THE EVOLVING PROCESS: NOMINATIONS, CONFIRMATIONS, AND PUBLIC OPINION By Jonathan Martin King A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Political Science—Doctor of Philosophy 2023 ABSTRACT The federal courts are increasingly important actors in the development of U.S. policy. Judicial decisions feature heavily in media coverage with even district court judges becoming household names as their rulings impact the entire nation. The increasingly contentious and politicized process by which judges gain their lifetime appointments, and how the public views case outcomes once on the bench, are important in understanding the federal judicial system. Yet, scholars understand little of lower court confirmation dynamics or how strategic opinion assignment alters public mood in salient cases. To remedy these issues, I use a combination of novel data and survey experiments to gain further insight into the evolving dynamics and politicization of the judiciary. Using data covering 1) all senator votes during judicial confirmations from 1981-2022, 2) all confirmation outcomes from 1981-2022, and 3) two survey experiments, I study the changing dynamics of federal judicial confirmations and public attitudes towards the courts. I demonstrate that the current confirmation environment is significantly more politicized for recent nominees compared to past administrations. Further, I provide an in-depth examination of the confirmation successes of the Trump presidency. Finally, I show that attempts to strategically select an opinion writer does not overcome ideological preferences in determining public support for salient cases but may mitigate negative support. For my daughter, Madison iii ACKNOWLEDGEMENTS By their nature, dissertations are supposed to be an undertaking by one individual, on your own. But, as anyone that has written a dissertation knows, this is not at all the case. The old adage, "it takes a village" definitely applies to finishing a dissertation. I have so many people to thank, and it is great that I am afforded this opportunity to thank people – in writing – for all their help. People who will now be immortalized as this dissertation is submitted in ProQuest (or at least immortalized for as long as ProQuest exists). My first set of thank yous, and the most important ones, go to the two most important people in my life: my wife, Amy, and my daughter, Madison. Amy has been my biggest supporter, my rock, and my best friend through this entire process. She has seen the highest of highs and the lowest of lows. I truly could not have done this without her and I will spend the rest of my life thanking her for everything she has done. And Madison: you are my inspiration for everything I do. You have made me realize how much more there is in life than just academia. That, when life was rough, I had your smile, your milestones, and your successes to make everything better. You are the best thing to ever happen to me and I’m so lucky I get to be your dad. Next, a big thank you to my family: Mom, Dad, Chad, Alex, Hannah. I am who I am today because of you. And, I got to where I am from the lessons I learned from all of you. From playing basketball on the slanted driveway to growing families of our own, it has been a hell of a journey. I cannot wait to see what the future holds for all of us. But also remembering what we have lost along the way. In many ways, this dissertation is in the memories of mom and Alex. While they are not here to share in this moment, I like to think they would be proud and know how much they motivated me the entire way. I also have to thank my friends and several other family members. Rick, Terry, Stephanie, and my amazing nieces and nephews, thank you so much for the continued support at every twist and turn. To my amazing cousins (Andy, Melanie, Scott, Erin, Karlie, Stefan, Becca, Jessie, Miles, Joe, Devon, Emma), I’m so glad we have grown closer over all these years and will always be there for one another. To my "Clueless" friends, thank you for pushing me to be better each season. Now that iv I am out of graduate school, I’ll have more time to get that second championship so watch out. In particular, I have to thank my best friend, Steve, for being the brother I chose and continue to grow with. And to my many graduate school friends that helped me along the way – Kesicia Dickinson, Jon Spiegler, Chrissy Scheller, Caleb Lucas, Christine Bird, Marcy Shieh, Kelsey Osborne-Garth, and Bailey Gardner – I cannot thank you enough. Finally, I have to thank The Peanut Barrel, for providing me with so much inspiration. Just, thank you all so much. There is a group of friends that I need to thank in more detail for helping me survive graduate school. Shane Wery, Lizzie Brannon, and Nico Bichay, you are the originals and the lifelong friends I’m so happy to have. Best friends are hard to come by, and I’m lucky I have you lot. To say I could not have made it through graduate school without you lot is an understatement. My fellow MSU judicial crew deserves special recognition. Miles Armaly, I am incredibly happy with how close we have gotten over the past few years and look forward to years to come of conference fun. Matt Cota, it has been such a pleasure to watch you grow this first year. You have made the last year of my graduate school career palatable, even trending towards enjoyable. It is going to be fun watching you grow as a scholar and working with you. Elizabeth Lane, the newest convert to Chelsea Football Club (sorry they are horrible this year). You are one of my best friends and I cannot understate how foundational you have been in making me the scholar I am today. And, finally, my academic big sister, Jessica Schoenherr. This is the one time I have to be nice to you. You know how much you mean to me and I don’t possibly have the space to adequately write it down here. Just know how much I appreciate you and what you mean to me. The Department of Political Science at Michigan State has provided me endless support over the past six years. And, as anyone who works in academia knows, the academic staff are really what keeps a department running. Rhonda Burns, Sarah Krause, Kelly Sweet, Karen Battin, Brian Egan, Heather Wilson, and Krista Zeig – thank you for everything you have done for me. From answering question upon question to the great conversations over the years, I truly could not have done it without you. Additionally, I need to thank Ani Sarkissian and Sarah Reckhow for their (often thankless) work as graduate program coordinators. The program is better because of you v two. Finally, a thank you to the numerous faculty members who have been along for the ride and helped me at every step with their advice and words of encouragement, with a special shout-out to Marty Jordan, Shahryar Minhas, and especially Mariana Medina. As I wrap up my thanks, I need to thank my committee – the four people who have shaped this dissertation (and my graduate career) more than any others. Matt Grossmann has been one of the most supportive individuals I could imagine and constantly gave words of wisdom, constructive advice, and did his all to get me a job. How you manage to do all you do, I am in awe. Thank you to Cory Smidt for being the person I could always expect to push back on my projects and make them better for it. Also, of course, for giving us all the list of best places to eat in Lansing during PLS 800. And for just always having your door open and being available to talk. I’m a better scholar because of the both of you. I want to make a special point to thank Ian Ostrander. Who would have thought that from being assigned to you as an RA my first year we would develop an entire research line! From being a first year in your office to developing a true friendship, I cannot thank you enough for all your help along the way. I am going to miss being able to come up to your office for a few hours and getting lost in conversation when I’m in West Virginia next year. But I am excited about the years to come of working with you! Finally, to my advisor Ryan Black, thank you. No person has shaped me as a scholar as much as you. From my teaching style to parts of my writing style (as Jessica loves to point out), you have fundamentally improved every aspect of my academic life. A world-class researcher, amazing teacher, and fantastic person, you are the type of academic I strive to one day become. Anyhoo, a simple thank you in a dissertation is nowhere near enough to full encapsulate how much I appreciate all you have done, but hopefully serves as a decent start. vi TABLE OF CONTENTS CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 CHAPTER 2: THE RISE OF IDEOLOGY AND THE FALL OF BIPARTISANSHIP IN LOWER COURT CONFIRMATIONS . . . . . . . . . . . . . . . . . 5 CHAPTER 3: PRESIDENT TRUMP AND JUDICIAL APPOINTMENTS . . . . . . . 24 CHAPTER 4: HOW UNEXPECTED OPINION AUTHORS INFLUENCE SUPPORT FOR SUPREME COURT DECISIONS . . . . . . . . . . . . . . . . . . 50 CHAPTER 5: CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 APPENDIX A: CHAPTER 2 APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . 90 APPENDIX B: CHAPTER 3 APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . 94 APPENDIX C: CHAPTER 4 APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . 96 vii CHAPTER 1: INTRODUCTION The federal judiciary has experienced significant changes in the relatively recent past. From the 2013 "nuclear option" dramatically altering the nominations and confirmation process to the overturning of Roe v. Wade and Planned Parenthood v. Casey in 2022, the judicial process has faced several shocks to its system. The politicization of the courts have seeped into almost every part of the judicial process. In turn, the judiciary – consistently viewed more favorably than the elected branches of government (Gibson and Caldeira 1992) – has seen its support reach historic lows (Jones 2022). Across the next 100 pages, I examine the impacts of the politicization of the judiciary across two distinct areas of judicial politics: lower court confirmations and public opinion of court decisions. In a system where the judiciary is often seen as an apolitical actor (Gibson and Caldeira 2009b,a; Bartels, Johnston and Mark 2015), how does increasing politicization impact previously bipartisan processes such as lower court confirmations? And, once confirmed, are there strategies that judges can use to shield themselves from partisan attacks of their decisions? Using data on senator voting behavior for lower court nominees, judicial confirmation outcomes, and a survey experiment of public support for case outcomes, I investigate how the breakdown of norms and politicization of the judicial process influences confirmation success and the public’s opinion of the judiciary. My goal over the next three chapters is to provide insight into the changing and evolving landscape of the judicial process. Specifically, I address three different questions: 1) how have institutional norms changed in the lower court confirmation process? 2) What can we learn about this evolving process by thoroughly examining nominations from a recent presidency? 3) What impact do strategies – such as strategic selection of opinion authors – have on public opinion of salient cases? I show that the confirmation process for all federal judges is now wholly a partisan endeavor, with ideology determining why senators vote for nominees – a dramatic departure from previous norms. And, that President Trump – the first to inherit this new system – achieved marked success in placing his nominees to the federal judiciary, though not as much as intuition would lead 1 to believe. Finally, I examine potential ramifications of this politicized process and explore public opinion of case outcomes written by "incongruent" justices and demonstrate that ideology – not the opinion author – drives public support for a decision. I conclude with a brief review and final thoughts on the project. 1.1 The Politicization of Lower Court Nominations While attention is often focused on nominations to the Supreme Court (Johnson and Roberts 2005; Moraski and Shipan 1999), lower court judges are incredibly influential actors in the federal judiciary. With the lower courts the final say in the majority of legal decisions (Bowie, Songer and Szmer 2014; Steigerwalt 2010), nominations to the district and circuit courts provide opportunities to significantly impact policy for decades (Goldman 1997). But, as their significance in U.S. law, politics, and policy has increased, the confirmation process has become a source of partisan conflict and scrutiny (Binder, Madonna and Smith 2007; Hartley and Holmes 2002). Past scholarship of senator voting behavior has solely focused on the confirmation of Supreme Court justices (Epstein et al. 2006; Kastellec, Lax and Phillips 2010) but has ignored voting for lower court judges. These studies have demonstrated the importance of ideology in how senators vote for Supreme Court nominees (Kastellec et al. 2015) while literature on lower court confirmations has assumed a largely bipartisan process (Binder and Maltzman 2004). With recent breakdowns in norms of the confirmation process (Smith 2007; Binder, Madonna and Smith 2007; Boyd, Lynch and Madonna 2015), past assumptions of this process driven by bipartisanship need to be re-evaluated. In Chapter 2, I answer this call with an examination of senator voting behavior on lower court nominations. To do so, I collect data on 26,763 opposing senators’ votes on district and circuit nominations from 2001-2022. I find that the once overwhelmingly bipartisan process of confirming lower court judges is no more. Instead, lower court confirmations are occurring mainly on party lines, with ideology being the main driver of why a senator votes for a nominee. Further, past indicators of a confirmation success – such as a nominee’s qualifications – no longer matter in voting for confirmation. Overall, I demonstrate that a process once dominated by bipartisan agreement 2 has broken down into an significantly partisan endeavor. With the establishment of a breakdown of norms in the confirmation process in Chapter 2, Chapter 3 provides a deep-dive into this process for the first president to inherit this new environ- ment: Donald Trump. Collecting data on all of President Trump’s 369 lower court nominations from 2017-2020, this chapter shows that President Trump achieved great success in reshaping the federal judiciary. While he was less successful than intuition would lead to believe, this is largely because of his prioritization of nominees based on the judicial hierarchy. Overall, Chapter 3 argues that the new confirmation environment allows presidents significant opportunity to transform the federal judiciary and influence law and policy for decades to come. 1.2 Strategic Opinion Writers and Public Support Having neither the power of the purse nor the sword, the courts rely on a "reservoir of good will" in order to enforce their decisions (Hamilton 2003; Gibson and Caldeira 1992). And, while public opinion is not supposed to impact judicial behavior (Rosenberg 2008), the courts frequently alter their behavior to account for public opinion in order to make implementation easier and not deplete the reservoir (Bartels and Johnston 2013; Gibson and Caldeira 2011). In doing so, judges strategically use a myriad of strategies in an attempt to counter potential attacks to their legitimacy. Chapter 4 examines one potential mechanism for shielding the court from negative public opinion: strategic opinion assignment of "incongruent" justices. Using two survey experiments on death penalty and abortion decisions, this chapter demonstrates that strategic opinion assignment does not increase public support for a decision. However, strategically assigning opinion writers can mitigate negative partisan feelings towards a politically salient decision. Taken together, this chapter provides evidence that, while the courts may use several strategies to combat negative perceptions, these strategies may not be as successful as believed. After three chapters of analysis, I conclude that many past assumptions of judicial politics must be updated. Over the past two decades, the confirmation environment has been significantly politicized with ideology now driving why senators vote for a nominee. The Trump administration exemplifies this trend with nominees making national headlines and receiving minimal Democratic 3 senator support. And this politicization has potential ramifications for support of judicial outcomes, with strategies such as strategic opinion assignment having no impact on the public’s opinion of cases. Instead, only partisanship and ideology drives much of the judicial process. Overall, I provide evidence of an evolving judicial system impacted by significant changes to institutional rules and norms. 4 CHAPTER 2: THE RISE OF IDEOLOGY AND THE FALL OF BIPARTISANSHIP IN LOWER COURT CONFIRMATIONS When Samuel Alito was confirmed to the U.S. Supreme Court in 2005, he received only four "yea" votes from Democratic senators, a marked decrease in bipartisanship compared to previous Supreme Court confirmations. However, just a few months later, Neil Gorsuch – a future Supreme Court justice – was confirmed without any opposition to his seat on the 10th Circuit Court of Appeals. This contrast – controversy for Supreme Court justices but consensus for lower court nominees – was the norm for judicial nominations for decades. Fast forward to 2017 and Supreme Court confirmation voting continues to largely fall along partisan lines as Brett Kavanaugh receives only one Democratic senator’s "yea" vote.1 Just a few weeks later, however, we see a dramatic shift in how senators vote for lower court judges as Amy Coney Barrett is confirmed to the 7th Circuit Court of Appeals by a vote of 54-42. Unlike the previous consensus for lower court confirmations, Coney Barrett receives support from just three Democratic senators. For decades, voting for Supreme Court justices was an almost entirely partisan endeavor while confirmations to the lower federal courts were a bastion of bipartisanship. Even controversial nominees to the lower federal courts were routinely confirmed, often without noted opposition (Goldman 1997). Comparatively, recent nominees routinely face significant partisan opposition going so far as requiring the vice president to cast tiebreaking votes (Raymond 2023; Carney 2018). Clearly, the state of play for lower court nominations has changed and now face a process similar to that of the Supreme Court. Research has demonstrated the importance of ideology in voting for Supreme Court nominees (Epstein et al. 2006; Segal, Cameron and Cover 1992), but the influence of ideology had not previously permeated to lower court confirmations (Binder and Maltzman 2002). Has this changed and is ideology now the determining factor in senator voting for lower court nominees? In this paper, I seek to better understand senator voting for lower court judicial nominations. 1 Joe Manchin, (D-WV). 5 Particularly, I am curious about the changing role ideology plays in the confirmation process. To do so, I use original data of all roll-call votes for lower court nominees from the beginning of the W. Bush administration through the first two years of the Biden administration (2001-2023). My results show that ideology, once unimportant, is now the driving factor in how senators vote in lower court confirmations. Further, attributes that once aided garnering bipartisan votes, such as qualifications, are no longer influential. These findings suggest that the primacy of ideology in judicial nominations extend past just Supreme Court confirmations and now impact all federal judicial nominations. This paper significantly contributes to the literature on federal judicial nominations in two significant ways. It provides the first examination of senator voting behavior on lower court nominations. Previous examinations of senator voting for judicial nominees has been limited to Supreme Court justices (Epstein et al. 2006; Kastellec, Lax and Phillips 2010; Kastellec et al. 2015) with studies of the lower courts being limited to aspects such as confirmation delay (Binder and Maltzman 2002; Scherer, Bartels and Steigerwalt 2008; Bond, Fleisher and Krutz 2009). Identifying how and why senators vote for lower court nominations is significant in our understanding of staffing the federal judiciary and mine is the first to do this. Second, it demonstrates that ideology is now the determining factor in how senators vote in lower court judicial nominations. While this has been evident for decades at the Supreme Court level, lower courts were shaped more by institutional constraints than ideology (Binder and Maltzman 2002, 2009). My findings are the first to empirically demonstrate the shifting considerations of senatorial voting behavior for lower court nominees and the primacy of ideology in lower court confirmation politics. 2.1 Declining Bipartisanship in Lower Court Confirmations The lower federal courts are increasingly influential actors in U.S. law and policy. From the growing prevalence of nationwide injunctions by single district judges (Ernst 2019) to circuit courts being the de facto courts of last resort for the majority of cases (Bowie, Songer and Szmer 2014), lower court judges influence almost every aspect of U.S. politics. With a "dysfunctional" Congress 6 featuring a decline in legislation (Binder 2015; Willis and Kane 2018) and Supreme Court that decides fewer cases each year (Owens and Simon 2012; Lane 2022), lower court judges – and the decisions they make – are increasingly consequential. Presidents have used the courts as a potential mechanism for policy gains for decades. Indeed, presidents have used lower court nominations as a way to create a "judicial legacy" with judges that will promote policy initiatives long after a president would leave office (Goldman 1997). However, presidents must weigh nominating judges that will promote their policies with who they can get confirmed by the Senate (Carter 1994). Consequently, the Senate has increasingly scrutinized lower court nominees because of their importance in law and policy (Hartley and Holmes 2002). Unlike the Supreme Court, lower court nominations have been driven by the norm of senatorial courtesy. Senatorial courtesy, institutionalized by the "blue slip" process, grants tremendous powers to individual senators in the confirmation of lower court judges (Binder 2007). While not limitless, senatorial courtesy provides senators with significant deference on nominees to their state (Binder and Maltzman 2004). This norm incentivized presidents to consult with senators – even opposition party senators – in order to confirm their nominees. Because of these norms, partisan obstruction was limited for lower court nominations. Often, the main mechanism of obstruction was the strategic delay of nominations (Binder and Maltzman 2002, 2009; Hendershot 2010; Martinek, Kemper and Winkle 2002). This, in turn, could lead to nominations failing due to "malign neglect" (Bond, Fleisher and Krutz 2009). But, for the most part, if home state senators signed off on a nominee – or at least did not issue a formal objection – they would be confirmed (Binder and Maltzman 2002; Binder 2007). While these norms have dictated the lower court confirmation process for centuries, they have gradually broken down over the past several decades. Beginning in the 1980s, the Reagan administration shifted the selection mechanism of lower court judges from a senator-driven process to a more centralized and politicized process (Goldman 1997). Senators still had significant say in the confirmation process (Binder and Maltzman 2004), but they had significantly less say in the selection process (Goldman 1997). Confirmations were still a relatively bipartisan and cooperative 7 process2, but the initial cracks in the process appeared. For the next several decades, these norms held strong even with the nomination and confirmation of more ideological judges (Goldman 1997). The next major attack on confirmation norms took place during the presidency of George W. Bush. Facing consistent partisan delay of his lower court nominees, W. Bush called for the Senate to "go nuclear" and change its confirmation procedure in order to confirm his nominees (Binder, Madonna and Smith 2007). Only through a compromise ending the obstruction of lower court nominations by a group of bipartisan senators, known as the "Gang of 14", was the confirmation process left unaltered (Scherer 2005). Although the Senate ultimately did not "go nuclear" in the early 2000s, the precedent was set for procedural reform to occur in the face of partisan obstruction. Akin to the gradual movement of tectonic plates eventually resulting in an earthquake, the steady attacks against confirmation norms eventually led to a breaking point. In 2013, following a patterns of minority obstruction for judicial nominees, the Senate "went nuclear" on all lower court nominations (Smith 2007; Boyd, Lynch and Madonna 2015). Through a series of procedural mechanisms, the cloture threshold – the required number of votes needed to end a filibuster – was reduced from 60 to a simple majority. In effect, this maneuver ended the need for any bipartisan cooperation. So long as a party controlled both the Senate and the presidency, judicial nominations would be confirmed and the minority party would not be able to block them. In doing so, the majority party could "stamp out the voices of the minority" and put forward nominees without any need to consult opposing party senators (Everett and Kim 2013). Put another way, past requirements for bipartisanship were no longer necessary because of institutional reforms. This breakdown in norms is a story that we have seen before play out at the Supreme Court level. Following the failed confirmation of Robert Bork, the process politicized and simply being nominated no longer guaranteed confirmation (Guliuzza, Reagan and Barrett 1994; Epstein et al. 2006). Moreover, senator behavior shifted. Confirmation hearings became an opportunity for 2 For example: now-Justice Sonia Sotomayor originally was nominated to the federal judiciary by President George H.W. Bush. This nomination was not because she was considered conservative – she was not – but instead because of an agreement the New York senators used to have where the minority party senator could choose one out of every four district seats (York 2009). 