Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T19:14:29.918Z Has data issue: false hasContentIssue false

Legislative Civility, Gridlock, Polarization, and Productivity

Published online by Cambridge University Press:  28 November 2023

William D. Schreckhise*
Affiliation:
Department of Political Science, University of Arkansas, Fayetteville, AR, USA
John C. Pierce
Affiliation:
School of Public Affairs and Administration, University of Kansas, Lawrence, KS, USA
Francis A. Benjamin
Affiliation:
Political Interaction Lab, Department of Psychology, Washington State University, Pullman, WA, USA
Nicholas P. Lovrich
Affiliation:
School of Politics, Philosophy, and Public Affairs, Washington State University, Pullman, WA, USA
Eric Button
Affiliation:
Department of Political Science, University of Arkansas, Fayetteville, AR, USA
*
Corresponding author: William D. Schreckhise; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

This article examines the relationship between legislative civility and legislative productivity in US state legislatures. The research employs data from the National Survey of State Legislative Lobbyists and from the State Policy Innovation and Diffusion (SPID) database. The former dataset is used to generate an overall civility index for each state as developed by Kettler et al. The SPID database allows one to measure the legislative productivity of a state legislature. Employing these data, negative binomial and Poisson regression models reveal that state legislatures rated as more civil by their own lobbyists produced significantly more pieces of noteworthy legislation than those legislative bodies rated as less civil. These results suggest that the quality of internal legislative dynamics matters for legislative productivity.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the State Politics and Policy Section of the American Political Science Association

Introduction

This article examines the extent to which legislative civility produces important results in the legislative process. Specifically, the research reported here explores the role that greater civility among state legislators can play in fostering legislative productivity. We report evidence that legislatures which are rated as more civil by people who are highly knowledgeable about their operations produce more legislation on important topics of contemporary public policy. We also report findings indicating that state legislatures which are more civil are less prone to the ill effects of political polarization as it relates to legislative gridlock. We employ a dataset derived from a national survey of state legislative lobbyists conducted in 2018 and 2019. Survey respondents were asked to rate the degree to which they believe the legislators with whom they work in their respective states treat each other in a civil manner (Lovrich Reference Lovrich, Benjamin, Pierce and Schreckhise2021). Over one thousand state legislative lobbyists from all 50 US states participated in the study, with many offering detailed comments and expressing concern over the decreasing levels of civility being displayed in the state legislatures in which they lobby. Many were former legislators and legislative staffers, many of whom reflected the widespread expression of concern over the state of civil discourse in Congress, state legislatures, local governments, school boards, public health districts, and even library boards across the country (Schuman Reference Schuman2016).

This study explores the question of whether greater civility among legislators is related to greater legislative productivity in the form of four specific outcomes – namely, passing new legislation, enacting greater proportions of bills submitted, passing larger numbers of major bills, and enacting a state budget in a timely manner. Our results show that civility is indeed linked to higher rates of policy adoption and lower levels of gridlock. These results suggest that internal legislative dynamics do matter for legislative productivity. This relationship remains even while considering the effects of other possible determinants shown to be related to legislative gridlock in prior research (e.g., Crosson Reference Crosson2019; Hicks and Smith Reference Hicks and Smith2009; Melusky Reference Melusky2020).

Civility, legislative productivity, and gridlock

The extent to which legislatures are productive has been the focus of considerable research, much of it examining the factors that inhibit the US Congress from passing legislation related to the important issues on the public agenda (e.g., Binder Reference Binder2003; Mayhew Reference Mayhew1991). Much of this research has examined the role that divided governments and partisan polarization have played in causing that gridlock in Congress (e.g., Coleman Reference Coleman1999; Howell et al. Reference Howell, Adler, Cameron and Riemann2000; Jones Reference Jones2001; Mayhew Reference Mayhew1991). Similarly, additional research has been conducted with an eye to determining the role that divided state government, polarized state legislative parties, the presence of legislative term limits, and the legislatures’ gender compositions play in state legislative productivity and gridlock (e.g., Bernecker Reference Bernecker2016; Bowling and Ferguson Reference Bowling and Ferguson2001; Hicks and Smith Reference Hicks and Smith2009; Melusky Reference Melusky2020). Collectively, this research has examined the relationship between these factors as they relate to productivity and gridlock as measured by things such as the total number of bills a state legislature enacts in a year or biennium, the percentage of bills introduced that were passed, the number of “important” bills passed, and whether a state’s budget bill was passed prior to the beginning of the states’ forthcoming fiscal year (e.g., Andersen, Lassen, and Westh Nielsen Reference Andersen, Lassen and Westh Nielsen2020; Bernecker Reference Bernecker2016; Crosson Reference Crosson2019; Rogers Reference Rogers2005).

We add to this discussion of legislative productivity by examining the role civility plays in shaping legislative productivity and legislative gridlock, with the notion of “productivity” being reflected in the number of statutes being enacted into law, and the concept “gridlock” being reflected in draft legislation that failed to gain passage or was unduly delayed. Although civil discourse in the political arena has been a concern for a number of years, knowledge about the extent to which incivility hampers governance and harms legislative deliberation remains in its relative infancy. Some early positive steps have been taken toward enhancing this knowledge with the advent of some recent publications, such as an edited volume (Lovrich Reference Lovrich, Benjamin, Pierce and Schreckhise2021) that reported findings derived from the National Survey of State Legislative Lobbyists conducted in 2018–2019. Along with Kettler, Fowler, and Witt (Reference Kettler, Fowler and Witt2021), these studies find that states with higher-rated levels of civility tend to have more professional legislatures, lack term limits for their members, have more nonwhite legislators serving, and feature less polarized political parties. Also, states with less competitive political parties and less economic inequality tend to have more civil legislatures (Kettler, Fowler, and Witt Reference Kettler, Fowler and Witt2021; Schreckhise and Benjamin Reference Schreckhise, Benjamin, Lovrich, Benjamin, Pierce and Schreckhise2021; Schreckhise, Chand, and Benjamin Reference Schreckhise, Chand, Benjamin, Lovrich, Benjamin, Pierce and Schreckhise2021).

As noted by Goovarts (2022, 2), [p]olitical incivility can be defined as “politicians’ use of a disrespectful, impolite, or rude communication style.” Incivility can include uncivil behavior that violates social norms as they relate to interpersonal interactions (see also Muddiman Reference Muddiman2017). More specifically, uncivil behavior refers to “discursive behaviors that represent the rejection of communication norms pertaining to considerate, courteous, and respectful discussion” (Hopp Reference Hopp2019, 206). As such, this article poses the specific research question: To what extent does incivility among legislators inhibit legislative productivity and enhance the danger of gridlock in state legislatures?

We suspect that less civil legislatures would be less productive and more prone to gridlock for several reasons. First, past research has found that uncivil behavior in the political arena by public figures weakens the public’s trust in political leaders (Goovaerts 2022; Mutz and Reeves Reference Mutz and Reeves2005; Skytte Reference Skytte2021) and in the political system in general (Forgette and Morris Reference Forgette and Morris2006; Mutz and Reeves Reference Mutz and Reeves2005). Such incivility-spawned mistrust even extends to such areas as news media credibility (Borah Reference Porismita2013; Prochazka, Weber, and Schweiger Reference Prochazka, Weber and Schweiger2018; Naab et al. Reference Naab, Heinbach, Ziegele and Grasberger2020) and public trust in the validity of science research (Chinn and Hart Reference Chinn and Hart2022). That same mistrust could translate into mistrust among politicians which would include those serving in the very state legislature from which the incivility emanates, including members of their own parties (see Druckman et al. Reference Druckman, Gubitz, Levendusky and Lloyd2018).

