Measuring the Effect of Direct Democracy on State Policy: Not All Initiatives Are
Created Equal*
Shaun Bowler
Department of Political Science
University of California, Riverside
Riverside, CA 92521
(909) 787-5585
[email protected]
Todd Donovan
Department of Political Science
Western Washington University
Bellingham, WA 98225
(360) 650-3018
[email protected]
AUTHOR INFORMATION:
Shaun Bowler is Professor of political science at the University of California, Riverside.
He and Todd Donovan have co-authored several books, including Demanding Choices,
Opinion, Voting and Direct Democracy (University of Michigan Press, 1998).
Todd Donovan is Professor of political science at Western Washington University. He
and Shaun Bowler have co-authored several books, including Reforming the Republic:
Democratic Institutions for the New America (Prentice Hall, 2004).
1
Abstract
Numerous studies attempt to assess direct democracy’s impact on state policy using
dummy variables or the frequency with which initiatives appear on a state’s ballots. We
offer an alternative to these measures that accounts for how rules governing the initiative
process vary among the states. We replicate several studies using different measures of
direct democracy and demonstrate that results of hypothesis tests are contingent on how
these institutions are measured. We contend that commonly used dummy variable
measures suffer validity problems, and that hypothesis tests using such measures produce
imprecise estimates of the initiative’s effect on policy.
2
Scholars and reformers believe that direct democracy can affect state politics and
policy directly and indirectly. Ballot initiatives allow groups outside the legislature to
propose and make laws directly. For example, voters may approve initiatives that alter
institutional rules defining how future legislators govern (Tolbert 1998). Such
governance policy initiatives may have long-term effects on state policy (Donovan and
Bowler 1998). The initiative process may also affect citizen behavior directly.
Participatory democratic theory (Pateman 1970; Barber 1984) suggests that simply the
use of the initiative alters the political context in which citizens reside. Empirical studies
find that the frequent use of initiatives stimulates greater discussion of policy issues,
leads citizens to seek out more political information, and increases voter mobilization
(Bowler and Donovan 2002; Smith 2001; 2002; Tolbert et al 2001; Nicholson n.d.).
Direct democracy can also have indirect effects on policy and political behavior.
Votes on initiatives may give legislators more accurate information about voter
preferences than what is available to legislators in non-initiative states (Romer and
Rosenthal 1979; Matsusaka 1992). Initiatives may increase the number of interest groups
active in a state (Boehmke 2002). Simply the threat of initiatives may influence how
legislators behave by prompting them to pass bills pre-empting groups seeking direct
control of the policy agenda (Gerber 1996; 1999; Matsusaka and McCarty 2001). Such
indirect effects have been argued to influence a variety of policies in this way, such as
abortion, death penalty, spending, and gay rights (Arceneaux 2002; Gerber 1996, 1999;
Matsusaka 1995; Gerber and Hug 2001). It is generally assumed that the existence of the
initiative process leads to public policy that better reflects mass preferences. Yet, some
empirical studies have challenged this assumption (Camobreco 1998; Hagen, Lasher and
1
Camobreco 2001; Lascher, Hagen and Rochlin 1996), and Matsusaka (2002) suggests
that differences in these results may be an artifact of model specification.
Theoretically, then, we have good reasons to expect the initiative process to shape
state politics and policy. But how can we test such a hypotheses? A key problem in
doing so has been how to measure the initiative process itself. Most studies of the impact
of direct democracy to date have simply assumed that all initiatives are the same (Gerber
1996, 1999, Camobreco 1998; Hagen, Lasher and Camobreco 2001; Lascher, Hagen and
Rochlin 1996).
In this article, we discuss ways in which the initiative process could be
represented empirically and argue in favor of measures that take into account the
considerable variety in how the process is implemented. We then compare various
initiative measures, replicating several previous studies to assess which measures are
worth exploring further.