8 senators to advance their political agendas (Farganis and Wedeking 2014; Schoenherr, Lane and Armaly 2020). The confirmation process was no longer about assessing the justices but instead a "vapid and hollow charade" with senators incentivized to vote against nominees put forward by opposition party presidents (Kagan 1995; Cottrill and Peretti 2013). Senator voting for Supreme Court nominees became driven by partisanship and ideology. Voting against a nominee was strategic as blocking a nomination could lead to a policy victory for the party (Smith 2007; Lee 2009). Nominations that a senator’s constituent supported still received "nay" votes as partisanship trumped representation (Kastellec et al. 2015). Even when voting against their constituents’ demands was electorally damaging, senators consistently voted along party lines for Supreme Court nominations (Bass, Cameron and Kastellac 2022). Essentially, modern Supreme Court nominees are nominated with the assumption they will garner little bipartisan support.3 We have seen a breakdown in confirmation norms for the entire federal judicial. Because of its importance in crafting law and policy, the Supreme Court confirmation process devolved into partisan politics while the lower courts maintained a bipartisan process. However, with the increased prominence of the lower courts in policy, the stakes have changed. Nominations to the federal judiciary are now a significant electoral issues (Badas and Simas 2021) and judicial nominations are a focal point of presidential campaigns (King, McAndrews and Ostrander 2022; Schonfeld 2023; Johnson 2023). With the changes in institutional norms combined with the increased importance of lower court judges, we need to reevaluate our past empirical expectations of lower court nominations being a largely bipartisan process and examine if they now reflect the partisanship previously only seen at the Supreme Court. 2.2 Empirical Expectations Drawing on insights from the Supreme Court nominations process, I argue that many of these mechanisms now also drive voting behavior in the lower courts confirmation process. That is, that the previously non-contentious – and often unanimous – confirmation of lower court judges now features the partisan vitriol previously only experienced by Supreme Court nominees. 3 This is exemplified by the confirmation of Justice Amy Coney Barrett in 2020 who, for the first time in history, was confirmed with only Republican senator support. 9 The first of these arguments is simple: the confirmation environment has significantly changed compared to past studies of lower court confirmations. Past literature has noted the confirmation process has become more contentious and nominees face more scrutiny (Hartley and Holmes 2002). For example, lower court confirmation hearings – once a formality – often reflect Supreme Court confirmation hearings (Dancey, Nelson and Ringsmuth 2020). With recent lower court nominees facing a confirmation environment similar to the more contentious Supreme Court process, my first hypothesis reflects this changing dynamic in senator voting for lower court nominees: H1: Recent presidents’ nominees are significantly less likely to receive an opposition senator’s vote than previous presidents’ nominees. My next argument focuses on the prevalence of ideology in lower court confirmation voting. The primacy of ideology has been apparent at the Supreme Court for decades (Segal, Cameron and Cover 1992; Epstein et al. 2006; Kastellec, Lax and Phillips 2010). The importance of ideology goes so far as superseding constituent preferences when voting for nominees (Kastellec et al. 2015). As my overall argument is the lower court confirmation environment now mirrors the Supreme Court, my next hypothesis is that the primacy of ideology has now trickled down to the lower courts and is the main driver of senator voting behavior for these nominations. H2: As the ideological distance between a senator and nominee increases for recent nomina- tions, the likelihood of receiving a senator’s vote decreases. Replacements to the federal judiciary do not take place in a vacuum. As we know from the Supreme Court, senators frequently look to the past – to who is being replaced – to explain their vote for a current nominee (Kastellec, Lax and Phillips 2010). With the increased importance of the lower federal judiciary in policymaking (Bowie, Songer and Szmer 2014), it is rationale to assume that senators follow similar behavior for lower court confirmations. This leads me to my third hypothesis: H3: Senators in more recent nominations are less likely to support nominees who are more ideologically distant than the judge they are replacing. Finally, past nominees were able to overcome ideological "problems" by having superior qual- 10 ifications (Epstein et al. 2006). Moreover, research on lower court nominations has consistently shown that qualifications matter with higher quality nominees more likely to be confirmed (Mar- tinek, Kemper and Winkle 2002; Black, Madonna and Owens 2014). However, as ideology is now the main driver of senator voting behavior for lower court nominees, I claim the impact of qualifications has diminished. This leads me to my final hypothesis: H4: The influence of nominee characteristics that previously increased support has decreased in recent presidencies. 2.3 Data and Methods To investigate the diminishing bipartisanship in senator voting on lower court nominations, I collect a novel dataset of roll-call votes for U.S. federal circuit and district court nominations from 2001-2022. This includes all lower court nominations from the administration of George W. Bush through the first two years of the Biden presidency. This timeline coincides with the increase in contentiousness of confirmation hearings (Dancey, Nelson and Ringsmuth 2020) and confirmation proceedings (Hartley and Holmes 2002). I test my hypotheses on nominations with a final, recorded vote on the Senate floor. Specifically, I examine how senators vote for nominees from the opposition party. That is, how Republican senators vote for nominees from Democratic presidents and vice-versa. I limit my analyses to opposition party voting behavior as that is where the variation lies. Copartisans – Democratic senators voting for Democratic president nominees and vice-versa – vote affirmatively for nominees 99% and lead to issues of perfect prediction. Accordingly, I examine the 26,763 roll-call votes cast by opposition party senators on lower court nominations. For each nomination, I collect several variables of interest. As I investigate the influence of ideology on receiving a senator’s vote, I include variables for the ideology of each individual senator as well as of the nominee. To measure a senator’s ideology, I use the first dimension of their DW-NOMINATE score (Lewis et al. 2022). For nominees, I gather their Judicial Common Space (JCS) score (Epstein et al. 2007).4 I then create a variable for the ideological distance between a 4 JCS highly correlates with another measure of ideology – CF Scores (Bonica 2016) – as shown in Figure A.1. 11 senator and a nominee.5 In addition to the ideological distance between a senator and a nominee, I examine the impact a nomination makes to the ideology of the seat they are replacing. That is, how does the shift in ideology from the departing judge to the nominee alter a senator’s voting for that nominee? To measure the effect of ideological shift, I generate a variable that accounts for this shift in ideology relative to the senator.6 Beyond ideology, I am also interested in how nominee characteristics may influence senator voting patterns. To account for a nominee’s qualifications, I include whether a nominee attended an "elite" law school for their legal training.7 Additionally, as I am concerned with trends over time by administration, I create a categorical variable for each presidential administration.8 In addition to the above covariates of interest, I collect several control variables for the nominees. As female nominees are more likely to be confirmed than male nominees Asmussen (2011); Solowiej, Martinek and Brunell (2005), I include the gender of a nominee. Similarly, I include the race or ethnicity of a nominee as found in the Federal Judicial Center’s (FJC) Biographical Database.9 As a nominee’s age influences confirmation outcomes (Goldman 1997; Binder and Maltzman 2004), I account for the nominee’s age at the time of their nomination. And, since elite law school attendance may not fully account for a nominee’s qualifications, I include the nominee’s American Bar Association (ABA) rating.10 However, CF scores do not have information on all judicial nominations and drop a sizeable number of nominees. As such, I use JCS scores for my empirical analyses. 5 Ideological distance is calculated by subtracting the DW-NOMINATE score of the senator from the JCS score of a nominee. I then take the absolute value of the result to generate the distance between a nominee and senator. 6 Ideological shift is calculated by taking the absolute value of the difference between the JCS score of the departing judge from the JCS score of the nominee. The ideological shift is then the absolute value of this difference subtracted from the DW-NOMINATE score of a senator. 7 For my purposes, a nominee is coded as having attended an elite law school if they received legal training from a traditional "Top 14" law school, according to the U.S. News and World Report. This includes the following law schools: Columbia, Cornell, Duke, Georgetown, Harvard, NYU, Northwestern, Stanford, UC-Berkely, Chicago, Michigan, Penn, Virginia, and Yale. 8 In 2013, the Senate enacted the "nuclear option", reducing the threshold for cloture on a nomination from 60 votes to a simple majority. To account for the institutional shifts of the rule change, I separate Obama administration nominations into two categories: pre-nuclear option and post-nuclear option. 9 The FJC’s Biographical database categorizes the race or ethnicity of a nominee into one of five categories: White, Black, LatinX, Asian, or Other. 10 Past research has demonstrated these ratings are biased against minority nominees (Sen 2014b) and frequently rate Republican nominees lower than Democratic nominees (Smelcer, Steigerwalt and Vining 2012). Further, these ratings 12 Further, I control for several senatorial or institutional factors that may influence how a senator votes on a nominee. As senators may need to worry about potential electoral considerations, I control for whether the senator is facing reelection as well as their previous win margin in their most recent election. Further, I account for the president’s popularity and include their Gallup approval rating at the time of the nomination (The American Presidency Project N.d.). Additionally, I consider institutional factors such as senatorial courtesy (Binder and Maltzman 2004; Binder, Madonna and Smith 2007) and code for whether a senator is from the state a nomination resides. Finally, I account for political considerations based on who is in control of the Senate majority and create a continuous variable for the Senate majority size. When the president faces an opposition Senate, commonly known as "divided government", the variable is negative while an allied Senate has a positive value. My dependent variable is the dichotomous "yea" or "nay" for vote a nominee by an individual senator. As my dependent variable is dichotomous, I estimate a logistic regression model with robust standard errors clustered on each nomination in my data. Consistent with the literature, I estimate separate regression models for the circuit and district levels to account for their unique institutional considerations (Primo, Binder and Maltzman 2008; Martinek, Kemper and Winkle 2002). 2.4 Results I begin my analysis of the changing dynamics of senator voting for lower court nominees with a descriptive examination of voting in circuit and district nominations. Figure ?? provides initial evidence of an increasingly contentious confirmation environment for lower court nominees. At both the circuit and district court levels, recent nominees are receiving dramatically fewer "yea" votes from opposition senators than previous nominees. This is perhaps most evident in Figure 2.2 with W. Bush’s district nominees almost unanimously received opposition senator support. Starkly contrasted are Biden’s nominees which gained less than 20% of opposition senator "yea" votes. From even a purely descriptive view, there is clear evidence of shifting voting strategies by senators are poor indicators of future judicial efficacy (Sen 2014a). However, ABA ratings are frequently used by senators in their evaluations of judicial nominations are are subjectively useful to senators in voting for nominees. 13 for lower court nominees. Figure 2.1 Senator Voting in Circuit Nominations Figure 2.2 Senator Voting in District Nominations But, are nominees significantly less likely to receive a senators vote in recent administrations compared to previous ones? Figures 2.3 and 2.4 provide the predicted likelihood of senator yea votes by presidential administration for circuit and district nominations, respectively.11 As is evident from Figures 2.3 and 2.4, recent nominations are significantly less likely to receive an opposition senator’s yea vote at both the circuit and district levels.12 11 To be conservative, I separate Obama nominations in two distinct categories: "pre" and "post" to account for the different institutional environments following the 2013 rules change. 12 Tables A.1 and A.2 provide the full regression tables. 14 Figure 2.3 Opposition Senator Yea Vote for Circuit Nominations by President At the circuit level, Figure 2.3 shows opposition senators consistently had a high likelihood of voting during the W. Bush and Obama administrations, never dropping below a predicted likelihood of 0.7 for a yea vote. However, this significantly decreases during the Trump administration and culminates with another significant decrease in the Biden administration with nominees having a significantly lower likelihood of receiving an opposition senator’s vote than any previous admin- istration. In the current confirmation environment, circuit court nominees are unlikely to receive any opposition party support much like Supreme Court nominees. At the district level, Figure 2.4 reveals the decline is more gradual. Yet, each administration is significantly less likely to receive an opposition senator’s vote than their predecessor. Up through the Trump administration, nominees were more likely than not to gain a senator’s yea vote. However, this significantly decreases for nominees in the Biden administration. From these results, I find support for my first hypothesis that recent nominees are significantly less likely to receive an opposition senator’s vote than nominees under previous presidents.13 13 A skeptic may argue that the decreasing bipartisanship is simply because presidents are now unrestrained and nominating more ideological nominees. Boyd, Lynch and Madonna (2015) find this not to be the case and Figures A.2 and A.3 show recent nominees are not significantly more ideological than past nominees. 15 Figure 2.4 Opposition Senator Yea Vote for District Nominations by President Having established that lower court nominations are receiving less opposition party support in recent times, I turn my attention to what I theorize is driving this decrease: ideology. Figures 2.5 and 2.6 provide the predicted probability of receiving an opposition senator’s vote based on the ideological distance between a senator and a lower court nominee. Smaller values indicate closer ideological proximity while larger values represent increased ideological distance. As my second hypothesis argues the impact of ideology on winning a senator’s vote is a recent phenomenon, Figures 2.5 and 2.6 compares a relatively older president (W. Bush) with the most recent (Biden) for circuit and district nominations, respectively. 16 Figure 2.5 Ideological Distance between Senator and Circuit Nominee As is evident in both figures 2.5 and 2.6, ideology has a significant impact in receiving an opposition senators votes for recent nominees while ideology did not influence senator voting behavior in the past. Figure 2.5 shows that, for W. Bush’s circuit nominees, ideology played no role in the likelihood of receiving an opposition senator’s vote. For Biden’s nominees, however, ideology is a crucial factor. Specifically, as the ideological distance between a senator and nominee increases, the likelihood of receiving that senator’s vote plummets to essentially zero for ideologically distant nominees. I find a similar pattern for district court nominees. As seen in Figure 2.6, W. Bush’s district nominees were virtually guaranteed to gain an opposition senator’s vote. Even the most ideologi- cally distant district nominee had greater than a 0.9 likelihood of a yea vote. For Biden’s nominees, increasing ideological distance significantly decreases the likelihood of a yea vote. Nominees with an ideological distance of 0.5 – the distance between President Biden and the most Republican senator, Susan Collins – have less than a coin flips chance of receiving a yea vote. Taken together, I find support for my second hypothesis. Ideology is driving factor in how opposition senators vote for lower court nominees and this is recent phenomenon. 17 Figure 2.6 Ideological Distance between Senator and District Nominee Nominations do not exist in a void and senators may look to who a nominee is replacing in determining their vote. That is, will the nominee shift the ideological makeup of the court closer to (or further from) an individual senator? Figure 2.7 looks into the impact of ideological shift in senator voting for circuit nominations. Specifically, negative values along the x-axis denote complete "flips" in a seat while positive values represent moving towards a senator’s ideological preferences. For example, Amy Coney Barrett replacing Ruth Bader Ginsburg would represent a "flip" of the seat relative to a Democratic senator. Using nominations from the Obama and Biden administrations as an example, Figure 2.7 demonstrates that as a nomination moves away from "flipping" a seat and towards maintaining the status quo, the likelihood of opposition senators voting for a circuit nominee increases. For Obama’s nominees, moving from nominees that would completely flip a seat to a nominee that would simply maintain the status quo represents a 20 percentage point increase in receiving a senator’s vote. And, if a nomination moves the seat towards a senator ideologically, they were virtually assured that senator’s yea vote. 18 Figure 2.7 Ideological Shift of Nomination For Biden nominees, moving from a seat flip towards a senator’s ideology is a significant 17 percentage point increase. However, the overall likelihood is much lower than previous presidents. Nominations that would alter the ideological composition of a court have essentially no chance at receiving an opposition senator’s vote. Even nominees with with an ideological shift of 0, those that simply maintain the status quo, have less than a 10% likelihood of receiving a yea vote from opposition senators. Interestingly, ideological shift is only significant for circuit nominations. Even though ideology is a key driver in receiving a senator’s vote for recent district nominees, it appears who a nominee is replacing matters less. This may, however, be an artifact of the current usage of blue slips in lower court confirmations. While blue slips are no longer honored for circuit nominations (Dinan 2019; Levine 2021), they are currently honored at the district level. However, this norm may be crumbling (Raymond 2022; Alder and Cohen 2022) and district nominees may face similar circumstances as circuit nominees in the near future. For decades, senators have used nominee characteristics, such as their qualifications, in eval- uating nominees and deciding their vote. But do qualifications still influence a senator’s vote in an era where ideology dominates? Figure 2.8 provides evidence that one measure of nominee 19 quality – attending an elite law school – no longer influences senator voting for circuit nominees. Specifically, Figure 2.8 provides the marginal effect of attending an elite law school on gaining a senator’s yea vote. Values above zero mean nominees attending a law school were significantly more likely to receive a senator’s vote while values below mean significantly less likely. If the confidence intervals cross over the dotted line at zero, there is no significant difference between the two groups. Figure 2.8 Elite Law School Attendance and Senator Voting for Circuit Nominees As seen in Figure 2.8, W. Bush’s circuit nominees that attended an elite law school were significantly more likely to achieve an opposition senator’s vote. However, for nominees since, attending an elite law school actually has no effect, and may even hurt a nominee as seen for Obama’s post-change nominees. This may be a result of forward-thinking by opposition senators. Attending an elite law school is almost a prerequisite for Supreme Court nominations (Cameron and Park 2011). Further, potential Supreme Court nominees almost exclusively arrive from the circuit courts (Badas 2020). As such, voting against circuit nominees from elite law schools may 20 be strategic for these senators in an attempt to keep potential Supreme Court candidates from the federal bench. For district nominees, I find a similar story. However, as most district nominees attend local law schools – the vast majority of which are not considered "elite" – I use the nominee’s American Bar Association (ABA) rating. Figure 2.9 provides the marginal effect of ABA rating, comparing well qualified and qualified nominees.14 Values above the dotted line at zero mean well qualified nominees are more likely to receive an opposition senators vote, below mean less likely, with confidence intervals crossing zero meaning no significant difference. Figure 2.9 American Bar Association Rating and Senator Voting for District Nominees As seen in Figure 2.9, nominees rated as well qualified were significantly more likely to receive an opposition senator’s vote than qualified nominees during the pre-nuclear option Obama administration.15 Interestingly, post-nuclear option Obama nominee qualifications did not influence 14 Less than 1% of district nominees with a recorded vote have been rated as not qualified. Of the four district nominees rated as not qualified in my data, one was nominated by W. Bush and three by Trump. 15 I exclude W. Bush from Figure 2.9 as qualifications are a perfect predictor of receiving an opposition senator’s vote. 21 receiving a senator’s vote. But, the positive impact of qualifications returns under the Trump administration with higher rated nominees more likely to receive an opposing senator’s vote. Yet, the influence of qualifications is yet again insignificant for Biden nominees. This finding makes sense when coupled with earlier results that demonstrated district courts lagged in their contentiousness compared to circuit nominees and that ideology is now the driving factor in senator voting for district nominees. Taking the results from Figure 2.8 and 2.9 together, I find support for my fourth hypothesis. While being highly qualified could buy nominees out of an ideological problem in the past, this is no longer the case with qualifications no longer influencing opposition senator support. 