Efficient Markets Theory and the Quality of Political Representation in the United States*
George A. Krause†
University of South Carolina
Draft as of
December 13, 2002
*
Paper prepared for delivery at the 2003 Public Choice Society Meetings, Nashville, TN. March 21–23, 2003
Earlier (and very different) manifestations of this paper w ere presented at the 200 0 Pub lic Choice Society M eetings,
Charleston, SC. March 10–12, 2000, 2000 American Political Science Association Meetings, Washington, D.C.
August 31–September 3, 2000, as well as seminars at the University of North Carolina–Chapel Hill American
Politics Research Group (APR G) and G eorge M ason University’s Center for the Study of Public Choice. I thank
John Aldrich, Bryan Caplan, Jeffrey Cohen, Roger Congleton, Mark Crain, Brad Gomez, Shingo Goto, Sara Gubala,
David Lowery, Michael MacKuen, Steven Mann, Bill Mishler, Irwin Morris, Carlos Ramirez, Brian Roberts, Dan
Sabia, Brian Sala, Ken Sho tts, Jim Stim son, T hom as Stratmann, LeeA nne K rause, and seminar particip ants for their
helpful suggestions and constructive criticisms o n earlier versions of this paper. I wish to extend special thanks to
HeeM in Kim for generously providing me with the government and voter ideology data from his joint cross–national
project with Richard Fording. None of the aforementioned individuals bear responsibility for any shortcomings of
this essay.
†
Associate P rofesso r of Political Science, De partm ent of G overnment and International Studies, U niversity of South
Caro lina. Co lumbia, Sou th Carolina 2 920 8. [email protected] (e–m ail).
Abstract
The principal–agent relationship involving voters delegating authority to elected officials is the
touchstone of representative democracy. Evaluating the quality of political representation is of utmost
importance to students of empirical democratic theory because it can show the extent to which the
government’s ideological preferences mirror those of those typical (median) voter. Existing research on
this topic provides limited insight into this issue since responsiveness is not mutually exclusive with
systematic shirking by politicians representing citizens. In this study, I address this dilemma by
advancing a Parity Representation model of political market equilibrium premised on efficient markets
theory. The theoretical benchmark of political market efficiency for each of these models are empirically
estimated using data on voter and government macro ideology for the 1948–1996 annual period. The
statistical findings clearly reject political market efficiency under this maintained model, thus indicating
that the government’s ideological position systematically deviates from the median voter. These
empirical results suggest that research on collective political representation contention that popular
preferences are effectively transmitted through electoral institutions are at a minimum, overstated.
“At the heart of the practice of every democracy is the need to
delegate authority from citizens to elected officials.” (Robert A.
Dahl, Dilemmas of Pluralist Democracy: Autonomy versus
Control. 1982: 48).
“Representative Government is not defined by particular actions
at a particular moment, but by long–term systematic
arrangements – by institutions and the way in which they
function.” (Hannah Fenichel Pitkin, The Concept of
Representation. 1967: 234).
Introduction
Modern democratic theorists, such as Robert Dahl and Hannah Pitkin, have long grappled with
conceptualizing the evaluative criteria by which to judge the performance of elected officials in a
democracy. Political representation entails the delegation of authority from the non–elected to the
elected, and also the subsequent means by which the latter represents the policy interests of the former.
Thus, this phenomenon cannot be accurately gauged as a series of single snapshots at one point in time.
Rather, its performance must be assessed over the ebb and flow of time by investigating the systemic
nature in which electoral institutions respond to the will of those whom they are elected to represent.
Empirical democratic theorists possess varying opinions on this subject. Many scholars maintain
that the efficacy of political representation is modest at best since voters are not well informed about
issues, policy, and candidates (e.g., Bernstein 1989; Campbell, et al. 1960; Conover and Feldman 1986;
Delli Carpini and Keeter 1996; Downs 1957; Kinder and Sears 1985 Miller and Stokes 1963; but see
Erikson and Romero 1990; Lupia and McCubbins 1998). As a result, this body of work implies that
government institutions are not responsive to voters’ preferences. Over the past decade or so, research
on macro political representation in the United States reveals that democratic institutions are
systematically responsive to mass public preferences from a majoritarian, Downsian perspective (e.g.,
Erikson, Wright, and McIver 1994; MacKuen, Erikson, and Stimson 2001; Page and Shapiro 1983; 1992;
Wittman 1995: Chapters 3 & 11; see also, Mishler and Sheehan 1993 for evidence regarding the U.S.
Supreme Court).
1
In this study, I attempt to arrive at a more complete and accurate understanding of political
representation by analyzing the extent to which a representative elected official mirrors the policy
preferences of a representative voter.1 To address this problem, I advance a parity representation model
that is grounded in an explicit theory of political market equilibrium. This approach is adapted from
efficient markets theory in financial economics where it has been employed to analyze the relationship
between asset prices and returns (e.g., Fama 1970, 1976) as well as the spot and forward rate in exchange
markets (e.g., Baillie and McMahon 1989; Frankel and Froot 1987). The efficient markets (EM)
theoretical approach proposed here overcomes two problems that plague existing research examining the
quality of political representation in a democracy. First, EM theory explicitly links voters to government
in market equilibrium terms by viewing political representation as consisting of consumers and
producers, respectively.2 Without an equilibrium perspective, the moral hazard problem of systematic
shirking can occur even in the presence of responsiveness (i.e., positive covariation between government
and voters’ policy preferences). Second, the EM approach provides a theory–laden evaluative standard
for assessing the quality of political representation that is lacking in present scholarship on this topic.
This evaluative standard is a more general criterion for assessing the quality of political representation
than simply examining responsiveness – i.e., the statistical significance associated with a positive
relationship between voters and government’s policy preferences. As a result, one can pinpoint the exact
nature of departures from the unattainable ideal of perfectly unbiased political representation.3
Departures from this ideal can reveal whether systematic political representation bias reflects ideological
drift, the lack of proportional responsiveness involving positive covariation between these actors’ policy
1
My use of the term representative in this context is common in political economy models that
refer to the typical or median actor drawn from a distribution of elected officials and voters.
2
This perspective is consistent with MacKuen, Erikson, and Stimson (2001: 5–12); however,
their formal model of political representation does not address issues of market efficiency presented here.
3
Policy positions and policy preferences are used interchangeably throughout this paper.
2
preferences, or perhaps both. Therefore, an EM–based model provides an empirically falsifiable test of
an explicit theory of political representation that is lacking in most scholarship on this topic.4
The outline of this paper is as follows. Next, I demonstrate the conditions by which positive
covariation between voters and government policy preferences do not constitute effective political
representation. A substantive justification for applying efficient markets theory to the analysis of political
representation is provided in the third section. Next, I advance a parity representation model of political
market efficiency and derive its empirically testable implications. The fifth section covers data,
measurement, and statistical estimation issues. The subsequent section involves presenting and
interpreting the empirical findings. The final section discusses the broader implications of this approach
for understanding political representation and suggests future directions for research on this topic.
Why “Responsiveness” Is Not Necessarily Indicative of Effective Political Representation
Responsiveness–based studies infer effective political representation when elected officials’
policy preferences move in tandem with voters’ policy preferences. In statistical terms, the
responsiveness criterion is satisfied when a statistically significant positive relationship exists between
constituent policy preferences and those held by elected officials.5 This phenomenon has been
investigated in the context of both dyadic representation between a singular elected official and
constituents (e.g., Cohen 1997; Erikson 1978; Herrera, Herrera, and Smith 1992; Miller and Stokes 1966;
Page et al. 1984; Powell 1982), and collective representation (e.g., Erikson, Wright, and McIver 1994;
Herrera, Herrera, and Smith 1992; Hurley 1982; MacKuen, Erikson, and Stimson 2001; Page and Shaprio
1983, 1992; Stimson, MacKuen, and Erikson 1995; Weissberg 1978; Wlezien N.d.).
4
One notable exception is MacKuen, Erikson, and Stimson (2001: Chapter 8 – Appendix). Their
model, however, is not concerned with the equilibrium relations between voters and government
concerning unbiased political representation. Instead, they deductively show that in aggregate,
government preferences are a function of the median voter’s preferences, and not vice–versa.
5
This characterization is hardly an inaccurate one given that Wittman (1995: 27–31) has a
section entitled “The Correlation Between Voters Preferences and Legislative Behavior”.
