Policy Balancing through Legislative Success
Eduardo Aleman
[email protected]
University of Houston
Ernesto Calvo
[email protected]
University of Houston
January/06
First (rough) Draft
PLEASE DO NOT CITE
Abstract: Most policy balancing models presume that moderate voters attempt to
maximize their policy preferences by granting control of Congress and the Presidency to
different political parties. The policy outcome is then presented as a mixture model
centered on the amendment process in Congress, which takes into consideration the
President’s preferences by anticipating veto intent. We propose here a different way of
thinking about the policy balancing model, centered on the relative legislative success
rates of bills initiated by the president and by individual representatives. We introduce the
critical issue of agenda control to explain why the success rate of presidents and
legislators varies under unified or divided government and show that the policy weight of
the president vis-à-vis legislators increases under divided government and decreases
during unified government. We provide statistical estimates of policy balancing through
legislative success using a multilevel model of legislative productivity.
Paper to be presented at the Conference on Party Politics in Europe at the Transnational
Level Institute for International Integration Studies, Trinity College, February 2-3, 2007.
Acknowledgments: We thank Isabella Alcañiz, Michael Alvarez, Kenneth Benoit, Tulia Falleti,
Orit Kedar, Robert Erikson, Juan Pablo Miccozi, Maria Victoria Murillo, Patricio Navia, Noah
Kaplan, Sebastian Saiegh, Matias Ircowitz, and Betsy Sinclair.
Introduction
A quarter of century has passed since policy balancing models were first
introduced to explain the existence of midterm cycles in US politics (Alesina and
Rosenthal, 1995; Fiorina, 1996). In most policy balancing models, moderate voters
approximate their policy preferences by casting votes for candidates of different parties
for Congress and the Presidency, under the presumption that the policy outcome will
approximate a weighted combination of the spatial location of each institutional actor.1
The importance of this model was immediately recognized among political scientists,
who initiated significant research efforts to measure the “moderate” intent of split-ticket
voters (Alvarez & Schousen, 1993; Born, 2000; Erikson, 1988; Fiorina, 1996; Garand &
Lichtl, 2000; Lewis-Beck & Nadeau, 2004).
At the core of the policy balancing model, however, rests the unsettled question of
whether unified/divided government leads in actuality to different policy outcomes. After
all, split-ticket voting will only serve the purpose of moderating policy implementation if
legislative output is affected by the degree to which there is policy congruence between
Congress and the President. Mixed evidence in the US case led to competing claims that
divided government had no discernible effect on legislative outcomes (Mayhew, 1991) or
that it produced observable but moderate declines in legislative success2 (Binder, 1999;
Cameron et. al., 2004). To our knowledge, however, there is little comparative research
measuring the effect of divided/unified government on legislative productivity.
1
In this article we do not distinguish between cases in which moderates split their vote in the same election
or in consecutive elections.
2
In this article, legislative productivity describes the number of bill initiatives approved by an actor or
institution. We distinguish legislative productivity from legislative success, which describes the rate of
legislation approved to legislation initiated by an actor or institution.
2
In this paper we tackle the question of whether divided government affects the
policy outcome of a regime in which the president has significant agenda setting power
and has the capacity to directly propose legislation to be voted by Congress.3 The fact
that presidents can propose legislation to Congress provides a window into the contextual
factors that shape the relationship between these two institutional actors. It also allows
researchers to estimate how contextual factors, such as the relative popularity of the
president or a change in the coalition that controls Congress, affect the relative success
rates of the president and of individual legislators.
Common to almost all policy balancing models is the presumption that the policy
weight of the President on the total legislation depends on exogenous institutional
features that remain unaffected by whether Congress changes political color, such as the
rules governing veto, insistence, amendments, etc (Alesina and Rosenthal, 1995; Fiorina,
1996). In this paper we allow the policy weight of the president to vary in response to
contextual political factors, and provide a policy balancing model based on the
differences on the success rates of legislation.
The most important finding presented in this paper is that the policy weight of the
president in legislative output increases under divided government. As we will show, this
situation stems from changes in the legislative impact of members of the majority party,
which exhibit lower success rates under divided government while presidential success
rates remain relatively unaltered.
3
With the notable exception of the US, almost all existing presidential regimes possess formal institutional
mechanisms that allow presidents to introduce legislation to be considered by Congress. Presidential
initiatives often carry significantly more political weight than bills introduced by individual members of
Congress, and their treatment produces some of the most public confrontations among parties.
3
To test our model we use a multilevel design that simultaneously estimates the
legislative productivity of the President and of members of Congress. We test our model
using a large legislative dataset that includes all bills initiated by the president and by
individual legislators in the Argentine Congress between 1983 and 2001. This dataset
allows us to estimate different legislative success rates for the President and for
individual legislators over time, and to evaluate the relative weight of the president in the
total legislative output.