2.5 Conclusion For decades, lower court nominees received high bipartisan support and a relatively non- contentious confirmation process. However, over the past several years, Senate norms for judicial confirmations have gradually broken down (Binder, Madonna and Smith 2007; Boyd, Lynch and Madonna 2015). How has this breakdown in norms impacted the lower court confirmation process? What factors are now the determinants of how senators vote on nominees? My findings demonstrate that lower court nominees now face a partisan, contentious process reflective of the rancor previously only seen at the Supreme Court. Specifically, ideology is now the driving factor in determining how a senator will vote. For lower court nominees, senators simply do not vote for ideologically distant nominees. Even factors that used to be able to overcome ideological problems – their qualifications – have no impact on gaining opposition senator’s support. The state of lower court confirmations has changed and now reflects the Supreme Court confirmation environment where partisanship and ideology reign (Kastellec, Lax and Phillips 2010; Kastellec et al. 2015). This change in confirmation environment has profound implications for the judiciary moving forward. My findings point to a scenario where unified government – the same party controlling the presidency and Senate majority – is required for nominees to be confirmed. While we have already seen a start to this with the failed nomination of Merrick Garland to the Supreme Court 22 and several failed lower court nominations (Slotnick, Schiavoni and Goldman 2017), presidents have still been able to confirm at least some of their nominees under divided government in the recent past. However, with ideology now primarily driving opposing senators’ votes, it is possible the opposition party may block all judicial appointments. Presidents may be able to counteract this by "going public" on nominations (Holmes 2007) but with the cost of other policy initiatives. Conversely, several positions may simply go unfilled, current judges may struggle to keep up with their caseloads, and the federal judiciary may become backlogged with cases. This paper adds to the literature in several ways. It provides the first empirical examination of senator voting behavior for lower court nominees- something previously limited to only the Supreme Court. Circuit and district court judges are creators of legal policy and it is important for us to understand the process by which they gain access to those powers. Second, and importantly, I demonstrate that the lower court confirmation process has significantly shifted in recent years and now is driven by partisan ideology. These findings imply that past intuitions about lower court confirmation politics may need to be re-examined in this new, partisan environment. There are several future studies scholars should pursue moving forward. Past scholarship has shown that constituents punish senators electorally when voting against their preferences for Supreme Court justices (Bass, Cameron and Kastellac 2022). Examining the electoral consequences of not representing constituents – particularly for district courts that directly rule on issues in a senator’s state – would be beneficial to determine if the electoral connection between senator voting behavior and judicial confirmations extends to the lower courts. Further, scholars should examine whether the polarized confirmation process impacts views of court legitimacy. Are decisions from judges that were confirmed by bipartisan majorities considered more legitimate or supported more by the public when compared to decisions from judges confirmed by only co-partisans? In short, there is plenty of work left to be done to determine the impact of this new, contentious era of judicial confirmations. 23 CHAPTER 3: PRESIDENT TRUMP AND JUDICIAL APPOINTMENTS Donald Trump began his presidency with an unprecedented ability to gain Senate confirmation for his judicial nominations after having campaigned on the promise of creating more conservative courts.1 His advantages were based on new Senate rules and norms. First, the 2013 use of the "nuclear option" in the Senate reduced the effective number required to advance an obstructed lower court nomination to confirmation from a 60 vote threshold to a simple majority of just 51. Donald Trump was the first president to enjoy both these new procedures and a Senate majority for an entire Congress.2 Second, by 2017 there was a large backlog of court vacancies due to a persistent Republican blockade of President Obama’s judicial nominations (Slotnick, Schiavoni and Goldman 2017). In short, President Trump inherited dozens of judicial vacancies (including a Supreme Court seat) at the same time that a Republican Senate was primed to efficiently confirm these nominations. And unlike President Trump’s legislative difficulties after losing the House in the 2018 midterms (Edwards 2021), the administration kept its advantages in judicial nominations throughout his entire term. The degree to which the Trump administration was advantaged by these changes in gaining judicial appointments is key to understanding both President Trump’s judicial legacy as well as understanding the future of federal court nominations. In particular, it is worth noting whether, how, and where the Trump administration was more successful in gaining confirmation as compared to prior presidents operating under the old rules. To what extent has the process been altered, or has the nuclear option merely "fizzled" without much real impact on outcomes (Ba, Cmehil-Warn and Sullivan 2020)? Prior studies note distinct epochs in judicial appointments (Hendershot 2010), and it is worth considering whether the rules and normative changes in the Senate about how far to press procedural advantages have ushered in a new era of appointment politics.3 Answering 1A version of this chapter was coauthored with Peter McAndrew and Ian Ostrander and is published in Justice System Journal. 2 President Obama had just over one year under the new rules while he also enjoyed a majority in the Senate. 3 For example, the majority party in the Senate is under no obligation to hold hearings or votes on presidential nominees. Republican senators violated Senate norms - but not the rules - as they blockaded President Obama’s judicial 24 these questions will provide new opportunities to test theories concerning the judicial appointments process as well as aiding researchers in updating their intuitions about how appointment politics now functions. We provide a descriptive overview of President Trump’s judicial nominations as well as an in-depth analysis of Senate confirmation politics during his administration. Our objectives are to provide a multitude of data allowing for comparisons across administrations and to use the Trump administration to demonstrate emerging trends in the judicial appointments process. In particular, we examine the kinds of nominees that the administration advanced, their success rates, and the pathways that nominees took. These factors will provide the opportunity to evaluate President Trump’s judicial legacy and allow us to assess the changes and continuity in judicial appointments evidenced during his administration. To analyze President Trump’s record in gaining confirmations, we examine all judicial nom- inations made to the federal courts during his term of office. Overall, we find that President Trump’s success with judicial appointments was impactful in the aggregate number of successes but ultimately uneven in its distribution of where that success occurred. Under President Trump, a friendly Senate was able to efficiently, and effectively, confirm three Supreme Court justices while circuit court nominations advanced quickly to confirmation. However, these gains came at the cost of lower and slower success on district court nominations. Furthermore, most of the confirmations advanced only by taking advantage of new Senate procedures. These findings suggest that while new pathways to Senate confirmation may aide presidential nominees overall, structural barriers still force prioritization. We conclude our analysis with a discussion of the Trump judicial legacy and an appraisal of changing judicial appointment politics. 3.1 Politics, Procedure, and Judicial Appointments Presidents nominate federal judges to fill vacancies in the courts, but these nominees must be confirmed by the Senate.4 Judicial nominations have become increasingly partisan and salient nominations (Slotnick, Schiavoni and Goldman 2017) 4 Recess appointments are possible (Graves and Howard 2010), but short-lived and increasingly unlikely given congressional adaptations (Black et al. 2011; Ostrander 2015). 25 over time. While the failed nomination of Robert Bork in 1987 often serves as a focal point for considering partisanship in judicial nominations (Epstein et al. 2006), President Reagan had been increasingly seeking ideologically allied nominees for the lower courts since the start of his administration (Goldman 1997). The Senate responded over time by increasingly scrutinizing judi- cial nominees (Hartley and Holmes 2002), which resulted in more political confirmation hearings (Dancey, Nelson and Ringsmuth 2020). Also, interest groups became more attentive to and active in judicial nominations (Cameron et al. 2020; Scherer, Bartels and Steigerwalt 2008; Steigerwalt 2010), which raised the public salience of these nominations. Senate procedure and norms dictate whether and how judicial nominations are confirmed. The lack of a simple majority mechanism to end Senate debate had until recently allowed senators to obstruct judicial nominations using the filibuster (Binder and Smith 1997; Koger 2010). Senators could effectively deter progress on a nomination by issuing a threat to filibuster – known as a "hold" – in secret (Howard and Roberts 2015, 2020). Judicial nominations for positions that fall within the boundaries of a single state are also subject to senatorial courtesy. This practice allows home state senators to reject a nomination, and has been institutionalized in the Senate Judiciary Committee through the use "blue slips," which allow a senator to formally object to a nomination and halt its progress at the committee stage (Binder 2007). Practice dictates that Senate Judiciary Committee Chairs determine whether to honor blue slips for circuit court nominations, given that circuit court jurisdictions cross state boundaries even while a judge "sits" within a given state. While historically senators have been able to obstruct judicial appointments, an important shift in judicial confirmation politics came from a 2013 change in Senate procedure. The maneuver, dubbed the "nuclear option" to convey both the gravity of the tactic as well as the threat of escalation (Smith 2014, 15), allowed a simple majority of 51 senators to advance a nomination to a final vote even in the presence of obstructions like a hold or a filibuster. While threats to go nuclear in the Senate over judicial nominations go back to at least the George W. Bush administration (Binder, Madonna and Smith 2007), the tactic was finally used in 2013 by Democratic Majority Leader Harry Reid in part because of the systematic obstruction of President Obama’s nominations to the 26 D.C. Circuit Court. Ultimately, the rules reforms created an environment in which judicial and other nominations were likely to be filled faster (O’Connell 2015), at least for key nominations (Ostrander 2017). 3.2 Appointment Politics During the Trump Administration The Trump administration is an especially interesting case for the study of judicial appointments. As a candidate, Donald Trump made his intentions to appoint federal judges similar to the recently deceased conservative Justice Scalia an explicit part of his public appeal to voters (Hollis-Brusky and Parry 2021). The fact that Donald Trump was campaigning for the presidency with a pending a Supreme Court vacancy made judicial nominations an unusually salient feature of the 2016 race. Judicial appointments are a key electoral issue (Badas and Simas 2021) and Donald Trump used the Supreme Court vacancy to his advantage. Indeed, 25% of Trump voters did so because they wanted him to nominate the next Supreme Court justice.5 But the Supreme Court seat – while the most visible vacancy in the federal courts – was not alone. In fact, the Republican Senate majority during President Obama’s final Congress effectively blockaded judicial confirmations (Slotnick, Schiavoni and Goldman 2017). This blockade resulted in President Trump inheriting a substantial backlog of judicial vacancies upon winning office. These inherited vacancies allowed President Trump to make more nominations than typical for a single term presidency. Of the 177 total judicial nominations made in the first Congress of the Trump administration, 114, or about 64%, were made to vacancies inherited by President Trump from previous administrations. This includes the highly contentious and publicized Supreme Court pick as well as 24 circuit and 89 district court nominations. Vacancies inherited by an incoming President may face unique circumstances (such as longer periods of consideration for replacements and pressure from understaffed courts to fill the post) and are potentially prioritized by incoming presidents (King and Ostrander 2020). The previous Senate’s blockade gave President Trump ample opportunity to begin his judicial legacy immediately upon entering office. President Trump’s judicial nominations enjoyed especially favorable conditions for confirma- 5 See: "A Quarter of Republicans Voted for Trump to Get Supreme Court Picks – and it paid off," The Washington Post as well as "Polling data shows Republicans turned out for Trump in 2016 because of the Supreme Court," Vox. 27 tion. His first Congress was in fact the first instance when a president enjoyed both the new Senate rules structure and a Senate majority for a full Congress.6 In this way, President Trump’s nominations could gain Senate confirmation in the face of obstruction with just copartisan votes. Furthermore, this advantage was maintained throughout his administration as the Republican party maintained control of the Senate after the 2018 midterm elections. As such, it may be no surprise that President Trump publicly touted his success in judicial confirmations. Judicial confirmations under the Trump administration demonstrate the continued use of creative innovations in Senate rules. The Senate is caught in a cycle of minority obstruction such as filibustering followed by majority restrictions such as cloture reform that has been previously dubbed "The Senate Syndrome" (Smith 2014). Such innovative adaptations beget yet more rule bending and breaking as they create new precedents and shift existing norms (Shepsle 2017). This is exactly what happened during the Trump administration. While the original 2013 nuclear option was carefully pitched so as to not apply to Supreme Court nominations, the Republican Majority under Mitch McConnell quickly went nuclear again in 2017 in order to break the filibuster on Supreme Court nominee Neil Gorsuch. And when nearly universal obstruction slowed down judicial appointments even after the rules change, the Senate went nuclear again in 2019 to reduce the maximum number of debate hours after cloture from 30 to just 2 for all district court nominations (Rybicki 2019). Importantly, these new rule structures will become a part of the procedural landscape for all future judicial nominations. In terms of norms, the Senate during the Trump administration continued to expand on pre- existing shifts in the practice of judicial nominations. While the blue slips process continued to be honored for district nominations, they were not honored for the more influential circuit courts.7 The degree to which blue slips are honored for circuit courts has long been a prerogative of the Judiciary Chair and is subject to change. However, it is worth noting that blue slips have primarily 6 In contrast, the nuclear option came in the middle of Obama’s penultimate Congress, which allowed him to work through a backlog of existing nominees. However, by the next Congress – the 114th – President Obama had lost his Senate majority. 7 See: "Grassley rips up ‘blue slip’ for a pair of Trump court picks," Politico and "Lindsey Graham: Blue slips won’t derail Trump appeals court picks," Associated Press 28 not been honored for circuit courts since the use of the nuclear option removed the filibuster as an effective bargaining tool. Finally, the Republican senators’ past suggestion that Supreme Court nominations would not be advanced during a presidential election year – which emerged during the Scalia vacancy – was quickly abandoned in the face of being able to fill Justice Ruth Bader Ginsburg’s vacancy just days before the 2020 election. Overall, Senate majorities now appear to use the rules of the chamber to advance – or withhold – confirmations to the maximum benefit of their own party. This is perhaps the natural conclusion of the "Senate Syndrome" and the rule innovation that has been observed on judicial nominations in recent administrations (Smith 2014). Furthermore, there may be little incentive to reverse these trends. American voters often view procedural tactics through a partisan lens, and fail to punish senators for obstructions such as filibusters (Smith and Park 2013). As such, one may expect that Senate majorities will continue to press their procedural advantages for their fullest partisan benefit on judicial nominations. While the Trump administration needed only Republican votes in the Senate to confirm a judicial nominee, they did not always win them. Even with more favorable Senate rules, the thin Republican majorities during the Trump administration implied that even a few co-partisan defections could doom a nominee. In particular, the administration ran into difficulties convincing co-partisans to vote for nominees of dubious quality or those mired in scandal. A series of especially embarrassing incidents of spectacular failure led critics to argue that the Trump administration was failing to properly vet its district court nominees (Savage 2017). Examples of failed Trump nominees are abundant. In one notable case, Matthew Petersen’s district court nomination was withdrawn after a hearing in which Senator Kennedy (R-LA) publicly exposed the nominee’s inexperience by asking a series of basic legal questions that Petersen was unable to answer (Savage 2017). Other nominees lost support due to perceptions of ideological impurity among Republican senators. For example, the nomination of Halil Suleyman Ozerden to the 5th Circuit Court of Appeals repeatedly foundered over fears that his views on religious liberty were out of sync with some Republicans as well as his lack of support from conservative judicial 29 organizations (Levine 2019). His nomination ultimately did not advance and was "returned" at the end of the Congress in accordance with Senate rules. 3.3 President Trump’s Nominations The judicial nominations process begins with an individual, and the characteristics of that nominee matter. In fact, nominee characteristics can directly impact the confirmation process and odds of success. For example, Asmussen (2011) finds that Republican presidents are more likely to nominate women and minorities in periods of gridlock, as a bid to gain more support among Democratic senators who would face a cost for obstructing these nominations. Because of their different support bases in the public, the Republican and Democratic parties have been found to approach female and minority judicial nominees differently (Solowiej, Martinek and Brunell 2005). Beyond demographics, measures of nominee quality are also predicted to influence the success and duration of the confirmation process (Martinek, Kemper and Winkle 2002, 348). As such, we start by examining the demographics of President Trump’s judicial nominations. We use presidents’ nominations as our unit of analysis throughout our work. This strategy is common in studies of lower court appointment politics (Binder and Maltzman 2002; Martinek, Kemper and Winkle 2002; Primo, Binder and Maltzman 2008). However, some studies examine judicial vacancies, individual nominees, or just confirmed cases. There are differences between these strategies because multiple nominees may be nominated to a vacant position before it is filled and nominees are sometimes renominated before confirmation because Senate rules require all nominations remaining at the end of a Congress to be "returned" to the president. While returns are often renominated, they are not guaranteed to be and furthermore they may not be renominated to the same vacancy. Each renomination represents a decision and comes with a cost that a confirmation would have avoided.8 For consistency, we utilize the nomination-level data to keep track of failures, return rates, examine prioritization by seeing when success occurs, and to make comparisons between Congresses. As we demonstrate below, returns are essential to understanding 8 For example, during the Trump administration, Patrick J. Bumatay was nominated three times to the lower courts. His first nomination to 9th Circuit was "returned" at the end of the 115th Congress, his second nomination to a district court (CA-S) in the 116th Congress was withdrawn, and his final successful nomination was also to the 9th Circuit but for a different vacancy than his first nomination. 30 how confirmation dynamics unfolded during the Trump administration. Table 3.1 Demographics of Trump’s Judicial Nominations: 2017-2020 District Circuit Supreme Total Nominations 294 75 3 372 Gender: Male 75.5% (222) 81.3% (61) 66.7% (2) 76.6% (285) Female 24.5% (72) 18.7% (14) 33.3% (1) 23.4% (87) Race/Ethnicity: White 84.6% (248) 84% (63) 100% (3) 84.6% (314) African American 5.5% (16) 0% 0% 4.3% (16) Hispanic 4.1% (12) 2.7% (2) 0% 3.8% (14) Asian 4.1% (12) 12% (9) 0% 5.7% (21) Other 1.7% (5) 1.3% (1) 0% 1.62% (6) White Male 65.2% (191) 68.0% (51) 66.7% (2) 65.7% (244) Elite School 21.8% 58.7% 66.7% 29.6% Average Age 50 47 50 49 ABA Ratings: Well Qualified 65.3% (192) 76.7% (56) 100% (3) 67.8% (251) Qualified 31.6% (93) 19.2% (14) 0% 28.9% (107) Not Qualified 3% (9) 4.1 (3) 0% 3.2% (12) Note: The unit of analysis is the nomination, which includes returns, failures and renominations. Table 3.1 contains demographic information on President Trump’s judicial nominations sepa- rated by court level.9 These are descriptions of President Trump’s nominations, not a summary of his actual successful appointments.10 In total, President Trump made 372 nominations split between 294 district, 75 circuit, and 3 Supreme Court nominations. As is common, many of these cases were re-nominations, where at the start of his second Congress the Trump administration nominated pending cases that were returned at the end of the 115th Congress. In terms of total 9 An enumeration of Trump administration nominations is available from Congress.gov. We use information from the Judicial Biographies (https://www.fjc.gov/history/judges) data to determine demographic information for confirmed nominees and online news reports for those nominees who were not confirmed. 10 We include a table of confirmed nomination demographics in our Appendix and the demographic makeup of nominations is very similar to the makeup of confirmed cases. 31 nominations, the Trump administration’s numbers are quite high for a single-term presidency. By comparison, President HW Bush’s one term included just 246 judicial nominations while President Obama made 520 judicial nominations over the course of two terms. As demonstrated in Table 3.1, President Trump’s judicial nominations skewed heavily towards white and male nominees at all court levels. In fact, nearly 66% of the Trump administration’s total judicial nominees were white men, making it by far the modal category. Overall, nearly 86% of the administration’s judicial nominees were white (314). The largest non-white racial/ethnic category was Asian, with 5.7% of nominees. For most minority groups, the administration was less likely to nominate minorities to higher court positions than for district courts. For example, there were no African American nominees to the circuit courts under the administration and all Supreme Court nominees were white. In terms of gender, approximately 23% of the administration’s nominees were women with the overwhelming majority of cases (72 of 87) being district court nominations. President Trump’s demographic numbers are roughly comparable to the George W. Bush administration, with a slightly higher proportion of female nominees (about 23% versus 20%) and a slightly lower proportion of white males (about 66% versus 69%) overall. The demographics, however, were significantly different as compared to his immediate predecessor.11 While the modal category for both presidents was a White-Male nominee, the differences between the two administrations are striking with President Obama significantly more likely to nominate women and minorities. This finding comports well with prior literature suggesting that Democratic presidents may have more incentive to nominate a diverse bench (Killian 2008, 270). As such, the Trump administration’s nominations, while significantly different from that of his predecessor, may not have been a departure from broader historical trends. For each level of the federal courts, we also examine the average age of President Trump’s nominees as well as the proportion with a law degree from an elite institution.12 While there is little 11 Using a t-test to compare President Obama and Trump’s first terms, President Trump was significantly less likely to nominate female or minority candidates for the federal bench at the p<0.01 level (a mean of .64 in Obama versus .34 in the Trump administration). 