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A Separation-of-Powers Approach to the Supreme Court’s Shrinking Caseload

Published online by Cambridge University Press:  21 October 2022

Elizabeth A. Lane
Elizabeth A. Lane*
Affiliation:
Louisiana State University, USA
*
Contact the author at elane8@lsu.edu.
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Abstract

Since the end of the 1980s the Supreme Court has cut its caseload nearly in half. While this decrease has not gone unnoticed, researchers have largely focused their explanations on institutional factors, such as changes in personnel, creation of the certiorari pool, or an increase in the amount of discretion justices have to set their agenda. Most existing work fails to consider how the preferences of members of Congress and the president also contribute to this staggering decrease. I provide the first systematic examination of how extrainstitutional influences affect the size of the Court’s caseload. I examine the 1951–2016 terms of the Court to reveal that a constraining political environment significantly reduces the number of cases the justices agree to hear each term. These results suggest that the justices consider the preferences of actors in the other branches of government much earlier than their decisions on the merits.

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Articles
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Journal of Law and Courts , Volume 10 , Issue 1 , Spring 2022, pp. 1 - 12
Copyright
© 2022 Law and Courts Organized Section of the American Political Science Association. All rights reserved.

Do Supreme Court justices alter their decision making to account for the preferences of the legislative and executive branches? Will justices, to avoid potential reprisal from the elected branches of government, vote for less optimal outcomes at the agenda or merits stage? Questions like these are critical to understanding the separation of powers in the United States. Yet, one aspect of the Court’s relationship with the other branches of government that has not received the same attention is the Court’s caseload, which is also commonly called its docket. Journalists, scholars, and members of the legal community have continually recognized the significant decrease in the size of the Court’s caseload since the 1980s (e.g., Owens and Simon Reference Owens2012; Harvey Reference Harvey2013; Moffett et al. Reference Moffett2016), but the explanations offered by research focus largely on internal changes on the Court such as personnel turnover, adjustments to the Court’s jurisdiction, and the creation of the certiorari pool, with little acknowledgment of the other branches. I contend that in addition to institutional changes on the Court, it is crucial to also account for extrainstitutional dynamics created by the United States’ separation-of-powers (SOP) system.

In this article, I assess whether the constitutionally created SOP system, designed to uphold judicial independence and fair and impartial checks and balances, actually allows Congress and the president to influence the size of the Supreme Court’s caseload. In doing so, I provide the first assessment of the size of the Supreme Court’s agenda and its decision to grant review over time, and how or whether fluctuations in docket size are influenced by the amount of interinstitutional constraint exhibited by various pivotal actors in the other branches of government. I analyze over a half a century’s worth of Supreme Court agenda-setting behavior and find a consistent SOP influence on the size of the Court’s docket regardless of the model of constraint that is used. During times of extrainstitutional ideological constraint, the results suggest that Supreme Court justices avoid as many as 12 cases. In an era in which the Court hears only 70 or so cases per term, even a single case has the potential to create profound legal, political, or social policy change, and so this result is especially noteworthy. Beyond their substantive importance, these results also inform the normative debate among legal professionals, commentators, and politicians.

Strategic Behavior and the SOP

The number of litigants petitioning the Court for review has steadily increased from 4,000 in the 1970s to upwards of 9,000 in recent terms. Yet, at the same time, the number of cases the Court actually decided has fluctuated over the same period, with a consistent downward trend from 200 cases in the mid-1980s to an average of 80 cases per term since the late 1990s. The justices claim that the majority of these cases simply do not merit their review (Barnes Reference Barnes2007), which may be true for some of these cases, but research suggests that the Court’s docket still consists of plenty of certworthy cases (see Edelman, Klein, and Lindquist Reference Edelman2008; Beim and Rader Reference Beim2019). I argue that their docket management is the result of strategic behavior.

Epstein and Knight (Reference Epstein2000, 626) explain that justices behave strategically to achieve their preferred policy outcomes yet understand that their choices and outcomes depend on other actors and the institutional setting, which structure these choices, and thus alter their decisions accordingly. At the agenda stage, justices consider their colleagues’ potential votes in order to determine whether the potential merits outcome is more optimal than the legal status quo (Black and Owens Reference Black2009). Justices’ strategic behavior also extends beyond their colleagues’ preferences on the bench to the preferences of members of the legislative and executive branches that exert power over the Court when justices are faced with an unfavorable or threatening political environment, which causes justices to rationally alter their behavior (Harvey and Friedman Reference Harvey2006, Reference Harvey2009; Segal, Westerland, and Lindquist Reference Segal2011; Harvey Reference Harvey2013).