Second, incivility negatively affects the needed interpersonal dynamics necessary for legislative productivity. As Mutz (Reference Mutz, Diana2007, 623) notes, “[w]hat is known about incivility – whether in political discourse or some other context – is that it heightens levels of arousal” (see also Mutz and Reeves Reference Mutz and Reeves2005). This heightened sense of arousal manifests itself in emotional responses such as anger and sensitivity to slight (Valentino et al. Reference Valentino, Brader, Groenendyk, Gregorowicz and Hutchings2011; Weber Reference Christopher2013), rendering an individual less willing to accept new information that contradicts their own views and less likely to compromise (Gervais Reference Gervais2019). Additionally, among the public, it has been found that over the course of the last four decades, Americans have expressed increasingly negative feelings toward members of the opposite party along with deepening loyalties to the ingroup of copartisans (Iyengar, Sood, and Lelkes Reference Iyengar, Sood and Lelkes2012; Iyengar and Westwood Reference Iyengar and Westwood2015). More importantly, perceived uncivil behavior increases negative feelings toward members of the opposite party (Mutz Reference Mutz, Diana2007; Skytte Reference Skytte2021). To be sure, these findings have been limited to studies among the general public. However, these dynamics may very well be in active play among state legislators as well.

Third, enhanced civility allows for communication and policy making free of substantive or procedural issues ancillary to the policy at hand that might impede the search for, discovery of, and production of shared benefits or the facilitation of the process producing compromise, bargaining, and exchange of benefits within the issue or across issues. Those attributes would enhance the probability of agreement on legislation and hence increase the number of bills passed. This process has traditionally been labeled “logrolling,” and while not confined exclusively to the legislative arena, is most visible there (see Thrasher Reference Thrasher2016). Along these lines, past research has found that incivility in the workplace is related to greater employee turnover, lower job satisfaction, and reduced organizational performance (e.g., Anjum et al. Reference Anjum, Ming, Siddiqi and Rasool2018; Estes and Wang Reference Estes and Wang2008; Miner et al. Reference Miner, Smittick, He and Costa2019).

To be sure, as Loomis (Reference Loomis and Loomis2004, 2) noted, “the connection between civility and [legislative] deliberation seems straightforward. Comity would seem a necessary, if not sufficient, condition for deliberation on the major issues of the day.” Although declining levels of civility in American politics are a concern for many, it remains unclear to what extent incivility hampers the deliberative process and impairs governance in the 50 US state legislatures.

Greater interpersonal civility among legislators is hypothesized to lead to greater productivity because legislative bodies are themselves social entities that require interpersonal cooperation for their successful operation. When individuals elected to legislative offices can cooperate in their legislative work because of their positive personal relationships and interpersonal trust, this diffuse trust will elevate their legislature’s level of productivity. As Uslaner (Reference Uslaner and Loomis2000, 35) noted in his observations of the relationship between the interrelated notions of civility, trust, and comity, and the productivity of the US Senate,

[s]trong friendship circles across party lines signify a legislature marked by trust. This is where civility turns into comity. Comity is more than being civil to others. It also involves reciprocity, which simply means that people must respect their promises and obligations to others. They must also recognize that another point of view is legitimate. Reciprocity involves respecting other people and their expertise. It means willingness to make deals and committing oneself to sticking with the agreement. Without reciprocity, you get either rule by a partisan majority or stalemate.

Thus, we suggest that members of more civil legislatures will be more likely to work in a collaborative fashion on issues on which they can agree. Relatedly, in less civil legislatures members are less likely to trust one another and less likely to engage in active collaboration. Much of the legislative process requires trust that members will keep their word when vote-trading deals are brokered. In this regard, Thurber (Reference Thurber and Loomis2000) found that declining levels of trust among US Senate Budget Committee members led to a decline in the quality of deliberations held on that committee. Interpersonal conflict resulting in uncivil behavior may lead to some members of those legislatures being unable to cooperate on otherwise consensual matters, and thus inhibit the passage of legislation.

At the national level, some other prior research has indeed observed such a relationship between civility and legislative productivity. Dodd and Schraufnagel (Reference Dodd and Schraufnagel2012) found that landmark legislation in the US Congress occurred during times when the combined effects of polarization and incivility were at moderate levels. Similarly, Schraufnagel (Reference Schraufnagel2005) found that longer delays in judicial nominees in the US Senate are related to greater interpersonal incivility among its members. At the local government level, Yuan and Schraufnagel (Reference Yuan and Schraufnagel2020) observed that local government bodies in the US were viewed as less productive when members noted incivility among the members of their boards. The research reported here thus examines the extent to which state legislatures with greater civility are more likely to adopt new legislation than are those with more uncivil behavior.

Data and hypotheses Footnote 1

Independent variable

For the primary independent variable, we measure civility as perceived civility. Data for this measure come from the National Survey of State Legislative Lobbyists. That survey was conducted by academic researchers at 12 US universities, drawing upon support from the National Institute for Civil Discourse at the University of Arizona, the Thomas S. Foley Institute for Public Policy and Public Service, the William Ruckleshaus Center, and the Division of Governmental Studies and Services at Washington State University. The lobbyists’ names and contact information (including email addresses) were collected from those secretaries of states offices which maintained lists of lobbyists registered with the state. In states without such listings, additional effort was expended to gather contact information through a variety of other strategies, such as searches of online legislative directories and internet searches. The survey was internet-based (Qualtrics platform) with follow-up mail surveys conducted in 2018–2019.Footnote 2

A US state-level civility index was created from the survey responses. We employed the method developed by Kettler, Fowler, and Witt (Reference Kettler, Fowler and Witt2021) calculating the “average” lobbyist’s rating of each respective state’s level of civility. The resulting index considers the scores which respondents gave for their own state legislature in terms of (1) the legislature’s quality of deliberation; (2) the prevailing level of civility in their state legislature; (3) the general level of civility in their state; and (4) the general level of civility among state legislators. The specific survey items are listed in Table 1.

Table 1. Civility index survey items

Multilevel-ordered regression models were estimated for the first three items with fixed effects for the individual-level variables and random effects for the state-level variables.Footnote 3 An intercept was then estimated for each state that reflects the differences for each state, controlling for the individual-level differences. This method considers individual-level demographic and political differences when producing the scores, generating “mean” ideal values for each state of the first three questions as deviations from the fourth survey question as a common point of comparison to ameliorate the potential for respondents from different states to interpret the first three questions differently. The result is the civility index which has a mean of near zero, the standard deviation of 0.38, and ranges from a minimum of −0.97 (Oklahoma) to a maximum of 0.76 (Maryland). Figure 1 displays a map of the score for each of the 50 states (see Kettler, Fowler, and Witt Reference Kettler, Fowler and Witt2021, 10–11). Summary statistics for this and all other variables used in our analyses are displayed in Table A.1 in the Supplementary Material and the sources, coding, and directionality of each variable are set forth in Table A.2 in the Supplementary Material.

Figure 1. Civility index scores by state.

Note: Higher scores reflect higher rates of perceived civility among legislators.

Source: Values from the Kettler, Fowler, and Witt (Reference Kettler, Fowler and Witt2021) and National Survey of State Legislative Lobbyists (2022).