12 A law school was coded as elite if it was: Harvard, Yale, Colombia, Stanford, University of Chicago, University of California-Berkeley, University of Michigan- Ann Arbor, or Northwestern University (Johnson, Wahlbeck and Spriggs 2006). 32 variation in the average age at each level, circuit court nominees tended to be slightly younger (at 47 year) than district or Supreme court nominees (50). This is consistent with a strategy of placing young judges on the circuit courts where they can rule for many years to come as well as provide future candidates for Supreme Court nominations. Unlike with age, there is a stark contrast among the court levels in the proportion of nominees who have attended an elite law school. District court nominees are far less likely to have attended an elite institution (21.8%) than circuit and Supreme court nominees (at 58.7% and 66.7% respectively). While district court nominees may benefit from the connections that a more local law school degree may convey, an elite degree is increasingly sought for circuit and Supreme court nominees. In fact, President Trump’s nomination of Amy Coney Barrett (with a law degree from Notre Dame) was a significant departure from prior Supreme Court nominees. The only other Supreme Court nominee since Reagan without an elite law degree was Harriet Miers.13 Among our demographic variables we also include measures of quality. In particular, we use a measure from the American Bar Association (ABA).14 The ABA rates judicial nominees as "Well Qualified," "Qualified," or "Not Qualified." ABA quality ratings are controversial (Haire 2001), and have been demonstrated to be biased against Republican nominees (Smelcer, Steigerwalt and Vining 2012) as well as women and minorities (Sen 2014a). However, these ratings are still used by the Judiciary Committee to evaluate nominees and they provide a systematic and external signal of quality. While the Trump administration’s nominees were overwhelmingly rated as Well Qualified, some interesting trends stand out. First, 12 nominations were rated as Not Qualified, which stands out as the highest rate relative to other administrations since President Reagan.15 Second, there is a clear pattern in the ratings of quality in which higher-level positions are consistently more likely to be rated as Well Qualified. Which, for Supreme Court nominees, is intuitive as they tend to have a more established record upon nomination and face higher hurdles in their vetting. 13 Her nomination was eventually withdrawn by the president when it became clear that - in part due to the lack of an elite law degree - she would not be confirmed by the Senate. 14 These data can be found at: https://www.americanbar.org/groups/committees/federal_judiciary/ ratings/. 15 G.W. Bush also had 12 nominations rated as Not Qualified, but he had far more nominees overall in his two terms. 33 Demographics are an important consideration for presidents because they ultimately determine the possible demographics of the federal bench. To the extent that the federal bench is unrepresen- tative of the population it serves, the courts may be viewed as illegitimate (Killian 2008; Scherer 2004). Nominating non-traditional judges is a means through which a president can change the character of the federal courts and establish a lasting judicial legacy (Slotnick, Schiavoni and Gold- man 2017, 397). Similarly, President Trump’s tendency to overwhelmingly nominate white and male candidates to the federal bench will remain an enduring part of his judicial legacy(Solberg and Waltenburg 2022). 3.3.1 Outcomes We continue our examination of Trump’s judicial nominations with a descriptive look at how these cases unfolded and ultimately ended. To begin, Table 3.2 provides an overview of nomination success by court level during each of the Trump administration’s Congresses. The table provides information on the number of nominations at each level ("Nominations"), the number of successful nominations ("Successful"), the number of nominations returned to the President by the Senate at the end of a Congress ("Returned"), and the number of nominations that were either formally withdrawn or, far more rarely, outright rejected ("Failure"). While the recent rule changes in the Senate may lead to the assumption that nominations during unified government would enjoy near certain success, this is not the case. President Trump’s Supreme Court nominees did indeed experience complete success. However, the administration had more difficulty with lower court confirmations with only about 43% (54/126) of district court nominations being confirmed in President Trump’s first Congress. Overall, President Trump’s success rate in gaining Senate confirmation was about 61% (229/372), which is roughly comparable to Presidents W. Bush and Obama at about 63% (319/506) and 60% (314/523) respectively. Several important patterns stand out in Table 3.2. First, President Trump’s success rate was driven down primarily by a high rate of "returned" cases for lower court appointments during his first Congress. Many - but not all - of these cases were then re-nominated in the 116th Congress and some were successfully confirmed. Success rates are far higher for the administration’s second 34 Table 3.2 Trump’s Judicial Nomination Outcomes by Congress: 2017-2020 Congress Nominations Successful (%) Returned (%) Failure (%) 115𝑡ℎ District 126 54 (42.9) 72 (57.1) 0 (0) Circuit 49 29 (59.2) 19 (38.8) 1 (2) Supreme 2 2 (100) 0 (0) 0 (0) 116𝑡ℎ District 168 119 (70.8) 45 (26.8) 4 (2.4) Circuit 26 24 (92.3) 2 (7.7) 0 (0) Supreme 1 1 (100) 0 (0) 0 (0) Total 372 229 (61.6) 138 (37.1) 5 (1.3) Congress. These data suggest that perhaps the high number of inherited vacancies created a bottleneck of nominees which took time for the Senate to clear. In terms of unsuccessful cases, the data in Table 3.2 are striking in that the Trump administration’s nominations have experienced very few withdraws or outright failures. The vast majority of unsuccessful nominations were cases that simply timed out at the end of a Congress. This is typical as most failed nominations fall victim to "malign neglect" rather than failing outright or being officially withdrawn (Bond, Fleisher and Krutz 2009). To more fully evaluate the Trump administration’s success with judicial appointments, we must compare it to prior presidencies. Table 3.3 examines nomination outcomes for presidents since the Reagan administration.16 In comparing President Trump to his predecessors, several trends stand out. First, the Trump administration was more successful – at 70.7% – on circuit nominations than compared with his most recent predecessors. Success rates had not been this high for circuit court nominations since the H.W. Bush administration, and success rates dropped for these nominations to as low as 39.3% under the W. Bush administration. With failure rates generally low across all administrations, it appears that circuit nominations that once would have been delayed to death were successful during the Trump administration. Second, the Trump administration appears to 16 Nomination data and outcome information comes from Congress.gov searches of judicial nominations for the "Latest Action." Also note, George W. Bush’s low success rate for the Supreme Court represents an unusual case due to the withdraw and renomination of John Roberts as Chief Justice after the unexpected death of William Rehnquist. 35 have been relatively unsuccessful on district court appointments. At a rate of 58.8% success, the Trump administration was about as successful on district court nominations as President Obama17, but far less successful than any other administration since President Reagan. These findings suggest that President Trump’s success with judicial appointments was bifurcated, with higher level appointments significantly more likely to be successful than lower court appointments.18 Table 3.3 Judicial Nomination Outcomes: Reagan–Trump Successes Success Rate Returned Rate Failure Rate Reagan District 284 87.1% 10.7% 2.2% Circuit 78 83% 12.8% 4.3% Supreme 4 80% 0% 20% HW Bush District 148 75.9% 23.6% 0.5% Circuit 37 75.5% 24.5% 0% Supreme 2 100% 0% 0% Clinton District 303 81% 16.6% 2.4% Circuit 61 56.5% 33.3% 10.2% Supreme 2 100% 0% 0% W Bush District 258 73.3% 25.3% 1.4% Circuit 59 39.3% 56% 4.7% Supreme 2 50% 0% 50% Obama District 264 61.1% 38% 0.9% Circuit 48 56.5% 41.2% 2.4% Supreme 2 66.7% 33.3% 0% Trump District 173 58.8% 39.8% 1.4% Circuit 53 70.7% 28% 1.3% Supreme 3 100% 0% 0% Note: The unit of analysis is the nomination, which includes renominations. The recently revised Senate rules also suggest that we examine the changing pathways through which nominations advance. Looking at the confirmation process for each presidency since Reagan 17 However, 14% (59 of 432) of President Obama’s district court nominations faced a hostile Senate and had an abysmal 31% (18 of 59) success rate due to an historic blockade of judicial nominations by Senate Republicans. 18 These general findings also hold when comparing the Trump administration to prior president’s first term only. See Appendix Table B.2. 36 by court level, Table 3.4 demonstrates how nominations were typically considered as well as how long it took for cases to proceed through the Senate. In particular, this table provides information on the number of cases that faced a roll call vote on either final passage or cloture vote ("Roll Call") and the number that experienced a cloture motion ("Cloture") at some point during Senate consideration.19 We also list the average number of days that nominations of each type were under consideration as a measure of duration and a very rough indication of relative obstruction across time. The data presented in Table 3.4 suggest that the judicial nominations process has been changing over time. In the past, all lower court nominations were routinely approved by the Senate with voice votes (Goldman 1997), but now recorded roll call votes – sometimes seen as a sign of more contentious politics – are much more common.20 Similarly, cloture motions on judicial nominations were generally a rarity before the rules change in the Obama administration and yet during the Trump administration cloture motions were present on the majority of district court nominations and a super-majority of circuit and Supreme Court nominations.21 These data make it clear that Senate Majority Leaders are routinely using the revised cloture thresholds to efficiently process judicial nominations and that contentious roll-call votes, often on cloture, may be the trend for the foreseeable future. Did the new Senate rules make President Trump’s judicial nominations proceed significantly faster through the Senate? The evidence is interestingly mixed. Specifically, the Trump admin- istration’s district court nominations – at an average of about 183 days – proceeded slower on average than any other recent president, though they are nearly identical to those experienced by President Obama. However, the Trump administration’s circuit court nominees – at an average of 19 These figures may even be a conservative picture of the new politics of judicial nominations in that sev- eral nominations were advanced and confirmed under an agreement that let Democrats head home to campaign (see: https://www.politico.com/story/2018/10/11/senate-democrats-judges-895168) leading to sev- eral confirmations without cloture that may otherwise have required it to advance. 20 In a comparison of first terms using t-tests, the only president since Reagan that didn’t have significantly fewer roll call votes on judges at the p<0.01 level was the George W. Bush administration, which also had significantly fewer roll call votes but at the p<0.06 level. 21 Using a t-test to compare cloture use in President Obama’s first term before the rules change versus the Trump administration, we find that the Trump years included significantly more cloture votes at the p<0.01 level. 37 Table 3.4 Judicial Confirmation Process: Reagan–Trump Roll Call Cloture Average # Votes (%) Motions (%) of Days Reagan District 1 (0.3) 1 (0.3) 66.2 Circuit 5 (5.3) 1 (1) 70.8 Supreme 5 (100) 1 (20) 69.4 HW Bush District 0 (0) 0 (0) 117.7 Circuit 1 (2) 1 (2) 140.5 Supreme 2 (100) 0 (0) 84 Clinton District 33 (8.8) 1 (0.3) 145.6 Circuit 15 (13.9) 4 (3.7) 217.5 Supreme 2 (100) 0 (0) 58.5 W Bush District 141 (40.2) 1 (0.3) 149.2 Circuit 58 (38.7) 19 (12.7) 231.4 Supreme 2 (50) 1 (25) 41.3 Obama District 177 (41) 100 (23.2) 182.9 Circuit 44 (51.8) 25 (29.4) 196.3 Supreme 2 (66.7) 0 (0) 148.7 Trump District 145 (49.5) 152 (51.7) 183.3 Circuit 54 (72) 51 (68) 100.2 Supreme 3 (100) 3 (100) 60 Note: The unit of analysis is the nomination, which includes returns, failures and renominations. about 100 days – proceeded faster than any other president since Ronald Reagan. But while these nominations were faster during the Reagan administration, it is also the case that they faced fewer procedural hurdles – such as cloture and roll call votes – as compared to the Trump administration nominations. In this sense the Trump administration’s speed remains impressive. The differences between administrations become even more stark with recent comparisons. President Trump’s circuit court nominations took on average about half the time that similar nominations took in the Obama, W. Bush, and Clinton administrations. Interestingly, President Trump is also the only one of the recent presidents to have circuit court nominations proceed on average faster than district court nominations. And while President Trump’s Supreme Court 38 nominations were not the quickest by comparison to prior administrations, they were far from the slowest. Overall, these findings are consistent with the expectation that President Trump’s allied Senate majority under Leader Mitch McConnell, prioritized Supreme Court and circuit nominations over district court nominations. Investigating Trump’s Nominations In the post-nuclear era, a president who can count on the support of a simple majority of the Senate can win on any one nomination, even in the face of strong partisan opposition and obstruction. However, the reality of managing the Senate floor may still suggest that a president can not count on winning all qualified nominations even after the rules change (Ostrander 2017). As such, even friendly Senate Majority Leaders are forced to prioritize nominations. This may be truer for the Trump administration than prior presidencies. The high volume of judicial nominations in the Trump administration due to inherited vacancies may have created a bottleneck inside the Senate that, when combined with near universal obstruction due to shifting Senate norms, forced choices to be made over which nominations to advance. Intuitively, the descriptive data above suggests that the Republican majorities in the Senate prioritized higher-level court positions over lower-level positions during the Trump administration. In the following sections, we develop and test expectations related to the prioritization of judicial nominations during the Trump administration. We specifically examine the kinds of factors that made some nominations move faster, and to be more likely confirmed, than others. In particular, we examine contexts in which the Trump years served as a departure from prior norms and expectations. We contend that the Republican Senate majorities’ prioritization explains why the Trump administration’s success on judicial confirmation was both bifurcated by court type and overall lower than expected given the Senate rules change. 3.3.2 Expectations All judicial nominations are important, but some are more important than others. In particular, filling circuit court vacancies provides substantially more value than filling district court positions. Given the Supreme Court’s downsized docket (Owens and Simon 2012; Lane 2022), circuit courts 39 now often serve as "courts of last resort" (Bowie, Songer and Szmer 2014, 26). Furthermore, circuit court nominations are often made with an eye towards future Supreme Court nominations. Because Senate confirmation takes time and effort, the decision to advance one nomination may imply that other nominations stand idle in the interim. As such, we expect to see a President Trump’s friendly Senate majority prioritize the advancement of circuit over district court nominations with circuit court nominations proceeding through the process relatively faster. As demonstrated descriptively above, this prioritization ultimately resulted in higher rates of success in circuit court nominations than compared to prior administrations. Expectation 1: Circuit court nominations will be confirmed more quickly through the Senate than district court nominations. We also suspect that the Senate prioritized Supreme Court nominations over all other judicial nominations. But with just three cases during the Trump administration it can be hard to generalize given such a low number of observations. One way to test this assertion is to observe the effects of a Supreme Court nomination on the other pending judicial nominations that exist at the same time. Prior research demonstrates that as a president expends more effort and political capital in support of a Supreme Court nomination, lower court nominations will proceed more slowly as a direct result (Madonna, Monogan and Vining 2016). With three Supreme Court nominations in a single term, we suspect that this effect slowed lower court nominations. The Trump years also provide an opportunity to test whether the effects of Supreme Court prioritization on lower court nominations remains in the post-nuclear Senate. Expectation 2: Lower court nominations that are pending during a Supreme Court nomina- tion will be confirmed significantly slower than nominations without a pending Supreme Court nomination. As we note, the blue slips process remained intact for district court nominations during the Trump administration. In some instances – especially where Democrats controlled both of a state’s Senate seats – the blue slips process allowed the minority party a virtual veto over district court nominations. For district court nominations, we believe that Senate Majority Leader McConnell’s 40 effort will have likely been used to more quickly advance nominees with allied Senate delegations, because mixed or opposed delegations had a tool for obstruction that could not be overridden with a cloture vote. Expectation 3: District court nominations with allied Senate delegations will move faster than nominations with opposed delegations. We expect that district court nominations – and especially those with opposed Senate delegations – will be less likely to succeed. Ultimately, this is a direct result of the Senate prioritization of higher-level judicial nominations over district court nominations. If Supreme and circuit court positions are filled first then district court positions will be filled last and are therefore less likely to succeed before the end of a Congress forces their "return to the President." This broad pattern can be observed in Table 3.2, where district court nominations are observed to be far more likely to be returned at the end of a Congress as compared to circuit court nominations. Expectation 4: District court nominations to states with opposed Senate delegations are less likely to be confirmed than those with allied delegations. 3.3.3 Data & Methods We collect data on all of President Trump’s lower court nominations to federal courts from the 115th through the 116th Congress (2017-2020).22 We focus on lower court nominations as there are too few Supreme Court cases for a statistical model. Because we are interested in how nominations have fared in the Senate under the new rules structure, our unit of analysis is the formal presidential nomination and not the vacancy itself or the ultimate fate of individual nominees. Judicial nominations that are "returned to the President" at the end of the Congress are not considered to be successful, even though they may have been renominated and confirmed in the next Congress. The details of each nomination are gathered from Congress.gov, which tracks actions, votes, and the final disposition for each nomination. President Trump issued 369 lower court nominations during our time frame. 22 President Trump did issue a few judicial nominations in the 117th Congress just before the end of his term, but these nominations were quickly withdrawn at the start of the Biden administration. We do not examine these transition cases. 41 We have two different dependent variables of interest: the duration and the success of President Trump’s nominations. To test expectations related to how quickly nominations proceed, we use a Weibull duration model to estimate the time between a formal nomination and the outcome of the process. This is a common strategy in studies of judicial and bureaucratic nominations (Boyd et al. 2021; Martinek, Kemper and Winkle 2002; Ostrander 2016; McCarty and Razaghian 1999), and are appropriate for instances in which one is confident that the probability of exit changes as time passes (Box-Steffensmeier and Jones 2004). To test expectations related to success, we use a logistic regression model on the dichotomous outcome variable of "success." While nominations may fail to succeed in different ways, the very rare cases of withdrawn nominations in the Trump administration make testing between failure types difficult and less valuable. Because of the difference in district and circuit court nominations, such as blue slip effects, we model these two kinds of nominations separately rather than pooling our models across all cases as is common in studies of judicial nominations (Martinek, Kemper and Winkle 2002; Primo, Binder and Maltzman 2008). We include a variety of relevant control variables in our models. First, we include basic demographic information as introduced in Table 3.1. In particular, we include dichotomies for female and minority nominees as these are traits that prior research notes to be influential especially for Republican presidents (Asmussen 2011). We also account for the role of the blue slips process by noting partisan alignment of the Senate delegation for the state in which the nominee will preside. Delegations are coded as "allied" (our baseline) when both senators are of the president’s party, "mixed" when there is a senator from each party, and "opposed" when both senators are not of the president’s party. We also include an indicator variable for whether the court is a D.C. district or circuit court, as these cases have no senate delegation. Because when a nomination occurs influences the duration and outcome of the process, we include a measure for how many days are left in the Congressional session – "Days Left." As a rough measure of quality, we include the ABA ratings of each nominee as described above. For models of duration, we also note whether each nomination’s consideration overlapped at any point with the Senate that of a Supreme Court 42 nomination. Finally, to account for the differences between Congresses – such as transition effects and re-nominations in the second Congress – we include an indicator for whether the nomination was in the 116th Congress as compared to the baseline of the 115th (President Trump’s first Congress). 3.4 Findings One way to demonstrate the impressive Senate prioritization of circuit over district court nominations during the Trump administration is to examine the trends in exit times for both. Figure 3.1 provides a Kaplan-Meier plot of these trends, with the lines representing the proportion of nominations remaining under Senate consideration at a given number of days since the initial nomination. The lines suggest that circuit court cases proceed substantially faster. For example, at the 150 day mark after nomination, approximately 75% of district court cases remain under consideration while at the same point in their nomination only 25% of circuit court cases remain. Our first expectation is thus strongly supported by descriptive evidence. 