Modeling the Effect of Initiatives: Interaction and Level of Measurement
There are several ways to represent ballot initiatives empirically in models
assessing their effect on state politics and policy: Each measurement strategy has
implications for the substantive interpretation of how we expect the initiative process to
work. Consider four ways we could model the effects of initiatives on policy, using the
following regression equations:
(1) State Policy = a + b1Dummy variable for direct democracy+ b2State opinion + ……..+ e
(2) State Policy = a + b1Dummy variable for direct democracy* State opinion +
b2 State Opinion + …..+ e
(3) State Policy = a + b1Type of direct democracy +…….+ e
2
(4) State Policy = a + b1Type of direct democracy *State opinion +
b2State Opinion +…….+ e
In Equation 1 direct democracy is measured as a dummy variable (1= initiative
state, 0=not), with public opinion modeled independently. In Equation 2, this dummy is
also interacted with a measure of public opinion. In Equations 3 and 4, direct democracy
is measured as at least an interval variable, so that two initiative states would have
different values if the rules for implementing their initiatives differed.
The literature to date has largely used the dummy variable approach to measuring
direct democracy (Gerber 1996, 1999, Camobreco 1998; Hagen, Lasher and Camobreco
2001; Lascher, Hagen and Rochlin 1996). One debate considered whether variants of
Equation 2 were an improvement over Equation 1 (Matsusaka 2001, Hagan, Lascher and
Camobreco 2001). A dichotomous measure of the process without an interaction
between the process and measure of state opinion on policy fails to capture how the
initiative conditions the effect of opinion on policy. It is likely that the initiative’s impact
on policy comes from its allowing public opinion to be more closely followed (an
interactive effect). That is, the initiative process affects how voter preferences are
transferred into policies (Matsusaka 2001; but see Hagan, Lascher and Camobreco 2001).
Thus, to understand the impact of the initiative, we need to know what policies voters
want. Assuming we can measure public opinion validly, Equation 2 would then be
preferable to Equation 1 in estimating the likely impact of the initiative process.
Although Equation 2 maybe an improvement over Equation 1, measuring the
initiative process as a dummy variable in either equation implies a substantive
interpretation that the simple presence or absence of an initiative process transforms
3
public policy. This representation is certainly consistent with the idea of the initiative
process as being important because it operates upon legislators both directly – by putting
policies into direct effect – and indirectly by threatening legislatures with the need to be
more beholden to popular preferences (Gerber 1996; 1999; Lascher, Hagan and Rochlin
1996). Yet there is a great deal of variation in the implementation of the initiative that
could influence the extent of its affect on politics and policy, and this is represented in
equations 3 and 4. Twenty-four states have some legal provision allowing laws to be
initiated outside the legislature and placed on the ballot by popular petition, with three
additional states (Kentucky, Maryland and New Mexico) allowing popular referendum.
These provisions vary on two dimensions that could significantly influence the
initiative’s effect: the ease of implementation of the initiative and the ease with which the
legislature can amend or undo laws produced by the initiative.
Ease of Initiative Implementation
Although the initiative process follows a very similar sequence across the states
(titling, qualification through petition, vote), there are critical differences among the
state’s rules for its implementation that structure the cost and difficulty of qualifying a
measure for the ballot. For example, some states require more petition signatures than
others, some require that these signatures be gathered in specific geographic locations.
Some states limit the time allotted for collecting signatures. Banducci (1998) and
Matsusaka and McCarty (2001) have demonstrated that higher signature requirements
and requirements for the geographic dispersion of signatures reduce the number of
4
initiatives in a state, and Matsusaka (1995; 2000) has even found that signature
requirements can condition how initiatives affect policy.
How might the ease of implementation influence the impact of an initiative
process? States with more measures on their ballots would likely experience greater
direct effects of initiatives simply because there would be a greater chance that more of
these measures would pass, producing substantive policies different than those that would
otherwise have emerged from the legislature. The theory of indirect initiative effects
hypothesizes that legislators respond to potential initiatives (Gerber 1996). In a state
where it is more difficult to qualify a measure for the ballot, legislators will feel less
pressure to respond to potential initiatives because the threat is less credible. Thus, the
indirect effect of the initiative in those states should be less. Therefore, initiatives will
likely figure differently in each state’s political system. For example, in states like
California and Oregon, where initiatives are a regular part of the political landscape, their
impact will be greater in states like Illinois, Mississippi, and Wyoming; where the law
nominally grants the option of using initiatives but few ever qualify for the ballot.