3
This approach, however, provides limited, and potentially misleading information concerning the
quality of political representation as astutely noted by Christopher Achen (1978: 475-476):
“....... Leaders’ opinions can strongly correlate with those of constituents even though the
representatives are distant from electors, and they can correlate weakly when the
representatives are close by (Achen, 1977).”
Achen’s remark reveals that positive covariation between voters and elected officials’ policy preferences
does not necessarily mean that these actors are ideologically proximate to one another. Moreover, one
cannot infer the representative voter’s policy preferences serves as an unbiased predictor of the
representative elected official. Evidence of responsiveness, defined as positive covariation between the
electorate’s and government preferences, might actually say little, if anything, about whether or not
democratic institutions are successful in mirroring the median voter’s policy preferences. This standard
approach for assessing the quality of political representation is only capable of indicating whether the
government’s preferences are moving in the same direction as those of the representative voter. The real
utility of such a test is not so much providing evidence of responsiveness, but instead by rejecting this
condition since it eliminates any possibility that government will behave in a manner that mitigates
agency loss between itself and voters.
I wish to argue that representative democracy must go beyond this rather limited evaluative
standard by assessing political market efficiency with respect to the representative voter and elected
official. This involves analyzing the extent to which the policy positions of elected officials’ mirror that
of the typical constituent, such as the median voter in a Downsian model. Unlike conventional analyses
of the relationship between government and voter preferences, efficient political representation involves
an empirical assessment of both the accuracy and bias by which electoral institutions’ policy positions
reflect those held by voters. In a representative democracy, agency loss – or the difference between the
median voter and electoral institutions’ respective policy positions will occur because of the imperfect
4
nature of the principal–agent relationship between voters (principal) and elected officials (agent).
Alternatively, this agency loss can be thought of as political representation error.
The key issue then becomes whether political representation error constitutes a random or
systematic bias? If random, then two conditions should approximately hold in empirical practice. First,
the representative voter’s and elected official’s policy positions will move in tandem by the same
magnitude, thus indicating proportional positive covariation. Second, the representative elected
official’s policy position should neither exhbit a liberal or conservative inclination, independent of its
relationship with the representative voter (absence of policy drift). Political representation errors exhibit
some bias in empirical settings, even if it is only of a random nature. Under these circumstances, the
expected value of this agency loss between principal (voters) and electoral institutions (agent) must equal
zero.6 In other words, liberal biases will cancel out with conservative biases through time consistent with
the opening quote of Pitkin (1967: 234). If these random shocks are the only source of bias, then one can
infer that political representation is efficient, and hence, consistent with conventional definitions of
efficient markets (e.g., Fama 1970, 1976; Baillie and McMahon 1989).
Efficiency within the context of this particular study refers to the idea that agents (electoral
institutions) do not make systematic representation errors in mirroring the policy preferences of their
principal (median voter), and that any representation errors which occur cannot be predicted from
available relevant information. This is because if democratic representation is efficient, then both voters
and electoral institutions each have an electoral–based incentive to ensure that the latter does not
systematically deviate from majoritarian preferences. Conversely, a systematic political representation
bias occurs when the policy positions adopted by electoral institutions, on average, do not equate with
those held by the median voter. Rejection of the political market efficiency condition is indicative of a
6
In the situation where no bias exists (i.e., perfectly unbiased political representation), the actual
value of agency loss would be zero for each observation or time period by definition.
5
systemic problem with political representation. In such instances, the majority of voters choose to
delegate formal policymaking authority to a government that systematically shirks from their policy
positions which reflects substantive friction between principal (voters) and their agent (elected officials).
One aspect of an efficient political market is unbiasedness – also termed weak efficiency.
Consistent with Achen’s (1978) conceptualization of “responsiveness”, which differs from conventional
uses of this term based on positive covariation of policy preferences discussed earlier and used
throughout this study, we can assume that the quality of political representation is a linear function of
.7 In the limiting case of perfect unbiased
drift (") and covariation ($) such that
representation that is void of any bias we should observe that " = 0 and $ =1. This means that the
relevant hypothesis test for $ should not be $ = 0 as commonly done in empirical investigations of
political representation, but rather $ = 1. In other words, preference covariation between the
representative voter and elected official should be both positive and proportional. The drift term is
important since " …0 implies a systematic political representation bias, even if $ =1.
A simple deterministic two period numerical illustration can highlight four general outcomes of
political representation. These are represented as capturing the relationship between government and
voter preferences with a one period transmission lag (see Figure 1). Let us assume that actors’
preferences lie along a unidimensional scale ranging from 0 (most liberal) to 100 (most conservative),
and a one period transmission lag exists from the representative voter
politician
to the representative
. Consider in period 1 that the representative politician is at 10 (
7
) and the
Achen originally advanced this concept in his reanalysis of the Miller and Stokes (1966)
cross–sectional data to determine whether individual members of Congress, on average, accurately
represented the typical constituent in their district at a single point in time. While an explicit test of the
joint hypothesis " = 0, $ = 1 was not performed, visual inspection of his findings appear to be consistent
with unbiased political representation with respect to social welfare and civil rights policies. Fama
(1991:1589–1599) discusses the theoretical and empirical limitations associated with cross–sectional
tests of market efficiency in the realm of financial economics (e.g., variations in market
size/capitalization across firms affecting expected asset returns). This criticism might also valid for
cross–sectional studies of political representation investigating n districts that range considerably in
terms of constituency size. This issue is beyond the scope of this study since my focus is macro
(collective) representation.
6
representative voter is at 45 (
) during period 0. In the second period, how the representative
politician responds to the representative voter’s move to
is crucial for determining whether or not
absence of drift and/or optimal prediction exist, and if so to what extent. If, however, the representative
, then we can state that both less than proportional covariation and liberal
politician moves to
policy drift occurs
(Line A).8
and
If the representative politician instead moves to 20
since
, then only liberal policy drift will occur
and
(Line B). Conversely, if we
suppose that the representative politician becomes increasingly more conservative than the representative
voter by moving from 70
to 51.42857
(declines) since
to 80
in response to the representative voter moving from 45
, then we shall observe a conservative (liberal) bias when
rises
and an absence of policy drift because
(Line C). In each of these three stylized cases,
responsiveness occurs since $ > 0, yet it has also been shown that the representative politician
systematically deviates from the representative voter’s policy preferences. Recall that perfectly unbiased
political representation (Line D) transpires only under the limiting case where a complete absence of
policy drift exists and exactly proportional positive covariation occurs between the representative voter
and politician, and thus
(i.e., agency loss is equal to zero).9
and
[Figure 1 About Here]
8
More generally, for n periods this can be computed as follows:
and
.
9
In empirical practice, achieving this exact condition is highly unlikely, though it can be
approximately accurate based on inferential statistical tests which allow for random bias in data.
7
This stylized illustration highlights the need for general evaluative standard that can serve as a
performance benchmark for assessing the extent to which political representation is effective. This
benchmark must be theory laden insofar that it is derived from an explicit model of political market
equilibrium that contains an isomorphism between the representative voter and politician. Next, I contend
that efficient markets theory satisfies this need for analyzing the quality of political representation.
The Applicability of Efficient Markets Theory to the Study of Political Representation
Efficient markets theory has its intellectual roots in financial economics where it is employed to
determine the processing of information and resource allocation in capital/asset markets (see Fama 1970,
1976: Chapter 5, 1991 for overviews). As Eugene Fama (1976: 133) notes:
“An efficient capital market is an important component of a capitalist system. In such a
system, the ideal is a market where prices are accurate signals for capital allocation.
That is, when firms issue securities to finance their activities, they can expect to get
“fair” prices, and when investors choose among the securities that represent ownership
of firms activities’, they can do so under the assumption that they are paying “fair”
prices. In short, if the capital market is to function smoothly in allocating resources,
prices of securities must be good indicators of value.”.
It can also be stated that an efficient political market is an important component for an effective system
of democratic governance, and hence, is relevant for evaluating the quality of political representation.
Similarly, if the political representation market is to function effectively, then government preferences
must be an accurate reflection of the representative voter’s preferences. In an ideal political market, that
is unrealistic to attain in a representative democracy (Bianco 1994), the representative politician’s policy
position will perfectly mirror that of the representative voter such that the latter can expect to obtain the
government that they want. Thus, a weakly efficient political market is one where the representative
voter’s policy preferences do not serve as a systematically random biased predictor of the representative
8
politician’s policy preferences. A strongly efficient political market occurs where any prediction errors
involving political representation cannot be explained by relevant information that represents potential
transactions costs associated with representative democracy.