The order of the paper is the following: in the first section we discuss three
different conceptions of policy balancing. In the second section we analyze what factors
affect the legislative success of the president and of individual legislators with a
particular emphasis on cases in which the president has significant bill initiation and
agenda setting powers. In the third section we provide a multilevel statistical model that
simultaneously estimates the legislative success and productivity rates of the president
and of individual legislators. The model allows us to estimate the relative weight of the
president on the total legislative output. Using data from Argentina, the fourth section
shows that divided government increases the policy weight of the President vis-à-vis
Congress and that unified government boosts the productivity of individual legislators of
the majority party. We conclude in the fifth section discussing some extensions of the
analysis.
1. Three Different Policy-Balancing Mechanisms in Presidential Democracies
Since it was first presented by Fiorina (1996), policy balancing models seek to
explain the conditions under which moderate voters maximize their preferred policies by
4
electing candidates from different parties to the presidency and congress. In the standard
model, the policy outcome is a weighted combination of the policy preferences of
representatives in congress and of the president. When major parties favor different and
extreme policies which are distant from the median voter’s preference, moderates split
their vote to promote policy outcomes that are in-between the policies advocated by these
parties. Alesina and Rosenthal (1995) provide a simple description of the model where
voters maximize the following random utility preference:4
K
U ik = αθ p + (1 − α )∑ skθ k , ∀ k ⊂ K
(1.1)
k =1
The utility of voting for party k for congress maximizes a policy outcome which
combines the policy preferences of the president, θp, and the seat-weighted5 policy
preferences of all legislative parties, skθk. The relative weight of the president in the total
outcome is given by α and the relative weight of Congress is its complement, (1-α).
Estimation of this utility function can be performed using a conditional logit design
where the probability of voting for candidate or party k to Congress has a multinomial
distribution with a log link given by equation (1.1):
P ( s ik ) =
eU
k
∑ e
ik
U
(1.2)
∀ i, k
ik
k =1
While the description of the policy balancing model is relatively unproblematic, it
should be noted that there are very different institutional mechanisms that can generate
policy outcomes that combine the preferences of the president and of congressional
4
Alesina and Rosenthal introduce a two party model. Equation (1.1) extends the model to K parties and
allows the president to have policy preferences that are different from all other parties. See also
Kedar(2005).
5
To simplify their presentation, Alesina and Rosenthal presume PR electoral rules in which the share of
votes obtained by a party results in an equivalent share of seats. The model can be adapted to account for
more complex electoral rules.
5
parties. In particular, equation (1.1) can describe a policy model in which (i) each bill is
amended back and forth (presumably on the floor) until it reflects the position of the
median voter, which usually has a different policy preference than the president and
needs to anticipate the president’s veto intent. In this idealized case, each bill approved
represents a compromise between the floor preferences of congress and the policy intent
of the president. Consequently, changes in the partisan composition of congress would be
reflected in the content of laws but not necessarily in the amount of legislation that is
approved under unified versus divided government.
A second model that would be consistent with equation (1.1) would see (ii) all
bills voted under closed rule and the median voter deciding whether to approve or reject
each one. The president would then veto any bill she opposes, leading to changes in the
status quo only if legislative proposals are acceptable to both institutional actors. In this
second idealized case, unified or divided government would produce different policy
outputs because the selection process will lead to changes in the type of legislation
approved in congress. The selection process should also result in an increase in the
number of bills rejected by the median congressional voter and vetoed by the president,
which will result in a decline in total legislative productivity. The number of bills failing
to pass because of a presidential veto should also increase under divided government.
Finally, equation (1.1) would also be consistent with a process in which (iii) an
agenda setter in Congress only proposes to the floor those bills which will not be divisive
to the party and be acceptable to the median congressional voter and the president.
Different from the mechanism in (ii), most bills reaching the floor should be approved,
however, divisive bills proposed by members of the majority party and bills proposed by
6
members of minority parties should be frequently denied a floor vote. When minority
presidents (or parties) are endowed with agenda control, they can take advantage of this
prerogative to push their own legislative proposals which will pass as long as they are
acceptable to the median voter on the floor.
