Abstract
Formal models of politics regularly combine assumptions about a variety of actors and institutions to produce equilibrium expectations, which serve as the primary target for empirical testing. Yet the underlying assumptions can vary in their accuracy among actors and across time and context. We focus on the pivotal politics model of lawmaking and argue that a full evaluation of the theory requires a granular analysis of its two primary components: the filibuster and veto “pivots” in Congress. We show that both types of pivots contribute to the success of pivotal politics in explaining postwar lawmaking, but that the relevance of each varies based on institution-specific contexts. Specifically, the filibuster pivot has little explanatory power before the 1970s, when norms of filibuster use were quite restrictive, while the veto pivot’s explanatory power is limited to situations in which the president has sufficient public backing to be a force in the legislative process.
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Notes
This statement assumes that status quos are distributed uniformly, a common assumption in the literature. The “distance” between the pivotal actors and the median is important because we assume that more status quos are gridlocked. With more status quos gridlocked, it is harder to pass new laws. While a uniform distribution assumption may seem to be unrealistic and demanding, Gray and Jenkins (2016) find that it has a relatively limited impact on pivotal politics testing.
The median member of each Congress is taken as the midpoint between the House median and the Senate median. Numerous ways of conceiving of “The Median” are possible in a bicameral legislature. We opt for the midpoint of the two chambers. Existing theories of legislation struggle to deal with bicameralism, but tend to assume that some median legislator’s preference exists to which policies will ultimately converge. We believe members of Congress are sophisticated actors who vote based on reasonable beliefs in the ultimate outcome of the total legislative process, which must include some resolution to the problem of bicameralism. One place where our measurement strategy may matter is that we calculate the Filibuster Interval as the space between the Filibuster Pivot and our created Congress Median (the midpoint between the two chamber medians). We believe this reflects the fact that the Senate is not a unicameral legislature, but instead is one-half of a bicameral legislature—and the ultimate bill that the Senate may pass must reflect a bicameral, rather than unicameral, process. The median we create reflects this.
For a more detailed explanation of this particular measure of significant legislation, see "Appendix 2".
About 38% of our observations represent unified government.
Specifically, we average each one-year growth rate within the Congress. These data come from the US Department of Commerce’s Bureau of Economic Analysis and are available at: http://www.bea.gov/national/.
The “War on Terror” is particularly difficult to code. We stop coding the United States as at war in 2010, when combined Afghanistan and Iraq troop deployments fell below 100,000.
Note that the first Congress in which all of these data are available is the 82nd (1951-1952), giving us a 32-Congress time series going up to the most recent complete Congress, the 113th.
This approach is followed for all subsequent tables herein.
The rule was formally changed on March 7, 1975 (Wawro 2010).
Filibusters are difficult to measure because their mere possibility can alter policy making, thus making them unobserved in some cases. Nevertheless, clear changes are evident in the observed numbers of filibusters. See http://www.senate.gov/pagelayout/reference/cloture_motions/clotureCounts.htm.
This quote is taken from prepared testimony given to the Senate Committee on Rules and Administration, April 22, 2010.
To the extent that the norms actually began to change one or two Congresses earlier, our tests would be biased against our expectations—thus creating more conservative interpretations.
We use the count of cloture motions filed, but the results using the number of cloture votes held are very similar. These counts come from: http://www.senate.gov/pagelayout/reference/cloture_motions/clotureCounts.htm These data are presented in the Figure 5 in Appendix.
This result is robust to entering a second interaction in the model, between the Filibuster Interval and the time trend. A separate model with an interaction between the time trend and no rule-change variable returns a significant coefficient similar to those in Table 2, albeit along with poorer model fit.
We define a “typical increase” as a one-standard-deviation increase, or about 0.08 on the DW-NOMINATE first-dimension scale.
Our results are robust to using a net measure as well, subtracting disapproval from approval ratings.
Two Presidents—Lyndon Johnson and Gerald Ford—took over in the middle of their predecessor’s term. In these cases, we use the scores of Presidents John F. Kennedy and Richard Nixon rather than their successors.
These data are presented in the Figure 6 in Appendix.
The same argument holds for a dependent variable based on both Sweep One and Two laws.
The theory could make predictions about productivity of non-ideological laws, however this would require separate measurement and analysis of alternative dimensions. If we are to use existing single-dimension ideological measures, then the outcome measured must also be ideological. In short, the revealed preferences must match the measured outcomes.
This definition of ideology is time-bound to the debates between mainstream liberals and conservatives in the postwar era. Lee provides more detail on each category and what should be included, as well as many examples.
Members’ preferences on these non-ideological issues may be orthogonal to their ideological preferences and thus the use of ideological preference measures potentially introduces substantial measurement error.
There are alternative ways to identify how topics fit on an ideological dimension. While we rely on human coding of topics based on a scholar-defined coding scheme, others might instead analyze which topics regularly produce high error rates in classification using existing measures, such as DW-NOMINATE. More opportunities exist for greater levels of empirical validation of Lee’s work.
Most laws have many sections and components that are difficult to evaluate in such a dichotomous way. We focus on the core features of the law rather than any add-ons or unrelated provisions—specifically, the aspect that Mayhew identified in his brief note on each law. When necessary, we use other historical descriptions of the laws to provide supplementary information.