This approach is very applicable as a paradigmatic framework to evaluate the quality of political
representation in a democracy for several reasons. First, conceiving political representation as a political
market consisting of supply (government) and demand (voters) is intuitively appealing way to view this
problem. Specifically, voters can be viewed as consumers who prefer (demand) a market basket of
policies based on their policy preferences and elect representatives (suppliers) accordingly. Second, this
theoretical framework has the advantage of containing an explicit equilibrium–based foundation that is
lacking in existing scholarship on political representation. As discussed in the previous section,
preference covariation is a necessary, but not a sufficient condition for obtaining effective political
representation. The basis of this critique is simple – the lack of an equilibrium relationship implies that
elected representatives are able to take policy positions that systematically deviate from majoritarian
preferences. The parity representation model advanced in the next section, for instance, is premised on
the equality of the conditional preference distributions of voters’ and elected officials. Third, efficient
markets theory is falsifiable and its empirical implications can be traced directly back to the assumptions
concerning the presumed nature of political market equilibrium. Thus, we can empirically discern
whether the rejection of an efficient political market holds for a given model of market equilibrium.
This, in turn, provides us with greater confidence concerning our inferences regarding effective political
representation. Finally, the efficient markets hypothesis explicitly allows us to determine whether or not
information is efficiently used by the political market. The transactions cost perspective of political
representation can reveal those factors which reduce the principal–agent slack between voters and
governments from those that do not. Thus, transactions costs impose a structural constraint on obtaining
efficient political representation.
9
The next task of this study is to formally define what constitutes market equilibrium, and then
subsequently derive its empirically testable implications. While there is no reason to expect perfect
political representation reflecting a complete absence of slack between the typical voter’s and
government’s ideological positions, we can still determine whether any bias we observe is random (i.e.,
weak efficiency condition) and cannot be systematically improved upon with relevant and available
information (i.e., strong efficiency condition). Next, I derive these evaluative criteria from a formal
model of political representation that is empirically falsifiable.
The Parity Representation Model of Political Market Efficiency: Theory and Testable Implications
I posit that the relationship between voters and politicians in a representative democracy is a
principal–agent relationship where the former delegates authority to the latter. This is reasonable given
that an information asymmetry naturally exists between elected officials and voters, as articulately
summarized by William T. Bianco (1994: 167):
“Democratic theory tells us how representative should act, but we cannot expect such
behavior under real–world conditions. This situation may be unfortunate, but it appears
inevitable. Representatives will always have private information about policy proposals.
Their motives will always be difficult to assess. Under these conditions, our inability to
give representatives the right incentives is no surprise: perfect control is is impossible in
all other situations characterized by asymmetric information. To put it another way, the
problem is not with representative government; rather, representative government is an
example of a generic and intractable problem. The solution is not to abandon this form
of government or to explain its failures by saying that voters or elected officials fall short
of some normative ideal. The solution is to accept the nature of the representation
game.”
This passage has two implications – (1) performance evaluation of political representation against an
10
(ideal) theoretical benchmark is not purposeful; and (2) representative democracy is hopelessly
imperfect. Contrary to the former issue, it is useful, and perhaps necessary, to evaluate the performance
of political representation against an ideal standard of a perfectly efficient market. Otherwise, the nature
of the transactions costs and the conditions by which it can affect the quality of political representation
cannot be fully understood. The latter issue raised by Bianco, however, is a critical insight suggestive of
imperfect political representation. The real question of interest to students of empirical democratic
theory becomes – Can government systematically (as opposed to randomly) deviate or shirk from what
the voters want them to do? This depends on whether voters are able to effectively, albeit imperfectly,
monitor elected officials. If they are not, the moral hazard problem that they experience will be severe
enough that a disequilibrium relationship ensues that is reflective of ineffective political representation.
In order to evaluate the standard of efficient political representation, there must be an
equilibrium basis to study the relationship between voters and elected representatives to whom authority
is delegated. I assume that a one period transmission lag occurs with respect to the government’s
ideological position being able to reflect the median voter’s ideological position as well as that of other
available and relevant information. This not only has a substantive basis in past research on this topic
(e.g., MacKuen, Erikson, and Stimson 2001; Page and Shapiro 1983, 1992), but also the added advantage
of eliminating any potential endogeneity problems that might arise if these actors’ information sets are
contemporaneous.
Political market efficiency is operationally defined within the context of a Parity Representation
Model of political market equilibrium which maintains that, on average, the representative politician’s
policy positions must be equal to those of the representative voter, and that transactions costs associated
with political representation in a democracy fails to explain the chasm between actors’ preferences.10 We
10
The Parity Representation model is simply a modified version of the Constant Expected
Returns model originally advanced by Fama (1970, 1976) which has served as the most common
theoretical foundation for empirically analyzing the efficient markets proposition in areas ranging from
11
begin by stating that median voter’s (ideological) policy position at time t–1 and that of the median
government (ideological) policy position at time t are:
(1a)
and
(1b)
where
where the former’s is a function of information at time t–2 used in forming the median voter’s policy
position at time t –1 i.e.,
, while the latter is a function of information at time t–1 used in
forming the government’s median policy position at time t – i.e.,
. In addition,
,
is additional relevant and available information that is employed by the government in the formulation of
its ideological position at time t that is distinct from
. For purposes of tractability and simplicity
consistent with standard work on efficient markets theory, I assume that each representative actor
possesses the same mapping process for each function – i.e., assume orthogonal projections and linear
transformations containing the same functional form. One important consequence of this assumption is
that under political representation market equilibrium (PRME) conditions,
is orthogonal to
by definition. This is a reasonable assumption for an efficient markets equilibrium since these
sources of information should be independent from one another by definition according to this theory.
For a political representation market equilibrium (PRME) to hold, it must be said that:
(2)
where the quantity or level of information voters possess (lagged one period) is equal to those of
government. PRME, and hence, an efficient political market implies
stock prices and asset returns to speculative behavior in international currency markets.
12
(3a)
and
(3b)
where the information set of voters is equal to those of government. PRME, and hence, an efficient
political market implies that
(4)
where m $ n (i.e., the number of voters’ policy positions is equal or greater than that of politicians). Both
the voters and politicians’ distribution of policy positions in the political representation market are
determined by their respective sets of information, plus an additional kth dimensional vector of information
at time t–1 that might explain variations in
independent of
. In other words, a political
representation market equilibrium represents a market clearing set of policy positions between voters
(“demand”) and politicians (“supply”), with a one–period transmission lag. If, however,
or
, then clearly voters experience an information asymmetry that results in
violation of weak and strong efficiency PRME conditions, respectively. For these conditions to hold, it
necessarily follows under weak and strong efficiency respectively that
(5a)
and
(5b)
where Z k
t–1
(i.e.,
is the adjustment to
as a result of the information contained in
that is net of
).
The general efficient markets condition characterizing the Parity Representation model of political
market equilibrium is given by:
13
,
(6)
where the conditional expected value of political representation errors is equal to zero. Based on (6), a
political market equilibrium model consistent with weak efficiency (unbiasedness) yields:
(7)
where the representative voters’ expected value of the representative politician’s policy position is a
function of the former’s policy position from the previous period (t–1), based on information from the
prior period (t–2). Thus, political representation errors that occur in a given period is simply the difference
between the representative politician and voter’s policy positions, with a one period transmission lag:
.
(8)
We should also observe that the expected value of political representation errors are zero such that:
.
(9)
Equation (9) is a special case of the PMRE condition insofar that it serves as a theoretical statement of
weak efficiency where the expected value of political representation errors is zero. In the weak or partial
efficiency case (i.e., only
) serves as information such that
efficient political markets condition that encompasses both
information
that might affect
. To arrive at the strong
and other relevant and available
involves combining (6) and (7) such that:
(10)
Or equivalently,
where
is the political representation slack (error) that exists between macro government and voter
ideological policy preferences that are net of
(
). Equation (10) can be viewed as satisfying the
14
strongly efficient political representation condition because no other factors will be able to systematically
explain movements in political representation errors if the principal-agent relationship between voters and
electoral institutions. One of the main benefits of applying the efficiency criterion in evaluating the
rationality of democratic representation is that it can show which factors contribute to either improving or
deteriorating the principal-agent relationship between voters and elected officials.