The three models have very different implications for the analysis of legislative
productivity in Congress. In the case of (i) there should be very little difference in the rate
of approval of legislation under divided or unified government. Incongruence between
the policy preferences of the president and the median voter in congress would lead to the
approval of different types of amendments6 rather than resulting in more bills been voted
down by congress or vetoed by the president. In the case of (ii), there should be a
substantive difference in the amounts of legislation enacted by Congress when the
median voter in the floor is from a different political color than the president, with
significantly higher rates of bill initiatives failing under divided government. Finally, if
(iii) captures more accurately the policy-balancing mechanism, the success rate of
legislation initiated by congress should be lower under divided government because
members of the majority party would still have to deal with an opposition president that
will veto legislation deemed unacceptable. The decline in productivity should be more
pronounce for those actors who lack control of the agenda. Consequently, minority
presidents with agenda power can prevent a decline in legislative success by pushing to
the floor their own bills knowing that they will pass as long as they are acceptable to the
median congressional voter. In consequence, few bills will be defeated by a floor vote,
few bills will be vetoed, and the impact of divided government on legislative productivity
6
The positional adjustment still results in proposals that change the SQ when this is outside of the
“gridlock” area. Presumably, divided government extends this area and reduces the set of acceptable
policies on certain dimensions of policy. As a result, passage of other types of bills may increase.
7
should stem from differences in the success rate of legislation initiated by members of
congress. In terms of legislative outcomes, the relative policy weight of members of
congress vis-à-vis the president should be greater under unified than under divided
government.
2. Divided Government, Agenda Control, and Legislative Productivity
First advanced with the U.S. model of presidential government in mind, policy
balancing models attached little importance to the parameter that measured the
institutional importance of the president on legislative output. In most models, the policy
weight of the president results from institutional rules which remain relatively fixed and
lacked substantive theoretical interest. However, as the institutions that regulate the
relationship between the Congress and the President vary, the empirical and theoretical
relevance of these rules becomes apparent.
In equation (1.1), changes in the institutional rules that regulate the relationship
between congress and the president will affect the value of the policy weight parameter α,
which estimates the relative importance of the president on legislative output. For
example, more extensive veto powers should result in legislation that is closer to the
policy preferences of the president, which will be reflected in higher values of α in
equation (1.1). Similarly, more extensive agenda setting power on the part of the
president will also result in policies that are spatially closer to the executive’s
preferences. This feature will become critical in most non-US presidential regimes, which
endow executives with significantly more agenda setting resources than those enjoyed by
US presidents.
8
There are important differences between the legislative process in the U.S. and in
most other presidential democracies. Most non-US presidential democracies, for
example, grant the executive significant institutional prerogatives to influence the
lawmaking process (Shugart and Carey 1992, Cox and Morgenstern 2002, Alemán and
Tsebelis 2005) such as the possibility to set deadlines for the approval of critical bills or
to call extraordinary sessions to treat important legislation. It is also common for
presidents to have the prerogative to introduce bills directly, instead of requiring
surrogate representatives to introduce legislation in their behalf. These executive-sponsor
bills make up a president’s legislative agenda clearly differentiable from that of
individual representatives.
The ability to prevent the passage of bills on which the president has exclusive
powers of introduction (i.e., gatekeeping power), as well as the ability to influence the
scheduling of bills and the proposals faced on the floor; provide unique opportunities to
executives in presidential regimes. The combination of positive and negative agenda
power in the hands of the executive makes the legislative process different that that
portrayed by the cartel agenda model (Cox and McCubbins, 2006), where the key actor is
the majority party leadership in Congress. In many presidential regimes, the agenda
setting powers is shared by the president and the majority in Congress and, as in
European parliamentary procedures, agenda control contributes to the legislative success
of the executive (Tsebelis 2002, Doering 2001). However, when the government is
divided among presidents and congressional majorities from different parties, the agenda
setting prerogatives of the president and those of the majority party in Congress will
collide.
9
Congresses are hierarchical organizations, with party leaders occupying decisive
positions that shape the plenary agenda (Cox and McCubbins, 2005). In all Latin
American countries, party leaders are given the task of scheduling legislative proposals,
as members of either a steering committee or the chamber directorate (Alemán 2006).
Committee assignments, chairmanships, bill referrals and scheduling decisions are
subject to bargaining among party leaders of the majority and minority parties. Although
most countries provide some sort of safety-valve for the plenary to challenge scheduling
decisions made by the leadership, such as a vote to discharge legislation from committee,
party discipline makes such moves very costly. As a result, de facto agenda control rests
on the hands of the chamber’s party leaders who use it to advance legislation they support
and prevent the passage of those they reject. The leadership of the Chamber, however,
needs not to correspond to the majority party. In many cases, leadership posts in the
Congress are negotiated with the executive, providing minority presidents with
significant clout to regulate the flow of legislation to be treated by both chambers.