Treating these types off initiatives the same in an empirical model with a dummy variable
will likely missspecify the impact of the initiative on state politics and policy.
Ease of Legislative Modification of Initiative Results
There is substantial variation in the power that state legislatures have to avoid or
modify initiatives that voters approve, and this may influence the impact of an initiative
on state politics and policy. To use an example from overseas, an initiative that was
merely advisory, as in New Zealand, would likely have a muted impact. Members of
5
Parliament in that country can simply ignore voter approved initiatives (Mulgan 1997).
Similarly, the indirect initiative in some American states (where proposals have to be
submitted to the legislature before being placed on the ballot) allows legislators to offer
voters alternative proposals. Some state legislatures are also able to amend voterapproved initiatives substantially after popular attention has moved on. Some states,
notably California, only allow an approved initiative to be amended by other initiative;
some states allow the legislature to amend any approved initiative immediately, by
simple majority vote. Others simply limit the scope of the original initiative by, for
example, by not allowing initiatives on budgetary matters or by restricting the proposal to
a single subject. Each of these procedures affects the power of the initiative relative to
the legislature, especially those that affect legislators’ power to ignore, amend, or undo an
approved measure.
Measures of Institutional Variation in the Initiative Process
Existing literature measures the effects of initiatives with a dummy variable
representing the presence or absence of the initiative, or with a measure of the average
annual number of initiatives that qualify for a state’s ballot. Neither of these provide
direct measures of the institutional rules that govern how initiatives are used in the states.
We develop two indices of the initiative process in a state that assess this
institutional variation. Our first is the sum of the number of formal provisions that
increase the difficulty of qualifying a measure for the ballot, giving special weight to a
state’s petition signature requirements.1 This index ranges from zero (least difficult) to
six (most difficult). Our second index is the sum of the number of provisions that
6
constrain how a legislature can change an initiative that has been approved by voters.
This index ranges from zero (the legislature can do little to affect voter-approved
initiatives), to nine (the legislature has great discretion in altering initiatives). The
appendix describes how each index was constructed.2
Table 1 illustrates the variation on these measures across the initiative states. The
ranking of states in Table 1 shows that these indices have face validity. California and
Oregon top the lists and these are often portrayed in the popular press as states where
direct democracy has run amok (Schrag 1999; Broder 2000). In contrast, at the bottom of
these rankings are states where initiatives are rare (Mississippi and Wyoming) and where
the legislature is known to circumvent voter-approved measures (Massachusetts) (Waters
2002).
Table 1 about here
We assess the construct validity of our measures by examining how they are
related to initiative use in these states (Carmines and Zeller 1979). We expect that
initiatives will be used more in states where procedures for ballot qualification are easiest
and where the legislature is least insulated from initiatives. We expect this because ease
of use means lower petition costs for initiative proponents, whereas ease of legislative
amendment may decrease incentives proponents have for using initiatives. Our
expectations are confirmed by the data. The 8 states with both a score of 2 or lower on
qualification difficulty and 4 or lower on legislative insulation had 1.58 initiatives per
year since adopting the process. The 11 states with a score of 3 or higher for
qualification difficulty and 5 or higher for legislative insulation averaged just 0.46
7
initiatives per year.3 Thus, initiative proponents appear to respond to the incentives that
these institutional rules create.
Returning to the idea that the mere threat of the initiative can influence policy
indirectly (Gerber 1996; Matsusaka 2001), this threat may vary with these
implementation rules. First, we call a threat in which the procedural framework allows
popular sovereignty to have quite direct expression (easy to qualify / less legislative
insulation) the Populist threat. On the other hand, we call a threat in which popular
sovereignty is harder to express and subject to amendment by elected elites (hard to
qualify / more legislative input) a Progressive threat. For states like California and
Oregon with a Populist version of the initiative the “gun behind the door” (Lascher,
Hagen and Rochlin 1996) is loaded and ready to go, and it has probably already been
fired many times by initiative proponents. In states with a Progressive model, like
Massachusetts and Wyoming, the gun may more closely resemble a water pistol. The
threat of initiative may well ring hollow where qualification is nearly impossible and the
legislature can easily undo anything voters approve. Conversely, where initiatives are
easier to qualify and legislatures have less power to alter their content, the threat is more
credible. Thus, it seems reasonable to expect that the overall impact of the initiative
process on a state’s politics and policy is more likely to be felt strongly in states with
Populist versions of the initiative process than places with Progressive versions. To return
to our models of the impact of the initiative, this suggests that we should adopt models
like Equations 3 and 4, rather than those in Equations 1 and 2.