Congress can exert power over the Court by introducing Court-curbing legislation to punish the Court for certain decisions, which has been shown to be an effective constraint (Clark Reference Clark2009; Mark and Zilis Reference Mark2018b, Reference Mark2019), and some research suggests it is even more effective than direct policy retaliation against the Court’s decisions (Mark and Zilis Reference Mark2018a). What is more is that Court-curbing legislation is a sweeping response and does not discriminate based on the type of decision (i.e., constitutional or statutory). That being said, research also demonstrates that the Court considers Congress’s preferences for both statutory and constitutional decisions because of the Court-curbing threats previously mentioned, override legislation, and constitutional amendments (Eskridge Reference Eskridge1991; Meernik and Ignagni Reference Meernik1997; Epstein, Knight, and Martin Reference Epstein2001; Harvey and Friedman Reference Harvey2006; Bailey and Maltzman Reference Bailey2011; Segal et al. Reference Segal2011; Hall Reference Hall2014).

The president also plays a role in congressional attempts to override Supreme Court decisions because he must sign any bill into law in order for the override to take effect. He also wields power over the Court directly via the Office of Solicitor General (OSG). Since the justices show a high amount of deference to the OSG, increased involvement in a case can affect both the agenda and merits stages (McGuire Reference McGuire1998; Johnson Reference Johnson2003; Bailey, Kamoie, and Maltzman Reference Bailey2005; Thompson and Wachtell Reference Thompson2009; Black and Owens Reference Black2011, Reference Black2012). As the chief bureaucrat, the president can also instruct his cabinet members and other agency officials to ignore Court decisions, and because the justices have no method of enforcement, they are at the president’s and bureaucracy’s will (Carrubba and Zorn Reference Carrubba2010).

Furthermore, it is clear that both Congress and the executive branch are mindful of the Supreme Court’s docket because they care about policy outcomes. They demonstrate this through their actions. Members of Congress regularly file amicus briefs, and the president as represented by the OSG is one of the most trusted friends of the Court. Furthermore, Bailey (Reference Bailey2007) demonstrates the abundance of overlapping issues that the president and members of Congress make statements and take positions on that relate to the Court’s policy outputs.

Despite the theoretical support for the SOP relationship between the Court and the other branches, results are inconclusive at the agenda-setting stage. Harvey and Friedman (Reference Harvey2009) examine the Court’s propensity to review laws enacted by Congress and find that a hostile political environment can force the justices to significantly deviate from the median justice’s preferred outcome. When the Court invalidates laws, other research reveals the justices granted review because they knew the law lacked support in Congress, which implies the Court is unlikely to strike down a statute that enjoys wide support among current lawmakers owing to fear of reprisal for their actions (Hall and Ura Reference Hall2015). Other work by Harvey (Reference Harvey2013) suggests that the Rehnquist Court was responsive to the House median alone. And Moffett et al. (Reference Moffett2016) theorize that the Court’s docket is influenced by other branches, but uncertainty caused by internal changes on the Court is also significant in determining the size of the Court’s caseload. However, Owens’s (Reference Owens2010) case-level examination only finds that case characteristics like a lower court striking a law as unconstitutional, circuit court conflict, and federal government support for a petition are significant in predicting the justices’ decisions to grant review. When considering the potential merits outcome, the ideological positions of the president and pivotal actors in Congress are insignificant in the justices’ decision calculus.

My research design differs from previous studies on SOP agenda influence because I examine the number of cases granted review each term as opposed to individual statutes, cases, or the total number of cases decided. This is significant for two reasons. First, it is important to consider this relationship in the aggregate as opposed to the individual case level. There is idiosyncrasy involved in the review process for each case at the individual level that can add noise to the analysis, thus resulting in mixed conclusions as in past research (Harvey and Friedman Reference Harvey2009; Owens Reference Owens2010). In fact, Segal et al. (Reference Segal2011) find that a hostile political environment makes justices less likely to strike legislation generally as opposed to altering their behavior on an individual case-by-case level, which suggests that taking a step back and viewing overall trends at the docket level can help shed some light on how SOP dynamics operate at the agenda stage of judicial decision making.