As a result of these provisions, the institutional structure of Latin American
legislatures is clearly different from that advanced by the floor-agenda model, where the
bills’ scheduling is determined by a majority vote on the floor. The lack of a legislative
majority does not mean the vanishing of agenda influence for the president. Although the
president may not govern around Congress, several tools allow the president to continue
to influence the treatment and passage of proposals despite the lack of a congressional
majority. This is achieved not only through the use of the constitutional prerogatives (or
the anticipated reactions they generate on congressional actors) but also through informal
norms that gives the executive branch the prerogative to appoint allies as congressional
10
authorities in charge of scheduling legislation. The president’s appointees can therefore
push the president’s initiatives to the floor or prevent those of the majority party from
ever being discussed.
While legislative proposals introduced by minor parties are regularly at the mercy
of the majority’s leadership, the president’s proposals enjoy a privileged position under
both unified and divided government. The bills initiated by individual representatives of
the majority party, however, may face significantly different hurdles when agenda setting
power is shared with a president of different political color. As we will show in the
following two sections, the fact that minority presidents retain agenda setting power has
significant implications for analyzing legislative productivity. In particular continuous
control of the agenda allows minority presidents to enjoy relatively stable legislative
success rates, while the productivity of the majority party increases during unified
governments and decreases under divided government.
3. A Statistical Model of Policy Balancing through Legislative Success
As indicated previously, legislative success describes the rate of approval of
legislation initiated by a political actor (i.e. the President, a Deputy, or a Senator).
Legislative productivity, on the other hand, refers to the total number of bill initiatives an
institutional actor approves in a given year. We begin this section describing the
institutional and contextual variables that explain legislative success and legislative
productivity.
We can think of legislative success in Congress as being shaped by political
processes at two different levels: First, at the floor level, the legislative success of the
11
executive, deputies, and senators is explained by effects that can be characterized as billspecific. Presidents can push their prioritized agenda forward by introducing initiatives in
a particular chamber or during a particular session of Congress or on some policy matter
on which they enjoy some advantage. Individual legislators can mobilize peer support or
take advantage of their expertise to push for particular bills while party leaders can force
floor votes at the right time or delay bills in multiple committees.
Second, at the aggregate level, contextual political processes also affect the
legislative success of these political actors. For example, the behavior of members of
Congress towards the President’s legislative agenda can be affected by contextual factors
such as the public’s perception of the executive or the electoral cycle. By “going public”,
popular presidents can oftentimes force Congress into a compliance mode (Kernell, 1988.
Similarly, presidents tend to benefit from a honeymoon with Congress after their
inauguration while electoral years often lead legislators to divert time away from
congressional work and towards more immediate campaign matters (Jones et.al. 2004).
If we had public opinion data on every bill, if individual legislators voiced their
legislative preferences explicitly, and so on, we could pool together all this information
and estimate a simple logistic model. However, the public often expresses vague
preferences about executive initiatives and representatives often remain silent regarding
their preferences on particular bills, forcing us to implement statistical models that
capture appropriately both the individual and the aggregate level information we have.
With a multilevel model we can estimate legislative success by measuring the effects of
both individual and contextual forces on the approval of legislation initiated by the
different actors.
12
Once we know the number of bills introduced by each actor and their different
success rates, we can also estimate the relative weight of the President and Congress in
formulating policies. Figure 1 provides an intuitive overview of the full model of
legislative productivity, estimating separate success rates π for the President and for
individual representatives. Legislative success is estimated from a matrix of explanatory
variables including individual and aggregate level factors, which vary among institutional
actors. The relative strength of the President and Congress, on the other hand, can be
estimated as a function of the number of initiatives approved, captured by the parameter
α. As in Alessina and Rosenthal (1995), α describes the legislative weight of the president
for a given congressional year with respect to Congress.
[Figure 1 about here]
While there usually is a positive correlation between legislative success and
legislative productivity, this is not always the case. For example, an increase in the
number of bills proposed by all legislators in a given congressional year can result in
lower success rates with a similar number of bills enacted, as more bills compete with
each other for scarce floor and committee time. A decline in legislative success holding
the number of bill initiatives constant, however, will indeed lead to a decline in
legislative productivity.
[Table 1 about her]
Table 1 presents aggregate rates of legislative success and total legislative
productivity in the Argentine Congress. As it is possible to observe, presidents have
moderate rates of legislative success, approving on average just over half of the bills they
propose. By contrast, legislative success is relatively low for bills initiated by individual
13
legislators, about 5 percent of the legislation introduced in Congress. Individual
legislators, however, initiate many more bills than the President. As a result, while
presidents display much higher rates of legislative success, executive legislative
productivity is about 20% lower than that of representatives. The table shows that success
rates are considerable higher for legislation initiated in the Senate, this is a regularity
observed across the border for all political actors, including non-Peronist parties which
have never controlled a plurality of the Senate seats. While legislative success appears to
be considerable higher for legislation introduced in the Senate, productivity favours the
Chamber of Deputies – partly the result of a larger number of bills being proposed in a
chamber that has three times the number of representatives.
