-1930: split between supporters of William Jennings Bryan in the rural west and
south, and anti-Bryan in the urban east (legislators coming from CT, DE, ME, NH,
NJ, NY, PA, MA, RI AND VT (Wiseman 1988, Kent 1928);
o
1930 to present: division between southern conservatives (Kentucky, Oklahoma and
the eleven states that seceded from the Union) and northern liberals (remainder of the
states) (Reiter 2001 and 2004, Aldrich 1995, Schousen 1994, Rohde 1991, Smith and
Deering 1984, Sinclair 1982, Brady and Bullock 1980, Shelley 1977, Stang 1974,
Manley 1973, Rohde and Shepsle 1973, Moore 1967, Patterson 1966, and Burns
1963).
12
My objective was to find a good measure of changes in the House across two consecutive congressional
terms that did not depend on roll call voting and legislator’s ideal points, essentially because such measures
are endogenous to my dependent variable. Votes on the floor are the product of the agenda set by
legislators who are given power by rules and procedures. For example, more heterogeneous voting from
one Congress to the next could be just indicating that, as a result of changes in rules and procedures, more
controversial issues could make it to the agenda. As a result, I tried to avoid a measure that was
endogenously determined by my dependent variable, changes in the organization of the House. Later in the
paper, I conduct robustness tests on my results using changes in DW-NOMINATE scores between two
successive congresses as the indicators of changes in House membership. The results show that the
findings are robust to changes in this measurement.
10

Republicans:
o
1898-1929: split between Progressives in the West (all states west of the Mississippi
river plus Wisconsin and Minnesota) and Conservatives in the East (all the states east
of the Mississippi river except Wisconsin and Minnesota) (Schousen 1994, Aldrich
1989, Shepsle 1978, Galloway 1976, Barfield 1970, Mayer 1967, Wilensky 1965,
Hofstadter 1963, Berdahl 1951, Gwinn 1957, Nye 1951, Chiu 1928, Hasbrouck
1927). Scholars also agree in that the Depression marked the end of the Progressives
as members either lost their seats to New Deal Democrats or joined the Democratic
Party.
o
Since 1940: division between liberals/moderates in the east (CT, DE, ME, MA, NH,
NJ, NY, PA, RI, AND VT) and conservatives from the Midwest, West and South
(Aldrich 1995, Schousen 1994, Rohde 1991, Rae 1989, Reinhard 1983, Sinclair
1982, Reichard 1975, Burns 1963).
After collecting and classifying the data on changes in rules and procedures, changes in
the president and Senate, and constant Houses from 1879 until 2001, I built a data set composed
of the procedural and organizational changes in the House from the 47th Congress until the 106th
Congress. As explained above, most of the data used has been previously compiled by leading
scholars in the field and is widely accepted as highly accurate. The dependent variable, i.e.
changes in the rules and procedures of the House, is binary, where 1 indicates a change in rules
and procedures and 0 represents no change. I also coded changes in the explanatory variables
(House, Senate and president) as defined above. These are also binary variables, with 1
indicating the existence of changes and 0 indicating the absence of variation. In order to
specifically test CTLO’s hypothesis against the null I selected Congresses where the identity of
the majority party remained constant across Congresses and the relative size of party factions
11
within the majority party was equivalent.13 Out of the total population of 61 Congresses from
1879 until 2001, 41 Congresses met the criteria.14
3. Methods and Results
As mentioned above, hypotheses derived from Traditional Theories and the
Constitutional Theory of Legislative Organization markedly differ with respect to the timing of
decisions regarding redistributions of power in Houses where members’ preferences across
consecutive terms did not change. Traditional Theories have very firm predictions with respect to
the 41 Houses in my study. Consider Table 2 below, in which I separate the Congresses based on
two criteria: whether or not changes in the Senate or president occurred, and whether or not the
House adopted new rules.