The correlation between our two indices is moderately strong (r = .74). States
where initiatives are relatively easy to qualify also tend to have a legislature that is less
8
insulated from the effects of initiatives that voter approve. Conversely, states with a
process that is hard to use also tend to limit voters’ direct influence on policy. This
combination of being hard to use and easy to amend is characteristic of states that
adopted the initiative process later than other states.4 That is, the Populist version of the
initiative is found in states that adopted direct democracy earliest; later adopters favored a
more Progressive model (Bowler and Donovan 2003).
In summary, the initiative is not the same process in Oregon as it is in Maine, and
to model the impact of the initiative on state politics and policy accurately, we must take
this difference into account. These institutional differences likely affect how open to
initiative proponents and voters a state’s process is, and how credible its threat is to the
legislature. The modeling consequence of this theoretical point is that a simple dummy
variable for the initiative does not adequately represent these complexities.
Alternative Specifications of the Initiative Process’s Effect: Testing the Measures
In this section, we assess how these different measures of the initiative process
affect the results of hypothesis tests in models of the effect of initiatives on state policy
and politics. We do this by replicating models from three published studies: one that
assessed the indirect effect of the initiative on state abortion laws restrictiveness
(Arceneaux 2002), one that assessed the direct effect of the initiative on the adoption of
state campaign finance laws (Pippen, Bowler and Donovan 2002), and one that assessed
the direct effect of the initiative on citizens’ attitudes toward government (Bowler and
Donovan 2002). We also use these measures to estimate variation in the “harshness”
(Chadha and Bernstein 1996) of state term limit laws. Each model was estimated using
each of five different measures of a state’s initiative process:
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1) a dummy variable where 1 = a direct democracy state (initiative or popular
referendum) and 0 = otherwise;
2) a dummy variable representing just initiative states (1 = state has the initiative,
0 = otherwise);
3) our legislative insulation index;
4) our qualification difficulty index; and
5) average annual state use of initiatives.
Non-initiative states are given scores of 10 on each of our indices. We obtained
similar substantive results when we reversed the order of each index, and coded noninitiative states as zero on each.
Given the discussion above, we expect that since the dummy variable measure
fails to capture the full range of variation in state rules for the initiative process, it will
underestimate the potential effects that initiatives might have on state policies and
politics. That is, since dummy variables lump all initiative states into one category, the
estimated effect will be the average for all initiative states. If some initiatives have a
weak effect (e.g. the Progressive version), then this will attenuate any estimated effect of
the more robust initiatives (e.g. the Populist version).
The model specification, measures and data used to estimate these abortion
policy, campaign finance policy, and political attitudes models are identical to those used
in the original published studies with the addition of measures of direct democracy.
Readers may refer to those articles for the full details of these.
State Abortion Policy Restrictiveness
10
In Table 2 we replicate Arceneaux’s (2002) study of state abortion policy
restrictiveness to examine how different measures of state initiatives affect the tests of
our hypotheses. Arceneaux expected that direct democracy conditioned the effect of
public opinion on policy, so interaction terms are used to test this hypothesis. He
concluded that abortion policy reflected public opinion on abortion better in direct
democracy states. Given the coding of these variables, our hypothesis is that the
coefficient for the interaction between public opinion and the initiative will be negative.