[Figure 2 about her]
Since 1983, significant variation can be observed both with regard to legislative
success and legislative productivity. This is the result of both the existence of political
cycles in the approval of legislation and the variation in the amount of legislation being
proposed. As it is possible to observe in the two panels in Figure 2, legislative success is
extremely variable while legislative productivity has generally increased.
3.1 A Statistical Model of Legislative Success
A straightforward interpretation of our argument is to model the law-making
process as a hierarchical process where, at the individual level, we observe the approval
of a bill rather than the true underlying preference that shapes the parties vote. The mean
bill approval rate across different institutional actors and Congresses, however, is
14
captured by a random intercept explained by other aggregate level factors. We can
describe this General Linear Multi-Level Model with the usual notation:
yik ~ Bern(θ ik )
∑
⎡ θ
⎤
ln ⎢ ik ⎥ =α k +
β t X tik , i = 1,.., nk , k = 1,.., K
⎣ 1 − θ ik ⎦
t
αk ~ N(
∑λ Z
j
(1.3)
2
jk , σ α ), k = 1,.., K
j
where yik is a dummy variable indicating whether the initiative i was approved by
Congress k,
∑β X
t
tik
describes the first level set of parameters and explanatory variables
t
for the individual level variation in bill approval, α k is a random intercept capturing the
mean bill approval rate for Congress k, and
∑λ Z
is a set of parameters and variables
j jk
j
explaining the aggregate level variation in approval rates. The first level logistic equation
explains within Congress approval for individual bills. The second level normal
equation,7 by contrast, explains the across Congress variation in bill approval rates for
each of the 19 Congressional periods.8 To estimate the model, we use WinBUGS,9 with
non-informative priors for all hyperparameters
{β , λ ,α ,σα },
t
j
k
2
although results can be
improved by using informative priors, particularly on parameters describing data
measured with error such as public opinion pools.10
7
Notice that the random intercept has been logistically transformed in the first level. Therefore, the second
level estimate of α should be interpreted as the log-odds mean bill approval ratio and the second level
equation is normal.
8
One Congressional period per year, including all types of sessions.
9
David Spiegelhalter, Andrew Thomas, Nicky Best, Dave Lunn. WinBUGS user Manual 1.4, (Cambridge
UK, 2003). {http://www.mrc-bsu.cam.ac.uk/bugs}.
10
For example, to model confidence intervals around the mean presidential approval rating in the second
level equation.
15
Equation (1.3) also estimates the legislative success of the president, which is
explained by a different set of contextual and institutional variables as described in
Figure 1. Finally, we measure the relative success rate11 of the president vis-à-vis
congress by comparing αck and αpk:
⎛ eα pk
PolicyWeight = ln ⎜⎜ α ck
⎝e
⎞
⎟⎟
⎠
(1.4)
3.2 Data
Between 1983 and 2001, close to 124,000 public and private bills were formally
proposed to Congress. Among these bills, 30,024 were substantive bills submitted by
Senators, Deputies, and the Executive, and approximately 94,000 were private bills with
only symbolic value. Among the 30,024 bills we find 2,384 executive initiatives, of
which 650 inform Congress about the promulgation of an executive decree. The rate of
approval of the remaining 1,739 initiatives is 54%. If we eliminate from the sample those
bills that (i) requests authorization for the president to leave the country, (ii) request the
confirmation of the presidential appointees,12 and (iii) request the ratification of good will
international treaties, we have 1,004 law initiatives, 51 per cent of which were approved
by Congress during the 1983-2001 period. The remaining 27,640 bills were introduced by
individual Deputies and Senators, with a number of co-sponsors that range from 0 to 61.
In order to measure legislative success we use a nominal dependent variable that
takes the value of 1 if a bill was approved by both the House and the Senate and 0
11
We use the exponential of the legislative success ratios to guarantee that values are positive.
Different from the US Congress, the confirmation of presidential appointees in Argentina is mostly a
formal procedure.
12
16
otherwise.13 On the aggregate level, the dependent variable is the latent rate of approval
of presidential bills once we control for the individual level factors, which is the natural
interpretation of the random intercept in our model.
The model of executive success incorporates individual and contextual variables
that measure the partisan support for the president; the electoral cycle; public approval of
the president’s performance; the chamber where the bill was first introduced; the number
of committee with jurisdiction over the bill; the size of the president’s agenda; policy
subject; and the issuance of executive decrees. The model of legislative success for bills
introduced by members of congress incorporates individual and contextual variables that
capture the effects of the party of the proponent; cross-party sponsorship; co-sponsorship;
the chamber where the bill was first introduced; the electoral cycle; the size of the
proponent’s party; the region of the country where the proponent was elected; and
number of bills introduced by the proponent and during the congressional year. An
explanation of how we coded each variable appears in the appendix.