TABLE 2.
 in Rules
 in Senate and /or president,
~  in House
~ in Senate and /or president,
~  in House
~  in Rules
Constitutional Theory predicts
more changes here
All theories predict no differences
Traditional Theories make very specific predictions about what the values of this table should be:
1. A substantial majority of Houses should not experience changes in rules and procedures.
Most Houses should fit the second column of Table 2. For Traditional Theories, changes
in the distribution of power in the House are a consequence of changes in the preferences
of House members. Therefore, if these preferences remain constant, as it is the case in
13
As mentioned before, it is precisely with respect to these constant Houses that Traditional Theories and
CTLO have very distinct predictions.
14
If we relax one of these conditions and only take into account those Congresses in which the majority
party from one period to the next is the same, then the total number of Congresses is 48. Later on I check
the robustness of my results to this measure.
12
the 41 Houses analyzed, there is no reason to expect a change in the power sharing
agreement.
2. A baseline number of Houses could change their rules and procedures even when their
membership remains constant. In some special circumstances, factors other than changes
in House members’ preferences may be important in determining redistributions of
power. Therefore, Traditional Theories expect that a small percentage of Houses could
change their rules and procedures even when the membership does not undergo changes.
3. Among the Houses that fit the first column (i.e. decided to change their rules) there
should be no differences between the number that experienced changes in the Senate
and/or president and those that did not. The same is true for Houses that fit the second
column (i.e. decided not to change their rules). The number of Houses that correspond
to each row in each column should be approximately equal. For Traditional Theories
decisions to redistribute power in the House are independent of the preferences of the
Senate and the president. Therefore, decisions regarding rearrangements of the balance
of power should not show any pattern depending on whether or not changes in those
outside institutions occurred.
Contrary to the conventional wisdom, the Constitutional Theory predicts important
differences between Houses that experience changes in the Senate and/or presidency and those
that do not:
1. Given a change in the Senate and/or president’s preference, a large proportion of
Houses should change their rules and procedures.
2.
Given no change in the Senate and/or president’s ideal point, a large proportion of the
Houses should NOT change their rules and procedures.
In contrast to Traditional Theories, the numbers across rows should not be identical because
the decision to change the House’s rules and procedures is not independent of changes in the
Senate and/or president. The Constitutional Theory predicts that the existence or absence of
13
changes in the partisan balance of the Senate and/or the identity of the president have an effect on
whether legislators decide to alter an existing power sharing arrangement. Moreover, we expect
the number in the top-left quadrant to be much higher than the bottom-left and the number of
cases in the bottom-right quadrant to be much higher than the upper-right. The Constitutional
Theory makes no predictions with respect to the total number that should fit each column. The
Constitutional Theory claims that most congresses that experienced change sin the Senate and/or
president should be concentrated in the upper-left and bottom-right cells. This prediction is very
different from Traditional Theories, which predict no difference between rows.
Table 3 shows the results. Do changes in the partisan balance of the Senate or the
identity of the president have a causal effect on House’s rules and procedures? Of the 22 Houses
that experienced changes in the partisan balance of the Senate and/or the identity of the president,
82% (18) changed their rules and procedures. Of the 19 Houses that did not experience a change
in the Senate and/or the president, 63% (12) did not change their rules.15 What do these results
mean for the null and alternative hypothesis?
TABLE 3. Each cell represents the number of ‘constant’ Houses from across consecutive congressional
periods that fit both categories.
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
 in Rules
82% (18)
~  in Rules
18% (4)
Total
100% (22)
37% (7)
63% (12)
100% (19)
First, a substantial majority of Houses are concentrated in the first column. More than 60% of the
41 Houses analyzed did change their distribution of power even though their membership
remained constant. Furthermore, 61% seems a much higher percentage than the baseline number
of Houses that could have changed their rules and procedures due to factors other than changes in
15
An extensive contingency table with all the Congresses included in each cell is presented in the appendix.
14
preferences of House members. These results are difficult to explain with Traditional models.
Traditional Theories would never have predicted that such a high proportion of Houses would
change their rules and procedures when House membership is held constant.