I am not the first to make this important contribution (Harvey Reference Harvey2013; Moffett et al. Reference Moffett2016), but I am the first to actually examine the number of cases granted review each term instead of a post hoc measure of the number of cases decided (Moffett et al. Reference Moffett2016) or the number of cases orally argued (Harvey Reference Harvey2013), which can (and does) differ from the number granted. Additionally, my approach provides a more complete and accurate testing of SOP theories. I am the first to examine multiple models of SOP constraint and how and whether each of these pivotal actors across the other branches of government influence the Court’s decision making at the agenda stage each term (Harvey Reference Harvey2013; Moffett et al. Reference Moffett2016). In doing so, I improve on the previous literature on this topic by using a continuous measure of constraint as opposed to a dichotomous indicator that assumes all levels of constraint are the same (Moffett et al. Reference Moffett2016).

Spatial Theory of Supreme Court Constraint

To connect the SOP and agenda-setting literature with the research on the Court’s caseload, I must identify how and when the legislative and executive branches can exert influence over the justices’ decisions to grant review. To model this relationship, I start with assumptions that are standard in the SOP literature: all actors have single-peaked preferences, which are plotted on a unidimensional policy space or a left-right ideological continuum (Harvey and Friedman Reference Harvey2009; Owens Reference Owens2010). These represent their preferred policy outcomes. And, within this policy space, all actors are aware of one another’s ideal points (Harvey and Friedman Reference Harvey2006; Owens Reference Owens2010).

I use the Court median’s ideal point as an estimate of the Court’s sincere ideological preference in a given term for two reasons. First, the bench median is what has traditionally been used in the SOP agenda literature (Harvey and Friedman Reference Harvey2009; Owens Reference Owens2010). Second, because I am looking at the overall number of cases in a term and not individual cases, I must choose an estimate that remains consistent across all cases the Court may choose to review.Footnote 1

Theories in the congressional literature identify different actors in the legislative branch who determine policy outcomes. The specific location of congressional pivots is dictated by the theory that is chosen, and whether the Court is constrained is contingent on the location of these pivotal actors relative to the Court. For example, the chamber median model posits that legislation passed through Congress will reflect the preferences of the median legislator in each chamber. This model is derived from the median voter theorem of electoral behavior, such that the candidate who appeals to the median voter is the election winner. As such, policies in Congress that appeal to the chamber median achieve the same success (Romer and Rosenthal Reference Romer1978). In what follows, I adopt the chamber median model to explain my measurement and identify the preferences of Congress. However, the results hold across all spatial theories of constraint.Footnote 2

Common Space of Chamber Median Pivots

The key actors in the chamber median model are the chamber medians and the president. If the Court median falls outside of these actors’ preferences, it is constrained. Take, for example, the 2009 term of the Court shown on the left side in figure 1. During the 2008 election, the Democrats gained control of both chambers of Congress and the presidency. This put a right-leaning Court at odds with the other branches. If the justices were to grant review and decide a case at the Court’s ideal point, all of the other branches would prefer a more liberal policy and could challenge the Court’s decision. In order to avoid backlash, the Court had to craft its opinion and any resulting policy at the closest pivot’s ideal point, which during this term was the House median. Compare this to a few terms later when the Republicans regained control of the House during the 2012 term. The House median was more conservative than the Court median, as shown on the right side in figure 1. Here, the Court is unconstrained because if the president or Senate were to challenge the Court’s outputs by introducing override legislation or Court-curbing legislation, the House would challenge them and vice versa, which allowed the justices to grant review and decide cases at their ideal point without fear of retaliation from the other branches.

Figure 1.

Estimates of common space locations from the 2009 and 2012 October terms (OT) of the US Supreme Court. The estimates come from Lewis et al. (Reference Lewis2019) and Epstein et al. (Reference Epstein2007). C = Court median, S = Senate median, H = House median, and P = president. The value below each pivot is its ideal point in common space. Smaller (more negative) values represent more liberal preferences; larger (positive) values represent conservative preferences.

I test the theory of SOP constraint in my model by including the amount of ideological distance by which the Court was constrained each term. If the Court was unconstrained during a term, as they were during 2012, then the distance equals 0. When the Court is constrained, the variable equals the absolute distance between the Court and the closest pivotal actor. For example, in the 2009 term, under the chamber median model, the House median was the closest actor at 0.467 common space units away (see fig. 1; distance highlighted in gray).Footnote 3 Coding constraint in this manner is more precise than the method of measuring constraint with a simple dichotomous indicator and more accurately models the environment in which the Court is operating.