4. Results
Table 2 presents the results of model (1.3). The first two columns provide the
coefficients and standard errors for first level variables, while columns three and four
provide the same information for the second level variables. Since the focus of this paper
is on presenting an alternative interpretation of inter-branch policy balancing through
13
Our data also includes 1194 bills that were only approved in one of the Chambers (media sanción), but
we do not use this information in this paper The information about media-sancion is highly relevant, given
that it gives information about legislation that is substantive but was could not be approved by the second
Chamber. This information is particularly useful for the 94 presidential bills that received media sanción
but could gain final approval.
17
legislative success and not on testing alternative theories about what variables influence
the approval of bills, we do not elaborate much on the particular results of model (1.3).
The goal is to be able to capture measures of success as a step to measure policy weight.
Yet, some findings reveal important implications.
The results show that changes in partisan support for the president do not affect
the legislative success of the president. They also show that bills introduced by members
of the majority party are more likely to succeed than others, and perhaps more
importantly, that this success is greater when the president is from the same political
party. These findings are consistent with the implications derived from the third
conceptualization of the policy balancing model discussed earlier in the paper.
Other interesting results show that at the second level the electoral cycle and
public opinion affect the success of bills initiated by both the president and legislators.
Increases on the positive image of the president lead to higher success rates for bills
initiated by the president and lower success rates for bills initiated by members of
congress. Presidential bills are more likely to be approved when they are introduced
during the first year in office and less likely to be approved if they are introduced during
the last year in office. During years of congressional elections, bills initiated by
legislators are less likely to be approved. In regards to the effects of individual level
variables, we observe that bills introduced in the Senate are more likely to pass regardless
of the proponent. Bills initiated by members of congress are more likely to pass when
they have multiple co-sponsors but less likely to pass when the support is cross-partisan.
Contrary to the conventional wisdom, presidential bills on economic matters do not
appear more likely to pass than others.
18
[Table 2 about here]
The most significant estimates from equations (1.3) and (1.4), however, are those that
measure the policy-weight of the president by comparing the relative success rates of the
president and legislators. Table 3 presents the relative success rate of representatives (BDAlphas)
and the relative success rate of the President (BPAlphas) as proposed in equation (1.4).
[Table 3 about here]
The measure of policy weight shown in Table 3 captures the extent to which the
legislative output is dominated by legislation proposed by the president or by legislation proposed
by individual legislators. The median policy weight ratio is 2.2 in favour of the president, which
indicates that on average the success rate of the president is 9 times higher than that of individual
representatives once we control for floor level variables.14 The president’s success rate is higher
for all Congressional periods examined, with a low of 1.3 (3.6 times higher) in 1998. There is,
however, considerable variance in our measure of policy weight across congressional periods.
A more intuitive interpretation of the results is showed in Figure 4, which plots the
policy-weight ratios of the president and that of individual legislators. The upper panel describes
the relative success rate of the president as in equation (1.4), with the horizontal line indicating
the score where the success rate of bills sponsored by the president is equal to that of bills
sponsored by individual representatives. The lower panel describes the relative productivity rate15
of the president, with the horizontal line indicating the score where the productivity rate of both
actors is equal. This lower panel, therefore, estimates the policy-weight of the president by taking
into consideration not only the success rate of each type of actor but also how much legislation
they introduce. Therefore, and as it was also noted in Table 1, while the relative success rate of
14
Notice that equation (1.4) estimates the log odds ratio of president success vis-à-vis that of the individual
legislators, e 2.2 = 9.02 .
15
The productivity rate is the product of equation (1.4) and the number of bills introduced by each actor:
⎛ eα p N p ⎞
Pr oductivity Rate = ln⎜ α p ⎟
⎜e N ⎟
c ⎠
⎝
19
the president is much higher than that of members of congress, the overwhelming majority of
bills are introduced by individual representatives.
[Figure 4 about here]
As it is possible to observe, the policy weight of the president increases under periods of
divided government and decreases under unified government, as a result of the higher sensitivity
or individual representatives to contextual political factors. As it was noted in the first and second
sections, the Argentine president controls significant agenda setting resources. In particular, it is
customary to allow the president to decide who will control key authority positions in Congress,
such as the presidency of the upper and lower chambers and the first legislative secretary. As a
result, minority presidents still retain extensive resources to keep legislation they do not want off
the floor.