The results provide no support for existing theories of legislative organization. First, in
each column the results are significantly different across the rows. If changes in the Senate and
president did not have any influence on decisions whether or not to change the balance of power
within the House, then the number of Houses in each row should be equal. To the contrary,
around 73% of those Houses that experienced changes in rules also experienced changes in the
Senate and/or president, while 75% of Houses that did not change their rules, did not experience
such changes in the Senate and/or president. These high percentages seem to suggest, in line with
the Constitutional Theory, that changes in outside institutions’ preferences may have an effect on
decisions regarding redistributions of power within the House.
In addition, 82% of those Houses that experienced changes in the preferences of the
Senate and/or president changed their distribution of power. Among those that did not experience
changes in the Senate and/or the president, 63% of Houses maintained a constant balance of
power across congressional terms. These results show a significant pattern: Houses do
concentrate on the upper left and bottom right cells. If changes in the preferences of outside
institutions with which the House must coalesce were not important in House members’
calculations, then we shouldn’t see such a pattern. That a significantly higher number of units do
correspond to those cells suggests that the preferences of the Senate and the president may enter
into House legislators’ estimations about the future bargaining environment.
Altogether, my findings show that a group of decisions that effectively change the
balance of power in the House can be explained by the Constitutional Theory, but not by
Traditional Theories. These results would never have been expected had we subscribed only to
the argument set forth by Traditional Theories. Empirical evidence seems to indicate that House
15
legislators may condition their decision to redistribute power within the House based upon the
preferences of the Senate and the president.
On the surface, the data may suggest support for the Constitutional Theory. However,
more formal analysis is needed to support my theory. I employ Fisher’s exact test to measure the
independence of outcomes (institutional change in the House) on treatments (change in the
president or Senate). In other words, the test determines whether a particular independent
variable has an effect on the dependent variable.16 In the test, every unit i (units in this case
correspond to each Congress) “exhibits a response that is observed some time after treatment. To
say that the treatment has no effect on this response is to say that each unit would exhibit the
same value of the response whether assigned to treatment or control” (Rosenbaum 2002:27). The
null hypothesis in Fisher’s exact test alleges that the response (r) is constant, and that it does not
vary with the treatment it receives: r1i  r0i , where 1 indicates the presence of treatment and 0
its absence, and i  1,..., N . The test statistic is the number in the upper left cell: the number of
responses equal to 1 in the treated group and its p-value represents the probability of a result as
rare or more rare than the actual observed value if the null hypothesis were true. Under the
hypothesis of no effect, the distribution of the test statistic is the hypergeometric distribution17:
“the upper corner cell (of the 2x2 table) has the hypergeometric distribution, and this is the basis
for Fisher’s exact test of the hypothesis of no effect” (Rosenbaum 2004:189).
This approach requires fewer assumptions than using either likelihood inference or linear
regression.18 It allows me to analyze the effect of changes in the Senate and the president on the
distribution of power in the House without requiring any assumptions about the conditional
The Fisher’s exact test is also commonly used with small sample sizes, as it doesn’t require any
asymptotic assumption. For more on information on Fisher’s exact test see Agresti 1995 and Rosenbaum
2002, chapter 2.
17
The hypergeometric distribution is a discrete probability distribution that describes the number of
successes in a sequence of n draws from a finite population without replacement.
18
Furthermore, “Birch (1964) and Cox (1966,1970) show that… [the Fisher exact test] with binary
responses posses an optimality property…Specifically, they show that the test statistic…together with the
significance level… is a uniformly most powerful unbiased test against alternatives defined in terms of
constant odds ratios” (Rosenbaum 2002:31).
16
16
distribution of the dependent variable (changes in the rules and procedures) given the explanatory
variable (changes in the Senate and/or president)
[these models] impose fewer incidental assumptions. Models for association between an
outcome and an explanatory variable bring with them mathematical structure…At a
minimum, likelihood based approaches introduce latent variables Pr(Y=1│Z=1) and
Pr(Y=1│Z=0), and commonly an entire latent distribution, that of Y│Z, X; this in turn
introduces a link function, to translate the linear predictor to the probability scale, and a
functional form for the regression of Y on Z and X…each person is assumed to have one
latent variable δ(i) which is either 0 if they respond because of the treatment and 1 if their
response did not occur because of the treatment. (Bowers and Hansen 2005:6)
In my analysis, the units are the 41 Congresses from 1879 until 2001 which are
considered constant. The treatment applied to the different units is changes in the partisan
balance of the Senate and/or the identity of the president. The binary response is either ri  1
which means that House legislators decided to change rules or procedures or ri  0 meaning that
rules and procedures were not changed. The null hypothesis claims that the response of the
House is independent of changes in the Senate and/or presidency. The test statistic is the number
of Congresses that received the treatment and changed their rules and procedures.