I also account for other possible influences on the size of the Court’s caseload recognized by previous research. These include institutional factors like the Case Selections Act of 1988, the invention of the certiorari pool, and personnel changes. Additionally, I control for political factors like the ideological heterogeneity of the Court and the ideological makeup of the judicial branch, as well as other SOP explanations, such as the amount of new legislation to review. See the appendix for a full description of these variables.Footnote 4

Data and Method

My dependent variable is a count of the number of cases granted review in each term from 1951 to 2016.Footnote 5 I employ a negative binomial regression model because of the dispersion of the data.Footnote 6 Examining the number of cases granted review over time introduces additional modeling concerns of serial correlation and stationarity. It is to be expected that there is institutional memory at the Court owing to lifetime tenure. As a result, the amount of cases the Court agrees to hear each term is related to the number of cases granted review in the previous term. To account for this relationship, I include a lagged dependent variable on the right-hand side of the model. Additionally, initial tests for stationarity on the dependent variable reveal that it possesses a unit root. Because of the nonstationarity of the dependent variable, I first differenced all of the independent variables that possess a unit root to avoid the pitfalls of regressing nonstationary variables on a nonstationary dependent variable (Box-Steffensmeier et al. Reference Box-Steffensmeier2014).Footnote 7 After accounting for the dynamics of the independent variables, the dependent variable is stationary.

Results

The SOP constraint variable exhibits a significant and negative effect on the number of cases the Court decides per term, regardless of which model is used (chamber median, party gatekeeping, etc.). The effect of the range of SOP constraint under the chamber median model on the number of cases the Court grants review is shown in figure 2 (see the appendix for a full table of results).

Figure 2.

Predicted number of cases the Court should hear in a given term across the 95th percentile range of constraint under the chamber median model. Cross bars, 95% confidence intervals estimated using the observed value approach (Hanmer and Ozan Kalkan Reference Hanmer2013). The rug plot along the x-axis displays the distribution of the data. The color of the tick marks represents the density of data at each level of constraint. Light gray marks represent one observation, and they darken as more data are concentrated at that level of constraint.

Figure 2 displays the expected number of cases the Court will hear in a given term across different levels of constraint under the chamber median model. When the justices are free to act without fear of Congress’s and the president’s response to their rulings, the Court is expected to hear 134 cases, yet when the Court is acting in a highly constrained environment (i.e., constraint equals 0.46, or the 95th percentile), its workload decreases to 122 cases, a relative change of approximately 9%. This means the low odds of seeking justice or policy change at the Supreme Court are further diminished because of extrainstitutional influence.

SOP influence on the Court’s agenda is even more stark when examined over time, which is shown in figure 3. In the left panel of figure 3, the gray bars depict the predicted number of cases each term, given the observed values in the data. The black bars represent the additional cases the justices would have granted review to had their institution not been influenced by the other branches of government. Across the 65 terms, the Court was constrained for 29 of them, which ultimately resulted in the avoidance of 182 cases. That number is more than double the number of cases the Court has heard in any term dating back to the first few years of the 2000s and still more than what the Court averaged at its most productive period. In other words, my results suggest that fear of retribution via Court-curbing legislation or being overturned by Congress and the president leads to the avoidance of one to two terms’ worth of cases left unheard by the Supreme Court.

Figure 3.

Values from the chamber median model. The gray bars in the left panel display the predicted values, given the data. The black bars stacked on top of the gray represent the additional number of cases to which the justices would have granted review if they were not constrained by the other branches. Conversely, the middle and right panels depict the number of cases the Court would have heard had they been constrained at the average level of constraint and maximum level of constraint, respectively. The black bars display the predicted values, given the data; the light gray bars represent the considerably fewer cases the justices would have heard in the same term, given they were under the average or highest level of constraint from the other branches of government.