This would suggest that the initiative pushes state policy toward public opinion.5
Table 2 about here
The models in Table 2 that include dummy variable measures of direct democracy
(Models 1 and 2) are out-performed by the models that include the more sensitive
measures of the process. In Model 1, with direct democracy represented by a dummy
variable coded 1 if a state had the initiative or the popular referendum, and 0 othewise,6
we find that the interaction between direct democracy and public opinion has no
statistically discernable effect on policy. In Model 2 we represent direct democracy with
a dummy variable coded 1 if a state had the initiative and 0 otherwise. This specification
is nearly identical to that used by Gerber (1996, 1999), but the interaction of public
opinion and direct democracy again has no statistically discernable effect on abortion
policy restrictiveness in these data.7
Models 3 and 4 use our legislative insulation and qualification difficulty indices,
respectively, in the interaction terms representing how direct democracy affects policy.
The coefficient for the legislative insulation index (Model 3) is not statistically
significant, but that of the qualification difficulty index (Model 4) is positive and
11
statistically significant. Given our variable coding this positive sign suggests that states
in which it is easier to qualify an initiative for the ballot are more likely to have abortion
policy that reflects public opinion. In Model 5, we use a measure of direct democracy
that is the frequency of initiative use in a state (the annual average from the year a state
adopted the process to 1998) in the interactive term with public opinion. The coefficient
for the interaction term using this measure of direct democracy is statistically significant.
In short, the hypothesis that direct democracy makes policy more reflective of public
opinion is supported in this instance if direct democracy is measured as qualification
difficulty or the frequency of initiative use, but the hypothesis is not supported when the
dummy variable or legislative insulation measures of direct democracy are used.
State Campaign Finance Policy
Next, we replicate Pippen, Bowler and Donovan’s (2002) study of changes in
state campaign finance policy, with each of the five measures of direct democracy. Since
the studies replicated in this section tested for direct effects of initiatives on policy,
measures of direct democracy are not interacted with public opinion in these models.
Rather than reporting the whole re-estimated model, Table 3 summarizes the results,
reporting only the relevant coefficients for the five measures of direct democracy used in
each estimation, along with the two-tailed probability level of the coefficient being
statistically distinct from zero. Coefficients for various control variables are omitted, but
the full results are available from the authors. Table 3 also reports these results for the
other models of direct democracy’s impact on state policy that we replicated.
Table 3 about here
12
Pippen, Bowler and Donovan (2002) found that states that used the initiative
frequently (high annual average usage) adopted more campaign finance regulations in
1984 - 1998. Their dependent variables were additive measures of the number of limits
on contributions from various sources. Table 3 shows that dummy variable direct
democracy measures are not consistent in estimating the effect of the initiative on
campaign finance reform. On the other hand, the coefficients for legislative insulation
and qualification difficulty indices suggest that variation in the rules for using the
initiative do in fact influence the strength of state campaign finance policies. The
qualification difficulty and frequency of use measures most consistently predict that
direct democracy affects policy in this area.
Attitudes Toward Government and Politics
Table 3 also reports a summary of estimates replicating Bowler and Donovan’s
(2002) study of the initiative process’s effect on citizen attitudes about politics. Bowler
and Donovan found that people living in states that used the initiative more frequently
had greater internal and external political efficacy. Comparing the results with the
different measures of direct democracy, we find that variation in institutional design
(initiative qualification difficulty and legislative insulation) tends to affect various
measures of political efficacy significantly, while dummy variables representing the
process tend to show no effects. The frequency of initiative use measure is the most
consistent predictor of these attitudes. Overall, the results in Tables 2 and 3 suggest that
more sensitive measures of direct democracy that account for the actual use of initiatives
and the variation in their institutional provisions tend to find more effects of direct
13
democracy on state policy and politics than do dummy variable measures of direct
democracy.
Estimating Harshness of State Term Limits
Another way to assess the relative validity of these direct democracy measures is
to examine how well they explain policy variation within initiative states. By definition,
dummy variable measures cannot do this, as they do not capture any variation in direct
democracy across the states. We assess the non-dummy measures by examining the
impact of direct democracy variation on variation on the harshness of state legislative
term limits laws.