1.00 0.90 Proportion of Nominations Remaining 0.80 0.70 0.60 0.50 0.40 0.30 0.20 Districts 0.10 Circuits 0.00 0 50 100 150 200 250 300 350 400 450 500 Days Since Nomination Figure 3.1 Kaplan-Meier Plot of District and Circuit Court Nominations: 2017–2020 43 While these results may be intuitive given the consideration of the relative value of these positions, this finding from during the Trump administration stands in stark contrast to prior presidencies. For example, Martinek, Kemper and Winkle (2002) find that circuit court nominations tend to take longer than district court nominations. In fact, Table 3.4 suggests that compared to all presidential administrations since Reagan, President Trump is the only one to have had circuit court nominations proceed faster on average than district court nominations. Furthermore, given the relative value of circuit versus district courts, this trend may continue into future administrations as long as a president enjoys the support of a Senate majority. Table 3.5 provides estimates from a Weibull duration model demonstrating the impact of nominee and political contexts on how much time a nomination takes for circuit and district nominations. Estimates are provided in terms of hazard ratios, where the baseline value of one is the comparison to a normal case. Estimates lower than one suggest that an increase in the given variable is associated with slower times to completion while estimates higher than one predict faster nominations. We note that the shape parameter in our Weibull model is significant in both models, which confirms our expectation that there is duration dependence in judicial nominations rather than a constant hazard. For both circuit and district court models we find support for our second expectation that lower court nominations will proceed more slowly if they overlap a Supreme Court nomination. The hazard ratio estimate for SCOTUS overlap is significant and less than one for both the circuit and district court model, suggesting that the presence of a Supreme Court nomination significantly slows all other federal court nominations. This finding suggests Supreme Court vacancies are indeed prioritized, supporting our second expectation, and furthermore that they still cause significant "collateral damage" on other lower court nominations even after the nuclear option (Madonna, Monogan and Vining 2016). The results of the duration models offer several other intuitive and interesting findings. First, both circuit and district models demonstrate that nominations proceeded significantly faster in the 116th Congress as compared to the 115th. These findings demonstrate the importance of 44 Table 3.5 Weibull Duration Estimates for Lower Court Nominations Circuit District Hazard Ratio z Hazard Ratio z Female 1.66 1.31 1.13 0.64 Minority 1.97 1.54 1.02 0.07 Mixed Delegation 1.05 0.12 0.46∗∗ -3.67 Opposed Delegation 0.74 -0.89 0.13∗∗ -8.09 D.C. Court 1.66 0.43 2.47∗∗ 2.11 Days Left 0.99∗ -2.17 1.00 1.12 Qualified 1.15 0.33 0.89 -0.66 Not Qualified 1.61 0.75 1.56 0.94 SCOTUS Overlap 0.20∗∗ -2.76 0.43∗∗ -2.78 116th Congress 4.95∗∗ 4.09 3.31∗∗ 4.51 Constant 0.00∗∗ -8.90 0.00∗∗ -16.77 ln(p) 0.88∗∗ 8.29 1.08∗∗ 18.62 N 71 293 Log Likelihood -50.25 -148.74 LR 𝜒2 39.21∗∗ 145.62∗∗ Note: ∗ p<0.1; ∗∗ p<0.05 Congress-level considerations such as the ability to re-nominate candidates and the constraints that administrations face in their first Congress (King and Ostrander 2020).23 Second, in support of our third expectation we find that district nominations with mixed and opposed Senate delegations proceed significantly more slowly than nominations made to states with fully allied delegations (the baseline). This finding suggests that the necessity of consulting Democratic senators for the blue slips process did indeed slow district nominations during the Trump administration. Interestingly, we find differentiation between the two court levels in duration for two variables – Days Left and D.C. courts. For circuit nominations, having more days left in a congressional session corresponds to a longer duration (p<0.1). This could be simply be an indication that later circuit court nominations were pushed through to confirmation before the hard deadline at the end of a Congress whereas this effort was not extended to district court nominees. Further, for 23 In total, 69 nominations or about 35% of cases within the 116th Congress were individuals who had also been nominated in the 115th Congress. 45 district courts, nominations to the D.C. court proceed significantly faster than others. This may be a reflection that – unlike other district court nominees – there were no senators to consult with for D.C. courts. Finally, neither nominee demographics nor ABA estimates of quality appear to influence duration in either the circuit or district models. Table 3.6 Lower Court Confirmations Circuit District Odds Ratio (SE) z Odds Ratio (SE) z Female 1.25 (1.06) 0.26 1.83 (0.64)∗ 1.73 Minority 2.40 (3.22) 0.65 0.80 (0.33) -0.55 Days Left 1.004 (0.00)∗∗ 2.21 1.005 (0.00)∗∗ 5.14 Mixed Delegation 0.84 (0.79) -0.19 0.79 (0.33) -0.56 Opposed Delegation 0.48 (0.35) -1.01 0.13 (0.05)∗∗ -4.99 D.C. Court – 0.85 (0.74) -0.18 Qualified 0.59 (0.45) -0.70 0.49 (0.16)∗∗ -2.18 Not Qualified – 0.38 (0.30) -1.22 116th Congress 6.89 (7.77)∗ 1.71 3.83 (1.36)∗∗ 3.79 Constant 0.50 (0.43) -0.81 0.23 (0.11)∗∗ -3.18 N 67 293 Log Likelihood -29.68 -146.44 LR 𝜒2 20.54∗∗ 103.66∗∗ Note: ∗ p<0.1; ∗∗ p<0.05 Table 3.6 provides estimates from logit models on the probability that a nomination will end successfully in confirmation for circuit and district nominations. Estimates are given in terms of Odds Ratios to make interpretation easier. Estimates higher than the one suggest that a variable is associated with a higher likelihood of success while an estimate lower than one is associated with a lower likelihood of success. Because the circuit court model has so few cases, and such cases were generally successful, there are a few instances in which estimates are omitted due to complete separation. Specifically, all D.C. circuit court nominations (2) were confirmed as were all circuit nominees with "Not Qualified" ABA ratings (3). Similar to models of duration, both models of success suggest that nominations were more likely 46 to be confirmed in the 116th Congress (Trump’s second) as compared to the 115th. This is intuitive as some of these nominations had already been partially processed beforehand and administrations are slow to nominate at the start of an administration.24 However, the significance of Congress in the circuit model was at the p<0.1 level. Both models also suggest that earlier nominations (with more "Days Left" in the Congress) are more likely to ultimately succeed. While not significant in the circuit model, there is also some support for prior literature’s suggestion that Republican presidents will leverage female nominees for greater success in that women nominated to district court positions appear more likely to succeed (at p<0.1). Intuitively, as shown in 3.6, district court nominations from states with opposed delegations are significantly less likely to succeed. In these cases, home state senators may have forced a less than palatable nominee on the majority party or resistance from these home state senators may bottle up a nomination in committee given the blue slips process. This provides strong support for our fourth expectation. Interestingly, merely Qualified district nominations – as opposed to the baseline of Well Qualified – appeared to be less successful while Not Qualified nominations were not significantly different from the baseline. This is likely, however, a product of the relatively few cases of unqualified nominees during the Trump administration. 3.5 Conclusions With an overall success rate of just under 62% during his presidency, President Trump did not appear to be especially accomplished with judicial confirmations as compared to his immediate predecessors.25 But the top line success rate belies an enormously bifurcated process in which district court nominations proceeded at an historically slow pace with low success rates while circuit court nominations proceeded both faster and more successfully. As such, President Trump’s success rate with circuit court nominations – at just over 70% – was significantly higher than Presidents Clinton, W. Bush, and Obama. Our findings from the Trump administration demonstrate the necessity of looking beyond top-line rates of success and duration by examining which of the many 24 In fact, 90% of renominations in the 116th Congress were successfully confirmed. 25 In fact, a t-test comparing Presidents Obama and Trump’s relative overall judicial confirmation success in their first terms is not significant (p=0.32 with means of 0.65 and 0.62 for Presidents Obama and Trump, respectively). 47 nominations are prioritized for quick confirmation. Prioritization is essential to understanding judicial appointment politics during the Trump administration and how it compares to the past. Our findings demonstrate that the Senate majority allied to President Trump consistently prioritized circuit over district court nominations. Such prioritization - made easier by the new Senate rules - led to dramatically different outcomes in confirmations. Trump was the only recent president to experience higher success rates on circuit as compared to district nominations. In terms of continuity, the prioritization of President Trump’s Supreme Court nominations continues to imply "collateral damage" to concurrent lower court nominations (Madonna, Monogan and Vining 2016). Overall, these examples of prioritization are evidence that, even after a significant Senate rules change, practical limitations continued to force choices over which nominations to pursue. Our descriptive findings demonstrate how President Trump’s judicial nominations and their outcomes compare and contrast to recent administrations. Overall, these data suggest that the Senate confirmation process for judges has become more contentious with the Trump administration experiencing both higher rates of roll-call votes as well as cloture motions compared to prior presidents. As a direct result of the Senate rules change, the Trump administration was the first in which cloture was the most common vehicle through which all types of judicial nominations were advanced to a final vote. Because the new rules were further entrenched by their use during the Trump administration, there is reason to expect that these trends will continue for future administrations having a co-partisan Senate majority. While the empirical models include only President Trump’s nominations, we can use our findings to directly examine the change and continuity expressed within this administration by comparing results to existing literature. Perhaps the most important change is that Donald Trump was the only recent president to have faster circuit as compared to district court outcomes and this finding directly contradicts the intuitions of pre-nuclear judicial appointments literature (Martinek, Kemper and Winkle 2002). In terms of continuity, the Senate rules reform has not altered the importance of blue slip considerations for district court nominations. Nominations to district 48 courts with opposed Senate delegations are still found to proceed slowly through the Senate and are less likely to succeed. In this way, we demonstrate that even presidents enjoying Senate majorities and more favorable rules will not be universally successful. Finally, given that President Trump’s female district court nominations were more likely to be confirmed, we find evidence in support of Asmussen’s (2011) contention that Republican presidents in particular will seek advantages in nominating women to these posts. This reinforces prior work suggesting that nominee characteristics matter for confirmation politics. What is Donald Trump’s judicial legacy? While high-profile fights over President Trump’s three Supreme Court picks captured most of the attention, it is also the case that events gave President Trump an historic opportunity to remake the lower federal courts. Even with relatively comparable top-line rates of success, the Trump administration’s advantage stemming from inherited vacancies allowed for a remarkable number of Senate-confirmed judges given just one term. Furthermore, the fact that President Trump’s success rates were higher in the circuit courts enhances this accomplish- ment. So while President Trump had mixed success with respect to his legislative agenda (Edwards 2021), his judicial nominations do stand out as an enduring accomplishment (Hollis-Brusky and Parry 2021). However, the administration’s legacy also includes a distinct lack of diverse nominees that contrasts starkly with other recent presidents. Ultimately, one of the enduring influences of the Trump administration may be that the once innovative procedural mechanisms utilized in the Senate to successfully confirm so many of his nominations may become routine. 49 CHAPTER 4: HOW UNEXPECTED OPINION AUTHORS INFLUENCE SUPPORT FOR SUPREME COURT DECISIONS At the end of the 2019 term, the Supreme Court dramatically increased workplace protections for LGBTQ individuals when the justices ruled that workplace discrimination based on someone’s sexual orientation or gender identity violated Title VII (Totenberg 2020).1 The opinion in Bostock v. Clayton County (2020) came from a surprising source: Justice Neil Gorsuch, who, despite not being particularly supportive of LGBTQ rights early in his tenure (Farias 2020), used textualism to explain that a plain text reading of Title VII confirmed that firing someone for being gay or transgender was discrimination because of sex (Stern 2020). Three of Gorsuch’s colleagues on the Court criticized the opinion as "a pirate ship" that "sails under a textualist flag" (Gersen 2020), but many legal analysts and commentators on both sides of the political aisle praised Gorsuch’s work (Poindexter 2020). They suggested that a conservative justice using a conservative approach to write an expansive liberal opinion like this one signaled the decision was legally principled and therefore beyond reproach. Chief Justice John Roberts undoubtedly had this outcome in mind when he assigned the opinion to Gorsuch in the first place (Biskupic 2020); the most conservative member of the coalition was the best possible defender of this sweeping and controversial liberal decision. Does knowing that an opinion writer’s ideological preferences or identity characteristics are at odds with the outcome of a Supreme Court case increase support for that decision? While people broadly view the Court as a legally-principled institution (Bartels and Johnston 2013), they use ideological and identity cues to react to individual Court opinions (Haglin et al. 2020; Ono and Zilis 2022; Zilis 2018). Media coverage of the Court, which tends to focus on ideological winners and losers, helps them do this (Collins and Cooper 2015; Hitt and Searles 2018; Zilis 2015). Such coverage keeps the public informed of the political consequences of newsworthy cases at the cost of discussing opinions’ principled legal underpinnings, which can negatively affect people’s 1A version of this chapter was coauthored with Jessica Schoenherr and is conditionally accepted at the Journal of Law and Courts. 50 perceptions of the Court (Gibson and Caldeira 2009b; Hall 2010). To avoid losing public support, the justices attempt to turn the conversation back toward the law (Krewson 2019); one way of doing this is asking a justice whose ideological preferences or identity characteristics are at odds with a path-breaking decision to write the majority opinion for it (Epstein and Knight 1998; Thomas 2019; Woodward and Armstrong 1979). From a legal standpoint, asking an incongruent justice to write an opinion helps the Court shut down dissent. Beyond that, an incongruent author’s presence can also signal the strength and credibility of a legal opinion (Gibson, Lodge and Woodson 2014; Krewson 2019). Is the public listening to that signal and responding to it? To answer this question, we fielded two survey experiments. In the first, we asked 733 par- ticipants to read and respond to a newspaper article about a Supreme Court decision upholding abortion rights, and in the second, we asked 1,497 participants to read about a decision upholding the death penalty. Across both experiments, we varied the ideology and gender of the decision’s opinion writer. All else being equal, we would expect to see that women and Democrats are more likely to support a decision upholding abortion rights (Reingold et al. 2021), and that men and Republicans are more likely to support a decision upholding the death penalty (Jones 2018). But if the justices’ instincts are correct and incongruent opinion writers increase support for a controversial and salient decision, we should see an increase in support for decisions written by ideologically- or identity-incongruent justices, especially among people least likely to support that position. Our results suggest that, despite judicial expectations, deploying incongruent justices does not broadly increase support for controversial and salient Supreme Court decisions. Instead, we find that aggregate support remains steady because asking an ideologically-incongruent justice to write a controversial opinion increases support among those least likely to approve of the decision and decreases support from those most likely to approve of it. This paper significantly contributes to the literature on Supreme Court opinion writing in two distinct ways. First, we connect judicial identity and judicial strategy. The well-developed literature on opinion assignment and construction shows that Supreme Court opinion writers produce decisions that move Court policy toward their preferred outcomes (Maltzman, Spriggs and 51 Wahlbeck 2000). Judicial ideology also influences popular support for decisions, as the public uses cues like the opinion writer’s ideology to evaluate the Court’s work (Armaly 2018; Boddery and Yates 2014; Zilis 2018). Additionally, scholars suggest that an opinion writer’s identity characteristics, namely their race, ethnicity, and gender, can influence acceptance (Boddery, Moyer and Yates 2020; Ono and Zilis 2022). Anecdotal evidence indicates the justices both understand and attempt to use identity cues to increase support for a decision (Epstein and Knight 1998; Woodward and Armstrong 1979). By examining the efficacy of this strategic behavior in two areas where it is most likely to appear, we are one of the first to connect these two lines of literature. Second, we offer insight into yet another way the justices can harness public support for their work. Because opinion enforcement is at least partially dependent on the Court’s public standing (Hall 2010), and the confirmation process and the justice’s own opinions can damage it (Badas and Simas 2021; Nicholson and Hansford 2014), the justices consistently attempt to reinforce the public’s trust in its work, doing everything from aligning their opinions with popular sentiment (Casillas, Enns and Wohlfarth 2011; Hall and Ura 2015), to emphasizing their dependence on precedent (Zink, Spriggs and Scott 2009), to traveling around the county and giving speeches in public forums about the Court’s apolitical role in American government (Black, Owens and Armaly 2016; Krewson 2019). We suggest the justices also anticipate negative reactions and attempt to head them off where they can by selecting a writer who can move the conversation away from ideology or identity and toward the law itself, which simultaneously fortifies the opinion and the Court’s legitimacy. 4.1 Supreme Court Opinions and the Public While much of the Supreme Court decision-making process is private, its end product is wholly public: an opinion, typically attributed to a single justice and joined by at least four others (Hitt 2019), that resolves a legal conflict and provides guidance for future cases (Hansford and Spriggs 2006). Despite the singular byline, the opinion is the collaborative product of ideological preferences and Court rules. The justices’ individual policy preferences and the Court’s broader ideological composition influence case outcomes (Hammond, Bonneau and Sheehan 2005; Car- 52 rubba et al. 2012; Lax and Cameron 2007), especially the Chief Justice’s, as he often assigns opinions (Johnson, Spriggs and Wahlbeck 2005).Additionally, past rulings can limit the justices’ ability to move policy in preferred directions (Black and Spriggs 2013); five justices must agree on the legal reasoning to establish a precedent (Hitt 2019); the need to complete work by the end of the term forces assignment equity across the justices (Maltzman, Spriggs and Wahlbeck 2000); the justices value issue expertise (Maltzman and Wahlbeck 1996); and dissents and concurrences can force modifications to the majority opinion (Corley 2010; Corley and Ward 2020). But once the opinion is complete and released, the Court owns it and is held responsible for its contents. Public opinion is not supposed to affect the decision-making process. The framers tried to remove the Court from public opinion by staffing it with lifetime appointees, but they then tasked popularly-elected officials with decision enforcement (Hamilton 2003; Rosenberg 2008). Implementation is thus more likely when the public supports the decision or believes the justices have the power to make it (Bartels and Johnston 2013), so the justices constantly attempt to buttresses their authority, creating a "reservoir of good will" that protects the Court from non- enforcement (Gibson and Caldeira 1992). The justices use the trappings of their office to show they work within a legal institution and not a political one (Enns and Wohlfarth 2013; Gibson, Lodge and Woodson 2014), make appearances and tell the public about the law’s role in their work (Black, Owens and Armaly 2016; Krewson 2019), and align most of their decisions with public opinion to avoid looking radical or untrustworthy (Casillas, Enns and Wohlfarth 2011; Gibson and Caldeira 2009b; Hall and Ura 2015; Nelson and Tucker 2021, but see Johnson and Strother 2021). These actions work. While people believe the justices are influenced by politics, they also believe the justices are principled decision makers (Bartels and Johnston 2013; Scheb and Lyons 2000), and they consistently express high feelings of legitimacy toward the Court (Gibson, Caldeira and Spence 2003), which pressures officials to implement its decisions. Because the reservoir of good will exists, Supreme Court justices can release unpopular de- cisions, but they cannot consistently act in a countermajoritarian manner without draining the reservoir (Gibson and Caldeira 2011). While the justices favor majoritarianism (Hall and Ura 53 2015), however, long-standing practices make it difficult for the Court to show it. The justices release their opinions without elaboration which, given the difficulty of reading them (Black et al. 2016), creates an informational vacuum around the Court’s work. The media fills the void, but outlets only cover a few cases each term (Collins and Cooper 2016), and the public consequently only learns about controversial and news-worthy cases (Zilis 2015). News outlets tend to summa- rize rather than quote the opinion, and they portray every decision as a battle won by one group and lost by another, typically with ideological implications woven throughout the narrative (Davis 2014; Hitt and Searles 2018; Johnston and Bartels 2010; Linos and Twist 2013). This coverage offers people the information and cues they need to understand the decision and react to it (Armaly 2020; Nicholson and Hansford 2014; Zilis 2022), but it also removes focus from the legal reasoning of the opinion, makes it easier for the public to disagree with the decision, and suggests the Court is only releasing controversial opinions. Put differently, Court conventions can lead to the justices looking radical, untrustworthy, and unprincipled, the exact things they want to avoid. Given the reality of the coverage, Supreme Court justices attempt to use the media-worthy parts of their opinion to convey the legal soundness of their decisions and move focus away from the outcome. They approach cases with greater media coverage with more care, taking longer to write opinions and producing more cognitively complex ones, too (Badas and Justus 2022). The justices can also use the opinion writer to cue legal soundness. The media may not explain the Court’s full legal justification for reaching a decision (Linos and Twist 2013), but it does mention the opinion writer in most of its coverage,2 and, in certain situations, that information can signal the legal propriety of a decision and accordingly increase support for it (Bartels and Johnston 2013, 2020). The justices have long believed there is power in asking incongruent justices to write controversial decisions. Court members asked a champion of civil liberties to defend the government’s relocation policies in Korematsu v. United States (1944) (Epstein and Knight 1998); a white Methodist Nixon appointee to write Roe v. Wade (1973) (Woodward and Armstrong 1979); and the only woman 2 Based on our analysis of 92 high-salience cases decided by the Court between the 1981 and 2014 terms, at least one newspaper mentioned the opinion writer in 92% of the cases. About a third of the time, an article that names the opinion writer will also mention his or her ideology. 