Another way to consider how extrainstitutional constraint affects the Court is shown in the middle and right panels of figure 3. The black bars show the predicted number of cases the Court is expected to hear each term, given the data. In the middle panel, however, the light gray bars that overlay the black bars represent the number of cases the Court would have heard under the average level of constraint each term. This amounts to 149 fewer cases heard and decided by the Court, which is more than a term’s worth of cases. Compare this to the right panel, which shows the same information but with constraint set to the maximum level. Had this been the Court’s reality, it would have heard an average of 12 fewer cases each term. Across 65 terms, this amounts to a staggering 779 fewer cases heard and decided by the Court. To put this in perspective, that is more cases than the sum of the Court’s workload in its nine most recent terms.

Discussion and Conclusion

This article began with the question of whether Supreme Court justices strategically alter their behavior based on the preferences of members of Congress and the president, and specifically whether their strategic response can shed light the Court’s shrinking docket. Many have expressed concern for the noticeably diminished docket, including journalists, legal scholars, judges, and political scientists (Barnes Reference Barnes2007; Owens and Simon Reference Owens2012; Harvey Reference Harvey2013; Moffett et al. Reference Moffett2016), all offering a number of explanations as to what has caused this decline. In this article, I provide strong evidence that a hostile political environment significantly reduces the size of the Court’s caseload and has contributed to their shrinking docket over time.

These findings are significant because they suggest that the elected branches of government influence the workload of the Supreme Court, which affects access to justice for those seeking review from the highest court in the land. These findings add to the growing evidence that the constitutionally constructed barriers intended to isolate the Supreme Court from the other branches and ensure judicial independence are perhaps not as solid and impenetrable as the Framers intended. Of course, internal changes on the Court like the retirement of Justice White and the expansion of the Court’s discretionary docket are also responsible for the significant drop in the Court’s workload. These changes caused a structural shift in the number cases the Court hears each term, whereas SOP dynamics account for term-by-term fluctuations. When the Court is unconstrained, the justices operate under normal conditions—business as usual—whereas when they are constrained, they proceed with caution. That is to say, current trends seem to suggest that the Court will not return to a workload of 150–200 cases per term without another major intervention, but an unconstrained Court can, and does, make more important policy decisions each term and provides more opportunities to litigants to get their day in the highest court in the land.

Footnotes

Thank you to the helpful reviewers and the editor and the Journal of Law and Courts for their thoughtful feedback and direction. Thank you to Michael Bailey for making his ideology data publicly available, to Sarah Binder for sharing her updated gridlock data, and to Ken Moffett and his coauthors—Forrest Maltzman, Karen Miranda, and Charles Shipan—for sharing data from their 2016 publication. Finally, thank you to Ryan Black, Ezra Brooks, Michael Colaresi, Eric Gonzalez Juenke, Jonathan King, Ian Ostrander, Jessica Schoenherr, Corwin Smidt, and Joe Ura for their thoughtful comments and help on several iterations of this project. Data and supporting materials necessary to reproduce the numerical results in the article are available in the JLC Dataverse at https://doi.org/10.7910/DVN/NGPLNC.

1. Lauderdale and Clark (Reference Lauderdale2012) suggest that it is important to consider issue area, but these issues are assigned after each case is decided, and there is good evidence to suggest issue codings suffer from confirmation bias (Harvey and Woodruff Reference Harvey2013).

2. This includes the veto-filibuster model, the committee gatekeeping model, and the party gatekeeping model. I chose the chamber median model because of its simplicity and because it is one of the best-fitting theories of constraint across the different time-series models. See the appendix for further discussion of these models and table A3 for full results.

3. Bailey (Reference Bailey2007) provides another possible measurement option.

4. I acknowledge that because the unit of analysis is at the term level as opposed to the traditional case level, there are some data limitations that prevent me from including the normal spectrum of controls that are typically included in agenda-setting research such as the status quo and certworthiness.