5. Concluding Remarks
This paper advances an alternative interpretation of the policy balancing model
which stresses the importance of contextual political factors and control of the agenda to
explain changes in the policy weight of the president in the legislative process. We
provide evidence of policy balancing trhough legislative success, which results from the
varying legislative success rates of the president, the majority, and minority parties.
In our analysis, the agenda setting power of presidents raises new difficulties to
the members of the majority party when trying to approve their preferred legislation. We
show that the higher sensitivity of individual legislators to contextual political factors
increases the political salience of presidents under divided government.
20
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23
Appendix:
Variable Descriptions
Coding of Variables -- Presidential
1. A dummy variable which takes the value of 1 if the bill was initiated in the Senate.
2. A dummy variable that takes the value of 1 if the president is in his first year in office.
3. A dummy variable that takes the value of 1 if the president is in his last year in office.
4 and 5. Two ordinal variables measuring the number of House and Senate committees to
which the law initiative was sent. If the bill was initiated in the house and never
made it to the Senate, the variable indicating the number of senate committee was
set to that year’s mean. Similarly, if the bill was initiated in the Senate and never
made it to the house, the value of the number of house committees was set to that
year’s mean.
6. The total number of bills proposed by the executive,
7. A dummy variable indicating whether the president controls a majority of the seats in
the House or the Senate.
8. The number of executive decrees during the congressional period.
9. The positive image of the president as reported by Mora y Araujo (1983-1988) and
Nueva Mayoria (1988-2001).
Legislators
1. The natural log of an ordinal variable ranging from 1 to 62 indicates the number of cosponsors (co-firmantes).
2. The natural log of the number of bills.
3. A dummy variable taking the value of 1 if the co-sponsors belong to more than one
partisan bloc.
4. A dummy variable takes the value of 1 if the president is in his first year in office.
5. A dummy variable that takes the value of 1 if the president is in his last year in office.
6. A variable to indicate if it is a legislative election year.
7. A dummy variable takes the value of 1 if the bill was introduced by a senator and 0 if it
was introduced by a deputy.
24
8-12. Five dummy variables are used to estimate the legislative success rates of
representatives from the PJ, the UCR, the FREPASO, the UCeDe, and from all
Provincial parties.
13-15. Three dummy variables also estimate legislative success for representatives from
the metropolitan region (CABA, Buenos Aires, Cordoba, Mendoza, y Santa Fe),
the North-West (Catamarca, Chaco, Jujuy, La Rioja, San Luis, Santiago del
Estero, Salta, Tucuman), and the South (Rio Negro, Chubut, La Pampa, Neuquen,
and Tierra del Fuego).
16. The natural log of the number of members of the sponsor’s party bloc is used to
measure the effect of the parties’ size.
17. A dummy indicating wether the legislator belongs to the majority party.
25
Figure 1: A Model of Legislative Productivity
First Level: Matrix of X independent variables indicating
the effect of Rules and Procedures at the floor level.
Presidential
Legislative Success
Pr(πp)=Xβ+Zλ+v
Second Level: Matrix of Z contextual variables e.g.
Presidential image.
Legislative Productivity
LP = α(θp)πp + (1-α)*(θs+θd)πL
First Level: Matrix of X independent
variables e.g. tenure, status, bloque
Diputados
Representative’s
Legislative Success
Pr(πL)=Xζ+Zδ+u
Second Level: Matrix of Z contextual
independent variables.
Senadores
First Level: Matrix of X independent
variables, e.g. tenure, status, bloque
Note: α describes the relative strength of the president vis-à-vis Congress, θ indicates the total number of initiatives initiated by an
actor, πp is the average legislative success rate of the President, and πL is the average legislative success rate of individual
representatives.
26
Figure 2: Legislative Success and Legislative
Productivity; 1984-2001.
0
.02
.04
Legislative Success
.06 .08 .1 .12
.14
.16
Legislative Success
100
105
110
Congressional Period
115
120
115
120
0
Number of Bills Approved
50
100
150
200
Legislative Productivity
100
105
110
Congressional Period
Note: Observations for Congress 101 (1983) were eliminated to
facilitate the reading. This congressional period only included the
legislation introduced in December of 1983, during the initial
month of the democratic period.
27
4
0
1
2
3
Unified Government
-1
Policy Weight of the President (Relative Success Rate)
5
Figure 3: Policy Weight of the President, Relative Success and Productivity Rates
1985
1990
1995
2000
4
0
2
Unified Government
-2
Policy Weight of the President (Relative Productivity Rate)
Congressional Year
1985
1990
1995
year
Note: Estimates from Table 4.