The empirically testable implications of the Parity Representation model are clear. Rearranging
terms in (8) and subtracting each side by
yields the testable implications of (9), predicated on a
first–difference model specification designed to handle the nonstationarity present in
and
:
(11a)
where
. Unbiased political representation in (11a) consistent with weak efficiency
(i.e., unbiased political representation) is not rejected by the data if " = 0, $ = 1. An equivalent test using
instead the difference between these actors policy preferences (
) as a dependent variable is
given by:
(11b)
where weakly efficient (unbiased) political representation is supported by the data if " = 0, $N= (1– $) = 0.
An appropriate specification that captures both the short–run and long–run dynamics of this relationship
is given by the following error correction model (ECM):
,
where
(11c)
and the long–run adjustment to past disequilibrium (shocks) involving
political representation (lagged one period) are captured by the 0 parameter. Political market weak
efficiency is satisfied when the joint (null) hypothesis a = 0, b = – 0 = 1 cannot be rejected (e.g., Engsted
15
1991; Hakkio and Rush 1989).11 This formulation of the test was has the advantage of controlling for
potential omitted variable bias attributable to the ECM term that captures the long–run dynamics between
these actor’s policy positions.
Testing strong efficiency consistent with (10) involves regressing the difference between the
representative politician’s and voter’s policy preferences on an information set that can be thought of as
the transactions costs of political representation:
(12a)
where strong efficiency in political representation is achieved when the joint (null) hypothesis
,
* k = 0 cannot be rejected. An alternative test of political representation errors can be reformulated so
that
appears on the right–hand side of the equation such that:
(12b)
where evidence of weak efficiency is supported if
,
and the strong efficiency hypothesis is consistent with the data when
are not rejected by the data;
,
,
cannot be rejected. This test has two advantages over the more common market efficiency test
reflected by (12a). First, it enables us to distinguish whether systematic bias in emanating from the
representative voter
or from the information set variables (Z k , t–1 ). This specification also serves
as a nested test that encompasses both the weak and strong efficiency hypotheses of political market
equilibrium based on the Parity Representation model.
11
The ECM term is simply used as a statistical control variable in this context to avoid omitted
variable bias. Liu and Maddala (1992) asserts that cointegration analysis makes estimation of (11a)–(11c)
moot for the pair of non-stationary series relevant to the unbiasedness hypothesis test because the
researcher imposes a priori long run parametric restrictions such that " = 0, $ =1 ($N= 0). However, more
recent research has shown that such a cointegrating vector does not necessarily hold in empirical practice
(Phillips, McFarland, and McMahon 1996), and also exhibits low statistical power based on Monte Carlo
simulation experiments (Lopes 1997). Thus, the ECM term based on the cointegrating vector is
noninformative for testing the weak efficiency hypothesis in isolation since it is incapable of yielding
sufficient evidence to confirm or deny evidence of unbiased political representation.
16
Still another nested test is performed that allows for both short–run and long–run dynamics
yields an ECM specification:
,
where weakly efficient political representation is supported if
efficiency cannot be rejected when
,
,
, and
(12c)
; and strong
.12
Data and Ancillary Hypotheses
Aggregate U.S. data on representative voter and politician ideology are employed for the 1948–
1996 annual period based on measures constructed by Kim and Fording (1998, 2000). Both measures are
computed along a common unidimensional ideological policy space between – 100 (most liberal) and 100
(most conservative). Voter ideology is measured as the median ideological position of voters within the
electorate based on election returns, conditioned by the ideological placement of the political parties
assessed by content analysis of platform policy positions on 26 different policy categories (Kim and
Fording 1998). Government ideology is an aggregate measure of central tendency capturing the
ideological policy positions of electoral institutions based on the partisan composition of its cabinet
portfolios that is conditioned by the ideological placement of the political parties through its platform
policy positions (Kim and Fording 2000).13 Party manifesto-based measures of Kim and Fording (1998,
2000), and others developed by others elsewhere (e.g., Huber and Gabel 2000; McDonald and Mendes
1999; Budge, Hofferbert, and Klingemann 1992), are shown to serve as both valid and reliable indicators.
Time series plots of voter ideology
, government ideology
, and political
12
Frankel and Stock (1987) analytically demonstrate that these regression–based tests are at a
minimum equally powerful, if not superior, to volatility (variance) tests.
13
Kim and Fording plausibly assume a steady change in ideology between elections (party
manifesto data once every four years, election returns and cabinet portfolios once every two years), and
thus use linear interpolation to estimate data values of these variables for non–election years.
17
representation bias
appear in Figure 2. These figures show that the representative
politician’s policy preferences are more liberal than those of the median voter, except for the first two
years of the Eisenhower administration where the Republicans possesses majority control of both
chambers of Congress (1953–1955) and the twelve contiguous years of the Reagan–Bush, I presidencies
(1981–1992). Consistent with Stimson’s (1998) Policy Mood measure, Fording and Kim (1998) note
that median voter ideology began to began its conservative ascent in the few years predating Ronald
Reagan’s electoral landslide in 1980.14 For the overall sample period, median voter ideology is
significantly more conservative than compared to mean government ideology (0 Voter Ideology = 11.34 , 0
Government Ideology
Ideology
= 1.89; t–statistic = 3.28, p .0.002), while the former is less volatile than the latter (S Voter
= 5.79 , S Government
Ideology
= 19.32; F–statistic = 11.13, p .0.000). Although this relationship follows
a cyclical pattern, the average value of political representation errors are not equal to zero (0 Political
Represe ntation Erro rs
= – 8.93,t–statistic = – 4.10, p .0.002), thus hinting at evidence of systematic bias.
[Figure 2 About Here]
For the purposes of evaluating the efficient markets theory of political representation, these
measures of government and voter ideology possess several advantages over public opinion and roll-call
vote data commonly utilized in existing scholarship on the topic. Most importantly, these ideological
policy positions are directly comparable to one another since they are on the same ideological scale.
Unlike existing studies that employ aggregate public opinion and roll-call legislative data as the means to
assess macro political representation, a voter ideology score of – 10 has the identical substantive meaning
as a government ideology score of – 10 (Hee-Min Kim, 7/31/2000 e– mail correspondence). This is
because these measures are simply aggregations of the same variables (party ideologies) using different
weights: votes and cabinet portfolios, respectively. In addition, only voters’ policy preferences are
explicitly considered, thus presuming that politicians cannot be held electorally accountable for the
14
Kim and Fording (1998) perform validity assessments of this measure and find that it closely
tracks Stimson’s (1998) policy mood measure.
18
policy preferences of non–voters. This outlook is compatible with Pitkin’s (1967: 234) claim that
elections ensure the possibility of regular, systematic responsiveness. Simply, if voters are not able to
ensure that their elected officials are responsive to their policy preferences, non–voters cannot be
expected to accomplish this task. Also, the use of party manifesto data suggests that government is
judged by its policy stances (unconstrained preferences) instead of the policy output that it has rather
limited control over (constrained preferences). Efficient political representation thus presumes “truth-inadvertising” insofar that voters, on average, will obtain a government whose revealed policy preferences
match their own. Finally, these measures also avoid the criticism of treating ideology as being temporally
fixed levied against congressional roll–call voting scores elsewhere (Groseclose, Levitt, and Snyder
1999).15 For these two reasons, use of both voter and government ideology data of Kim and Fording
(1998, 2000) will provide conservative statistical tests insofar that they create a less demanding standard
for finding empirical support in favor of effective political representation than existing research on this
topic. If any statistical bias exists, these voter and government ideology–based measures are biased in
favor of failing to reject the null hypothesis of unbiased and efficient democratic representation. This is
because a voter–based measure only considers those individuals who actually exercise their ability to
hold electoral institutions accountable via the mechanism of elections consistent with principal–agent
theory, and a policy position (preference) measure of government ideology makes it more difficult to
ascribe blame towards one another under circumstances when legislative output (in the form of roll–call
votes) deviates from revealed preferences, thus making it more difficult for voters to attribute
responsibility to electoral institutions for such actions.
Statistical tests of the strong efficiency in political representation require estimating (12a) – (12c)
15
Stimson, MacKuen, and Erikson, (1995) use a sophisticated dynamic latent variable
measurement approach involving multiple indicators. Much of their motivation underlying this approach
is due to a concern that nominal interest group rating scores suffer from longitudinal validity problems
(549 and Note 15). This is a legitimate concern for their study given that the voting scores that they
employ are nominal values that are not adjusted for temporal and cross–chamber differences.