In the survey, many lobbyists commented on the connection between civility and legislative effectiveness in accomplishing the work of legislating in the public interest. Among the 1,200+ survey respondents, 72 were former state legislators and 288 were persons who served as legislatives aides or committee staffers prior to becoming lobbyists. Many “lobbyists” were serving as contract lobbyists or lobby firm employees, and many others were in-house lobbyists for private interests, nonprofit advocacy groups, or worked for public entities such as local governments, special districts, or state agencies.

Dependent variables and hypotheses

The following analysis presents four models predicting separate dependent variables, each of them used in previous research as a measure of legislative productivity (or conversely, legislative gridlock), along with corresponding control variables that have been employed in conjunction with those dependent variables. We provide a brief description of each of the dependent variables below with a further description provided as we present each model further below.

The first model examines the total number of bills passed by each of the state legislatures in 2018 and 2019, an approach that has been used in several prior studies of legislative productivity and innovation, including Bowling and Ferguson (Reference Bowling and Ferguson2001), Crosson (Reference Crosson2019), Gray and Lowery (Reference Gray and Lowery1995), Hicks and Smith (Reference Hicks and Smith2009), and Rogers (Reference Rogers2005). The data for total counts of legislation passed are extracted from The Book of States. Using this as the primary dependent variable in our first model, we test our first hypothesis:

H1: State legislatures with higher civility index scores will pass a larger number of bills than states with lower civility scores.

Similar to previous studies of legislative gridlock (i.e., Hicks and Smith Reference Hicks and Smith2009; Rogers Reference Rogers2005), our second model tests another hypothesis regarding the percentage of bills passed of those introduced by each of the state legislatures in 2018 and 2019. Data for the percentage of bills passed are likewise extracted from The Book of States.

H2: State legislatures with higher civility index scores will pass a larger percentage of bills than states with lower civility scores.

Testing our third hypothesis, another model examines whether the state passed notable or needed legislation in 2017, somewhat similar to Mayhew’s (Reference Mayhew1991) measures of “important” legislation (see Bernecker Reference Bernecker2016; Crosson Reference Crosson2019). The data for this dependent variable are extracted from the Boehmke et al. (Reference Boehmke, Brockway, Desmarais, Harden, La Combe, Linder and Wallach2020) State Policy Innovation and Diffusion (SPID) Database.

H3: State legislatures with higher civility index scores will pass a larger number of important pieces of legislation than states with lower civility scores.

The final model examines whether the state passed a budget “on time” (i.e., prior to the start of the state’s next fiscal year) in 2019 for the 2020 fiscal year as similarly employed by Andersen, Lassen, and Westh Nielsen (Reference Andersen, Lassen and Westh Nielsen2020), Birkhead (Reference Birkhead2016), Klarner, Phillips, and Muckler (Reference Klarner, Phillips and Muckler2012), and Melusky (Reference Melusky2020). The data for on-time budgets are from the National Conference of State Legislatures. Thus, we present our fourth hypothesis:

H4: State legislatures with higher civility index scores will be more likely to pass state budgets on time than states with lower civility scores.

Also included in our analyses are variables that past research has shown to be related to each of the dependent variables to represent legislative outcomes. We discuss these measures in greater detail below.

Results

To determine the relationship between legislative civility and legislative productivity, we first examine the link between the civility index and the total number of bills passed by state legislators, using a count of the total numbers of bills passed by each state legislature for the 2018–2019 period. Such a measure has been used by others (see, e.g., Bowling and Ferguson Reference Bowling and Ferguson2001; Crosson Reference Crosson2019; Gray and Lowery Reference Gray and Lowery1995; Hicks and Smith Reference Hicks and Smith2009; Rogers Reference Rogers2005). As Tsebelis notes, “policies are the principal outcome of a political system” (Tsebelis Reference Tsebelis2002, 6), and as such, this measure serves as an appropriate starting point for examining the civility-productivity link.

The analytical model includes other covariates that have been linked to the quantities of legislation passed by state legislatures. These specific variables of interest include: levels of legislative professionalizationFootnote 4 as developed by Bowen and Greene (Reference Bowen and Greene2014), state elite ideology, the presence of unified partisan state government, the size of the state’s population), whether the state’s voters can propose their own legislation via ballot initiatives, and the economic health and economic capacity of the state (Berry and Berry Reference Berry and Berry1990; Boehmke and Skinner Reference Boehmke and Skinner2012; Gray Reference Gray1973; LaCombe and Boehmke Reference LaCombe and Boehmke2021; Mallinson Reference Mallinson2020; Mallinson Reference Mallinson2021; Rom, Peterson, and Scheve Reference Rom, Peterson and Scheve1998; Walker Reference Walker1969).

We also include in the first model a measure indicating whether legislators in either or both chambers in each state legislature have a legislative rule or constitutional provision limiting the number of bills introduced, either as a number that each legislator may introduce or a deadline for when they can be introduced. We do this because Squire (Reference Squire1998) found that state legislatures with such rules passed a higher percentage of bills. These rules are chamber-specific, and they vary from state to state. For example, Florida’s House Rule 5.3 permits a member to introduce only six bills during each legislative session. Some states impose the limit only after a certain period in the session has passed, like Arizona’s House Rule 8(c) which only imposes a cap on bill introduction after the fourth day of the session. Each state was given a value of “2” if both chambers had some type of limit on bill introductions, “1” if such a rule existed for only one chamber, but not the other, and “0” if neither chamber had such a rule.

The results for the first model are presented in Table 2. We employed a negative binomial model due to the discrete and nonnegative nature of our dependent variable, which is heavily skewed. The over-dispersed nature of these count data can render OLS and Poisson model results that are biased and inconsistent (Hilbe Reference Hilbe2011; Paternoster and Brame Reference Paternoster and Brame1997), potentially resulting in Type II errors (Payne et al. Reference Payne, Gebregziabher, Hardin, Ramakrishnan and Egede2018). We report the incident rate ratios (IRR) which indicate the extent to which a one-unit change in the independent variable leads to a percentage change in the dependent variable, reflected in the extent to which the value of IRR is greater or less than 1.0 (Piza Reference Piza2012).

Table 2. Total number of bills enacted in state legislatures, 2018–2019

Note. The total number of bills enacted in 2018 and 2019 is the dependent variable in this negative binomial model.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

The results reported in Table 2 reveal a statistically significant relationship between the civility index scores and the number of laws adopted by states. Specifically, as one moves from the state with the lowest civility score (−0.97) to a state at the mean civility score value (0.0), one moves roughly 1 unit on the civility index. This one unit of change is thus reflected in a roughly 64% increase in the number of laws passed by state legislatures in 2018–2019, ceteris paribus. Additionally, states with more bills introduced, more liberal states, and larger states passed more legislation. The predicted values of legislative productivity in Table 2 are presented graphically in Figure 2 across the range of civility index scores.

Figure 2. The relationship between legislative civility and the predicted number of bills enacted, 2018–2019.

Note: Shaded area reflects 95% confidence intervals. Estimates are obtained from Table 2.

We also employed a second measure of legislative productivity, namely the percentage of bills introduced that were passed by each legislature for the 2018–2019 period. Such an approach has been used in past research to examine such things as the effect of divided government on gridlock, with “gridlock” being associated with the failure of proposed legislation (Coleman Reference Coleman1999; Hicks and Smith Reference Hicks and Smith2009; Howell et al. Reference Howell, Adler, Cameron and Riemann2000; Rogers Reference Rogers2005). As Edwards (Reference Edwards, Barrett and Peake1997, 547) and his colleagues note when examining the effects of divided government on legislative activity, “[i]f our concern is whether or not divided government obstructs the passage of legislation, we ought to investigate legislation that was proposed that did not pass” [emphasis original]. We employ the same logic here as it relates to legislative civility, including the percentage of bills as the dependent variable.