Table 4 about here
Of the 21 states to adopt term limits by 1996, all but one (Louisiana) was an
initiative state. Term limits, then, are typically the direct result of the initiative granting
non-legislative actors influence over the policy agenda. However, there is meaningful
variation in the harshness of state term limits policies having to do with how quickly
limits set in, how long legislators may serve, and how long they must sit out before
seeking office again (Chadha and Bernstein 1996). Assuming that legislators would want
term limits that were less harsh than non-legislators, we expect that term limits will be
harshest in states where the initiative process is most freewheeling, that is, where
proposals are easy to qualify and where the legislature has little discretion over measures
that voters approve. We test this by regressing Chadha and Bernstein’s (1996) term
limits harshness index on our measures of variation in the initiative process (controlling
for a state’s level of legislative professionalism).8
14
Table 4 reports ordinary least squares regression estimates of the impact of our
direct democracy measures on term limits harshness in the 21 states with term limits and
in the 20 term limits states that allow initiatives. We also report these estimates
controlling for legislative professionalism.9 The results demonstrate that variation in
state rules for the initiative have a statistically significant effect on state term limits
policy within those states that adopted term limits. As expected, where legislators are
more insulated from initiatives, having more ability to amend their results, the limits are
less harsh. Likewise, where it is more difficult to qualify measures, the limits are less
harsh. The former effect is not statistically significant when Louisiana is removed from
the dataset, while the latter does effect remains significant when the cases are limited
only to the 20 initiative states that adopted term limits. Also as expected, where
initiatives are used more frequently, term limits are more severe.
Discussion and Conclusion
Modeling the initiative process as a simple dummy variable lumps into a single
category states that make active and repeated use of the process with those that do not
and states that have easy to use and hard to amend initiatives with those that do not. A
dummy variable also makes a false distinction between states that do not have the
initiative and those that have it in on the books but that barely use it in practice. As a
result, modeling the impact of direct democracy with such a dummy variable reduces the
precision of our estimates and probably increases the risk of making Type II errors,
making conclusions that there is no impact of the initiative on politics and policy when
one may, in fact, exist. In this study, using more sensitive measures of direct democracy
15
across a range of policy areas and a range of political attitudes, we have shown that the
initiative process has an impact. Critics and defenders of the process alike are right in
arguing that the process makes for different kinds of state politics and policy.
Furthermore, we have shown that the impact of the initiative process varies
depending on how it is designed and used. The initiative has a greater impact where it is
easier to get a measure on the ballot, where it can more easily circumvent the legislative
process, and perhaps most important, where it is used the most. Thus, the initiative
process is not monolithic. The difficulty of using the process and the degree to which the
legislature is insulated from initiative measures varies widely. The initiative operates
differently in each state and is more of a threat to the legislature in some states than in
others.
Of course, one may disagree with specific aspects of our measures of the initiative
process presented above. Experts on the process in specific states may be able to point to
subtle differences in implementation that are not captured by our indices. We do not
pretend that this is the last word on how to measure the initiative process as an institution.
Nevertheless, our results suggest that we have valid and meaningful measures of the
variation in the institutions of direct democracy that can be used to shed light on the
relationship between institutions and public policy. Further refinements of these
measures and applications of them to understand other aspects of the causes and effects
of direct democracy can be pursued in the future.
16
Appendix: Coding of Initiative Indices
Qualification Difficulty Index:
Higher scores indicate greater difficulty. Points are added to the index score for a state if:
1) only statutes or only constitutional measures are allowed, 2) the length of the
qualifying period is limited, 3) geographic distribution of signatures is required, 4) the
proportion of voters’ signatures required for qualification is between 7.0 percent and 10.0
percent; 5) the proportion of voters’ signatures required for qualification exceeds 10.0
percent, and 6) there are substantive limits on the subject matter of initiatives.
Sources: Magleby 1984; National Conference of State Legislatures 2000 and 2002.
Legislative Insulation Index:
Higher scores indicate that the legislature has greater ability to affect initiatives and,
therefore, is more insulated from their effects. Points are added to the index score for a
state if: 1) the state has a single-subject rule, 2) there are limits on the substance of an
initiative, 3) there are restrictions on fiscal initiatives, 4) the legislature can amend or
repeal a statutory initiative, 5) the legislature can repeal initiative statutes without a
waiting period, 6) if the legislature can repeal a statutory initiative without a
supermajority, 7) the state allows no constitutional amendment initiatives, 8) the state
allows direct and indirect initiatives, and 9) the state allows indirect initiatives only.