19
with a set of relevant information variables from the previous year (Z k, t–1 ) consistent with the Parity
Representation model. If strong efficiency is rejected absent the inclusion of median voter ideology as a
right–hand side variable in the empirical model, then this these variables must jointly predict political
representation errors in a significant manner (* k … 0). Three types of information contained in this set
that can explain the sources of inefficiencies inherent to political representation: environmental,
institutional, and electoral. Environmental factors pertain to those items that neither occur within
political institutions nor are electoral in nature, such as the state of the economy and major wars. As the
state of the economy deteriorates (measured as the misery index of summed inflation and unemployment
rates lagged by one year), electoral institutions will be granted less slack from their principals – the
American voting public. In terms of directional ideological bias, a higher economic misery index value
will result in less of a conservative (liberal) bias during times when Republicans (Democrats) occupy the
White House since administrations are not only held accountable for the economy, but will also need to
move closer towards the representative voter. This requires that is the economic misery index variable is
multiplied by +1 for Republican presidents and – 1 for Democratic administrations (lagged one year) in
order to keep this coefficient’s sign consistent. Likewise, electoral institutions are hypothesized as being
afforded greater policy slack from voters under such instances, mainly via presidents. The Wartime
variable equals 1 for the Korean War (1950–1952) and Vietnam War (1964–1972), all other years this
measure is assigned a value of zero, and is subsequently multiplied by +1 for Democratic administrations
and – 1 for Republican counterparts in order to assess directional bias. This variable’s coefficient should
be positive.
Institutional variables refer to the sources of information related to electoral institutions that are
capable of predicting the policy chasm between voters and elected officials. The first two measures
capture government fragmentation in assessing the nature of democratic representation. Unified (party)
government captures this phenomenon on a macro level, and is measured as a dummy variable that equals
20
1 when the majorities in each legislative chamber and the president share the same party affiliation, and
zero otherwise, then multiplied by – 1 and +1 under Democratic and Republican administrations,
respectively. This transformation allows for negative (liberal) biases positively to correspond with
unified Democratic control, while positive (conservative) biases do likewise with unified Republican
control. On one hand, we should expect greater policy slack involving the voter–elected official
relationship will transpire during times of divided government because elected officials can attribute
responsibility for deviations from voters’ desired policy positions to their partisan counterparts, all else
being equal. On the other hand, divided party government can actually attenuate the tendencies for
electoral institutions to move to the ideological extremes, and thus provide institutional balancing
(Alesina and Rosenthal 1995; Fiorina 1995) that lends itself to more faithful representation to the median
voter’s policy preferences, ceteris paribus. In addition, partisan fragmentation in the legislative chambers
(House and Senate) may also serve as a potential mezzo-institutional source of political representation
bias. This is measured as the average Republican–Democratic party seat differentials from the preceding
year (expressed in percentage terms) displayed in both legislative chambers. As this variable’s value
rises, members will have a greater incentive for extolling a conservative political representation bias. The
U.S. federal budget deficit (measured as the annual percentage rate of change in public debt/GNP ratio)
might also explain political representation bias. Since fiscal policy is jointly determined by the president
and Congress, the budget deficit variable is multiplied by the unified/divided government dichotomous
variable transformed for directional bias described above. We should observe that surges in the federal
budget deficit will reduce liberal (conservative) biases when Democrats (Republicans) control electoral
institutions. Finally, partisan change in the occupants of electoral institutions also may matter in
explaining agency loss that occurs between voters and elected officials. The first year in office for a new
(partisan) administration, for example, may account for greater slack than otherwise when presidents’
cycle of influence is at its apex and their on-the-job experience/effectiveness is at its lowest (Light 1999).
21
This transition event variable is coded 1 for years when there is a partisan change in presidential
administration or majority status in at least one chamber of Congress, and zero otherwise, then multiplied
by – 1 and +1 for Democratic and Republican administrations, respectively. If political representation
slack occurs under these circumstances, then conservative (liberal) bias should transpire newly elected
Republican (Democratic) presidents, all else being equal.
Electoral factors are also posited of affecting the principal–agent relationship between voters and
government. Aggregate voter turnout should affect the degree by which electoral institutions’ ideological
policy position mirrors that of voters. This variable is measured as the percentage of eligible voters who
cast a vote in U.S. House elections in the preceding election cycle during mid–term election years, and
the average between this value and that of the percentage of eligible voters casting a vote in presidential
contest in years where such an election takes place lagged one year. Aggregate voter turnout in the
preceding election cycle is hypothesized to be inversely related to political representation bias since
higher overall turnout rates are typically thought to benefit Democratic or liberal party candidates such
that we should expect higher turnout to result in greater liberal bias, all else being equal. Relatedly, this
also means that political institutions will also face greater pressures during election years; therefore, they
will become less inclined to deviate from the median voter’s policy position, all else being equal,
compared to non–election years. This is operationally defined as being equal to zero in non–election
years, 0.50 in midterm election years, and 1.0 in presidential election years and multiplied by the divided
government variable described earlier that is appropriate for testing directional bias.16 The electoral
margin from the preceding election (lagged one period) for victorious presidents provides them with a
good sense of how strong of a policy mandate they enjoy. This variable is measured as the difference in
the major two–party vote share multiplied by – 1 and +1 for Democratic and Republican administrations,
16
The weighting of these variables is reasonable when one considers that all 435 House seats
and one–third of all 100 Senate seats are open to elections every two years.
22
respectively. If this information is not already incorporated into the chasm between representative
politician and voter’s policy preferences, then we should observe Republican (Democratic) presidents
enjoy greater flexibility or conservative (liberal) slack from the ideological policy preferences of the
median voter because the officeholder possesses a stronger electoral mandate. For the directional bias
models, this variable is transformed by multiplying this measure by multiplied by – 1 and +1 for
Democratic and Republican administrations, respectively. Finally, the public approval of presidents’ job
performance may also provide information into the amount of slack that occurs between voters and
electoral institutions. For instance, higher levels of presidential approval – measured as the annual
average of the Gallup presidential job approval ratings multiplied by – 1 and +1 for Democratic and
Republican administrations – should result in greater conservative (liberal) slack for electoral
institutions’ ideological policy positions in relation to those held among voters when Republicans
(Democrats) occupy the White House. This is because popular administrations can better afford not to
faithfully respond to the median voter than unpopular ones.
Statistical Findings
This is further corroborated by the descriptive statistics involving predictive accuracy and bias
displayed in Table 1. This consists of political representation error measures wit and without a one
period transmission lag between government and voters. The political representation errors across these
measures are strikingly similar. Specifically, the both indicate that the mean political representation error
reflects a liberal bias – i.e., the median voter tends to be more conservative than government. These
political representation errors are normally distributed based on the non–significant Jarque–Bera test
results. The Theil–U inequality coefficient is a standardized forecasting/prediction statistic that is the
ratio of the root mean square error to the sum of the individual root mean square components for each
series (Pindyck and Rubinfeld 1998: 387–388). This measure of predictive accuracy is moderately large
(0.55 and 0.54), and thus indicates that the median voter’s policy preferences do not accurately track
23
those of the representative politician. This statistic is comprised of three components: bias (UM ), variance
(US), and covariance proportions (UC ). In the extreme case where all bias is random, we should observe
UM = 0, US = 0, UC = 1. The most important of these is the bias proportion since it captures systematic
error by assessing the relative mean difference of these series. Since UM values for each measure are
above the 0.2 threshold (Pindyck and Rubinfeld 1998: 388), this corroborates our earlier hunch of
systematic bias in political representation. This chasm in political representation also reveals that the
variance between these series are noticeably difference given that the US statistics are nearly 0.60. The
smallest proportion of the prediction error is attributable to unsystematic error, UC = 0.15 and 0.13,
respectively. These tests do not provide inferential evidence, lack precision concerning the exact nature
of the bias, and fail to explicitly consider the unique properties of time series data.
[Table 1 About Here]
To address the limitations of the simple univariate descriptive prediction statistics, one must
employ regression–based tests to obtain a definitive handle on this issue. The tests of weak efficiency
denoted by equations (11a) – (11c) are displayed in Table 2. Because of the heteroskedasticity and
residual non–normality problems present in these statistical models, the data are analyzed using OLS
(with White and bootstrapped standard errors), robust, and quantile least absolute deviation (LAD)
regression (with bootstrapped standard errors) estimation procedures.17 The results are striking. While
there is no evidence of systematic drift observed in the data consistent with the Parity Representation
model (" , a = 0), the coefficient on the slope parameter relating to the median voter ideology variable
strongly suggests that responsiveness is substantively small in all cases, save ECM–OLS (11c) based on
the significant coefficient restrictions test where ($, b =1; $N= 0). In robust and quantile regression
17
Achen (1978) notes that these unbiasedness tests may suffer from measurement error, thus
rendering $ being biased downward to zero and " biased in either direction. This, however, is not a
problem here since information on all voters who cast their ballot at a given point in time, and not merely
a subset of the overall constituency, is being utilized in constructing the median voter’s ideological
policy position.