54 on the Court to strike down a women-only college admissions policy in Mississippi University for Women v. Hogan (1982) (Thomas 2019). In each of these cases, the media reported its delighted surprise that that justice wrote this controversial but obviously legally correct opinion. The justices believe that if they can find an incongruent justice to write the opinion in a salient case, that justice’s presence can increase support for the Court’s decision. Seeing that a female justice wrote an opinion is a useful and disruptive signal that the law might matter, though that signal is issue specific. People use the gender of the majority opinion writer, which is a readily-available cue, to evaluate the procedural correctness of an opinion. Research suggests that Democrats believe female judges are fairer than male ones and Republicans believe the opposite, particularly on issues like abortion or immigration where they fear women’s "soft" natures will lead to lenient rulings (Ono and Zilis 2022). Simultaneously, people are more likely to support a "tough on crime" search and seizure decision or an anti-abortion ruling when a female justice writes it (Boddery, Moyer and Yates 2020; Matthews, Kreitzer and Schilling 2020), which suggests people respond positively when women act against (heavily-stereotyped) behavioral expectations (Heilman and Eagly 2008). On family and women’s issues, then, seeing that a man wrote the opinion should lend credibility to the proceedings and increase support among the Republicans least likely to support them; on criminal issues, however, seeing that a woman wrote the opinion should increase support among those least likely to support it, namely women and Democrats. Ideologically-incongruent justices are also easy to identify and their presence sends a strong message about the power of the law. The public struggles to evaluate Supreme Court outcomes without the help of heuristics like partisanship or ideology (Nicholson and Hansford 2014; Zilis 2022), but when they have that information, they use it and respond accordingly (Hitt and Searles 2018; Zilis 2015). When that cue is not clearly available, people use opinion writers’ ideologies to work through decisions (Boddery and Yates 2014; Clark and Kastellec 2015; Zilis 2021). But what happens when the press reports competing messages, like announcing that a justice wrote an ideologically-distant opinion? The justices clearly believe competing cues draw attention toward the legal correctness of the decision, but this effect should be conditional. For the people pleased 55 with the outcome, seeing that an ideologically-incongruent justice wrote the opinion should simply bolster their belief the Court got the answer right (Armaly 2020; Bartels and Johnston 2020), and their support should remain high. But for the people displeased with the outcome, seeing that an ideologically-incongruent justice wrote an opinion should draw attention away from the outcome and toward the legal correctness of the decision and increase support from those people in the process. Given these expectations and the justices’ own assumptions about incongruent opinion writers, we hypothesize that: H1: Opinions written by an identity-incongruent justice should have higher overall support than those written by an identity-congruent justice. H2: Opinions written by an ideologically-incongruent justice should have higher overall support than those written by an ideologically-congruent justice. Because our theory leads us to believe incongruent opinion writers target specific groups, we also hypothesize that: H3: Opinions written by an identity-incongruent justice should increase public support for a Supreme Court decision among people most likely to disagree with the opinion. H4: Opinions written by an ideologically-incongruent justice should increase public support for a Supreme Court decision among people most likely to disagree with the opinion. 4.2 Motivation and Approach We want to know if and how support for a salient and ideologically-charged Supreme Court opinion changes when the public sees that an ideologically- or identity-incongruent justice wrote the opinion. To do this, we conducted two separate 2 x 2 survey experiments. Participants in the first experiment read about an unnamed Supreme Court decision overturning a state law that unduly burdened women’s access to abortion, based on the Court’s ruling in Whole Women’s Health v. Hellerstedt (2016) (Liptak 2016), and participants in the second experiment read about a ruling allowing three death row inmates’ executions to proceed, based on Glossip v. Gross (2015) (Liptak 56 2015).3 In both experiments, participants in the treatment groups learned that either a liberal or conservative justice, who was male or female, wrote the majority opinion in the case, while participants in the control group did not see any information about the opinion writer.4 We used Lucid Theorem to recruit two nationally representative samples of participants to complete our surveys (Coppock and McClellan 2019).5 In the first survey, fielded between March 29 and April 11, 2021, we asked 733 participants to respond to the decision upholding abortion rights.6 For the second survey, fielded between September 23 and October 14, 2022, we asked 1,497 participants to respond to the decision allowing inmates’ death sentences to proceed.7 Table 4.1 provides a summary of the treatments as well as the number of participants assigned to each group.8 Table 4.1 Experimental Conditions Issue Area Opinion Writer Identity Summary of Condition N No identity (control) Supreme Court opinion strengthened protections for abortion rights 145 Liberal Male Opinion by a liberal male justice strengthened protections for abortion rights 148 Pro Abortion Conservative Male Opinion by a conservative male justice strengthened protections for abortion rights 142 Liberal Female Opinion by a liberal female justice strengthened protections for abortion rights 150 Conservative Female Opinion by a conservative female justice strengthened protections for abortion rights 148 No identity (control) Supreme Court opinion allowed three inmates’ executions to go forward 295 Liberal Male Opinion by a liberal male justice allowed three inmates’ executions to go forward 302 Pro Death Penalty Conservative Male Opinion by a conservative male justice allowed three inmates’ executions to go forward 297 Liberal Female Opinion by a liberal female justice allowed three inmates’ executions to go forward 302 Conservative Female Opinion by a conservative female justice allowed three inmates’ executions to go forward 301 We structured both experiments the same way. After consenting to take the survey, participants answered a handful of questions about the Court before they were randomly sorted into their treatment or control groups and asked to read the newspaper vignette. We next asked participants to identify the profile of the justice that wrote the opinion,9 then asked several questions about the 3 Vignettes are available in the supplemental appendix. 4 While most Americans cannot name a justice without prompting (Birnbaum 2018), at least half of all Americans can identify some of the justices’ names from a list, and certain justices are easier to identify than others (Wolf and Gilbert 2019). To avoid capturing reactions to the justices themselves (Brutger et al. Forthcoming), we decided not to name the justices in our experiment. 5 Demographic breakdowns of our sample are available in Table A1 in the supplemental appendix. 6 We also conducted an initial death penalty experiment with that sample, the results of which are available in Tables A2 and A3 as well as in Figures A3 and A4 in the supplemental appendix. While the results broadly conform with our hypotheses, we conducted the experiment again in October 2022 to better investigate the between-group comparisons (see Gelman and Loken 2013) and present those results here. 7 To guard against concerns of declining data quality in online platforms, particularly during the COVID-19 pandemic (Peyton, Huber and Coppock 2020), we implemented suggestions from Aronow et al. (2020) to screen out inattentive respondents at the beginning of the study. 8 We provide power analyses for both experiments in the supplemental appendix. 9 Results of the manipulation check are available in Tables A6 and A7 in the supplemental appendix. Most participants either recognized the profile of the opinion writer or admitted they did not know rather than answer the question incorrectly. Similar to Ono and Zilis (2022), we restated the opinion writer’s profile before asking about the 57 participant’s feelings regarding the decision, the Supreme Court broadly, and their broader feelings regarding abortion or the death penalty. We measure participant feelings using a combination of feeling thermometers (0 to 100) and agree/disagree/no opinion questions, and we preface these questions by restating the profile of the justice who wrote the opinion, asking, "On a scale from 0 to 100, how would you rate the [conservative/liberal] [male/female] justice’s decision in this [abortion/death penalty] case?" Participants in the control group were asked about the Court’s unattributed decision. For our final question, we asked participants if they thought the Court should be deciding cases in this particular issue area. At the end of each survey, we debriefed the participants and told them the news article they read was fictional. We focus our analysis on abortion and the death penalty for several different reasons. First, abortion and the death penalty are salient issues that garner media coverage (Collins and Cooper 2016), which means people realistically learn about and respond to the Supreme Court’s work in these areas (Hitt and Searles 2018; Zilis 2015); that is, these are issues where the justices would realistically deploy an incongruent opinion writer if one was in the majority coalition.10 Second, we selected two issue areas with policy preferences that are easily associated with specific ideologies: Democrats support abortion rights and Republicans oppose them,11 while Republicans support the death penalty and Democrats oppose it.12 Third, abortion is considered a woman’s issue and the death penalty is not (Reingold et al. 2021), so these issues allow us to examine the role gender plays in response to decisions on a woman’s issue and a more general one. Finally, the Court did not review any cases in these areas during our experimental periods, which limited the potential for external or recency bias to interfere with our results. decision to ensure the treatment worked. 10 We should note an important caveat here: while our vignettes are based on real decisions written by two male justices that received significant media coverage, the situations described in our vignettes are not unique to those particular cases and could feasibly have been heard by the Court at the time we fielded the surveys. Appeals regarding similarly restrictive abortion laws continued to make their way up to the Supreme Court through the 2021 term, as did death penalty cases involving lethal injection (Greenhouse 2021; Sarat 2022). At the time we deployed our surveys, it was also theoretically possible that a liberal male, conservative male, liberal female, or conservative female could have written the decision in either vignette. 11 https://pewrsr.ch/32c6h2a 12 https://pewrsr.ch/3uU3ZRL 58 4.3 Results To examine participants’ support for the Supreme Court’s decision in a pro-abortion or pro-death penalty decision, we used feeling thermometers.13 The higher the score, the greater the support for the decision, with a zero indicating cold and negative feelings toward the decision and a 100 indicating warm and positive feelings toward it. For both the abortion and death penalty vignettes, the median thermometer score was 60 and the mean was between 58 and 59, indicating that, on average, participants were more likely to support the Court’s decision than oppose it. Generally speaking, there are significant ideological differences in overall support. When con- sidering support for a decision upholding abortion rights, participants who identified as Democrats had an average thermometer score of 67.6, which is significantly higher than the average ther- mometer score for participants who identified as Republicans (51, p<0.05) and Independents (53, p<0.05). The opposite is true regarding a decision upholding the use of the death penalty, as par- ticipants identifying as Republicans had an average thermometer score of 69, which is significantly higher than the scores for participants who identified as Democrats (55, p<0.05) and Independents (54, p<0.05). Women are not significantly more supportive of a pro-abortion decision (average thermometer of 61 for women and 57 for men, p=0.13), but they are significantly less supportive of a decision supporting the death penalty than men (average thermometer of 57 for women and 64 for men, p<0.05). Our first objective is to see if participants broadly respond differently to opinions attributed to certain justices. As we stated in Hypotheses 1 and 2, the justices’ historical use of incongruent opin- ion writers leads us to hypothesize that overall support for salient and controversial decisions should increase when an identity-incongruent (Hypothesis 1) or ideologically-incongruent (Hypothesis 2) justice writes the opinion. To test these hypotheses, we turn to the direct treatment effects. We utilize ordinary least squares (OLS) regression, with the feeling thermometer of support for the Court’s opinion as the dependent variable, the different treatment groups (liberal male opinion 13 We also asked participants the simpler "Do you agree or disagree with the Supreme Court’s decision in this case?" The results do not substantively change if we examine treatment response using that dependent variable, as we show in Table A8 in the supplemental appendix. 59 writer, conservative male opinion writer, liberal female opinion writer, conservative female opin- ion writer) as the independent variables, and the control group acting as the comparison category. Table 4.2 contains our analysis of the support for the abortion rights decision in Model 1 and for the death penalty decision in Model 2. Table 4.2 OLS Results, Decision Thermometer, Direct Effects (1) (2) Abortion Death Penalty Liberal Male Justice −9.5∗ −4.9∗ (3.4) (2.3) Conservative Male Justice −8.4∗ −3.3 (3.5) (2.3) Liberal Female Justice −5.7 −3.4 (3.4) (2.3) Conservative Female Justice −8.9∗ 1.2 (3.4) (2.3) Constant 65.8∗ 60.6∗ (2.4) (1.6) Observations 733 1497 R2 0.014 0.007 F Statistic 2.59∗ (df = 4; 728) 2.54∗ (df = 4; 1492) ∗ p<0.05 If incongruent justices increase broad support for a pro-abortion decision, we would expect to see that people are more supportive of a pro-abortion decision when an ideologically-incongruent conservative justice or an identity-incongruent male justice wrote the opinion. As the results in Model 1 of Table 4.2 show, contrary to our hypotheses, we do not find that to be true. Instead, par- ticipants who read about an unattributed decision upholding abortion rights expressed significantly higher support for the decision (66) than did the participants who read about a liberal male writing the decision (56, p<0.05), a conservative male justice writing it (56, p<0.05), or a conservative female justice writing it (57, p<0.05). Participants who read about an identity-incongruent male justice or an ideologically-incongruent conservative justice writing the opinion did not express higher support for it. Interestingly, participants who read about a liberal female justice writing 60 such an opinion were as supportive as the participants who read about an unattributed opinion (60, p=0.09). Applying the same logic to the death penalty experiment, if incongruent justices increase broad support, we would expect to see that people are more supportive of a pro-death penalty experiment when an ideologically-incongruent liberal justice or an identity-incongruent female justice wrote the opinion. Turning to Model 2 of Table 4.2, we again see that no one opinion writer profile increases broad support for a Supreme Court decision that upholds the death penalty. The average participant who read about an unattributed decision allowing inmates’ executions to go forward had a feeling thermometer score of 61, which is no different from the feeling thermometer scores for anyone who read about a liberal female justice writing the opinion (57, p=0.13), a conservative male justice writing it (57, p=0.15), or a conservative female justice (59, p=0.61). Participants who read about a decision written by a liberal male justice, however, were significantly less supportive of the Court’s decision to uphold the death penalty than were those in the control group (56, p<0.05), again showing the opinion writer does little to increase broad support for the decision, congruent or not. Despite having found no support for our hypotheses that incongruent opinion writers universally increase support for a Supreme Court decision, we still wanted to know if seeing that a certain justice wrote an opinion increased support for it among those predisposed not to like it. As we explain in Hypotheses 3 and 4, we expect that incongruent justices specifically increase support among those least likely to support the Court’s decision in the first place. To address these hypotheses, we look at participant support for a Supreme Court decision given their treatment group, partisanship, and gender. We again used OLS for this analysis, and the results are presented in Table 4.3 as well as Figures 4.1 and 4.3.14 Again beginning with the abortion experiment, the results in Figure 4.1 show that Democrats (left) are more likely to support the decision than are Republicans (right).15 As the left side of 14 We provide results with a full set of participant controls in Table A10. 15 We analyze the responses of participants who identify as Republicans and Democrats because partisanship influences responses to Supreme Court decisions (Armaly 2020; Bartels and Johnston 2020). As we explain in the supplemental appendix, we code "leaners" as partisans because they act like partisans (Smidt 2017). That leaves a 61 Figure 4.1 shows, there are small differences in support between male and female Democrats. Male Democrats’ support for a pro-abortion decision did not significantly change based on the treatment they received, but they did feel significantly more positive about an unattributed majority opinion (77) than they did about one written by a liberal male (62, p<0.05), a conservative male (62, p<0.05), or a conservative female justice (57, p<0.05). Mirroring the results of the direct treatment effects in Table 4.2, only a liberal female justice writing an opinion garners as much support as the unattributed decision in the control group (77 vs. 68, p=0.22). Conversely, female Democrats’ support for a pro-abortion decision remains high across all four treatments and the control group; no matter who wrote the opinion, female Democrats felt supportive of it. The right side of Figure 4.1 demonstrates that, for the most part, support among Republican participants does not differ from an unassigned opinion, regardless of gender. The only outlier is when female Republican are less supportive of opinions written by liberal male justices (45, p<0.05). small number of participants who identify as true Independents (see Tables A11 and A12 for numbers and comparison to our larger treatment groups). We control for them in our models but do not discuss them here because Independents do not react like partisans (Klar and Krupnikov 2016). We provide an analysis of Independents in Figures A5, A6, and A7 in the supplemental appendix. 62 Table 4.3 OLS Results, Decision Thermometer, Expanded Models Abortion Death Penalty Liberal Male Justice −15.6∗ −5.9 (7.6) (4.7) Conservative Male Justice −15.9∗ −6.9 (7.8) (4.5) Liberal Female Justice −9.0 1.2 (7.3) (4.5) Conservative Female Justice −20.3∗ −0.3 (7.0) (4.7) Female Respondent −10.0 −11.0∗ (6.5) (4.6) Liberal Male Justice 18.8 7.9 x Female Respondent (9.6) (6.4) Conservative Male Justice 15.2 7.7 x Female Respondent (10.0) (6.5) Liberal Female Justice 17.9 3.7 x Female Respondent (9.7) (6.6) Conservative Female Justice 19.4∗ 6.6 x Female Respondent (9.1) (6.7) Independent Respondent −19.9 0.7 (10.3) (5.6) Republican Respondent −27.7∗ 9.9 (8.7) (5.2) Female 12.5 −5.2 x Independent Respondent (13.8) (8.2) Female 24.8∗ 4.7 x Republican Respondent (11.2) (7.4) Liberal Male Justice 10.4 −12.6 x Independent Respondent (14.6) (9.3) Conservative Male Justice 11.3 2.0 x Independent Respondent (14.9) (8.4) Liberal Female Justice 8.4 −13.8 x Independent Respondent (14.7) (8.2) Conservative Female Justice 17.3 −11.3 x Independent Respondent (14.0) (8.4) Liberal Male Justice 10.6 −8.3 x Republican Respondent (11.7) (7.6) Conservative Male Justice 18.9 3.1 x Republican Respondent (12.0) (7.2) Liberal Female Justice 7.6 −16.0∗ x Republican Respondent (11.4) (7.2) Conservative Female Justice 23.0∗ 0.9 x Republican Respondent (11.5) (7.3) Liberal Male Justice x Female −26.4 16.7 x Independent Respondent (19.5) (12.3) Conservative Male Justice −18.3 −0.2 x Female x Independent Respondent (19.2) (11.8) Liberal Female Justice −21.9 5.3 x Female x Independent Respondent (19.3) (11.7) Conservative Female Justice −42.6∗ 7.3 x Female x Independent Respondent (20.3) (11.8) Liberal Male Justice −33.7∗ 1.3 x Female x Republican Respondent (15.3) (10.3) Conservative Male Justice −31.3∗ −10.3 x Female x Republican Respondent (15.6) (10.3) Liberal Female Justice −30.1∗ 5.7 x Female x Republican Respondent (15.1) (10.3) Conservative Female Justice −37.2∗ −5.9 x Female x Republican Respondent (15.3) (10.2) Constant 77.4∗ 62.6∗ (5.1) (3.2) Observations 733 1,497 F Statistic 3.02∗ (df = 29; 703) 3.35∗ (df = 29; 1467) ∗ p<0.05 63 Approval for Pro-Abortion Decision Democrat Republican 100 90 80 Predicted Value - Decision Thermometer 70 60 50 40 30 20 10 0 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups Women Men Figure 4.1 Mean differences in participant feelings toward Supreme Court’s decision strengthening abortion rights for Democrat (left) and Republican (right) participants. Vertical bars show 95% confidence intervals. Examining these results in more detail, the expectations outlined in Hypotheses 3 and 4 suggest that seeing that an incongruent male or conservative justice wrote an opinion upholding abortion rights should increase support for that decision among those least likely to agree with it, namely among men and Republicans. Figure 4.2a shows the differences in support between male and female participants for pro-abortion decisions, broken down by partisanship. Aligning with the results we provided in Figure 4.1, there are no gendered differences in support in our data, period: male and female Democrat participants are equally likely to support an Supreme Court decision upholding the death penalty regardless of who wrote it, as are male and female Republican participants. This finding is unsurprising giving that gendered differences in abortion support are not always as obvious as the partisan ones (Lizotte 2020), but it does not align with our expectation in Hypothesis 3. 64 Differences between Female and Male Participants, Approval for Pro-Abortion Decision Democrat Republican 45 Difference in Feeling Thermometer (Female - Male) 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (a) First Differences by Gender Differences between Republican and Democrat Participants, Approval for Pro-Abortion Decision Men Women Difference in Feeling Thermometer (Republican - Democrat) 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (b) First Differences by Partisanship Figure 4.2 First differences of participant feelings toward Supreme Court’s decision strengthening abortion rights by (a) participant gender (Democrats left, Republicans right) and (b) participant partisanship (male left, female right). Vertical bars represent 95% confidence intervals. 