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Appendices
Appendix 1: Additional models and data visualization
See Figs. 5, 6 and Tables 4, 5, 6.
Count of cloture motions, 80th–113th Congresses
Initial presidential approval, 80th–113th Congresses
Appendix 2: The ideological content of landmark legislation
In this Appendix, we briefly describe the process of getting from Mayhew’s counts of significant legislation to the counts of ideological legislation we use in this paper. This section is a condensed version of what appears in Gray and Jenkins (2016).
Mayhew (1991) developed a list of significant enactments based on a two-sweep method. In Sweep One, laws are marked as significant based on their appearance in newspaper recaps of major acts of Congress. In Sweep Two, additional bills are picked as significant by expert evaluators of policy areas. Sweep One, being contemporaneous, covers the entire period from the 80th to 113th Congresses. Sweep Two, being retrospective and taking considerable time for the impacts of policy to become apparent, only covers legislation up through the 1980s. This poses the first problem for using Mayhew’s total counts. Because both sweeps are not applied to the full time sequence, the potential for bias exists owing to extra laws being counted in earlier periods. This is not something that simply “dummying” these earlier years can be guaranteed to correct. Moreover, Howell et al. (2000) persuasively argue that the two sweeps have different time-series dynamics that make combining them problematic. Therefore, we rely exclusively on Sweep One counts, which have been applied across the entire time range.
When just Sweep One counts are used, they represent one justifiable means of measuring important legislation: what journalists remarked upon at the end of a session. However, when Sweep One counts are incorporated as the dependent variable in a pivotal politics model, a disconnect between theory and testing occurs.Footnote 22 Pivotal politics describes the process of producing ideological laws. Legislators have preferences over policies based on their ideal points on a single dimension of ideological positions. If a status quo cannot be placed on the line because it does not invoke political ideology, then the theory cannot make predictions about it.Footnote 23 An ideal test of pivotal politics should assess whether the ideological distance between pivotal actors in Congress influences the production of ideological legislation. Yet, Mayhew’s laws were chosen only for their importance, not for whether they represent any genuine conflict between liberal and conservative values. As Lee (2009) argues, not all issues generate conflict, and not even all issues for which there is partisan conflict can be coherently placed on an ideological line.
This weakness provides an opportunity for constructing a new dependent variable, but does not require departing from Mayhew. Instead, we create subsets of Mayhew’s Sweep One laws, separating those that fit into a liberal-versus-conservative ideological conflict from those that do not. In this, we rely on Lee’s (2009, pp. 63–64) definition of ideology and resulting coding scheme for classifying Senate roll-call votes as ideological or non-ideological. We code based only on the content of legislation; we do not consider the size or partisan composition of voting coalitions.
Lee identifies four issue categories of ideological conflict: economic, social, hawk-versus-dove, and multilateralism versus unilateralism.Footnote 24 The economic category includes laws that change levels of economic regulation (such as environmental regulations for businesses) or redistribution (for example, changing tax schedule income brackets or expanding Medicaid funding) or affect the overall level of government spending and share of the economy (such as large economic stimulus spending). The social category includes civil rights legislation and criminal punitiveness, as well as laws that push policy away from traditional gender, family, sex, and race norms (such as “Don’t Ask, Don’t Tell,” abortion rights, or school prayer). The hawk-versus-dove category involves authorizations for the use of military force, weapons investment, and limitations on weapons testing. Finally, the multilateralism versus unilateralism category includes debates over the importance of international organizations to America’s foreign policy (for example, policies that promote the United Nations).
Laws outside of these four categories do not have a clear place in modern American ideological debates. In these cases, placing a policy alternative to the “left” or “right” of a status quo is very difficult or impossible, which makes the common logic of Pivotal Politics theory testing inapplicable.Footnote 25 Many laws fall into this non-ideological category, including those that deal with good governance (such as Freedom of Information policies) and departmental reorganization, non-redistributive and non-regulatory programs (such as the anti-cancer efforts begun by the National Cancer Act of 1971), disaster relief, and the distribution of power between the branches of the federal government (such as the War Powers Resolution).
For each law that Mayhew identified in his Sweep One, we determine whether it fits into one of Lee’s categories of ideological conflict.Footnote 26 If so, the law is coded as ideological; if not, it is coded as non-ideological.Footnote 27 Figure 7 illustrates the resulting time series of ideological and non-ideological landmark laws. The mean level of ideological laws (5.85) is higher than non-ideological laws (4.09), but both series display meaningful variation (standard deviations of 2.83 and 2.19, respectively). The minimum and maximum for ideological laws are two (106th and 109th Congresses; 1999–2000 and 2005–2006) and 12 (111th Congress; 2009–2010), while the minimum and maximum for non-ideological laws are one (86th, 95th, 98th Congresses; 1959–1960, 1977–1978, and 1983–1984) and 12 (109th Congress; 2005–2006). Overall, the two series exhibit no meaningful correlation (r = 0.03) (See Fig. 7).
Landmark laws split into ideological and non-ideological categories, 80th–113th Congresses
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Gray, T.R., Jenkins, J.A. Unpacking pivotal politics: exploring the differential effects of the filibuster and veto pivots. Public Choice 172, 359–376 (2017). https://doi.org/10.1007/s11127-017-0450-z
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DOI: https://doi.org/10.1007/s11127-017-0450-z