24
models, the error correction term coefficients are trivial, thus indicating the lack of long–run
responsiveness to shocks. In the OLS model, this coefficient is both negative and statistically significant
consistent with movement towards a steady–state long–run equilibrium, yet its value is far from negative
unity that is required to satisfy the unbiasedness proposition of (11c). Most importantly, all nine
alternative specifications unequivocally reject the maintained hypothesis of weak efficiency at p <.01.18
In other words, political representation is systematically biased, and hence, implies a substantive
disconnect between the representative politician and voter in the post–War United States. At the macro
level, the political market of representation is indicative of market failure since systematic shirking by
the principal (representative politician) occurs based on this empirical evidence. These findings paint a
less sanguine portrait of political representation compared to recent works that emphasize its
considerable effectiveness and responsiveness (e.g., MacKuen, Erikson, and Stimson 2001; Page and
Shapiro 1983, 1992; Stimson, MacKuen, and Erikson 1995; Wittman 1995)
[Table 2 About Here]
Since the weak efficiency criterion has been refuted by the data, it necessarily follows that the
strong efficiency condition will also fail to hold by definition. However, an analysis of strong efficiency
in the political market of representation remains useful for two reasons. First, the nested tests of (12b)
and (12c) that encompass both weak and strong efficiency predictions can enable us to discern whether
or not the systematic bias is coming from transactions costs, independent of the median voter’s
ideological preferences. This type of analysis also performs the useful function of identifying those
transactions costs that shape the chasm existing between the representative politician and voter’s policy
18
ideology
I have also performed this analysis using the contemporaneous median voter
and arrive at the same substantive conclusions using both OLS and GMM estimation
techniques in the latter case designed to handle endogeneity bias that might plague the former set of
estimates under these circumstances. These results appear in Table A–1.
25
preferences. Thus, the strong efficiency tests can provide us answers regarding what information is
accounted for in the representative actors’ policy preferences from that which is not. In other words, if
democratic representation is efficient, then policy representation bias should not be predicted by
information that may either increase or reduce this agency loss.
The tests for strong efficiency hypothesis appear in Table 3. Obviously, the strong efficiency
condition [i.e ., P2 - (11/12/13/14): (", a = *j = 0, $, b =1; $N= 0, – 0 = 1) / F–Test: (", a = *j = 0;
$, b =1; $N= 0, – 0 = 1] is soundly rejected by the data in every instance. Once again, the constant term is
indicative of an absence of drift (", a = 0), yet the impact of the median voter’s policy preferences is not
consistent with unbiased political representation given that the hypotheses $N= 0 in equation (12b) and
b = – 0 = 1 in equation (12c) are clearly rejected at p < .01 significance levels. Thus, weak efficiency is
rejected across all models where it is embedded in the strong efficiency specification.
[Table 3 About Here]
The transaction cost (information set) variables, for the most part, are not of consequence for
explaining political representation errors consistently across various model specifications and estimation
techniques. Some exceptions, however, do occur. Most notably, each one percent increase annual budget
deficit growth will lead to a 1.35 unit reduction in political representation errors (Model 12a). In
addition, when a partisan change occurs in either Congress or the White House, the incoming party of
power is granted slightly more than a one unit rise in slack from the representative voter’s policy
positions. Some counterintuitive patterns are also observed in these relationships. For example, a one
percent rise in the combined inflation and unemployment rates for model (12b) estimated via robust
regression results in 0.31 unit rise in political representation errors in a conservative (liberal) direction
for Republican (Democratic) administrations. This suggests that poor economic conditions yield greater
slack to electoral institutions. This rather limited pattern of countercyclical partisan politics also holds for
the impact of presidential approval on political representation errors where each one percent rise in
Republican presidents’ job approval produces a small, yet significant, 0.03 decline in conservative
26
government bias when estimated by robust regression. Overall, the statistical results indicate that the
sum of these transaction cost variables appear to be more important than its individuals parts.
What is most telling about these set of empirical results is that once the representative voter’s
policy preferences are controlled for as an exogenous variable in (12b) and (12c), the transaction cost
(i.e., information set) variables become noticeably less meaningful than they were when median voter
ideology is omitted in (12a). Given this evidence and rejection of the weak efficiency hypothesis
component of these models, it is safe to infer that the median voter’s policy preferences trump transaction
cost variables in terms of being the main source of this bias. This is borne out by rejecting both the weak
and strong efficiency hypotheses. Therefore, we cannot conclude that the combination of transaction
costs are the source of poor government responsiveness to public preferences, but rather the source of
systematic bias relates to a systematic disjoint between the representative politician’s and voter’s policy
preferences. Thus, the inefficiency or ineffectiveness associated with political representation is
attributable to the systematic bias exhibited by electoral institutions with respect to voters more so than
third–party influences which independently hinders the representative politician’s ability to more
accurately mirror the representative voter’s policy positions.
Discussion
The touchstone of representative democracy is the principal–agent relationship between voters
and their elected representatives. This is captured by the Hobbesian view that “representation is
authority, the right to make commitments and incur the consequences for one another.” (Pitkin 1969: 8).
Thus, a representative acts in the name of another (voters) so that their actions represent the represented.
The extent to which elected officials (agents) faithfully represent the will of the people (principals) is
the normative basis for evaluating the quality of political representation. This normative ideal has
relevance for students of empirical democratic theory concerned with understanding the actual chasm
that exists between governments and citizens.
27
Much of the research on political representation in a democracy has emphasized the policy
responsiveness of electoral institutions to the mass public (e.g., Erikson, Wright, and McIver 1994;
MacKuen, Erikson, and Stimson 2001; Page and Shapiro 1983; 1992; Weissberg 1978; Wittman 1995;
Wlezien N.d.). These studies claim that evidence of quality political representation is determined by
whether a statistical relationship exists involving the positive covariation between these actors’ policy
preferences/positions. These responsiveness oriented studies are useful for providing low–level criterion
to analyzing political representation insofar that it is desirable to attain this condition in a democracy
where power is delegated from voters to elected officials. If the responsiveness hypothesis is rejected
(i.e., $ = 0), then clearly it eliminates the possibility that politicians will alter their preferences of public
policy in accordance with those who elected them to office. This criteria for evaluating political
representation effectiveness, however, is limited since responsiveness might exist, yet electoral
institutions’ policy preferences systematically deviate from those of the median voter. In other words,
the concept of responsiveness is not predicated on necessary equilibrium conditions that serve as a
criterion for attaining effective representation in a political market. The fundamental question scholars
must pose when studying the quality of political representation is simple – On average, does the
government’s policy preferences mirror those held by the electorate? If not, why?
In this study, I argue that past research does not explicitly link the representative politician and
voter in a model of political market equilibrium. This is the task that I have set out to accomplish by
proposed a theoretically motivated attempt at connecting efficient markets theory to the study of
empirical democratic theory. Based on a Parity Representation model of political market equilibrium
adapted from efficient markets theory in financial economics, I assert that the quality of political
representation must be viewed in terms of an absence of systematic bias (weak efficiency condition) and
also the failure of relevant transactions costs to explain any bias since it must be purely random by
definition (strong efficiency). The statistical results jointly reject this model of political market
28
equilibrium and also the efficient political representation hypotheses analyzed in this study. The reason
for rejecting this model of political market equilibrium is not as much as due to the transaction costs that
inhibit effective political representation by governments, as it is attributable to the systemic bifurcation
between the majoritarian preferences of elected officials and voters, respectively.
These findings are especially important to consider given that they call into question the well
accepted view among scholars that the political system functions in a collectively rational manner, and
thus representation linkages are robust (e.g., MacKuen, Erikson,and Stimson 2001; Page and Shapiro
1983, 1992; Weissberg 1978; Wittman 1995). They also suggest that political representation on a dyadic
level is even more likely to observe evidence of moral hazard problems via shirking given that “...citizens
as a whole are better represented by Congress than are citizens in each district by their particular
legislators.” (Weissberg 1978: 541). This is because collective representation represents broad national
interests, as opposed to local or particularistic concerns, and thus is part and parcel of responsible party
government theory as originally proposed by Key (1966) as originally noted by Weissberg (1978: 537).