The p-value resulting from the Fisher’s exact test is 0.0001. This p-value allows me to
reject the null hypothesis that treatment variable has no effect. Substantively, it could mean that
changes in the majority of the Senate and/or identity of the president may have an effect on
changes in rules and procedures. Under the null hypothesis, the distribution of the different
Congresses in the table above would only occur in 0.01% of possible cases if Senate and
president were irrelevant. The test casts great doubt on the belief that the 18 constant Congresses
that experienced changes in the Senate and/or president changed their distribution of power
merely due to chance. These results may suggest that the alternative hypothesis that claims that
changes in the Senate and the president can cause radical changes in the distribution of power in
the House is plausible. Under this hypothesis, rational House members take into account the
17
preferences of outside institutions like the Senate and the president when deciding issues of
power distribution.
4. Bias in Observational studies
Could these results be merely due to chance? Is this causal effect believable? Could the
results be the product of the influence of an omitted variable? Is there any systematic difference
between the ‘constant houses’ that experience changes in the Senate and/or the president and the
others that do not? If confounders exist, are they driving the results? When working with
observational data, one of the central concerns of researchers is that treatments may be assigned
to subjects in a biased manner: “An observational study is biased if the treated and control
groups differ prior to treatment in ways that matter for the outcome under study” (Rosenbaum
2002:71). To reduce the probability that the treated and control groups are different in
substantive ways, scholars attempt to control for both overt and hidden bias. Overt bias refers to
the probability that the result is generated by differences between treated and control groups
before the treatment is applied, through variables that were observed. Hidden bias refers to the
bias introduced by variables that were unobserved. Addressing both types of bias is crucial, since
if “relevant variables are omitted, our ability to estimate causal inferences correctly is limited”
(King, Keohane, and Verba 1994:174).
Investigators seeking to avoid omitted variable bias typically include ‘control’ variables
that are correlated with the dependent and independent variables. Recently investigators have
also turned to matching and stratification to combat bias. By using these techniques, the
differences between groups that receive the treatment and those that do not in specific covariates
are removed. However, “even after adjustments have been made for observed covariates (i.e.
characteristics that were measurable prior to treatment assignment), estimates of treatment effect
can still be biased by imbalances in unobserved covariates.” (Rosenbaum 1984:41, emphasis
mine). A less common tool for checking biases is sensitivity analysis. In contrast to control
18
variables and matching, sensitivity analysis19 asks: “How large would the effect of unobserved
confounders need to be to explain the observed association?” The result of the analysis is a
quantitative measure of uncertainty based on the data that assesses both types of bias.
In the next section, I analyze both the overt and hidden bias present in my model. First, I
examine different covariates suggested by previous literature to be important in causing changes
in rules and procedures in the House. In particular, I stratify the data by the identity of the
majority party and conduct a test similar to the test above. Second, I address hidden bias by
conducting sensitivity analysis.
1. Controlling for Overt Bias
As mentioned above, to assume that the influence of observed covariates is zero is to
assume a priori all Congresses are equally likely to change their rules and procedures when faced
with changes in the Senate or president. My test would be incorrect if constant Houses that
experience changes in the Senate and/or president and those that did not, differ prior to treatment
in ways that are important for the outcome under investigation. If overt bias is present,20 we can
adjust the data with several techniques like multivariate regression, matching or stratification:
“the first step is to adjust for observed covariates, to compare subjects who appear similar in
terms of observed covariates prior to treatment” (Rosenbaum 2004:153). As a result, we can
account for the differences between constant Houses by matching them or dividing the Houses
into stratums depending on the values of the covariates.