We also take into direct account the total number of bills introduced. This is because it is possible that state legislatures may very well have a maximum capacity for legislation each year; thus, some states may eventually pass a smaller percentage only because more bills were initially introduced. Indeed, considerable variation exists across the states in the number of bills introduced for the two years we examine. For example, Ohio’s legislature saw a total of 1,832 bills introduced in 2018 and 2019, while New Jersey’s legislators introduced more than 10 times that number for the same two-year period.

Table 3 reveals that states with legislatures rated as more civil produce a higher percentage of the bills originally introduced. This is the case even when taking into the account the extent to which a state’s political parties are ideologically polarized or whether the state’s legislative chambers impose bill introduction limits, as well as other variables that have been found in past research to have a relationship with a legislature’s bill passage rate (Bowling and Ferguson Reference Bowling and Ferguson2001; Gray and Lowery Reference Gray and Lowery1995; Hicks Reference Hicks2015; Hicks and Smith Reference Hicks and Smith2009; Jones Reference Jones2001; Rogers Reference Rogers2005; Squire Reference Squire1998). Regardless, our model in Table 3 reveals that a one-unit change in the civility index is related to a 17% increase in the percentage of bills being passed in 2018–2019.

Table 3. Percentage of bills enacted, 2018–2019

Note. The percentage of bills introduced that were enacted in each state in 2018–2019 is the dependent variable in this OLS regression model. Values are unstandardized coefficients.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

To be sure, measures of the absolute volume of legislation passed and the percentage of bills passed obscure one important fact: not all legislation is of equal importance. Each year, states collectively pass thousands of bills, some of which are important, while others are quite less so (e.g., noncontroversial “technical bills” submitted by state agencies to address administrative needs). As such, employing only measures of the total number of bills passed and the percentage that passed may obscure the reality that some legislatures may be more effective at enacting a plethora of legislation that has little effect, while other states may pass relatively very few pieces which have profound ramifications. Faced with a similar concern, David Mayhew (Reference Mayhew1991) in his study of the effect of divided government on Congressional productivity included in his tally of enacted statutes bills only those mentioned in post-congressional wrap-up news articles appearing in the New York Times and the Washington Post, or which were subject to later noteworthy historical examination. Others, such as Howell et al. (Reference Howell, Adler, Cameron and Riemann2000), expanded their tally to include somewhat similarly the number of pages CQ Almanac dedicated to these statutes as an additional measure of each law’s importance.

Clearly, employing similar methods to indicate the importance of laws at the state level would be impossible. The newspapers mentioned above do not produce similar end of year wrap-ups for all 50 states. Examining other state-specific major newspapers that may write end-of-session articles in every state would yield inconsistent data since the myriad different newspapers in each of the 50 states would likely employ very different standards for what constitutes “important” legislation. Thus, we are confronted with finding another source of data on legislation that could be deemed “noteworthy.” To be sure, Crosson (Reference Crosson2019) took a slightly different approach; he examined whether a state had passed a law or set of laws stemming from, and essentially mandated by, the passage of the federal Affordable Care Act in 2010, such as state bills creating state health care exchanges or expanding state Medicaid coverage (see also Bernecker Reference Bernecker2016 for a similar approach). Unfortunately, during the period our study examines, we were unable to find a similar federal law that triggered similar essential state legislative implementation activity.

However, the dataset developed by Boehmke and his colleagues (Boehmke et al. Reference Boehmke, Brockway, Desmarais, Harden, La Combe, Linder and Wallach2018) in their SPID dataset allows us to identify the number of “important” new laws adopted by each state. Their scores include additional measures of policy adoption recorded by other researchers such as laws related to health policy by Silver and Macinko (Reference Silver and Macinko2013), abortion policy by Kreitzer (Reference Kreitzer2015), elections legislation by Biggers and Hanmer (Reference Biggers and Hanmer2015) as well as others (e.g., Boushey Reference Boushey2016; Warshaw and Caughey Reference Warshaw and Caughey2015). They also include state laws passed that were based on model state laws drafted by the Uniform Law Commission such as the model state Administrative Procedure Act and the model state Uniform Commercial Code. To be sure, it is admittedly impossible to determine whether the laws included in this set include all possible laws that could be deemed “important,” but they do include a very broad range of state laws – 728 laws, in total – that are sufficiently noteworthy to be examined by past researchers or proffered by the Uniform Law Commission and frequently are passed in multiple states.

Our dependent variable in this analysis consists of a count of the total number of laws in this dataset passed by each of the states in the years 2016 and 2017. This includes a total of 105 instances of laws passed by 43 states during that two-year period, reflecting in each instance laws comparable to those that had been passed by an average of 19 other states by the end of 2017. By adopting this approach our analyses are not directed toward examining which states were early adopters of specific policies (i.e., “innovators”), nor do we examine patterns of diffusion of new policies across states. Instead, we examine the number of new pieces of important legislation as identified by Boehmke et al. (Reference Boehmke, Brockway, Desmarais, Harden, La Combe, Linder and Wallach2018) as adopted by state legislatures during those years, the most recent years available.

We include as a control variable in this model a count of the number of laws among the 728 pieces of important legislation included in the SPID which each state had already adopted prior to 2016. We do this because if a state had adopted fewer of these laws prior to 2016, then there would remain a larger number in the set of potential laws the state could pass in 2016 and 2017. We also consider the other factors which Crosson (Reference Crosson2019) included in his study of effective predictors of passage of the important post-Affordable Care Act state legislation and which Bernecker (Reference Bernecker2016) found to be linked with the passage of state welfare reform laws. As with the model presented in Table 2, the count and skewed nature of our dependent variable in this model warrants the use of a negative binomial statistical model. Our findings from this model are set forth in Table 4, similarly employing incident rate ratios.

Table 4. Number of “important” bills passed, 2016–2017

Note. The total number of bills enacted in 2016 and 2017 identified by Boehmke et al. is the dependent variable in this negative binomial model.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

Even when considering a state’s degree of legislative party polarization along with the other factors found by Bernecker (Reference Bernecker2016) and Crosson (Reference Crosson2019) to predict the likelihood that important state legislation passes, civility plays a very prominent role. As one moves from the state with the lowest rated civility to the state with the median civility index score (i.e., moving one unit of value of the civility scale), ceteris paribus, the state with the higher score produced 97% more of this type of legislation than the state with the lowest score. In fact, this is the only predictor in this model that reaches the standard levels of statistical significance.

Studies of gridlock at the state level have also explored the area of budgets (e.g., Andersen, Lassen, and Westh Nielsen Reference Andersen, Lassen and Westh Nielsen2020; Melusky Reference Melusky2020). As partial shutdowns of the federal government garnered national attention in 2013, scholars looked to US states to examine what factors determine when similar budget impasses occur at the state level. To be sure, not all state budget impasses result in government shutdowns. Like Congress, some state legislatures may pass continuing resolutions or take other measures to prevent the shuttering of state government office doors (NCSL 2010). Nonetheless, a state legislature’s failure to produce a new budget by the beginning of the start of the new fiscal year brings uncertainty about paychecks for state government workers and payments to state government contractors. Despite these problems, state budget impasses do occur. In fact, Klarner, Phillips, and Muckler (Reference Klarner, Phillips and Muckler2012, 998) found that in the years between 1961 and 2006, over 15% of all state budgets were passed after the beginning of their respective fiscal years. This state of affairs obtained even though all 50 states, either through state statute or via a state constitutional mandate, require that legislation authorizing a new budget be passed by the legislature and signed by the governor before the start of that next fiscal year (Kirkland and Phillips Reference Kirkland and Phillips2018). Thus, annual, or biennial budget legislation certainly can be considered another type of “important” legislation.