On a broader level, this study complements the comparative research on political representation
and electoral systems that focus on the accuracy of political representation. Specifically, coordination
failures are higher in single–member district systems with high electoral thresholds such as the U.S.
result in less accurate legislative (political) representation compared to proportional representation
systems with low electoral thresholds (Cox 1997). The cross-national empirical evidence supports this
theoretical conjecture. For instance, Powell (2000) and Powell and Vanberg (2000) provide empirical
evidence showing that correspondence between the median legislator and median voter in the U.S. (i.e.,
accuracy) was inferior compared to multi–party democracies. While my analysis differ from theirs in that
it is limited to the U.S. case and my government ideology measure accounts for occupant of the White
House, I demonstrate that systematic bias in majoritarian–based political representation occurs in one
such single–member district system possessing a high electoral threshold. This is a fundamentally
distinct point from the aforementioned comparative representation literature since a high degree of
accuracy can be observed, yet a systematic bias can still plague political representation, while a low
29
degree of accuracy can exist in the presence of random bias.
This is an initial step towards arriving at a richer understanding as to what constitutes quality
political representation beyond existing notions of responsiveness centered on evidence of positive
covariation between voter and government preferences. There are several avenues which this work can be
extended. First, since tests of efficient markets theory are conditional since they are predicated on a
particular maintained theoretical model, alternative political market equilibrium models need to be
advanced and empirically tested to determine whether this study’s inferences concerning systematically
biased political representation are robust. Second, this work can be extended to see how the systemic
moral hazard problems inherent to political representation are “corrected” by voters through the
mechanism of elections. Finally, the chasm between the representative politician and voter’s policy
preferences are more informative for understanding majoritarian and countermajoritarian forces involved
in policymaking than simply looking at either one in isolation. This is because relative deviations
between these actors can provide a sense whether the agency loss associated with moral hazard problems
involving political representation has tangible effects on public policymaking. Addressing these
questions can provide students of empirical democratic theory with a broader thematic research agenda
that directly addresses agency problems part and parcel to delegation within a representative democracy.
30
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35
FIGURE 1
Alternative Hypothetical Relationships Involving the Representative Voter
and Representative Politician in Unidimensional Policy Space
FIGURE 2
Time Series Plots of Government Ideology
Political Representation Bias
, Voter Ideology
(1948–1996)
, and
TABLE 1
Descriptive Statistics for Assessing Predictive Accuracy and Bias in U.S. Political Representation, 1948–1996
Statistic
Mean Political Representation Error
–8.93
–9.44
Mean Square Political Representation Error
303.23
306.70
Mean Absolute Political Representation Error
15.04
15.09
Root Mean Square Political Representation Error
17.41
17.51
Political Representation Error Variance
228.22
222.07
Minimum Political Representation Error
–33.51
–31.27
Maximum Political Representation Error
15.13
11.89
Theil–U (Inequality)
.55
.54
Theil–U (Bias Proportion)
.26
.29
Theil–U (Variance Proportion)
.59
.58
Theil–U (Covariance Proportion)
.15
.13
Normality of Political Representation Errors
Jarque–Bera Statistic: P2 ~ (2)
3.70
(.16)
2.87
(.24)
Notes: Mean Political Representation Error =
, Mean Square Political Representation Error =
Mean Absolute Political Representation Error =
, Root M ean Square Po litical Representation Error =
, Political Representation Error Variance =
, Theil–U (Bias Proportion) =
, Theil–U (Covariance Proportion) =
Jarque–Bera Test Statistic =
,
.
, Theil–U (Inequality) =
, Theil–U (Variance Pro portion) =
,
TABLE 2
Empirical Testing of Weak Efficiency Condition: Parity Representation Model of Political Market Equilibrium (1948–1996)
Independent Variables
Constant ( ", a)
1 st Difference
OLS (11a)
1 st Difference
Robust (11a)
1 st Difference
LAD(11a)
1 st Difference
OLS (11b)
1 st Difference
Robust (11b)
1 st Difference
LAD (11b)
ECM
OLS (11c)
ECM
Robust (11c)
ECM
LAD (11c)
–1.58
(1.20)
{1.24}
.26
(.32)
–.17
{.42}
–1.58
(1.20)
{1.24}
.26
(.32)
–.17
{.42}
–1.79
(1.19)
{1.11}
.003
(.37)
–.17
{.46}
.23*
(.12)
{.12}
–.02
(.02)
–.01
{.04}
–.77***
(.12)
{.12}
–1.02***
(.02)
–1.01***
{.04}
__________
___________
_________
___________
___________
__________
___________
___________
__________
1.11 *
(.58)
{.60}
.30 *
(.17)
–.02
{.37}
___________
___________
__________
___________
___________
__________
–.24 *
(.13)
{.12}
–.001
(.02)
.01
{.03}
P2 - (1): ( ", a = 0)
F–T est: ( ", a = 0)
1.64
[.20]
.65
[.42]
.17
[.68]
1.64
[.20]
.65
[.42]
.16
[.69]
2.59
[.11]
.00
[.99]
.14
[.71]
P2 - (1): ( $, b =1 ; $N= 0)
F–T est: ( $, b =1 ; $N= 0)
39.27 ***
[.00]
3224.82***
[.00]
724.08***
[.00]
39.27***
[.00]
3225.94***
[.00]
723.88 ***
[.00]
.03
[.86]
16.90***
[.00]
7.60**
[.01]
52.50 ***
[.00]
2235.08***
[.00]
420.70***
[.00]
52.50***
[.00]
2235.90***
[.00]
423.50 ***
[.00]
2.66
[.27]
8.78***
[.00]
4.98**
[.01]
P2 - (2): (b = – 0 = 1)
F–Test: (b = – 0 = 1)
___________
___________
___________
___________
___________
___________
75.70 ***
[.00]
1150.72 ***
[.00]
511.27 ***
[.00]
P2 - (3): (a = 0, b = – 0 = 1)
F–Test: (a = 0, b = – 0 = 1)
___________
___________
___________
___________
___________
___________
86.21 ***
[.00]
1053.21 ***
[.00]
342.43 ***
[.00]
Adjusted R 2 / Pseudo R 2
.11
________
.0009
.59
___________
.65
.10
_________
.001
P2 - (2): ( ", a = 0, $, b =1 ; $N=
0)
F–T est: ( ", a = 0, $, b =1 ; $N= 0)
F–statistic
5.78 *
[.02]
.84
[.37]
___________
67.13***
[.00]
3225.94***
[.00]
___________
3.61**
[.04]
1.53
[.23]
_________
Q– statistic: P2 - (7)
3.93
[.79]
_________
___________
3.93
[.79]
___________
___________
3.28
[.86]
___________
__________
ARCH statistic: P2 - (1)
142.14 ***
[.00]
_________
___________
142.15***
[.00]
___________
___________
.12
[.73]
___________
__________
Jarq ue–Bera statistic: P2 - (2)
.10
[.75]
_________
___________
.10
[.75]
___________
___________
121.41 ***
[.00]
___________
__________
W hite statistic: P2 - (2) / P2 - (3)
7.66 **
[.02]
________
___________
7.66**
[.02]
___________
___________
10.36 **
[.04]
___________
__________
Notes: Robust standard errors inside parentheses for OLS models: Newey–West (1987): (12a), White (1980): (12b) and (12c); Robust regression
models: Street, Carroll, and Ruppert (1988): (12b) and (12c). Bootstrap standard errors inside curly brackets. Probability levels inside regular
brackets.
*
p < .10
**
p < .05
***
p < .01.