5. I obtained these data from the Federal Judicial Center.

6. Additional discussion of the modeling choices can be found in the appendix.

7. The results of these tests can be found in table A2.

References

Bailey, Michael A. 2007. “Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency.” American Journal of Political Science 51 (3): 433–48.CrossRefGoogle Scholar
Bailey, Michael A., Brian Kamoie, and Forrest Maltzman. 2005. “Signals from the Tenth Justice: The Political Role of the Solicitor General in Supreme Court Decision Making.” American Journal of Political Science 49 (1): 72–85.CrossRefGoogle Scholar
Bailey, Michael A., and Forrest Maltzman. 2011. The Constrained Court: Law, Politics, and the Decisions Justices Make. Princeton, NJ: Princeton University Press.Google Scholar
Barnes, Robert. 2007. “Roberts Supports Court’s Shrinking Docket.” Washington Post, February 2. https://www.washingtonpost.com/wp-dyn/content/article/2007/02/01/AR2007020102213.html.Google Scholar
Beim, Deborah, and Kelly Rader. 2019. “Legal Uniformity in American Courts.” Journal of Empirical Legal Studies 16 (3): 448–78.CrossRefGoogle Scholar
Black, Ryan C., and Ryan J. Owens. 2009. “Agenda Setting in the Supreme Court: The Collision of Policy and Jurisprudence.” Journal of Politics 71 (3): 1062–75.CrossRefGoogle Scholar
Black, Ryan C. 2011. “Solicitor General Influence and Agenda Setting on the U.S. Supreme Court.” Political Research Quarterly 64 (4): 765–78.CrossRefGoogle Scholar
Black, Ryan C. 2012. The Solicitor General and the United States Supreme Court: Executive Branch Influence and Judicial Decisions. New York: Cambridge University Press.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., John R. Freeman, Matthew P. Hitt, and Jon C. W. Pevehouse. 2014. Time Series Analysis for the Social Sciences: Analytical Methods for Social Research. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Carrubba, Clifford J., and Christopher Zorn. 2010. “Executive Discretion, Judicial Decision Making, and Separation of Powers in the United States.” Journal of Politics 72 (3): 812–24.CrossRefGoogle Scholar
Clark, Tom S. 2009. “The Separation of Powers, Court Curbing, and Judicial Legitimacy.” American Journal of Political Science 53 (4): 971–89.CrossRefGoogle Scholar
Edelman, Paul H., David E. Klein, and Stefanie A. Lindquist. 2008. “Measuring Deviations from Expected Voting Patterns on Collegial Courts.” Journal of Empirical Legal Studies 5 (4): 819–52.CrossRefGoogle Scholar
Epstein, Lee, and Jack Knight. 2000. “Toward a Strategic Revolution in Judicial Politics: A Look Back, a Look Ahead.” Political Research Quarterly 53 (3): 625–61.CrossRefGoogle Scholar
Epstein, Lee, Jack Knight, and Andrew D. Martin. 2001. “The Supreme Court as a Strategic National Policymaker.” Emory Law Journal 50:583–612.Google Scholar
Epstein, Lee, Andrew D. Martin, Jeffrey A. Segal, and Chad Westerland. 2007. “The Judicial Common Space.” Journal of Law, Economics, and Organization 23 (2): 303–25.CrossRefGoogle Scholar
Eskridge, William N., Jr. 1991. “Overriding Supreme Court Statutory Interpretation Decisions.” Yale Law Journal 101 (2): 331–455.CrossRefGoogle Scholar
Hall, Matthew E. K. 2014. “The Semiconstrained Court: Public Opinion, the Separation of Powers, and the U.S. Supreme Court’s Fear of Nonimplementation.” American Journal of Political Science 58 (2): 352–66.CrossRefGoogle Scholar
Hall, Matthew E. K., and Joseph Daniel Ura. 2015. “Judicial Majoritarianism.” Journal of Politics 77 (3): 818–32.CrossRefGoogle Scholar
Hanmer, Michael J., and Karem Ozan Kalkan. 2013. “Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models.” American Journal of Political Science 57 (1): 263–77.CrossRefGoogle Scholar
Harvey, Anna. 2013. A Mere Machine: The Supreme Court, Congress, and American Democracy. New Haven, CT: Yale University Press.CrossRefGoogle Scholar
Harvey, Anna, and Barry Friedman. 2006. “Pulling Punches: Congressional Constraints on the Supreme Court’s Constitutional Rulings, 1987–2000.” Legislative Studies Quarterly 31 (4): 533–62.CrossRefGoogle Scholar
Harvey, Anna. 2009. “Ducking Trouble: Congressionally Induced Selection Bias in the Supreme Court’s Agenda.” Journal of Politics 71 (2): 574–92.CrossRefGoogle Scholar
Harvey, Anna, and Michael J. Woodruff. 2013. “Confirmation Bias in the United States Supreme Court Judicial Database.” Journal of Law, Economics, and Organization 29 (2): 414–60.CrossRefGoogle Scholar
Johnson, Timothy R. 2003. “The Supreme Court, the Solicitor General, and the Separation of Powers.” American Politics Research 31 (4): 426–51.Google Scholar
Lauderdale, Benjamin E., and Tom S. Clark. 2012. “The Supreme Court’s Many Median Justices.” American Political Science Review 106 (4): 847–66.CrossRefGoogle Scholar
Lewis, Jeffrey B., Keith Poole, Howard Rosenthal, Adam Boche, Aaron Rudkin, and Luke Sonnet. 2019. “Voteview.” Congressional Roll-Call Votes Database. https://voteview.com/.Google Scholar
Mark, Alyx, and Michael A. Zilis. 2018a. “Blurring Institutional Boundaries: Judges’ Perceptions of Threats to Judicial Independence.” Journal of Law and Courts 6 (2): 333–53.CrossRefGoogle Scholar
Mark, Alyx. 2018b. “Restraining the Court: Assessing Accounts of Congressional Attempts to Limit Supreme Court Authority.” Legislative Studies Quarterly 43 (1): 141–69.CrossRefGoogle Scholar
Mark, Alyx. 2019. “The Conditional Effectiveness of Legislative Threats: How Court Curbing Alters the Behavior of (Some) Supreme Court Justices.” Political Research Quarterly 72 (3): 570–83.CrossRefGoogle Scholar
McGuire, Kevin T. 1998. “Explaining Executive Success in the U.S. Supreme Court.” Political Research Quarterly 51 (2): 505–26.Google Scholar
Meernik, James, and Joseph Ignagni. 1997. “Judicial Review and Coordinate Construction of the Constitution.” American Journal of Political Science 41 (2): 447–67.CrossRefGoogle Scholar
Moffett, Kenneth W., Forrest Maltzman, Karen Miranda, and Charles R. Shipan. 2016. “Strategic Behavior and Variation in the Supreme Court’s Caseload over Time.” Justice System Journal 37 (1): 20–38.CrossRefGoogle Scholar
Owens, Ryan J. 2010. “The Separation of Powers and Supreme Court Agenda Setting.” American Journal of Political Science 54 (2): 412–27.Google Scholar
Owens, Ryan J., and David A. Simon. 2012. “Explaining the Supreme Court’s Shrinking Docket.” William and Mary Law Review 53 (4): 1219–85.Google Scholar
Romer, Thomas, and Howard Rosenthal. 1978. “Political Resource Allocation, Controlled Agendas, and the Status Quo.” Public Choice 33 (4): 27–43.CrossRefGoogle Scholar
Segal, Jeffrey A., Chad Westerland, and Stefanie A. Lindquist. 2011. “Congress, the Supreme Court, and Judicial Review: Testing a Constitutional Separation of Powers Model.” American Journal of Political Science 55 (1): 89–104.CrossRefGoogle Scholar
Thompson, David C., and Melanie Wachtell. 2009. “An Empirical Analysis of Supreme Court Certiorari Petition Procedures: The Call for Response and the Call for the Views of the Solicitor General.” George Mason Law Review 16 (2): 237–302.Google Scholar
Figure 0