Which are important confounds in the congressional literature? As mentioned above,
Traditional Theories of legislative organization claim that changes in the preferences of House
members trigger institutional reforms. That is, when the House experiences significant changes
“Cornfield et al (1959) contains the first sensitivity analysis in an observational study, replacing the
qualitative statement that ‘association does not imply causation’ by a quantitative statement about the
magnitude of hidden bias that would need to be present to explain away the observed association between
treatment and response” (Rosenbaum 2004b:1).
20
“Overt bias is one that can be seen in the data at hand” Rosenbaum 2002:71).
19
19
either in the identity of its majority party or in the ideological position of its members, then
redistribution of power should be more likely. This literature suggests that the identity of the
majority party and ideological preferences of House members (as measured by DW-NOMINATE
scores) are significantly related to changes in the rules and procedures of the House (Schickler
2001). In what follows I stratify the data to determine whether controlling for majority party
alters the relationship between changes in the Senate and/or president and changes in
institutions.21
Table 4 presents the data for 41 constant Houses stratified by majority party. The
stratification allows the relationship between treatment and result to be compared among
Congresses that are homogeneous in the identity of the majority party. Is the relationship
between House rules and exogenous actors affected by whether the majority belongs to the
Democratic or Republican Party?
TABLE 4: Exact Stratification on Majority Party
Democratic
Majority
Republican
Majority
 Rules
~  Rules
 S/P
14
3
~  S/P
5
8
 S/P
4
1
~  S/P
2
4
To learn whether a difference exists between treated and controlled Congresses on this
covariate, I employed the Mantel-Haenszel test, which is analogous to Fisher’s exact test in cases
21
I decided to not include a control variable for ideology based on DW-NOMINATE. As explained above,
ideology scores are derived from roll call votes. But votes on the floor are the product of the agenda set by
legislators who are given power by rules and procedures. As a result, these scores would have been
endogenously determined by my dependent variable, changes in the distribution of power in the House. In
section 5, I conduct robustness tests on the dependent variable using DW-NOMINATE scores, to check
how robust the results are to different measures of the dependent variable.
20
with more than 1 stratum (i.e. 2 x 2 x S contingency tables). The test statistic is again the number
of positive responses among units that have received the treatment across all strata. The p-value
of the test statistic is 0.01. This small p-value implies that the results found in the previous
section are not an artifact of which party controls the House.
2. Assessing the Impact of Hidden Bias
Unmeasured covariates may still create biased estimates. Sensitivity analysis in this case
is very informative as it shows how large the effect of the unobserved variables would need to be
to alter the results. Sensitivity analysis asks: “How would inferences about treatment effects be
altered by hidden biases of various magnitudes?... How large would these differences have to be
to alter the qualitative conclusions of a study?” (Rosenbaum 2002:106).
Most statistical analyses implicitly assume the absence of hidden bias. “In many
empirical studies of the effect of social program researchers assume that, conditional on a set of
observed covariates, assignment to the treatment is exogenous or unconfounded (aka selection on
observables). Often this assumption is not realistic, and researchers are concerned about the
robustness of their results to departures from it” (Imbens 2003:126). How would my results
change if we relaxed the assumption of exogeneity? Sensitivity analysis provides a quantitative
answer:
Sensitivity analysis is conceptually related to the practice of assessing sensitivity of
estimates by comparisons with results obtained by discarding one or more observed
covariates…The attraction of the sensitivity analysis is that it is more directly relevant:
one is not interested in what would have happened in the absence of covariates actually
observed, but in biases that are the result from not observing all relevant covariates.
(Imbens 2003: 126)
How does sensitivity analysis work? “The sensitivity model assumes the following: in
the population…treatments were assigned independently, and two subjects…may differ in their
odds of receiving the treatment, pr(Z=1)/pr(Z=0), by at most a factor of   1 .” (Rosenbaum
2004:155). Suppose we have two different Houses, (j) and (k), where the identity of the majority
21
party from one Congress to the next one is similar and the relative size of the factions in the
majority party is equivalent. The odds that that these two ‘constant’ Houses experience an effect
coming from changes in the Senate and the president are
j
1 j
and
k
and the odds
1  k
ratio is the ratio of these odds:
1  j (1   k )

   j , k with
  k (1   j )
x j  xk .