Examinations of the passage of on-time budgets provide an additional benefit when examining states’ capacity for legislative productivity. This is because it avoids the problem of measuring the demand for policy change, or as Kirkland and Phillips (Reference Kirkland and Phillips2018, 179) deem “the denominator problem.” That is, it is difficult (if even possible) to measure the demand for potential policy change across all policy areas, either in the form of political support for this change or in the form of genuine need for change in some objective sense. However, states must pass budgets each year, or in a few cases with biennial budget cycles, every two years.

To explore whether more civil legislatures are more likely to pass their budgets on time, we created a dichotomous variable indicating whether the state legislature had passed (and the governor signed) an annual state appropriations bill prior to the start of the succeeding year fiscal year 2020 (usually on July 1). The dichotomous variable created is coded “1” if the budget was enacted prior to the fiscal year’s start date, 0 if it was not (see Melusky Reference Melusky2020 for a similar approach).

We also include other covariates linked in past research to the probability a state adopts an on-time budget. These include measures of the size of the state budget, whether the state experienced a budget surplus during the previous fiscal year, and whether the state’s constitution required the passage of a balanced budget (as opposed to merely being required in statute). Like Melusky, who found that a legislature’s gender balance was a significant predictor of whether a legislature passed an on-time budget, we include a variable indicating the percentage of the state legislature who were women. We also take into account the degree of polarization between the legislative parties, whether control of the state government was exercised by one political party, the length of the legislative session (Birkhead Reference Birkhead2016; Kirkland and Phillips Reference Kirkland and Phillips2018; Klarner, Phillips, and Muckler Reference Klarner, Phillips and Muckler2012; Melusky Reference Melusky2020), each of these variables have been found to offer additional predictive value in previous studies. This model also includes measures of whether each state’s governor can exercise a line-item veto, the states’ populations, and the states’ per capita GDPs.

To be sure, unlike our previous models, the following model considers the role of the governor in the legislative process. We do this by measuring not only whether the budget was passed by the state legislature, but also whether the governor signed the legislation approving the budget. We do this because, as with Melusky (Reference Melusky2020), we consider it important to factor in the role that governors’ potential power play via line-item vetoes on budgetary items. Such gubernatorial powers might prompt legislatures to pass on-time budgets. If a governor is granted considerable authority to veto parts of a budget as passed by the legislature, the debate could be less intense and shorter in the legislature by virtue of the fact the side wanting to include something would be doing so with the knowledge their attempts could come to naught should the governor feel inclined to veto that portion of the budget.

Surprisingly, Model 5.1 in Table 5 reveals that states with constitutional requirements for passing a balanced budget prior to the beginning of the next fiscal year were less likely to do so. As suspected, however, states with governors possessing the power to execute line-item vetoes were more likely to pass a budget on time, as were states with lengthier legislative sessions. Considering these and the other covariates in the model, we once again find that a state’s civility score serves as a significant predictor. States that scored higher were more likely to pass budgets on time than states with lower scores.Footnote 5 Additionally, more polarized state legislatures are less likely to pass an on-time budget, as well.

Table 5. On-time budgets

Note. Whether the state enacted a budget for FY20 prior to the beginning of that fiscal year for the state is the dependent variable in each of these logit models.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

We also explored the possibility that polarization may interact with civility – in particular, we suspect that higher levels of legislative civility could moderate the effect that polarization might have on legislative adoption, since past research has shown that state legislatures with more ideologically polarized political parties are less likely to pass budgets on time (Kirkland and Phillips Reference Kirkland and Phillips2018) and produce less legislation, in general (Bowling and Ferguson Reference Bowling and Ferguson2001; Jones Reference Jones2001). Model 5.2 in Table 5 tests this possibility by including an interaction term for those two variables. The interaction term yielded is both positive and statistically significant.Footnote 6

To get a better sense of the meaning of the positive interaction term between the civility index and polarization, we display in Figure 3 the predicted probability of the passage of an on-time budget at three estimated values for the polarization variable: one standard deviation below the mean, the mean, and one standard deviation above the mean. The probability of passing a budget on time is then plotted across the range of civility score values. At the left-hand side of the x-axis where states are rated as being very uncivil, there is considerable variation among these three groupings. Highly polarized states are relatively unlikely to pass their budget on time (less than 14% on time). Moderately polarized states (i.e., those at the mean of the polarization variable) found in the middle of the y-axis are more successful at passing on-time budgets (at roughly 50% on time). Finally, states that are rated the most civil are even more likely to pass a timely budget (75% of the time). Conversely, on the right-hand side of the x-axis where the civility scores are at their highest, polarization plays no role in on-time budget passage. In other words, all states with higher rated values for civility are equally likely to pass their budget on time (at a rate approaching 100% on time), regardless of how polarized their state legislative parties may be. This finding suggests that the negative effect of polarization on legislative productivity, insofar as it involves passing state budgets, can be mitigated by higher degrees of civility with the state legislature.

Figure 3. The relationship between legislative civility, polarization, and the probability of the passage of a budget prior to the start of FY2020.

Note: Shaded area reflects 95% confidence intervals. Estimates are obtained from Model 5.2 in Table 5.

Discussion and conclusion

To what extent are more civil legislatures able to produce more new legislation? Our findings suggest that there is indeed a positive relationship between legislative statutory productivity and civility, confirming H1 – namely, state legislatures that lobbyists rate as more civil do indeed pass more pieces of legislation. The model in Table 1 estimates that the most civil legislature passed more than 64% more legislation than the least civil ones in 2018 and 2019, while Model 2 estimates that the most civil legislature passed a higher proportion of bills that were introduced, providing support for H2. Perhaps even more importantly, the results reported here suggest that our measure of civility would seem to be the best predictor in our models of total legislative productivity. Only three other state-level variables included in our first two analytical models – a state’s population and the total number of bill introductions in the first model, and the presence of limits on bill introductions in the second – offer any additional significant predictive power.

Such a pattern remains when we turn our attention to more significant pieces of legislation. The model presented in Table 4 (testing H3) reveals that states with more civil legislatures passed significantly more pieces of the “important” state legislation identified in the Boehmke et al. (Reference Boehmke, Brockway, Desmarais, Harden, La Combe, Linder and Wallach2018) SPID dataset than less civil ones. Such a relationship between civility and legislative productivity is also evident when examining the probability that a state passed their FY 2020 budget on time (see Model 5.1), providing evidence for H4. Additionally, our findings suggest that civility and polarization interact in a way to combat both partisan polarization and legislative gridlock – namely, that states with legislatures rated as being more civil are more likely to pass these on-time budgets. This is true regardless of the extent to which their state legislative parties are ideologically polarized. Highly polarized, yet civil, legislatures are just as likely to pass their budgets on time than less polarized and less civil ones.