Sources: National Conference of State Legislatures (2002); Gerber (1995).
17
Endnotes
*An earlier version of this article was presented at the Third Annual Conference on State
Politics and Policy, sponsored by the APSA State Politics and Policy Section and the
University of Arizona March 2003. Co-authorship is equal. We thank Kevin Arceneaux
for providing us his data. The authors bear responsibility for any errors in the analysis or
interpretation of these data.
1
Each rule in our indices may not have equal effect on policy. Given findings in
Matsusaka (1995; 2000) and Banducci (1998), our qualification difficulty index gives
double weight to petition requirements. States requiring 7.0 percent to 10,0 percent of
voters signatures receive one point, while states requiring more than 10.0 percent receive
an additional point. See the Appendix for details about other items in each index. An
assessment of the impact of each rule on the initiative’s impact is beyond the scope of
this article, but will be pursued in future work.
2
Coding states for each index was quite straight-forward, except for Florida. The
legislative insulation index includes, among other things, measures of how the legislature
may respond to statutory initiatives. Florida does not allow statutory initiatives.
Alternative codings for Florida do not affect the substantive results discussed in this
article.
3
Data on annual use of initiatives through the year 2002 were obtained from the Initiative
and Referendum Institute, and from the National Conference of State Legislators. For
details, see: http://www.ac.wwu.edu/~donovan/inituse.pdf
4
The correlation between the year of adoption and the qualification difficulty index is
.66; it is .46 for the year of adoption and the legislative insulation index.
18
5
Matsusaka (2001) argues that unless surveys ask about the exact policy the public wants
in a state, there is no way to interpret this coefficient as showing that direct democracy
does a better job of producing the public’s desired policy. But, he also claims that logic
underlying Gerber’s (1996, 1999) studies, and her statistical results, support the view that
policies are closer to what voters want in initiative states. Table 2, reports the results of
models specified as Gerber’s were, with measures of state public opinions on abortion
that are similar to Gerber’s measures of state public opinions on abortion and the death
penalty.
6
Arceneaux (2002) coded states with the popular referendum or the initiative as 1 on his
direct democracy dummy variable. However, he coded some states where initiatives are
rarely used as 0. Our data are coded differently, so that all initiative states, including
Florida, Illinois, and Mississippi, are represented as having direct democracy.
7
Therefore, we code Kentucky and Maryland as 0 since they have the referendum, but no
the initiative.
8
The term limits harshness index ranges from a low of -2.23 (Utah) to a high of 1.22
(Idaho) (Chadha and Bernstein 1996).
9
In the 21-state models, Louisiana is coded “10” on the legislative insulation and
qualification difficulty measures. Professionalism is a rank-ordered indicator of
professional (3), hybrid (2), and citizen legislatures (1), adapted from Hamm and
Moncrief (1999).
19
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23
Table 1. Measures of Formal Provisions for Statewide Ballot Initiatives
Qualification
Difficulty
Index
Oregon
California
Colorado
North Dakota
Arkansas
Ohio
Michigan
South Dakota
Idaho
Arizona
Washington
Oklahoma
Montana
Missouri
Massachusetts
Utah
Nebraska
Maine
Nevada
Florida
Illinois
Alaska
Mississippi
Wyoming
0
1
1
1
2
2
2
2
2
3
3
3
3
3
3
3
4
4
4
4
4
5
5
6
Legislative
Insulation
Index
California
Arkansas
Arizona
Michigan
North Dakota
Oregon
Colorado
Idaho
Oklahoma
South Dakota
Utah
Washington
Florida
Illinois
Nevada
Alaska
Missouri
Montana
Nebraska
Ohio
Mississippi
Maine
Massachusetts
Wyoming
1
2
3
3
3
3
4
4
4
4
4
4
5
5
5
6
6
6
6
6
7
8
8
9
Note: For the qualification difficulty index, higher scores indicate more difficulty. For
the legislative insulation index, higher scores indicate that the legislature has greater
ability to modify voter approved measures and is more insulated from their effects.
Sources: Magleby (1984); Gerber (1995).National Conference of State Legislatures
(2000, 2002). See appendix for details.