(1.1)
Sensitivity analysis shows the number   1 that shows the ratio of those odds.
When  equals 1, then the study is free of hidden bias and we can proceed in the analysis as if
changes in the Senate and president were randomly assigned to different constant congresses.
However, if   2 , then those two Congresses could differ in their odds of experiencing the
treatment by as much as a factor of 2: one Congress is twice as likely as the other to receive the
treatment:
 is a measure of the degree of departure from a study that is free of hidden bias. A
sensitivity analysis will consider several possible values of Γ and show how the
inferences might change. A study is sensitive if values of Γ close to 1 could lead to
inferences that are very different from those obtained assuming the study is free of hidden
bias. A study is insensitive if extreme values of Γ are required to alter the inference.
(Rosenbaum 2002:107)
The model (1.1) can be re-written in terms of an unobserved covariate u: Congress j has
an unobserved covariate u j . “Then the model has two parts, a logit form linking treatment
assignment  j to the covariates (u j ) and a constraint on u j ” (Rosenbaum 2002:107).
 j
log 
1 
j


  u j , with 0  u j  1, (1.2)


where  is an unknown parameter.
If we have two Congresses, j and k, the ratio of the odds that both Congresses receive the
treatment is:
22
 j 1   k 
 exp   u j  u k  
 k 1   j 
Therefore, if
two units…differ in their odds of receiving the treatment by a factor that involves the
parameter  and their difference in their unobserved covariates u…sensitivity analysis
will display the sensitivity of inferences to a range of assumptions about (γ, u).
Specifically, for several values of γ, the sensitivity analysis will determine the most
extreme inferences that are possible for u satisfying the constraint in (1.2). (Rosenbaum
2002:108, 110)
“If conclusions are insensitive over a range of plausible assumptions about u, the number of
interpretations of the data is reduced, and causal conclusions are more defensible” (Rosenbaum
and Rubin 1983:213).
How is sensitivity analysis conducted in this framework? For a range of Γ  1, we need
to find the upper and lower bounds on inference quantities, like p-values (or endpoints of
confidence intervals). We need to report these p-values for several values of Γ until the
conclusion begins to change. “For several values of Γ, the sensitivity analysis computes the
maximum possible values of the significance level…For Γ = 1, there is only one possible
significance level, namely the usual one from randomization test…Sensitivity analyses for tests
are inverted to yield confidence intervals” (Rosenbaum 2004:156) How large must Γ be, that is,
how far must Γ depart from the randomization distribution, to alter materially the conclusions of
the study? Table 5 shows the upper and lower bounds of significance levels for different values
of Γ.22
TABLE 5. Sensitivity analysis. Range of Significance levels for the Fisher
Exact Test.
Γ
1
2
3
4
22
Minimum
< 0.0001
< 0.0001
< 0.0001
< 0.0001
Maximum
< 0.0001
0.006
0.035
0.09
The full results may be obtained from the author upon request.
23
In the case Γ = 3, the significance level might be less than 0.0001 or it might be as high
as 0.035, but for all unobservable variables the null hypothesis of no effect of changes in the
Senate and/or president on decisions to change the rules and procedures of the House is
implausible. The null hypothesis fails to reach conventional statistical levels for implausibility
when Γ  4. So, to attribute the observed pattern of change in the distribution of power in the
House to an unobserved covariate rather than to an effect of changes in the Senate and/or the
president, that unobserved covariate would need to produce a fourfold increase in the odds of
changes in the Senate and/or the president and it would need to be a near perfect predictor of
decisions to change rules and procedures.
Furthermore, this unobserved covariate u would have to more than quadruple the odds of
changes in the Senate and the presidency and more than quadruple the odds of changes in the
distribution of power in the House, before altering the conclusion that Houses tend to change their
distribution of power after changes in the presidency and the Senate. The difference between
Congresses that experience changes in the preferences of the Senate and/or the president and
those that do not does not seem to be easily explained as the result of an imbalance due the
existence of unobserved covariates.