It is worth noting that our findings may generate additional questions requiring attention. First, our measure of civility does not locate the locus of the incivility documented in the national survey of state legislative lobbyists. Although one might assume uncivil behavior is more likely to occur between individuals on opposite sides of the ideological spectrum in different parties, the Kettler, Fowler, and Witt (Reference Kettler, Fowler and Witt2021) measure we employ does not reveal this. In other words, however unlikely it may seem, it very well could be the case that the perceived incivility is not just occurring between Democrats and Republicans but within legislative party caucuses as well. A good number of the comments made by state legislative lobbyists working in one-party dominant settings report that the incivility within dominant parties is a major factor inhibiting effective caucus deliberations (Benjamin et al. Reference Benjamin, Lovrich, Pierce and Schreckhise2022). At the very least, even if one can assume that the bulk of uncivil legislative behavior is directed across the aisle, some of it also occurs within party caucuses.

To this point, one of the themes of lobbyists’ comments in the National Survey of State Legislative Lobbyists was that the proportion of “true believers” has grown and the presence of “practical problem solvers” has ebbed in the state legislatures in which they ply their craft -- leading to incivility and diminished prospects for “getting to yes” in the terminology of alternative dispute resolution (ADR). Additionally, in one state, it was noted that the “real contest for influence is among factions in the GOP wherein the uncompromising nut cases must be fought tooth and nail.” In ADR the observance of ground rules to which all parties pledge faithfulness is essential to dispute resolution. It is the routine violation of these fundamental ground rules (such as truthfulness) which characterizes much of the incivility being witnessed in the contemporary state legislative processes according to many of the lobbyists surveyed. It is worth noting, however, that we found that the correlation between the civility index and the degree of partisan competition in the state legislatures is mildly negative; that is, state legislatures dominated by a single party see only slightly higher civility scores than states that have roughly equal numbers of Democrats and Republicans in their legislative chambers.Footnote 7

A couple of caveats are in order regarding our findings. First, because of the relatively small number of cases in our analyses and the number of covariates included in our models, the possibility exists that the stability of the coefficients in these models is less than optimal. To be sure, the fact that our “sample” consists of the total population of American states, issues related to sampling distributions are less of a concern. Nonetheless, we examined the stability of our core independent variable by running our models using different combinations of the other covariates and examining whether the value of coefficient for civility index changed when used in these different combinations. The direction of the civility index remained consistently positive; however, the extent to which the coefficients for this variable reached standard levels of statistical significance was less consistent.Footnote 8

Second, although the models presented in this article provide evidence of a link between legislative productivity and legislative civility, we cannot discern the direction of the casual arrow with absolute certainty. That is, we cannot say lower levels of civility cause lower levels of legislative productivity. It is possible, if perhaps unlikely, that the causal direction runs the opposite of what we have suggested, and that more productive legislative sessions lead to more civil relations among legislators. Perhaps, buoyed by a productive legislative session, legislators are “nicer” to each other than they would otherwise be.Footnote 9 Moreover, this could occur as an iterative process, that is, when one important bill passes, relations among legislators improve, opening the door for future collaboration. Conversely, when an important bill fails or a budget bill is delayed, relations sour, and further collaboration is impeded, causing additional legislation to founder.

Regardless of these caveats, the analyses and results presented here show that a demonstrable relationship between legislative civility and legislative productivity does exist. Even when a number of other factors linked to legislative productivity and gridlock are taken directly into account, we find evidence that legislatures that produce more (and more important) legislation are rated by those individuals who work with them closely as being a more civil body. Such findings not only provide insight into the legislative dynamics of states but offer insight to the productivity of legislatures in general. Future work should feature comparative cases studies of the most productive state legislatures and the least productive state legislatures, noting the extent to which civility appears as a key element of discrimination between these two groups along with other factors of interest. Doing so can provide a richer and more nuanced understanding of the relationship between legislative civility and legislative productivity.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1017/spq.2023.23.

Data availability statement

Replication materials are available on SPPQ Dataverse at https://doi.org/10.15139/S3/QOSJLL (Schreckhise et al. Reference Schreckhise, Pierce, Benjamin, Lovrich and Eric Button2023).

Acknowledgment

The authors dedicate this article to the memory of Burdett A. Loomis.

Funding statement

The authors received financial support from the following sources for the research for this article: the National Institute for Civil Discourse at the University of Arizona, the Division of Governmental Studies & Services at Washington State University, the Thomas S. Foley Institute for Public Policy and Public Service at Washington State University, the School of Public Policy at Oregon State University, and the PhD Program in Public Policy and Administration at Boise State University. They received no financial support for the authorship of the article. They received financial support for the publication of this article from the University of Arkansas.

Competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biographies

William D. Schreckhise is a Professor of Political Science at the University of Arkansas. His research interests include public policy and American political institutions.

John C. Pierce is an Affiliate Faculty in the School of Public Affairs and Administration at the University of Kansas.

Francis A. Benjamin is an Information Systems Coordinator, managing the Political Interaction Lab, in the Psychology Department at Washington State University. He has 13 years of elected service in local government.

Nicholas P. Lovrich is a Regents Professor Emeritus in the School of Politics, Philosophy and Public Affairs at Washington State University. He served as a state and local government specialist on partial appointment in WSU Extension for 25 years.

Eric Button is a Teaching Assistant Professor of Political Science at the University of Arkansas. He researches state and local public policy and veterans policy.

Footnotes

1 The data that support the findings of this study are openly available in UNC Dataverse at https://doi.org/10.15139/S3/QOSJLL.

2 Names, email addresses, and physical addresses were obtained from state offices, ethics commissions, and the like for all 50 states. Two hundred registered lobbyists selected at random from each state were then invited to participate via email in a Qualtrics online survey, with two waves of printed surveys being sent to the lobbyists surveyed via the U.S. Postal Service. We obtained over 1,200 usable responses with the online survey and two follow-up mailings combined, with many questionnaires containing extensive comments on individual questions and commentaries on legislative affairs in their own states. The participation rate of the survey was 25%.

3 The individual variables in the model included the respondents’ age, gender, race, partisan affiliation, preferences on social and fiscal issues, and whether they represented businesses, business associations, trade association, labor unions, public agencies, or public interest nonprofits.

4 State legislative professionalization scores, as computed by Bowen and Green (Reference Bowen and Greene2014), were computed by applying multidimensional scaling related to each state’s legislature’s legislative legislator salary (see Bowen and Green Reference Bowen and Greene2014) and are somewhat like Squire’s (Reference Squire2007) measure of state legislative professionalism (see also LaCombe and Boehmke Reference LaCombe and Boehmke2021, 293).

5 It should be noted that a variable indicating whether the state would face a shutdown if the budget did not pass in time was excluded from this model. The inclusion of this variable in the model caused computational difficulties, likely due in large part to the fact that none of the states that failed to produce an on-time budget faced mandatory shutdowns when they fail to do so.

6 It is worth noting that in separate analyses we added Civility index-Polarization interaction terms in each of the models presented in Tables 2–4. Of these, only the model in Table 2, predicting the total number of bills passed, presented a statistically significant coefficient. The coefficient for the interaction term was positive. Analyses of the estimates reveal that the least polarized legislatures pass roughly the same number of bills, regardless of the extent of their degrees of civility, while the number of bills the most polarized legislatures is influenced by the extent to which these legislatures are civil – specifically, the more civil a polarized legislature is, the larger number of bills it passes. At the same time, however, the estimates reveal as well that the most civil and least polarized legislatures pass fewer bills than the most polarized and most civil legislatures enacted in the 2-year period in question. Because of this unexpected finding, it is necessary to conduct additional research for additional years. Such research is currently being planned and a replication of the original study, with additional content on COVID-19 events and legislative committee dynamics, is being fielded in 2023.