28
2000
Table 1
Legislative Success by Chamber of Origin
Initiator
Bills Introduced
Bills Passed
Success Rate
Executive
919
919
376
566
40.9%
61.6%
Ch. of Deputies
Senate
20,635
5,484
780
432
3.8%
7.9%
Ch. of Deputies
Senate
9,007
2,937
402
257
4.5%
8.8%
6,668
1,818
271
131
4.1%
7.2%
4,960
729
107
44
2.2%
6.0%
Ch. of Deputies
Senate
Legislators
PJ
UCR
Ch. of Deputies
Senate
Others
Ch. of Deputies
Senate
29
Table 2: Determinants of Legislative Success
Diputados & Senadores
First Level Equation
Representative from
Frepaso (Left)
representative from
PJ
Representative from
Provincial Party
Representative from
UCeDe (Rigth)
Representative from
UCR
Initiated in the Senate
Number of CoSponsors
Belongs to Majority
Coalition
Multi-Block Proposal
Bills Introduced by
Rep (N)
Size of Party
Delegation
Region: NW
President PJ
Region: South
Rep. Metro
-0.4744
(0.2332)
0.4157
(0.2472)
0.4218
(0.1832)
-0.7121
(0.453)
0.4556
(0.2279)
1.217
(0.1097)
0.1687
(0.04327)
0.5204
(0.09269)
-0.9998
(0.08555)
-0.1034
(0.02959)
0.04071
(0.05637)
-0.0793
(0.0838)
-0.85
(0.6149)
-0.03856
(0.0805)
-0.04286
(0.07137)
President
Diputados & Senadores
First Level Equation
Second Level Equation
Initiated in the
Senate
Number of
Diputado
Committees
Number of
Senate
Committees
PJ President
Target:
Individual
Target: Local
Target:
Provincial
Target: National
General
Legislation
Pork
General
Economic
Election Year
1.258
(0.1625)
-1.21
(0.6086)
Honeymoon Year
-0.05874
(0.1028)
President
Second Level Equation
Provincial
Spending
Honeymoon year
-0.4319
(0.6861)
Positive image of the
President
-0.005679
(0.1164)
Last year of
Presidency
-0.03536
(0.2276)
-3.694
(1.055)
-0.4411
(0.9308)
0.3626
(0.2704)
Positive image of
the President
Last year of
Presidency
Ratio of Decrees
-0.354
(0.4934)
0.1099
(0.4664)
-0.4019
(0.5055)
-0.04457
(0.442)
-0.1668
(0.3306)
0.04946
(0.2956)
-0.0448
(0.2924)
Note: Estimates from equation(1.3).
30
1.102
(0.8462)
2.193
(1.158)
-0.1371
(0.3595)
-1.998
(1.156)
Table 3: Legislative Success and Policy Weight of the President
Year
1983
1984
1985
1986
1987
1988
1989
1990
1991
Mean
Success
(Diputados)
Mean
Success
(Presidente)
Policy Weight
BDAlphas
BPALPHAS
RATIOS
-2.459
0.9936
3.452
(0.4603)
(0.5669)
(0.7096)
-2.459
1.026
3.485
(0.2129)
(0.4993)
(0.5355)
-3.335
0.1326
3.467
(0.2399)
(0.5951)
(0.6281)
-3.101
-0.01051
3.091
(0.2372)
(0.545)
(0.582)
-3.302
0.07192
3.374
(0.2743)
(0.5)
(0.5585)
-2.461
-0.2658
2.196
(0.2155)
(0.5284)
(0.5633)
-2.867
0.02521
2.892
(0.2296)
(0.5459)
(0.5845)
-1.625
-0.2546
1.37
(0.6201)
(0.589)
(0.7957)
-2.067
-0.3033
1.764
(0.625)
(0.5318)
(0.7652)
Year
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Note: Estimates from equations (1.3) and(1.4).
31
Mean Success
(Diputados)
Mean
Success
(Presidente)
Policy
Weight
BDAlphas
BPALPHAS
RATIOS
-1.991
-0.1021
1.889
(0.621)
(0.608)
(0.8181)
-1.892
0.2307
2.122
(0.6238)
(0.5022)
(0.7534)
-1.664
0.03503
1.699
(0.6229)
(0.5822)
(0.8022)
-1.943
-0.1266
1.816
(0.6223)
(0.5793)
(0.8126)
-1.838
-0.2407
1.597
(0.6202)
(0.5297)
(0.7788)
-1.982
-0.4084
1.574
(0.6226)
(0.4997)
(0.7515)
-2.237
-0.9683
1.268
(0.6172)
(0.5901)
(0.8117)
-3.442
-0.892
2.55
(0.2301)
(0.5175)
(0.5597)
-2.943
0.1006
3.044
(0.2089)
(0.5477)
(0.5846)
-3.637
-0.3285
3.309
(0.2268)
(0.4824)
(0.5324)