5. Robustness tests
Do my results depend on the operationalization of the concept “constant Congresses”? In
this section I assess whether my results are contingent on my definition of “constant House.”
Throughout the paper, I considered a House constant when (1) the majority party from one
Congress to the next is the same and (2) the relative size of the factions of the majority party is
similar. In what follows I will first relax condition 2 and analyze the model including all Houses
where the relative size of its factions changed across consecutive congressional terms. Second, I
will use a totally different operationalization of the variable “constant House.” Specifically, I will
24
use DW-NOMINATE scores to define which Houses are ‘constant’ across consecutive
congressional terms.
1. Robustness test for the ‘constant’ House variable
First, I relax the definition of constant House and include all Congresses whose majority
party did not change from one Congress to the next in the following 2x2 contingency table.23
Thus Table 6 encompasses more Congresses than the previous analysis since it includes
Congresses where the relative size of party factions changed.
TABLE 6. Robustness test for the “constant’ House variable.
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
 in Rules
20 (77%)
~  in Rules
6 (23%)
Total
26 (100%)
9 (40%)
13 (60%)
22 (100%)
The table shows that more than 77% of the 26 Houses that experienced changes in the
Senate and/or president did change their distribution of power even though their membership
remained constant. Of the 22 Houses that did not experience changes in the Senate and/or
president, 60% did not change their rules and procedures. Traditional Theories would never
predict such significant differences across rows in each column (p-value 0.001, from Fisher’s
exact test). If the data supported extant theories of legislative organization, then for Houses that
either decided or not to change their distribution of power, we shouldn’t see differences between
situations where the Senate and/or president changed and those where they did not. This
difference is predicted by the Constitutional Theory but is difficult to explain using the
conventional models. Again, we can reject the null hypothesis that decisions to rearrange
distributions of power in the House are independent of changes in the Senate and/or the president.
23
The additional Congresses included in this analysis are the following: 55 th, 59th, 60th, 73rd, 78th, 79th and
82nd.
25
Thus the results obtained in the previous section are robust to a different
operationalization of the variable “constant Congress.”
2. Robustness test using DW-NOMINATE scores
To further test the robustness of the ‘House constant’ definition, I decided to conduct
tests using the DW-NOMINATE24 scores employed by congressional researchers. Using this
variable, when the score for the median member of the House (or the party) changes substantially,
the new House is considered significantly different from the previous one. Applying this
definition to my analysis, those Houses that experience a “substantial change” in the scores of the
median member of the House and majority party cannot be considered constant from one
congressional period to the next one.
But what is a “substantial change”? As there is no answer to this question in the
literature, I decided to use different definitions of “substantial” change. Specifically, I computed
the differences in contiguous Congresses25 between the median member of the House and the
median member of the majority party26 in the first and second dimension.27 The table (Table 2 in
the Appendix) shows the movement in the median of the House and the median of the majority
party from one Congress to the next. However, how large should the magnitude be to consider it
substantial? What is the threshold below which a House is considered constant?
Rather than selecting a threshold ad hoc, I tested several plausible values. Each threshold
took the form, “If the difference in one of the medians in one of the dimensions is  X, then that
Congress cannot be considered constant and is not included in the analysis,” with
“DW nominate is a dynamic version of W-Nominate and is very similar to the original D-Nominate
procedure. The only differences is that DW-Nominate is based on normally distributed errors rather than
on logit errors and that each dimension has a distinct (salience) weight (the weight for the first dimension is
always 1.0)” (http://voteview.com/pmediant.htm).
25
DW-Nominate scores in one Congress are directly comparable with scores in another Congress
(http://voteview.com/pmediant.htm).
26
Both measures of the median were taken from Poole’s website: http://voteview.com/pmediant.htm.
27
The first dimension refers to economic issues. The second refers to the north-south split between 1930
and 1970’s.
24
26
X   0.10, 0.15, 0.20, 0.25  . Tables 7 through 10 present the distributions resulting from
each threshold, along with the results of a Fisher’s exact test. The small p-value produced in
every case provides further evidence that the effect of the Senate and the president on House rules
is not an artifact of variable coding.