7 To determine the relationship between party competition and civility index scores, we computed a measure of legislative party competition from the absolute value difference between the number of members of the two parties in each chamber, divided by the total number of members in the two chambers combined, then multiplied by −1. This rendered for each state values with a minimum score of 0.14, a mean score of 0.68, a standard deviation of 0.21, and a maximum score of 0.97. Higher scores indicate more party competition, and lower scores reflect less competition. The Pearson correlation coefficient between the legislative party competition values and civility index score is −0.25 (p = .08), indicating more competitive states are rated as being somewhat less civil.

8 This was done by using Stata’s add-on command, checkrob. Results are available from the authors by request. Additionally, as a check for multicollinearity, variance inflation factors (VIFs) were computed for each of the variables in our models. The largest VIF for any of the variables in the models was 3.65 (for the variable Population(ln)) in models 5.1 and 5.2.

9 The authors are willing to share the data, metadata, survey comments, and a coding manual for comments collected for the National Survey of State Legislative Lobbyists with other researchers working in the area of state legislative policy and politics. Our website (https://labs.wsu.edu/outside-looking-in/) features these key research materials, and we encourage researchers to contact coauthors Nicholas Lovrich and Francis Benjamin at Washington State University directly for assistance in making use of the data serving as the foundation for this article.

References

Andersen, Asger Lau, Lassen, David Dreyer, and Westh Nielsen, Lasse Holbøll. 2020. “Irresponsible Parties, Responsible Voters? Legislative Gridlock and Collective Accountability.” PLoS One 15 (3): e0229789. doi: 10.1371/journal.pone.0229789.CrossRefGoogle ScholarPubMed
Anjum, Amna, Ming, Xu, Siddiqi, Ahmed Faisal, and Rasool, Samma Faiz. 2018. “An Empirical Study Analyzing Job Productivity in Toxic Workplace Environments.” International Journal of Environmental Research and Public Health 15 (5): 1035. doi: 10.3390/ijerph15051035.CrossRefGoogle ScholarPubMed
Benjamin, Francis, Lovrich, Nicholas, Pierce, John, and Schreckhise, William. 2022. “Outside Looking In: Lobbyists’ Views on Civil Discourse in US State Legislatures.” Paper presented at the 2022 Annual Meeting of the Western Political Science Association, Portland, Oregon, March 8–13.Google Scholar
Bernecker, Andreas. 2016. “Divided we Reform? Evidence from U.S. Welfare Policies.” Journal of Public Economics 142: 2438.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1990. “Innovation and Diffusion Models in Policy Research.” In Theories of the Policy Process, eds. Christopher M. Weible and Paul A. Sabatier, 253–297. New York: Westview Press.CrossRefGoogle Scholar
Biggers, Daniel R., and Hanmer, Michael J.. 2015. “Who Makes Voting Convenient? Explaining the Adoption of Early and no-Excuse Absentee Voting in the American States.” State Politics & Policy Quarterly 15 (2): 192210.CrossRefGoogle Scholar
Binder, Sarah. 2003. Stalemate: Causes and Consequences of Legislative Gridlock. Washington, DC: Brookings Institution.Google Scholar
Birkhead, Nathaniel A. 2016. “State Budgetary Delays in an Era of Party Polarization.” State and Local Government Review 48 (4): 259–69.CrossRefGoogle Scholar
Boehmke, Frederick J., Brockway, Mark, Desmarais, Bruce, Harden, Jeffrey J., La Combe, Scott, Linder, Fridolin, and Wallach, Hanna. 2018. “State Innovativeness – Dynamic Rate Scores from SPID v1.2,” https://doi.org/10.7910/DVN/GMVOI5, Harvard Dataverse, V3, UNF:6:aqnXTm8O/6l5nsjCmwl4EQ==[fileUNF] (accessed February 24, 2022).Google Scholar
Boehmke, Frederick J., Brockway, Mark, Desmarais, Bruce A., Harden, Jeffrey J., La Combe, Scott, Linder, Fridolin, and Wallach, Hanna. 2020. “SPID: A New Database for Inferring Public Policy Innovativeness and Diffusion Networks.” Policy Studies Journal 48 (2): 517–45.CrossRefGoogle Scholar
Boehmke, Frederick J., and Skinner, Paul. 2012. “State Policy Innovativeness Revisited.” State Politics & Policy Quarterly 12 (3): 303–29.CrossRefGoogle Scholar
Boushey, Graeme. 2016. “Targeted for Diffusion? How the Use and Acceptance of Stereotypes Shape the Diffusion of Criminal Justice Policy Innovations in the American States.” American Political Science Review 110 (1): 198214.CrossRefGoogle Scholar
Porismita, Borah. 2013. “Interactions of News Frames and Incivility in the Political Blogosphere: Examining Perceptual Outcomes.” Political Communication 30 (3): 456473.Google Scholar
Bowen, Daniel C., and Greene, Zachary. 2014. “Should we Measure Professionalism with an Index? A Note on Theory and Practice in State Legislative Professionalism Research.“ State Politics & Policy Quarterly 14 (3): 277–96.CrossRefGoogle Scholar
Bowling, Cynthia J, and Ferguson, Margaret R.. 2001. “Divided Government, Interest Representation, and Policy Differences: Competing Explanations of Gridlock in the Fifty States.” The Journal of Politics 63 (1): 182206.CrossRefGoogle Scholar
Chinn, Sedona, and Hart, P. Sol. 2022. “Can’t you all Just Get along? Effects of Scientific Disagreement and Incivility on Attention to and Trust in Science.” Science Communication 44 (1): 108–29.CrossRefGoogle Scholar
Coleman, John J. 1999. “Unified Government, Divided Government, and Party Responsiveness.” American Political Science Review 93 (4): 821–35.CrossRefGoogle Scholar
Crosson, Jesse M. 2019. “Stalemate in the States: Agenda Control Rules and Policy Output in American Legislatures.” Legislative Studies Quarterly 44 (1): 333.CrossRefGoogle Scholar
Dodd, Lawrence C, and Schraufnagel, Scot. 2012. “Congress and the Polarity Paradox: Party Polarization, Member Incivility and Enactment of Landmark Legislation, 1891–1994.” Congress & the Presidency 39 (2): 109–32.CrossRefGoogle Scholar
Druckman, James, Gubitz, R., Levendusky, Matthew S., and Lloyd, Ashley. 2018. “How Incivility on Partisan Media (De-)Polarizes the Electorate.” Journal of Politics 81 (1): 291–95.CrossRefGoogle Scholar
Edwards, George C., Barrett, Andrew, and Peake, Jeffrey. 1997. “The Legislative Impact of Divided Government.” American Journal of Political Science 41 (2): 545–63.Google Scholar
Estes, Brad, and Wang, Jia. 2008. “Integrative Literature Review: Workplace Incivility: Impacts on Individual and Organizational Performance.” Human Resource Development Review 7 (2): 218–40. doi: 10.1177/1534484308315565.CrossRefGoogle Scholar