TABLE 3
Empirical Testing of Strong Efficiency Condition:
Parity Representation Model of Political Market Equilibrium (1948–1996)
Independent Variables
Forecast Error
OLS (12a)
Forecast Error
OLS (12b)
Forecast Error
Robust (12b)
Forecast Error
LAD (12b)
ECM
OLS (12c)
ECM
Robust (12b)
ECM
LAD (12c)
Constant (", a)
–3.80
(11.03)
–4.79
(8.33)
{9.06}
–1.93
(1.52)
.57
{10.96}
–4.27
(8.73)
{11.94}
– 2.23
(1.34)
–1.64
{6.75}
__________
–.72***
(.21)
{.19}
–1.01***
(.04)
–.99**
{.39}
__________
__________
_________
__________
___________
__________
___________
– .01
(1.06)
{1.11}
.40**
(.19)
.24
{1.18}
__________
___________
__________
___________
–.34
(.30)
{.29}
.08
(.04)
.06
{.25}
.88**
(.34)
.05
(.37)
{.38}
.31***
(.07)
.31
{.31}
.10
(.43)
{.49}
.25***
(.07)
.29
{.38}
–6.43**
(2.95)
–2.56
(3.24)
{3.23}
–.29
(.66)
–1.39
(5.43}
–3.07
(4.74)
{5.12}
.41
(.63)
–.17
{4.25}
–9.41
(6.68)
–15.66
(6.26)
{7.65}
–2.82
(1.29)
–4.52
{7.88}
–14.43
(5.54)
{13.68}
–3.47***
(1.15)
–5.68
{8.86}
–.23
(.22)
.02
(.21)
{.21}
– .02
(.04)
.05
{.32}
– .01
(.29)
{.29}
.02
(.04)
.05
{.23}
Economic Misery t– 1 (*1 )
Wartime Dummy
t– 1
Unified Government
t– 1
(*2 )
(*3 )
Net Republican (* 4 )
House Seat Differential t– 1
Net Republican (* 5 )
Senate Seat Differential t– 1
.70***
(.14)
.35
(.21)
{.20}
–.01
(.04)
–.02
{.29}
.41
(.29)
{.28}
–.07
(.04)
–.05
{.26}
Budget Deficit t– 1 (* 6 )
–1.35**
(.48)
–.66
(.71)
{.85}
.04
(.11)
–.20
{.92}
–.72
(.80)
{1.00}
.10
(.09)
–.05
{.58}
2.65
(3.19)
3.61
(2.75)
{2.96}
1.12*
(.63)
1.16
{2.85}
3.47
(2.99)
{3.27}
1.16**
(.54)
.89
{3.00}
–.10
(.20)
.03
(.15)
{.16}
.01
(.03)
– .04
{.17}
.02
(.16)
{.17}
.01
(.02)
– .002
{.09}
10.51
(7.75)
5.38
(9.57)
{9.29}
.19
(.90)
.98
{8.45}
5.54
(9.46)
{9.15}
.21
(.77)
1.43
{10.73}
–.25
(.17)
– .06
(.15)
{.17}
–.05
(.04)
–.07
{.17}
– .12
(.23)
{.26}
.03
(.04)
– .01
{.14}
.05
(.10)
.10
(.07)
{.08}
–.03*
(.01)
–.01
{.12}
.09
(.08)
{.10}
–.03**
(.01)
–.02
{.10}
P2 - (1): (", a = 0)
F–Test: (", a = 0)
.12
(.73)
.28
[.60]
1.62
[.21]
.00
[.96]
.13
[.72]
2.79
[.11]
.06
[.81]
P2 - (1): ($, b =1; $N= 0)
F–Test: ($, b =1; $N= 0)
__________
14.66***
[.00]
815.13***
[.00]
6.63**
[.01]
.82
[.36]
9.60***
[.00]
.42
[.52]
P2 - (2): (", a = 0, $, b =1; $N= 0)
F–Test: (", a = 0, $, b =1; $N= 0)
___________
15.83***
[.00]
407.62***
[.00]
3.45**
[.04]
.88
[.65]
5.44***
[.01]
.25
[.78]
P2 - (2): (b = – 0 = 1)
F–Test: (b = – 0 = 1)
___________
___________
___________
___________
13.74***
[.00]
564.15***
[.00]
22.22***
[.00]
Transition Year Dummy
t– 1
(*7 )
Voter Turnout
(*8 )
in Preceding Election Cycle t– 1
Election Year
t– 1
(*9 )
Presidential Election Margin
Presidential Approval
t– 1
t– 1
(*10 )
(*11 )
P2 - (3): (a = 0, b = – 0 = 1)
F–Test: (a = 0, b = – 0 = 1)
___________
____________
___________
___________
13.92***
[.00]
376.19***
[.00]
15.06***
[.00]
P2 - (2)
(*j(Environm ental) = 0)
16.05***
[.00]
.63
[.73]
8.79***
[.00]
.50
[.61]
.36
[.84]
8.19***
[.00]
.32
[.73]
P2 - (5)
(*j(Institutional) = 0)
29.38***
[.00]
7.24
[.20]
2.54**
[.05]
.16
[.98]
5.94
[.31]
4.06***
[.01]
.24
[.94]
P2 - (4)
(*j(Electoral) = 0)
5.36
[.25]
2.08
[.72]
1.40
[.26]
.15
[.96]
1.48
[.83]
1.72
[.17]
.03
[.998]
P2 - (11/12/1 3/14 ): ( ", a = *j = 0, $, b =1 ; $N= 0, – 0 = 1)/
F–T est: ( ", a = *j = 0; $, b =1 ; $N= 0, – 0 = 1)
304.01***
[.00]
320.94***
[.00]
399.78***
[.00]
149.75**
[.00]
256.85***
[.00]
498.51***
[.00]
256.54***
[.00]
Adjusted R2 / Pseudo R2
.46
.60
__________
.70
.09
__________
.16
F–statistic
4.68**
(.00)
6.81***
[.00]
306.78***
[.00]
___________
1.37
[.23]
6.48***
[.00]
_________
Q–statistic: P2 - (7)
9.68
[.21]
9.57
[.21]
__________
__________
8.50
[.29]
__________
_________
ARCH statistic: P2 - (1)
.07
[.80]
.08
[.77]
___________
___________
.12
[.73]
__________
_________
Jarque–Bera statistic: P2 - (2)
1.31
[.52]
42.46**
[.00]
___________
___________
36.86**
[.00]
__________
_________
White statistic: P2
31.78*
[.08]
36.30*
[.05]
___________
___________
36.62*
[.08]
__________
_________
Notes: Robust standard errors inside parentheses for OLS models: Newey–West (1987): (12a), White (1980): (12b) and (12c); Robust regression
models: Street, Carroll, and Ruppert (1988): (12b) and (12c). Bootstrap standard errors inside curly brackets. Probability levels inside regular
brackets.
*
p < .10
**
p < .05
***
p < .01.
TABLE A–1
Empirical Testing of Weak Efficiency Condition Using Contemporaneous Voter Ideology:
Parity Representation Model of Political Market Equilibrium (1948–1996)
(OLS and GMM Estimation)
Independent Variables
1 st Difference
OLS (11a)
1 st Difference
GM M (11a)
1 st Difference
OLS (11b)
Constant (", a)
–1.79
(1.19)
–2.14
(1.30)
–1.79
(1.19)
.24*
(.13)
.29**
(.15)
__________
1 st Difference
GM M (11b)
ECM
OLS (11c)
ECM
GM M (11c)
–2.14
(1.30)
–1.86
(1.16)
–1.71
(1.13)
–.76***
(.13)
–.71**
(.15)
________
__________
_________
__________
_________
1.36
(.83)
1.27*
(.66)
__________
__________
__________
__________
–.23*
(.12)
–.16
(.10)
P2 -(1): (", a = 0)
2.26
[.13]
2.73*
[.10]
2.26
[.13]
2.73*
[.10]
2.59
[.11]
2.30
[.13]
P2 -(1): ($, b =1; $N= 0)
36.28***
[.00]
23.78***
[.00]
36.28***
[.00]
23.78***
[.00]
.19
[.66]
.17
[.68]
P2 -(2): (", a = 0, $, b =1; $N= 0)
50.21***
[.00]
42.59***
[.00]
50.21***
[.00]
42.59***
[.00]
2.66
[.27]
2.35
[.31]
P2 - (2): (b = – 0 = 1)
__________
__________
__________
__________
58.98***
[.00]
147.51***
[.00]
P2 - (3): (a = 0, b = – 0 = 1)
__________
__________
__________
__________
76.95***
[.00]
150.24***
[.00]
Adjusted R2
.11
.11
.58
.57
.14
.13
F–statistic
6.94**
[.01]
_________
66.34***
[.00]
_________
4.71**
[.01]
_________
Hausman Test: P2 - (1)
(Endogeneity Bias)
1.17
[.28]
_________
1.17
[.28]
_________
.00
[.99]
__________
J–statistic: P2 - (2) / (4)
_________
.00
_________
.00
________
1.58
(Overidentifying Restrictions)
[1.00]
[1.00]
[.81]
Notes: Robust standard errors inside parentheses. Probability levels inside brackets. The instruments for
(11a) and (11b) via GMM estimation consisted of a constant and
. The instruments for (11c)
via GMM estimation comprised of a constant,
*
p < .10
**
p < .05
***
p < .01.
,
, and
.