Figure 1.Estimates of common space locations from the 2009 and 2012 October terms (OT) of the US Supreme Court. The estimates come from Lewis et al. (2019) and Epstein et al. (2007). C = Court median, S = Senate median, H = House median, and P = president. The value below each pivot is its ideal point in common space. Smaller (more negative) values represent more liberal preferences; larger (positive) values represent conservative preferences.

Figure 1

Figure 2.Predicted number of cases the Court should hear in a given term across the 95th percentile range of constraint under the chamber median model. Cross bars, 95% confidence intervals estimated using the observed value approach (Hanmer and Ozan Kalkan 2013). The rug plot along the x-axis displays the distribution of the data. The color of the tick marks represents the density of data at each level of constraint. Light gray marks represent one observation, and they darken as more data are concentrated at that level of constraint.

Figure 2

Figure 3.Values from the chamber median model. The gray bars in the left panel display the predicted values, given the data. The black bars stacked on top of the gray represent the additional number of cases to which the justices would have granted review if they were not constrained by the other branches. Conversely, the middle and right panels depict the number of cases the Court would have heard had they been constrained at the average level of constraint and maximum level of constraint, respectively. The black bars display the predicted values, given the data; the light gray bars represent the considerably fewer cases the justices would have heard in the same term, given they were under the average or highest level of constraint from the other branches of government.

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