65 But, as we showed in Figure 4.1, there are real partisan differences in support for a decision upholding abortion rights, and, when examining the different reactions across partisans, we see that having an ideologically-incongruent justice write the opinion matters. Looking at Figure 4.2b, partisan differences disappear when certain opinion writers take the lead. Aligning with our expectations, we see that when a conservative justice wrote the opinion, the partisan difference in support disappears for male participants. But upon further examination, this result is slightly more complicated: when either a male or female conservative justice wrote the opinion upholding abortion rights, Republican men become more supportive of the decision, but when a female conservative justice wrote the opinion, support among male participants identifying as Democrats decreased. This means that in the aggregate, the Court ends up with about the same level of support for the decision. The right side of Figure 4.2b provides evidence that, among women, Democrat participants are always more supportive of a decision than Republicans, unless the opinion is unattributed, at which point support is high from both female Democrat and female Republican participants. We consequently find some support for Hypothesis 4, though the results suggest that increasing support with the people least likely to support the decision comes at the cost of decreasing support among those most likely to support it in the first place. Shifting our attention to the death penalty experiment, Figure 4.3 shows Democrats (left) are less supportive of a pro-death penalty decision than Republicans (right). As the left side of Figure 4.3 demonstrates, neither male nor female Democrats vary in their support of opinion writers for death penalty decisions. That is, who writes the opinion does not alter support for a decision for Democratic participants. The right side of Figure 4.3 shows a similar pattern for Republican women, whose support for a pro-death penalty decision remains constant regardless of the opinion writer. Republican men, however, do express different levels of support for an opinion when the author changes. Republican men prefer opinions authored by a conservative female justice (73, p<0.05) or opinions attributed to the Court (73, p<0.05) compared to a liberal male justice (58). Similarly, Republican men show higher support for a decision penned by a conservative male justice (69, p<0.05), conservative female justice (73, p<0.05) or the Court (73, p<0.05) than they do for 66 an opinion written by a liberal female justice (58). Approval for Pro-Death Penalty Decision Democrat Republican 100 90 80 Predicted Value - Decision Thermometer 70 60 50 40 30 20 10 0 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups Women Men Figure 4.3 Mean differences in participant feelings toward Supreme Court’s decision upholding the use of the death penalty for Democrat (left) and Republican (right) participants. Vertical bars show 95% confidence intervals. For our death penalty experiment, Hypotheses 3 and 4 lead us to expect that support for a pro-death penalty decision increases among female and Democratic participants when a female or liberal justice wrote the opinion. Figure 4.4a provides the differences in support by male or female participants by partisanship. Across all but one category, there is no difference in female and male support for death penalty opinions for Democrats or Republicans. The sole exception is in the control group for Democrats, as female participants are significantly less supportive of death penalty opinions "by the Court" than male participants (52 vs. 62, p<0.05). When combined with the results of the abortion experiment, these results suggest that, contrary to Hypothesis 3, seeing that an identity-incongruent justice wrote an opinion does not increase support for the Court’s decision. Turning next to Figure 4.4b, we see the first differences in support for death penalty decisions by partisanship amongst male and female participants. The left side of Figure 4.4b provides evidence of partisan variation in support by opinion author for male participants. Republican men are more 67 supportive than Democratic men of a pro-death penalty opinion written by a conservative male justice (69 vs. 56, p<0.05) or a conservative female justice (73 vs. 62, p<0.05). Interestingly, when a liberal male justice (p=0.77), liberal female justice (p=0.22), or the Court itself (p=0.06) produced the opinion, those partisan differences disappear. But, as we saw with the abortion experiment, the elimination of this gap is not necessarily what the justices want to see, as its driven by Republican men, who are most likely to support the death penalty, withdrawing support when a liberal justice wrote the opinion. The right side of Figure 4.4b shows similar results for the partisan differences between female participants. Republican women exhibit higher support than Democratic women when an pro-death penalty opinion is written by a conservative female justice (68 vs. 58, p<0.05) or "by the Court" (66 vs. 52, p<0.05), and there are no partisan differences when a liberal male or a liberal female justice produces the difference. Once again, these results are driven by decreases in Republican support and not increases from Democrats. When combined with our findings from the abortion experiment, these results suggest that ideologically-incongruent justices do modify support for a Supreme Court decision, just not in the manner the justices intended. They eliminate partisan differences, but they do so by holding steady or slightly increase support from one group at the expense of those most likely to support the decision in the first place. 68 Differences Between Female and Male Participants, Approval for Pro-Death Penalty Decision Democrat Republican 45 Difference in Feeling Thermometer (Female - Male) 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (a) First Differences by Gender Differences Between Republican and Democrat Participants, Approval for Pro-Death Penalty Decision Men Women Difference in Feeling Thermometer (Republican - Democrat) 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (b) First Differences by Partisanship Figure 4.4 First differences of participant feelings toward Supreme Court’s decisions strengthening the death penalty by (a) participant gender (Democrats left, Republicans right) and (b) participant partisanship (male left, female right). Vertical bars represent 95% confidence intervals. 69 4.4 Conclusion At least a few times each term, the typically placid Supreme Court wades into a salient and controversial debate and draws media attention and fire when the justices eventually release their decision in it to the world. While the justices cede control over the direction that conversation takes (Hitt and Searles 2018; Zilis 2015), they can use certain high-value signals, like the opinion writer’s ideology and identity, to show the public their dedication to the law and increase support for that decision. History shows that Supreme Court justices believe that an opinion writer’s attributes can influence acceptance of a case by the public, and the justices strategically assign certain opinions with that belief in mind. We sought to better understand how those strategic decisions influence public support for the Court’s decision. We found that incongruent opinion writers never broadly increase support for a decision. Instead, we found that incongruent opinion writers specifically target the people least likely to support a controversial decision, at the cost of pre-existing support. In this manuscript, we find that strategically-selected opinion writers whose ideology are at odds with a decision can influence support for Supreme Court decisions, though not in the manner the justices intended. While identity-incongruent justices do not move public opinion at all in other pro-abortion or pro-death penalty decisions, ideologically-incongruent justices can shift opinion, though they are essentially robbing Peter to pay Paul: they incrementally increase or hold steady the support offered by those least likely to support the Court’s decision, but they do so at the cost of losing support among those most likely to agree with the justices. The justices have long acknowledged they strategically select justices to write opinions and our results suggest that, while this strategy may not increase support in the aggregate, it can reduce partisan divides in support. Is asking an incongruent justice to write an opinion worth the effort, then? While our exper- iment suggests employing incongruent opinion writers results in limited benefits, we still believe attempting to reduce negative support is always worth the effort. The Court’s approval has declined in recent years (Haglin et al. 2020), and the justices have both acknowledged this problem and done things to correct it. They go to law schools, policy centers, and think tanks to explain the legal nature their jobs to the public (Barnes 2022; deVogue 2021; Ramsey 2021); they transmit 70 oral argument in real time so the public can hear their process (Cordova 2021); and sometimes the justices even shift their positions to keep the Court from looking ideological (Toobin 2012). None of these actions are entirely successful – Justices Barrett and Alito got lambasted for deliv- ering their comments to fawning conservative crowds (Benen 2021; Lithwick 2021), the novelty of listening to oral argument eventually dropped off (Houston, Johnson and Ringsmuth 2023), and Chief Justice John Roberts is persona non grata in most conservative circles because he voted to uphold the Affordable Care Act (Kaplan 2018) – but the justices still try to protect their institution (Biskupic 2019; Litman, Murray and Shaw 2020). Asking incongruent justices to write opinions in salient cases is just another way of doing this. And, importantly, this option is an increasingly available one as the Court continues to diversify in different ways (Greenhouse 2021; Howe 2022). Would incongruent opinion writers make a difference in a case about affirmative action? Campaign spending? Gun rights? We can be sure that if the option is available, the justices will try to use it. In the future, scholars could expand this research by looking at other salient issue areas, like the ones we just discussed, and by looking at different types of identities. We focused on two obvious identity characteristics here, but many other identities can be salient to Supreme Court decision making at different times (Baum 2006; Epstein and Knight 2013). Future work could examine how a justice’s race might affect support for a decision in an affirmative action case, or how a justice’s status as a parent might affect support for gun rights or the death penalty, or how religion affects support for the death penalty. Scholars could also compare effects across decisions that uphold or restrict certain rights and see if the public responds differently when the Court gives and takes. Our decision to use a single survey experiments limits our ability to examine the more dynamic effects of this process, but future scholars could employ multiple survey waves to examine this process. Scholars could also use real-time public opinion measures of support to see how support changes over time. While we designed our experiment to simulate the real world process through which people consume information about the Supreme Court and therefore maximize external validity (Zilis 2015), the design also limits our ability to see how long this effect lasts. One could use survey data to look at real time effects both immediately after an opinion gets released and over the course 71 of several months. Our results suggest that strategically assigning opinions affects immediate support for a decision, but looking at these effects long term is important too. There is, in short, always more work to be done. 72 CHAPTER 5: CONCLUSION For decades, judges and the courts have portrayed themselves as simple, non-political "umpires" who only interpret the law (Segal and Spaeth 2002). This strategy was relatively successful, with the judiciary having high levels of support relative to the other constitutional branches (Gibson and Caldeira 2009b). However, the federal judiciary now finds itself at the center of political discourse. From public outcry following the decision in Dobbs v. Jackson Women’s Health Organization (2022) (Reserach 2022) to detailed examinations of the financial disclosures of district court judges (Tolan and Chapman 2023), the judiciary is under the microscope of public scrutiny. But why could this be happening and what are the potential repercussions? In the preceding essays, I provide insight into both why this has occurred as well as one potential repercussion for the courts. A significant shift in why this has been happening is the polarization of the lower federal courts. The recent presidential campaigns of Trump and Biden have emphasized nominations to the federal judiciary and plans to reform the bench (King, McAndrews and Ostrander 2022; Kapur 2020). Coupled with reforms to the confirmation process (Binder, Madonna and Smith 2007; Boyd, Lynch and Madonna 2015), the confirmation of federal judges is now a wholly partisan process. The norms of bipartisanship that once shielded judges from being considered politicians in robes have broken down and the majority of the public believes reforms are necessary to rein in overly-political judges (Ross 2022). There are likely several ramifications for the politicization of the judiciary. One such ramification is that judges may have to alter their behaviors as past strategies – aimed at increasing public perceptions of court outcomes – no longer work. The selection of an opinion author to bolster public support for a case may have worked in the past (Woodward and Armstrong 1979), but no longer influences public opinion. Cases that are salient in the eyes of the public – such as the death penalty or abortion – are not impacted by these strategies. It is likely that, with the continued publicity and politicization of the courts, other strategies likely may also need to be reevaluated. My goal in the previous pages was to demonstrate the evolving nature of the judicial process 73 and its potential outcomes. From the nomination process to the public’s support for case outcomes, how the courts are viewed in the eyes of elites and the public has significantly changed over recent decades. All levels of the federal judiciary now face intense scrutiny during their confirmation process, are the focal point of national elections, and are continuously evaluated by the public. Change is one of the few constants we have in life. 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Zink, James R., James F. Spriggs and John T. Scott. 2009. “Courting the Public: The Influence of Decision Attributes on Individuals’ Views of Court Opinions.” Journal of Politics 71(3):909–925. 89 APPENDIX A: CHAPTER 2 APPENDIX A.1 Supplemental Tables and Figures Figure A.1 Correlation: JCS and CF Scores 90 Table A.1 Senator Voting for Circuit Nominees Dependent variable Senator Vote Female 0.307 (0.360) Black Nominee -0.490 (0.608) LatinX Nominee 1.435∗∗ (0.583) Asian Nominee 0.162 (0.581) Nominee Age 0.085∗∗∗ (0.032) Elite Law School 1.743∗ (0.892) Qualified -0.117 (0.422) JCS Difference -1.098 (1.523) JCS Shift -1.584∗∗ (0.781) Senate Majority Size -0.113∗ (0.063) Home State Senator 0.979∗∗∗ (0.257) Senator Election Year 0.597∗∗∗ (0.230) Senator Previous Win % -0.004∗∗ (0.002) Presidential Approval -0.020 (0.024) Obama (Pre) 3.156∗ (1.648) Obama (Post) 3.334∗ (1.848) Trump 1.509 (1.491) Biden 2.814∗∗ (1.411) JCS Difference × Obama (Pre) -1.300 (1.850) JCS Difference × Obama (Post) -1.144 (1.647) JCS Difference × Trump -4.564∗∗∗ (1.728) JCS Difference × Biden -8.802∗∗∗ (1.896) JCS Shift × Obama (Pre) 3.240∗∗∗ (1.237) JCS Shift × Obama (Post) 1.997 (1.664) JCS Shift × Trump 1.043 (0.908) JCS Shift × Biden 0.286∗∗∗ (1.066) Elite School × Obama (Pre) -2.192∗ (1.166) Elite School × Obama (Post) -4.653∗∗∗ (1.446) Elite School × Trump -2.442∗∗ (1.077) Elite School × Biden -2.340∗∗ (1.094) Constant -1.283 (2.219) Observations 7,573 Log Likelihood −2,876 Akaike Inf. Crit. 5,815 Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01 91 Table A.2 Senator Voting for District Nominees Dependent variable Senator Vote Female 0.583∗∗ (0.274) Black Nominee 0.039 (0.349) LatinX Nominee 0.839∗∗∗ (0.319) Asian Nominee -0.214 (0.535) Other Nominee 1.442∗∗∗ (0.459) Nominee Age 0.010 (0.017) Elite Law School -0.397∗ (0.240) Qualified -0.515 (0.461) JCS Difference -10.181∗∗∗ (1.857) JCS Shift -6.442∗∗∗ (1.846) Senate Majority Size -0.037 (0.030) Home State Senator 1.945∗∗∗ (0.300) Senator Election Year 0.321∗∗∗ (0.115) Senator Previous Win % -0.009∗∗∗ (0.001) Presidential Approval -0.009 (0.018) Obama (Pre) -7.591∗∗∗ (1.257) Obama (Post) -9.468∗∗∗ (1.359) Trump -8.200∗∗∗ (1.326) Biden -8.471∗∗∗ (1.395) JCS Difference × Obama (Pre) 7.669∗∗∗ (1.896) JCS Difference × Obama (Post) 9.533∗∗∗ (1.914) JCS Difference × Trump 6.619∗∗∗ (1.906) JCS Difference × Biden 2.758 (2.159) JCS Shift × Obama (Pre) 7.391∗∗∗ (1.938) JCS Shift × Obama (Post) 6.958∗∗∗ (1.974) JCS Shift × Trump 6.863∗∗∗ (1.906) JCS Shift × Biden 5.181∗∗∗ (1.919) Qualified × Obama (Pre) -0.472 (0.647) Qualified × Obama (Post) 0.442 (0.709) Qualified × Trump -0.685 (0.576) Qualified × Biden Constant 11.666 (1.872)∗∗∗ Observations 19,190 Log Likelihood −7,790 Akaike Inf. Crit. 15,643 Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01 92 Figure A.2 Correlation: JCS and CF Scores Figure A.3 Correlation: JCS and CF Scores 93 APPENDIX B: CHAPTER 3 APPENDIX B.1 Supplemental Tables & Models Table B.1 Demographics of President Trump’s Confirmed Judges District Circuit Supreme Total Confirmations 173 53 3 229 Gender: Male 75.5% 79.4% 66.7% 76.4% Female 24.5% 20.6% 33.3% 23.6% Race/Ethnicity: White 87.3% 84.9% 100% 86.9% African American 7.0% 0% 0% 5.2% Hispanic 2.3% 1.9% 0% 2.2% Asian 1.1% 11.3% 0% 3.5% Other 2.3% 1.9% 0% 2.2% White Male 66.5% 67.9% 66.6% 66.8% Elite School 19.7% 56.6% 66.6% 28.8% Average Age 50 47 50 ABA Ratings: Well Qualified 68.2% 77.4% 100% 70.7% Qualified 28.9% 17.0% 0% 25.8% Not Qualified 2.9% 5.7% 0% 3.5% 94 Table B.2 First Term Judicial Nomination Outcomes: Reagan–Trump Successes Success Rate Returned Rate Failure Rate Reagan District 125 86.8% 11.8% 1.4% Circuit 32 82.1% 12.8% 5.1% Supreme 1 100% 0% 0% HW Bush District 148 75.9% 23.6% 0.5% Circuit 37 75.5% 24.5% 0% Supreme 2 100% 0% 0% Clinton District 167 83.5% 15% 1.5% Circuit 29 72.5% 25% 2.5% Supreme 2 100% 0% 0% W Bush District 168 79.3% 20.3% 0.5% Circuit 34 35.4% 54.2% 10.4% Supreme 0 – – – Obama District 138 66.7% 32.4% 1.0% Circuit 27 58.7% 39.1% 2.2% Supreme 2 100% 0% 0% Trump District 173 58.8% 39.8% 1.4% Circuit 53 70.7% 28.0% 1.3% Supreme 3 100% 0% 0% 95 APPENDIX C: CHAPTER 4 APPENDIX C.1 Treatments Abortion Vignette: The Supreme Court strengthened constitutional protections for abortion rights this week, striking down parts of a restrictive Texas law that would have drastically reduced the number of abortion clinics in the state. The decision, written by one of the Court’s [liberal/conservative] [male/female] justices, reaffirmed the constitutional right to an abortion, ruling that Texas’s law placed an undue burden on a woman’s ability to obtain an abortion. Figure C.1 Abortion Experiment Sample Vignette: Liberal Male Opinion Author 96 Death Penalty Vignette: This week, the Supreme Court ruled against three death row inmates who claimed the drugs that would be used in their executions would cause them excruciating pain, vio- lating the Eighth Amendment’s protections against cruel and unusual punishment. The decision, written by one of the Court’s [liberal/conservative] [male/female] justices, said the inmates’ executions could go forward as they failed to identify a preferable execution method or make the case that the challenged drug entailed a substantial risk of severe pain Figure C.2 Death Penalty Experiment Sample Vignette: Liberal Male Opinion Author 97 C.2 Participant Demographics from Experiments We treat participants as Democrats if they identified as a "Strong Democrat," "Not very strong Democrat," "Independent Democrat," or "Other - leaning Democrat." We follow the same coding scheme for Republicans. Participants who answered "Independent - neither" or "Other - neither" are coded as Independents. Questions are available later in the appendix. Table C.1 Participant Demographics Variable Abortion Death Penalty Democrat 354 (48%) 703 (47%) Republican 259 (35%) 492 (33%) Independent 120 (16%) 302 (20%) Male 321 (44%) 719 (48%) Female 412 (56%) 778 (52%) White 571 (78%) 1110 (74%) Black 61 (8%) 178 (12%) Asian or Pacific American 47 (6%) 83 (6%) Native American 10 (1%) 29 (2%) Some other race 20 (3%) 91 (6%) Prefer not to answer 24 (3%) 6 (1%) Some high school or less education 29 (4%) 82 (5%) High school graduate 134 (18%) 402 (27%) Other post high school vocational training 29 (4%) 49 (3%) Completed some college, but no degree 123 (17%) 275 (18%) Associate’s degree 70 (9%) 158 (11%) Bachelor’s degree 187 (26%) 335 (22%) Master’s or professional degree 115 (16%) 143 (10%) Doctorate degree 30 (4%) 42 (3%) None of the above 16 (2%) 11 (1%) Not of Hispanic, Latino, or Spanish origin 659 (90%) 1307 (87%) Mexican 30 (4%) 91 (6%) Cuban 4 (1%) 10 (1%) Puerto Rican 2 (1%) 0 (0%) Another Hispanic, Latino, or Spanish origin 16 (2%) 79 (5%) Prefer not to answer 22 (3%) 10 (1%) Less than $14,999 103 (14%) 274 (18%) $15,000 to $19,999 39 (5%) 84 (6%) $20,000 to $24,999 44 (6%) 114 (8%) $25,000 to $29,999 32 (4%) 96 (6%) $30,000 to $34,999 46 (6%) 64 (4%) $35,000 to $39,999 24 (3%) 85 (6%) $40,000 to $44,999 28 (4%) 53 (4%) $45,000 to $49,999 28 (4%) 60 (4%) $50,000 to $54,999 27 (4%) 82 (5%) $55,000 to $59,999 11 (2%) 39 (3%) $60,000 to $64,999 26 (4%) 43 (3%) $65,000 to $69,999 15 (2%) 25 (2%) $70,000 to $74,999 25 (3%) 56 (4%) $75,000 to $79,999 25 (3%) 45 (3%) $80,000 to $84,999 11 (2%) 19 (1%) $85,000 to $89,999 12 (2%) 26 (2%) $90,000 to $94,999 12 (2%) 18 (1%) $95,000 to $99,999 17 (2%) 27 (2%) $100,000 to $124,999 52 (7%) 78 (5%) $125,000 to $149,999 39 (5%) 64 (4%) $150,000 to $174,999 16 (2%) 58 (4%) $175,000 to $199,999 16 (2%) 22 (1%) $200,000 to $249,999 9 (1%) 20 (1%) $250,000 and above 11 (2%) 23 (2%) Prefer not to answer 65 (9%) 22 (1%) 98 C.3 Additional Death Penalty Experiment In the manuscript, we show the results from two experiments: an abortion experiment conducted in April 2021 and a larger death penalty experiment conducted in October 2022. We also conducted the death penalty experiment in April 2021 and we present the results of the baseline and full models in the last column (3) of Tables C.2 and C.3 as well as in Figures C.3 and C.4. As the results presented here show, while some of the conditional effects are different in the October 2022 sample, our broad findings remain substantively similar across the two experiments and align with our expectations. Table C.2 April 2021 Death Penalty Experiment OLS, Decision Thermometer, Direct Effects (1) (2) (3) Abortion Death Penalty Death Penalty Manuscript Manuscript Additional April 2021 October 2022 April 2021 Liberal Male Justice −9.5∗ −4.9∗ −0.2 (3.4) (2.3) (3.1) Conservative Male Justice −8.4∗ −3.3 −2.0 (3.5) (2.3) (3.2) Liberal Female Justice −5.7 −3.4 −6.2 (3.4) (2.3) (3.1) Conservative Female Justice −8.9∗ 1.2 −3.6 (3.4) (2.3) (3.1) Constant 65.8∗ 60.6∗ 62.2∗ (2.4) (1.6) (2.2) Observations 733 1497 747 R2 0.014 0.007 0.007 F Statistic 2.59∗ (df = 4; 728) 2.54∗ (df = 4; 1492) 1.33 (df = 4; 742) ∗ p<0.05 99 Approval for Pro-Death Penalty Decision April 2021 Experiment Democrat Republican 100 90 Predicted Value - Decision Thermometer 80 70 60 50 40 30 20 10 0 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups Women Men Figure C.3 Mean differences in participant feelings toward Supreme Court’s decision upholding the use of the death penalty for Democratic (left) and Republican (right) participants in the April 2021 experiment. Vertical bars show 95% confidence intervals. 100 Table C.3 Expanded Models Abortion Death Penalty Death Penalty Manuscript Manuscript Additional Liberal Male Justice −15.6∗ −5.9 15.1∗ (7.6) (4.7) (6.9) Conservative Male Justice −15.9∗ −6.9 14.9∗ (7.8) (4.5) (6.7) Liberal Female Justice −9.0 1.2 −1.5 (7.3) (4.5) (6.8) Conservative Female Justice −20.3∗ −0.3 3.3 (7.0) (4.7) (6.7) Female Respondent −10.0 −11.0∗ −0.4 (6.5) (4.6) (6.4) Liberal Male Justice 18.8 7.9 −14.8 x Female Respondent (9.6) (6.4) (9.1) Conservative Male Justice 15.2 7.7 −20.4∗ x Female Respondent (10.0) (6.5) (8.9) Liberal Female Justice 17.9 3.7 0.3 x Female Respondent (9.7) (6.6) (9.1) Conservative Female Justice 19.4∗ 6.6 −7.7 x Female Respondent (9.1) (6.7) (9.0) Independent Respondent −19.9 0.7 22.9∗ (10.3) (5.6) (10.2) Republican Respondent −27.7∗ 9.9 28.2∗ (8.7) (5.2) (7.1) Female 12.5 −5.2 −23.6 x Independent Respondent (13.8) (8.2) (12.6) Female 24.8∗ 4.7 −15.2 x Republican Respondent (11.2) (7.4) (9.4) Liberal Male Justice 10.4 −12.6 −42.1∗ x Independent Respondent (14.6) (9.3) (13.7) Conservative Male Justice 11.3 2.0 −24.2 x Independent Respondent (14.9) (8.4) (18.8) Liberal Female Justice 8.4 −13.8 −29.8∗ x Independent Respondent (14.7) (8.2) (14.6) Conservative Female Justice 17.3 −11.3 −39.2∗ x Independent Respondent (14.0) (8.4) (13.3) Liberal Male Justice 10.6 −8.3 −31.5∗ x Republican Respondent (11.7) (7.6) (10.4) Conservative Male Justice 18.9 3.1 −22.9∗ x Republican Respondent (12.0) (7.2) (10.1) Liberal Female Justice 7.6 −16.0∗ −15.9 x Republican Respondent (11.4) (7.2) (9.9) Conservative Female Justice 23.0∗ 0.9 −4.3 x Republican Respondent (11.5) (7.3) (10.1) Liberal Male Justice x Female −26.4 16.7 44.3∗ x Independent Respondent (19.5) (12.3) (17.6) Conservative Male Justice −18.3 −0.2 36.1 x Female x Independent Respondent (19.2) (11.8) (21.4) Liberal Female Justice −21.9 5.3 27.1 x Female x Independent Respondent (19.3) (11.7) (18.3) Conservative Female Justice −42.6∗ 7.3 47.7∗ x Female x Independent Respondent (20.3) (11.8) (17.2) Liberal Male Justice −33.7∗ 1.3 39.0∗ x Female x Republican Respondent (15.3) (10.3) (13.6) Conservative Male Justice −31.3∗ −10.3 20.8 x Female x Republican Respondent (15.6) (10.3) (13.5) Liberal Female Justice −30.1∗ 5.7 11.2 x Female x Republican Respondent (15.1) (10.3) (13.3) Conservative Female Justice −37.2∗ −5.9 4.8 x Female x Republican Respondent (15.3) (10.2) (13.4) Constant 77.4∗ 62.6∗ 54.1∗ (5.1) (3.2) (4.4) Observations 733 1497 747 R2 0.110 0.062 0.132 F Statistic 3.02∗ (df = 29; 703) 3.35∗ (df = 29; 1467) 3.76∗ (df = 29, 717) ∗ p<0.05 101 Differences Between Female and Male Participants, Approval for Pro-Death Penalty Decision April 2021 Experiment Democrat Republican Difference in Feeling Thermometer (Female - Male) 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (a) First Differences by Gender Differences Between Republican and Democrat Participants, Approval for Pro-Death Penalty Decision April 2021 Experiment Difference in Feeling Thermometer (Republican - Democrat) Men Women 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (b) First Differences by Partisanship Figure C.4 First differences of participant feelings toward Supreme Court’s decisions strengthening the death penalty by (a) participant gender (Democrats left, Republicans right) and (b) participant partisanship (men left, female right) in the April 2021 experiment. Vertical bars represent 95% confidence intervals. 