TABLE 7. Cutting point 0.1.
Probability that the null hypothesis of no effect is true: 0.01 (from Fisher’s exact test)
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
 in Rules
14 (88%)
~  in Rules
3 (18%)
Total
17 (100%)
5 (33%)
10 (67%)
15 (100%)
TABLE 8. Cutting point 0.15.
Probability that the null hypothesis of no effect is true: 0.05 (from Fisher’s exact test)
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
 in Rules
14 (74%)
~  in Rules
5 (26%)
Total
19 (100%)
8 (40%)
12 (60%)
20 (100%)
TABLE 9. Cutting point 0.2.
Probability that the null hypothesis of no effect is true: 0.01 (from Fisher’s exact test)
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
 in Rules
18 (78%)
~  in Rules
5 (22%)
Total
23 (100%)
8 (40%)
12 (60%)
20 (100%)
27
TABLE 10. Cutting point 0.25.
Probability that the null hypothesis of no effect is true: 0.008 (from Fisher’s exact test)
 in Senate and /or
president, ~  in
House
~ in Senate and /or
president, ~  in
House
6.
 in Rules
20 (80%)
~  in Rules
5 (20%)
Total
25 (100%)
9 (41%)
13 (59%)
22 (100%)
Conclusion
The chapter shows that the House does not need a single member to change her
preferences to alter its organizational arrangement. By examining institutional changes adopted
in the US House from 1879 until 2001, I showed that whenever House members face a new
bargaining situation caused by changes in the Senate and/or the president, they are far more likely
to modify their organizational arrangements. There is a significant difference between the
proportion of Houses that redistribute power among its members immediately following a change
in the Senate or the president and the proportion that do it when no such change occurs.
By studying major rule changes in House procedures over several decades, I have shown
that holding constant House member preferences, changes in the identity of the president and/or
the composition of the Senate has regularly been followed by radical alterations of the
distribution of power in the House in ways that my theory predicts but the conventional wisdom
does not.
This paper suggests that analyses of the internal dynamics of the House need to
incorporate exogenous factors such as the president and the Senate. The incorporation of these
factors produces new insights regarding the timing and causes of legislative organization that
Traditional Theories do not provide. While Traditional Theories predict no difference between
the number of Houses that would embark on significant rule changes after changes in the Senate
28
and/or president have occurred or not, the results show that substantial differences exist. The
Constitutional Theory helps us understand this result by incorporating into the analysis the
preferences of exogenous institutions like the Senate and the presidency.
Furthermore, these results have both normative and practical implications. Normatively,
the implications are critical to the notion of representation. If policy outcomes are closely tied to
the distribution of power, then knowing which actors are powerful allows us to know precisely
which interests are privileged. In addition, the ability to predict changes in the distribution of
power has significant practical implications for group representation. Groups attempting to
influence policy in Washington need to know where they should invest their scarce resources. If
return on investment depends on the ability to affect legislation, an investment strategy should be
based on a coherent understanding of how the House distributes power among its members and
the conditions under which it will change its power sharing agreements. Such knowledge could
dictate when it would be wise for social interest groups to change their lobbying strategies.
In conclusion, I find that the constitutional requirement to bargain with non-House actors
can induce House members to make different leadership decisions than they would if they were,
as commonly represented, an isolated entity unaware of other chambers or branches of
government. Put another way, rational, foresighted and policy-oriented House members have an
incentive to incorporate aspects of the Constitution’s Article I, Section 7 into the organizational
decisions they make.
29
Appendix
Expansion of contingency table
 in
Rules
 in Senate and /or president.
~  in House
Change in the president
49th
58th
61st
67th
68th
87th
91st
93rd
95th
~  in Senate and/or president
~  in House
57th
64th
65th
69th
92nd
98th
99th
Changes in the Senate
46th
74th
90th
94th
96th
100th
Changes in the Senate and the president
53rd
89th
97th
~  in
Rules
Change in president
71st
101st
103rd
Change in Senate and president
63rd
50th
56th
70th
75th
76th
77th
85th
86th
88th
102nd
105th
106th
30
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