24
Table 2. Public Opinion and Abortion Policy: Estimating the Effect of Direct
Democracy Using Five Different Measures
Initiative & Referendum dummy
State ideology * I & R dummy
State public opinion on abortion *
I & R dummy
(1)
7.700
(0.10)
-1.792*
(1.92)
-5.020
(0.27)
Initiative dummy
(2)
(3)
(4)
(5)
-6.804
(0.09)
-1.727
(1.66)
-0.898
(0.05)
State ideology * initiative dummy
State public opinion on abortion *
initiative dummy
Legislative insulation index
7.837
(0.70)
-0.255
(1.48)
-2.433
(0.94)
State ideology * leg. insulation index
State public opinion on abortion * leg.
insulation index
Qualification difficulty index
-27.847**
(2.73)
-0.564**
(2.17)
6.760***
(2.80)
State ideology * qualification difficulty
index
State public opinion on abortion *
qualification difficulty index
Average annual use of initiative
State ideology * Avg. annual use of
initiative
State public opinion on abortion * Avg.
annual use of initiative
Control Variables:
State public opinion on abortion
State ideology
% fundamentalist protestants in state
% of legislators female
Divided government
Constant
N
R-squared
61.518
(1.63)
-1.133
(1.51)
-15.959*
(1.84)
-45.654***
(3.20)
1.664***
(3.34)
-0.475
(1.21)
-1.452**
(2.38)
0.991*
(1.81)
249.404***
(4.13)
40
0.72
-50.232***
(3.81)
1.538***
(2.99)
-0.505
(1.31)
-1.452**
(2.36)
1.102
(1.68)
264.128***
(4.68)
40
0.71
-38.787***
(2.84)
1.807***
(3.43)
-0.205
(0.50)
-1.609***
(2.84)
1.289*
(1.99)
219.523***
(3.80)
40
0.74
-60.248***
(5.31)
1.289***
(2.75)
-0.656*
(1.79)
-1.681**
(2.69)
0.964*
(1.91)
305.017***
(6.45)
40
0.72
25
-33.847***
(2.77)
1.648***
(3.60)
0.024
(0.06)
-1.796**
(2.69)
1.074**
(2.07)
200.284***
(4.03)
40
0.75
Note: The unit of analysis is a state. Dependent variable is NARAL ‘s index of state abortion
policy restrictiveness as of the end of 2000. The table reports the authors’ weighted OLS
estimates using data from Arceneaux (2002), with robust t-statistics in parentheses.
* p < .10; ** p < .05; *** = p < .01
26
Tables 3 in separate file
27
Table 4. The Impact of Direct Democracy Variation on the Harshness of State Term
Limits Policy
All Term Limit States
Term Limits States w/ Initiative
_____________________________________________________________________
Legislative
-.18** -----.08
----insulation
(.09)
(.10)
Qualification
difficulty
---
-.29***
(.08)
---
---
-.28*
(.14)
---
Average annual
initiative use
---
---
.51**
(.25)
---
---
.39*
(.21)
Constant
.91* . 94*** -.48
.54
.87
-.27
_______________________________________________________________________
Adjusted R2
.17
.36
.15
.04
.13
.10
Number of states
21
21
21
20
20
20
_______________________________________________________________________
Controlling for Legislative Professionalism
All Term Limit States Term Limits States w/ Initiative
________________________________________________________________________
Legislative
-.17*
-----.07
----insulation
(.09)
(.10)
Qualification
difficulty
---
-.30***
(.09)
---
---
-.25
(.16)
---
Average annual
use
---
---
.49*
(.25)
---
---
.36
(.24)
Legislative
professionalism
.18
(.25)
.01
(.25)
.01
(.24)
.24
(.27)
.12
(.27)
.11
(.28)
Constant
.55
. 76
-.49
.02
.58
-.43
______________________________________________________________________
Adjusted R2
.10
.32
.11
.01
.09
.06
Number of states
21
21
21
20
20
20
______________________________________________________________________
* = p < .10 ; ** = p < .05; *** = p < .01 (two-tailed tests)
Note: Ordinary least squares coefficients are reported (with standard errors in parentheses
beneath).
28