102 Table C.4 April 2021 Death Penalty Experiment, Participant Demographics Variable Death Penalty Democrat 348 (46%) Republican 274 (37%) Independent 125 (17%) Male 307 (41%) Female 440 (59%) White 577 (77%) Black 77 (10%) Asian or Pacific American 46 (6%) Native American 4 (1%) Some other race 27 (4%) Prefer not to answer 16 (2%) Some high school or less education 25 (3%) High school graduate 132 (18%) Other post high school vocational training 26 (3%) Completed some college, but no degree 151 (20%) Associate’s degree 77 (10%) Bachelor’s degree 180 (24%) Master’s or professional degree 121 (16%) Doctorate degree 26 (3%) None of the above 9 (1%) Not of Hispanic, Latino, or Spanish origin 661 (88%) Mexican 32 (4%) Cuban 4 (1%) Puerto Rican 3 (1%) Another Hispanic, Latino, or Spanish origin 21 (3%) Prefer not to answer 26 (3%) Less than $14,999 97 (13%) $15,000 to $19,999 41 (5%) $20,000 to $24,999 32 (4%) $25,000 to $29,999 38 (5%) $30,000 to $34,999 37 (5%) $35,000 to $39,999 25 (3%) $40,000 to $44,999 30 (4%) $45,000 to $49,999 29 (4%) $50,000 to $54,999 33 (4%) $55,000 to $59,999 19 (3%) $60,000 to $64,999 21 (3%) $65,000 to $69,999 25 (3%) $70,000 to $74,999 17 (2%) $75,000 to $79,999 29 (4%) $80,000 to $84,999 11 (1%) $85,000 to $89,999 15 (2%) $90,000 to $94,999 10 (1%) $95,000 to $99,999 21 (3%) $100,000 to $124,999 52 (7%) $125,000 to $149,999 49 (7%) $150,000 to $174,999 16 (2%) $175,000 to $199,999 11 (1%) $200,000 to $249,999 17 (2%) $250,000 and above 13 (2%) Prefer not to answer 59 (8%) 103 Table C.5 April 2021 Death Penalty Experiment, Manipulation Check Treatment Incorrect Correct Don’t No Total Profile Selected Profile Selected Remember Answer Liberal Male Justice 28 60 50 11 149 Conservative Male Justice 30 45 60 10 145 Liberal Female Justice 36 58 45 10 149 Conservative Female Justice 55 44 43 10 152 C.4 Power Analysis We conducted a power analysis using the pwr package in R to confirm the minimum sample size required to test the study hypotheses. For the abortion experiment, the results indicated the required sample size to achieve 90% power for detecting a small effect size (f2 = 0.04), at a significance criterion of 𝛼 = 0.05, was N = 741; to achieve 80% power under the same conditions, the sample size would need to be 598. Our sample has 733 participants (f2 = 0.042 at 90% power, f2 = 0.034 at 80% power). For the death penalty experiment, the results indicated the required sample size to achieve 90% power for detecting a small effect size (f2 = 0.02), at a significance criterion of 𝛼 = 0.05, was N = 1,491. Our sample has 1,497 participants. Both studies are adequate to test the study hypotheses. 104 C.5 Manipulation Checks As a manipulation check, we asked participants that were not in the control group, "What was the profile of the justice that wrote the opinion? Liberal male; Conservative male; Liberal female; Conservative female; Don’t remember." As Table C.6 and Table C.7 show, most participants did not answer the question incorrectly. They either got it right or admitted they did not remember which justice wrote the opinion. To ensure participants knew who wrote the opinion, we restated the profile of the justice in their vignette before asking them about their feelings toward the decision itself. Table C.6 Abortion Vignette Manipulation Check Treatment Incorrect Correct Don’t No Total Profile Selected Profile Selected Remember Answer Liberal Male Justice 28 (18%) 60 (40%) 50 (33%) 11 (7%) 149 Conservative Male Justice 30 (21%) 45 (31%) 60 (41%) 10 (7%) 145 Liberal Female Justice 36 (24%) 58 (39%) 45 (30%) 10 (7%) 149 Conservative Female Justice 55 (35%) 44 (29%) 43 (28%) 10 (7%) 152 Table C.7 Death Penalty Vignette Manipulation Check Treatment Incorrect Correct Don’t No Total Profile Selected Profile Selected Remember Answer Liberal Male Justice 116 (38%) 96 (32%) 90 (30%) 0 (0%) 302 Conservative Male Justice 81 (27%) 108 (36%) 108 (36%) 0 (0%) 297 Liberal Female Justice 114 (38%) 107 (35%) 81 (27%) 0 (0%) 302 Conservative Female Justice 116 (38%) 96 (32%) 89 (30%) 0 (0%) 301 105 C.6 Alternative Dependent Variables In addition to the dependent variable we use for analysis in the paper, we ran the same models using a simpler, two-part dependent variable. In the manuscript, we used responses to the question, "On a scale from 0 to 100, how would you rate the [liberal/conservative] [male/female] justice’s decision in this case?" Here, we ask, "Do you agree or disagree with the Supreme Court’s decision in this case?"1 We asked this question shortly after the one we used in the manuscript. These results are in Table C.8, and they are substantively similar to the ones we present in the manuscript, with one exception: we do not have estimates for the seven female Independent participants from the abortion experiment. While their feeling thermometers vary between 0 and 55 degrees, none of the participants agreed with the decision, and their unanimous response thus offered no variation. We also analyzed our results using a feeling thermometer toward the Court itself. We asked, "On a scale from 0 to 100, how would you rate the Supreme Court?" We asked this question immediately following the question we used in the manuscript. These results are in Table C.9. Following long- established findings that a single decision does not fundamentally alter support for the Supreme Court (see Gibson and Nelson 2014 for a full overview), the results presented in Table C.9 suggest feelings are stable across treatments. Notably, however, the results from the death penalty experi- ment show an ideological split in support, with Democrat participants feeling less warmly toward the Court overall and Republican participants feeling more warmly toward the Court. This finding is not surprising; the Court’s explosive ruling in Dobbs v. Jackson Women’s Health Organization (2022) capped several years of politicized responses to the Court (Armaly 2020; Carrington and French 2021; Krewson and Schroedel 2020), and surveys suggest Democrats and Republicans split in their support for the Court after the Dobbs ruling.2 1 Participants could select from three different potential answers for this question: "agree," "disagree," or "no opinion." In the results that we present here, our dependent variable is whether or not the participant agreed with the decision, which means we coded anyone who answered "disagree" or "no opinion" as a zero and anyone who answered "agree" as a 1. The results remain the same if we remove the "no opinion" answers from the analysis or use multinomial logistic regression to analyze the three-part dependent variable. 2 http://bit.ly/3V5ea39 106 Table C.8 Logistic Regression Results, Agree with Supreme Court Decision Abortion Death Penalty Liberal Male Justice −0.3 −0.01 (0.6) (0.3) Conservative Male Justice 0.3 −0.1 (0.6) (0.3) Liberal Female Justice −0.2 0.2 (0.5) (0.3) Conservative Female Justice −0.2 −0.1 (0.5) (0.3) Female Respondent 0.01 −0.8∗ (0.5) (0.3) Independent Respondent −2.1∗ 0.02 (0.9) (0.4) Republican Respondent −2.2∗ 0.3 (0.7) (0.4) Independent Respondent 0.3 −0.5 x Female (1.1) (0.6) Republican Respondent 1.2 0.5 x Female (0.9) (0.6) Liberal Male Justice 0.5 0.7 x Female (0.7) (0.5) Conservative Male Justice −0.5 0.6 x Female (0.8) (0.5) Liberal Female Justice −0.1 0.3 x Female (0.7) (0.5) Conservative Female Justice 0.03 0.6 x Female (0.7) (0.5) Liberal Male Justice −0.6 −0.9 x Independent (1.4) (0.7) Conservative Male Justice 0.7 −0.3 x Independent (1.2) (0.6) Liberal Female Justice 1.6 −1.1 x Independent (1.1) (0.6) Conservative Female Justice 1.3 −0.7 x Independent (1.1) (0.6) Liberal Male Justice 1.0 0.2 x Republican (0.9) (0.6) Conservative Male Justice 0.4 0.4 x Republican (1.0) (0.5) Liberal Female Justice 0.7 −0.2 x Republican (0.9) (0.5) Conservative Female Justice 1.3 −0.02 x Republican (0.9) (0.5) Liberal Male Justice 0.8 0.1 x Female x Independent (1.7) (1.0) Conservative Male Justice −0.8 −0.1 x Female x Independent (1.5) (0.9) Liberal Female Justice −1.3 0.9 x Female x Independent (1.5) (0.9) Conservative Female Justice ˘ 0.3 x Female x Independent ˘ (0.9) Liberal Male Justice −2.1 −1.3 x Female x Republican (1.2) (0.8) Conservative Male Justice −0.4 −1.6∗ x Female x Republican (1.2) (0.8) Liberal Female Justice −1.2 −0.4 x Female x Republican (1.2) (0.8) Conservative Female Justice −3.2∗ −0.4 x Female x Republican (1.3) (0.8) Constant 0.7 0.3 (0.4) (0.2) Observations 732 1494 Log Likelihood -440.2 -996.8 ∗ p<0.05 107 Table C.9 OLS Results, Feeling Thermometer, Supreme Court Abortion Death Penalty Liberal Male Justice −4.6 −8.4 (6.0) (4.4) Conservative Male Justice −11.6 −0.5 (6.1) (4.3) Liberal Female Justice −8.6 −2.0 (5.7) (4.2) Conservative Female Justice −11.2∗ 0.5 (5.5) (4.4) Female Respondent −16.6∗ −7.5 (5.1) (4.3) Independent Respondent −14.1 6.5 (8.1) (5.3) Republican Respondent −13.3 14.2∗ (6.8) (4.9) Independent Respondent 17.6 −11.2 x Female (10.8) (7.7) Republican Respondent 22.3∗ −0.4 x Female (8.7) (6.9) Liberal Male Justice 12.5 11.1 x Female (7.5) (6.0) Conservative Male Justice 16.6∗ 1.6 x Female (7.8) (6.1) Liberal Female Justice 15.1∗ 2.3 x Female (7.6) (6.2) Conservative Female Justice 16.6∗ 1.4 x Female (7.1) (6.3) Liberal Male Justice 5.4 −9.6 x Independent (11.4) (8.8) Conservative Male Justice 15.7 −16.2∗ x Independent (11.7) (7.9) Liberal Female Justice −3.0 −13.7 x Independent (11.5) (7.7) Conservative Female Justice 4.8 −15.1 x Independent (11.0) (7.9) Liberal Male Justice 7.2 5.7 x Republican (9.1) (7.1) Conservative Male Justice 14.7 −3.7 x Republican (9.4) (6.8) Liberal Female Justice 17.2 −9.1 x Republican (9.0) (6.8) Conservative Female Justice 4.2 −2.2 x Republican (9.0) (6.9) Liberal Male Justice −19.9 12.6 x Female x Independent (15.3) (11.6) Conservative Male Justice −25.3 19.8 x Female x Independent (15.0) (11.1) Liberal Female Justice −12.4 14.3 x Female x Independent (15.1) (11.0) Conservative Female Justice −19.0 13.9 x Female x Independent (15.9) (11.1) Liberal Male Justice −16.3 −11.4 x Female x Republican (12.0) (9.7) Conservative Male Justice −27.2∗ 3.7 x Female x Republican (12.2) (9.7) Liberal Female Justice −21.5 7.0 x Female x Republican (11.8) (9.7) Conservative Female Justice −21.3 −7.1 x Female x Republican (12.0) (9.6) Constant 76.5∗ 57.8∗ (4.0) (3.0) Observations 733 1497 R2 0.058 0.075 F Statistic 1.49∗ (df = 29; 703) 4.07∗ (df = 29; 1467) ∗ p<0.05 108 C.7 Full Models with Complete Set of Participant Controls Table C.10 OLS Results, Decision Thermometer, Full Models Abortion Death Penalty Liberal Male Justice −17.6∗ −6.2 (8.0) (4.8) Conservative Male Justice −21.5∗ −5.7 (8.8) (4.6) Liberal Female Justice −11.6 1.5 (7.7) (4.5) Conservative Female Justice −20.1∗ −0.9 (7.6) (4.7) Female Respondent −13.1 −10.2∗ (7.0) (4.7) Independent Respondent −22.2∗ 3.3 (11.0) (5.7) Republican Respondent −29.5∗ 9.2 (9.3) (5.3) Independent Respondent 18.6 −7.0 x Female (14.5) (8.3) Republican Respondent 26.5∗ 4.0 x Female (11.7) (7.4) Liberal Male Justice 22.3∗ 8.0 x Female (10.1) (6.5) Conservative Male Justice 21.7∗ 6.4 x Female (10.9) (6.5) Liberal Female Justice 21.9∗ 3.4 x Female (10.3) (6.6) Conservative Female Justice 22.0∗ 7.4 x Female (9.7) (6.7) Liberal Male Justice 17.9 −11.0 x Independent (15.6) (9.5) Conservative Male Justice 20.2 −1.5 x Independent (16.7) (8.5) Liberal Female Justice 10.7 −14.7 x Independent (15.1) (8.3) Conservative Female Justice 24.9 −12.0 x Independent (15.0) (8.4) Liberal Male Justice 11.8 −7.5 x Republican (12.1) (7.6) Conservative Male Justice 22.2 2.6 x Republican (13.0) (7.3) Liberal Female Justice 8.9 −15.7∗ x Republican (12.0) (7.2) Conservative Female Justice 24.7∗ 0.9 x Republican (12.0) (7.4) Liberal Male Justice −40.9 17.2 x Female x Independent (21.6) (12.5) Conservative Male Justice −31.4 3.4 x Female x Independent (20.9) (11.9) Liberal Female Justice −26.7 7.5 x Female x Independent (20.1) (11.8) Conservative Female Justice −57.5∗ 9.0 x Female x Independent (21.7) (11.9) Liberal Male Justice −32.9∗ 2.0 x Female x Republican (15.8) (10.4) Conservative Male Justice −33.9∗ −8.2 x Female x Republican (16.6) (10.4) Liberal Female Justice −28.2 6.5 x Female x Republican (15.9) (10.3) Conservative Female Justice −39.4∗ −5.0 x Female x Republican (16.0) (10.3) 109 Table C.10 (cont’d) Table C.11 Table C.10 (Cont.) Abortion Death Penalty Participant Income −0.1 0.3∗ (0.2) (0.1) Participant Education 1.5∗ 0.3 (0.6) (0.4) Participant Age 0.04 0.1∗ (0.1) (0.04) Constant 70.3∗ 50.7∗ (7.1) (4.0) Observations 656 1464 R2 0.123 0.078 F Statistic 2.74∗ (df = 32; 623) 3.78∗ (df = 32; 1431) ∗ p<0.05 110 C.8 Treatment Group Breakdowns Table C.12 Abortion Group Breakdown Treatment Democrat Democrat Independent Independent Republican Republican Male Female Male Female Male Female Liberal Male Justice 25 45 11 12 29 26 Conservative Male Justice 23 36 10 20 25 28 Liberal Female Justice 29 32 10 16 30 33 Conservative Female Justice 35 47 12 7 25 22 Control 31 51 10 12 16 25 Table C.13 Death Penalty Group Breakdown Treatment Democrat Democrat Independent Independent Republican Republican Male Female Male Female Male Female Liberal Male Justice 62 91 17 36 42 54 Conservative Male Justice 71 71 26 33 52 44 Liberal Female Justice 76 62 28 35 51 50 Conservative Female Justice 64 65 27 35 51 59 Control 74 67 35 30 43 46 111 C.9 Analysis of Independents in the Death Penalty Experiment Approval for Pro-Death Penalty Decision Independent Participants 100 Predicted Value - Decision Thermometer 90 80 70 60 50 40 30 20 10 0 Liberal Conservative Liberal Conservative Control Male Male Female Female Justice Justice Justice Justice Opinion Writer Treatment Groups Women Men Figure C.5 Mean differences in Independent participant feelings toward Supreme Court’s decision upholding the use of the death penalty. Female Independent participants are represented in light grey and male Independent participants are represented in dark grey. Vertical bars show 95% confidence intervals. 112 Differences Between Female and Male Participants, Approval for Pro-Death Penalty Decision Independent Participants Difference in Feeling Thermometer (Female - Male) 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Male Male Female Female Justice Justice Justice Justice Opinion Writer Treatment Groups Figure C.6 First differences of participant feelings toward Supreme Court’s decisions strengthening the death penalty by participant gender. Vertical bars represent 95% confidence intervals.. 113 Differences Between Democrat and Independent Participants, Approval for Pro-Death Penalty Decision Difference in Feeling Thermometer (Democrat - Independent) Men Women 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (a) First Differences by Partisanship, Democrats and Independents Differences Between Republican and Independent Participants, Approval for Pro-Death Penalty Decision Difference in Feeling Thermometer (Republican - Independent) Men Women 45 35 25 15 5 -5 -15 -25 -35 -45 Liberal Conservative Liberal Conservative Control Liberal Conservative Liberal Conservative Control Male Male Female Female Male Male Female Female Justice Justice Justice Justice Justice Justice Justice Justice Opinion Writer Treatment Groups (b) First Differences by Partisanship, Republicans and Independents Figure C.7 First differences of participant feelings toward Supreme Court’s decisions strengthening the death penalty by comparing (a) participants who identified as Democrats and Independents (men left, women right) and (b) participants who identified as Republicans and Independents (men left, women right). Vertical bars represent 95% confidence intervals. 114 C.10 Survey Questions C.10.1 Demographic Questions 1. What is your age? [] 2. What is your gender? [Male; Female] 3. What is your current annual household income before taxes? [Less than $14,999; $15,000- $19,999; $20,000-$24,999; $25,000-$29,999; $30,000-$34,999; $35,000-$39,999; $40,000- $44,999; $45,000-$49,999; $50,000-$54,999; $55,000-$59,999; $60,000-$64,999; $65,000- $69,999; $70,000-$74,999; $75,000-$79,999; $80,000-$84,999; $85,000-$89,999; $90,000- $94,999; $95,000-$99,999; $100,000-$124,999; $125,000-$149,999; $150,000-$174,999; $175,000-$199,999; $200,000-$249,999; $250,000 and above; Prefer not to answer] 4. What is your race? [White; Black, or African American; American Indian or Alaska Native; Asian (Asian American; Chinese; Filipino; Japanese; Korean; Vietnamese; Other); Pacific Islander (Native Hawaiian; Guamanian; Samoan; Other Pacific Islander); Some other race; Prefer not to answer] 5. Are you of Hispanic, Latino, or Spanish origin? [No, not of Hispanic, Latino, or Spanish origin; Yes – Mexican, Mexican American, Chicano; Yes – Cuban; Yes – Puerto Rican; Yes – Another Hispanic, Latino, or Spanish origin (Argentina; Colombia; Ecuador; El Salvadore; Guatamala; Nicaragua; Panama; Peru; Spain; Venezuela; Other Country); Prefer not to answer] 6. What is the highest level of education you have completed? [3rd Grade or less; Middle School – Grades 4-8; Completed some high school; High school graduate; Other post high school vocational training; Completed some college, but no degree; Associate Degree; College Degree (such as B.A., B.S.); Completed some graduate, but no degree; Masters degree; Doctorate degree; None of the above] 7. Generally speaking, do you think of yourself as a Republican, a Democrat, an Independent, or what? [Strong Democrat; Not very strong Democrat; Independent leaning Democrat; Independent - neither; Independent leaning Democrat; Other - neither; Other - leaning Republican; Not very strong Republican; Strong Republican] 8. What is your region? [Northeast; Midwest; South; West] 9. What is your zip code? [] C.10.2 General Dispositions toward the Court (Pre Treatment) 1. How well do you think the U.S. Supreme Court does its main job in government? Would you say it does a great job, a pretty good job, not a very good job, or a poor job? [Great job; Pretty good job; Not a very good job; Poor job] 2. In general, would you say that the Supreme Court is too liberal, or too conservative, or about just right in its decisions? [Much too liberal; Too liberal; Just right; Too conservative; Much too conservative] 115 3. How much confidence do you have in the U.S. Supreme Court? [A great deal of confidence; Only some confidence; Hardly any confidence] C.10.3 Legitimacy Battery (Pre Treatment) 1. If the U.S. Supreme Court started making a lot of decisions that most people disagree with, it might be better to do away with the Supreme Court altogether. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] 2. The right of the Supreme Court to decide certain types of controversial issues should be reduced. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] 3. The Supreme Court can usually be trusted to make decisions that are right for the country as a whole. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] 4. The decisions of the U.S. Supreme Court favor some groups more than others. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] 5. The U.S. Supreme Court gets too mixed up in politics. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] 6. The U.S. Supreme Court should have the right to say what the Constitution means, even when the majority of the people disagree with the Court’s decision. [Agree strongly; Agree somewhat; Neither agree nor disagree; Disagree somewhat; Disagree strongly] C.10.4 Attention Check (Pre Treatment) 1. Some people received a story about an election. If you read about the election scenario, where was the election being held? [New Jersey; A Midwestern State; Germany; New York; I did not read about an election scenario] C.10.5 Supreme Court Knowledge (Pre Treatment) 1. Who is the current Chief Justice of the United States? [Stephen Breyer; Brett Kavanaugh; John Roberts; Clarence Thomas] 2. Some judges in the U.S. are elected; others are appointed to the bench. Do you happen to know if the justices of the U.S. Supreme Court are elected or appointed to the bench? [Elected; Appointed; Don’t know] 3. Some judges in the U.S. serve for a set number of years; others serve a life term. Do you happen to know whether the justices of the U.S. Supreme Court serve for a set number of years or whether they serve a life term? [Set number of years; Life term; Don’t know] 116 4. Do you happen to know who has the last say when there is a conflict over the meaning of the Constitution – the U.S. Supreme Court, the U.S. Congress, or the President? [U.S. Supreme Court; U.S. Congress; President; Don’t know] 5. Do you happen to know if the Supreme Court has made decisions on gay marriage? [Yes, it has; No, it has not; I do not know] 6. Do you happen to know if the Supreme Court has made decisions on the rights of Black Americans? [Yes, it has; No, it has not; I do not know] 7. Do you happen to know if the Supreme Court has made decisions on the maximum income tax rate? [Yes, it has; No, it has not; I do not know] C.10.6 Manipulation Check (Post Treatment) 1. What was the profile of the justice that wrote the opinion? [Liberal male; Conservative male; Liberal female; Conservative female; Don’t remember] C.10.7 Opinions Regarding the Court and the Treatment Decision (Post Treatment, Abor- tion Vignettes Only) 1. Do you think abortion should be legal in all cases, legal in most cases, illegal in most cases, illegal in all cases? [Legal in all cases; Legal in most cases; Illegal in most cases; Illegal in all cases] 2. On a scale from 0 to 100, how would you rate the Supreme Court? A rating of zero means you feel as cold and negative as possible. A rating of 100 means you feel as warm and positive as possible. You would rate the decision at 50 if you do not feel particularly positive or negative. [0-100] 3. On a scale from 0 to 100, how would you rate the [liberal/conservative] [male/female] justice’s decision in this abortion case? A rating of zero means you feel as cold and negative as possible. A rating of 100 means you feel as warm and positive as possible. You would rate the decision at 50 if you do not feel particularly positive or negative. [0-100] 4. Do you agree or disagree with the Supreme Court’s decision in this case? [Agree; Disagree; No opinion] 5. Do you think the Supreme Court should be deciding abortion cases? [Yes, should be; No, should not be; No opinion] C.10.8 Opinions Regarding the Court and the Treatment Decision (Post Treatment, Death Penalty Vignettes Only) 1. Do you strongly favor, favor, oppose, or strongly oppose the death penalty for persons convicted of murder? [Strongly favor; Favor; Oppose; Strongly oppose] 117 2. On a scale from 0 to 100, how would you rate the Supreme Court? A rating of zero means you feel as cold and negative as possible. A rating of 100 means you feel as warm and positive as possible. You would rate the decision at 50 if you do not feel particularly positive or negative. [0-100] 3. On a scale from 0 to 100, how would you rate the [liberal/conservative] [male/female] justice’s decision in this death penalty case? A rating of zero means you feel as cold and negative as possible. A rating of 100 means you feel as warm and positive as possible. You would rate the decision at 50 if you do not feel particularly positive or negative. [0-100] 4. Do you agree or disagree with the Supreme Court’s decision in this case? [Agree; Disagree; No opinion] 5. Do you think the Supreme Court should be deciding death penalty cases? [Yes, should be; No, should not be; No opinion] 118