Forgette, Richard, and Morris, Jonathan S.. 2006. “High-Conflict Television News and Public Opinion.” Political Research Quarterly 59 (3): 447–56.CrossRefGoogle Scholar
Gervais, Bryan T. 2019. “Rousing the Partisan Combatant: Elite Incivility, Anger, and Antideliberative Attitudes.Political Psychology 40 (3): 637655.CrossRefGoogle Scholar
Goovaerts, Ine. 2022. “Highlighting Incivility: How the News Media’s Focus on Political Incivility Affects Political Trust and News Credibility.” Journalism & Mass Communication Quarterly 2022. doi: 10.1177/10776990221084608.Google Scholar
Gray, Virginia. 1973. “Innovation in the States: A Diffusion Study.” American Political Science Review 67 (4): 1174–85.CrossRefGoogle Scholar
Gray, Virginia, and Lowery, David. 1995. “Interest Representation and Democratic Gridlock.” Legislative Studies Quarterly 20 (4): 531–52.CrossRefGoogle Scholar
Hicks, William D., and Smith, Daniel A.. 2009. “Do Parties Matter? Explaining Legislative Productivity in the American States.” Paper presented at The State of the Parties: 2008 & Beyond Conference, October 15–16, 2009, Bliss Institute, University of Akron, Ohio.Google Scholar
Hicks, William D. 2015. “Partisan Competition and the Efficiency of Lawmaking in American State Legislatures, 1991-2009.American Politics Research 43 (5): 743770.CrossRefGoogle Scholar
Hilbe, Joseph M. 2011. Negative Binomial Regression, 2nd edition. New York: Cambridge University Press.CrossRefGoogle Scholar
Hopp, Toby. 2019. “A Network Analysis of Political Incivility Dimensions.” Communication and the Public 4 (3): 204–23. doi: https://doi.org/10.1177/2057047319877278.CrossRefGoogle Scholar
Howell, William, Adler, Scott, Cameron, Charles, and Riemann, Charles. 2000. “Divided Government and the Legislative Productivity of Congress, 1945–94.” Legislative Studies Quarterly 25 (2): 285312.CrossRefGoogle Scholar
Iyengar, Shanto, Sood, Gaurav, and Lelkes, Yphtach. 2012. “Affect, Not Ideology: A Social Identity Perspective on Polarization.” Public Opinion Quarterly 76 (3): 405–31.CrossRefGoogle Scholar
Iyengar, Shanto, and Westwood, Sean J.. 2015. “Fear and Loathing across Party Lines: New Evidence on Group Polarization.” American Journal of Political Science 59 (3): 690707.CrossRefGoogle Scholar
Jones, David R. 2001. “Party Polarization and Legislative Gridlock.” Political Research Quarterly 54 (1): 125–41.CrossRefGoogle Scholar
Kettler, Jaclyn J., Fowler, Luke, and Witt, Stephanie L.. 2021. “Is It Us? Is It Them? Or is it this Place? Predicting Civility in State Legislatures.State Politics & Policy Quarterly, 22: 5069. doi:10.1017/spq.2021.18.CrossRefGoogle Scholar
Kirkland, Patricia A., and Phillips, Justin H.. 2018. “Is Divided Government a Cause of Legislative Delay?Quarterly Journal of Political Science 13 (2): 173206.CrossRefGoogle Scholar
Klarner, Carl E., Phillips, Justin H., and Muckler, Matt. 2012. “Overcoming Fiscal Gridlock: Institutions and Budget Bargaining.” Journal of Politics 74 (4): 9921009.CrossRefGoogle Scholar
Kreitzer, Rebecca J. 2015. “Politics and Morality in State Abortion Policy.” State Politics and Policy Quarterly 15 (1): 4166.CrossRefGoogle Scholar
LaCombe, Scott J., and Boehmke, Frederick J.. 2021. “The Initiative Process and Policy Innovation in the American States.” State Politics & Policy Quarterly 21 (3): 286305.CrossRefGoogle Scholar
Loomis, Burdett A. 2004. “The Senate as a Representative Body.” In Esteemed Colleagues: Civility and Deliberation in the U.S. Senate, ed. Loomis, Burdett A., 112. Washington, DC: Brookings Institution Press.Google Scholar
Lovrich, Nicholas P., Benjamin, Francis A., Pierce, John C., and Schreckhise, William D. (eds). 2021. “Outside Looking in: Lobbyists.” In Views on Civil Discourse in the U.S. State Legislatures. Pullman, WA: Washington State University Press.Google Scholar
Mallinson, Daniel J. 2020. “Policy Innovation Adoption across the Diffusion Life Course.” Policy Studies Journal 49 (2): 335–58.CrossRefGoogle Scholar
Mallinson, Daniel J. 2021. “Who Are your Neighbors? The Role of Ideology and Decline of Geographic Proximity in the Diffusion of Policy Innovations.” Policy Studies Journal 49 (1): 6788.CrossRefGoogle Scholar
Mayhew, David R. 1991. Divided we Govern: Party Control, Lawmaking, and Investigations, 1946–1990. New Haven: Yale University Press.Google Scholar
Melusky, Benjamin. 2020. “Gender and Fiscal Gridlock in the American States.” Journal of Women, Politics & Policy 41 (3): 249–77.CrossRefGoogle Scholar
Miner, Kathi N., Smittick, Amber L., He, Yimin, and Costa, Paula L.. 2019. “Organizations Behaving Badly: Antecedents and Consequences of Uncivil Workplace Environments.” Journal of Psychology 153 (5): 528–54. doi: 10.1080/00223980.2019.1578191.CrossRefGoogle ScholarPubMed
Muddiman, Ashley. 2017. “Personal and Public Levels of Political Incivility.” International Journal of Communication 11: 3182–202.Google Scholar
Mutz, Diana, C. 2007. “Effects of ‘in-your-Face’ Television Discourse on Perceptions of a Legitimate Opposition.” American Political Science Review 101 (4): 621–35.CrossRefGoogle Scholar
Mutz, Diana C., and Reeves, Byron. 2005. “The New Videomalaise: Effects of Televised Incivility on Political Trust.” American Political Science Review 99 (1): 115.CrossRefGoogle Scholar
Naab, Teresa K., Heinbach, Dominique, Ziegele, Marc, and Grasberger, Marie-Theres. 2020. “Comments and Credibility: How Critical User Comments Decrease Perceived News Article Credibility.” Journalism Studies 21 (6): 783801. doi: 10.1080/1461670X.2020.1724181.CrossRefGoogle Scholar
National Conference of State Legislatures. 2010. Late State Budgets. https://www.ncsl.org/research/fiscal-policy/late-state-budgets.aspx (accessed August 23, 2022).Google Scholar
National Survey of State Legislative Lobbyists. 2022. Washington State University. https://labs.wsu.edu/outside-looking-in/.Google Scholar
Paternoster, Raymond, and Brame, Robert. 1997. “Multiple Routes to Delinquency? A Test of Developmental and General Theories of Crime.” Criminology 35 (1): 4984.CrossRefGoogle Scholar
Payne, Elizabeth H., Gebregziabher, Mulugeta, Hardin, James W., Ramakrishnan, Viswanathan, and Egede, Leonard E.. 2018. “An Empirical Approach to Determine a Threshold for Assessing Overdispersion in Poisson and Negative Binomial Models for Count Data.” Communications in Statistics: Simulation and Computation 47 (6): 1722–38. doi: 10.1080/03610918.2017.1323223.CrossRefGoogle ScholarPubMed
Piza, Eric L. 2012. “Using Poisson and Negative Binomial Regression Models to Measure the Influence of Risk on Crime Incident Counts.” Rutgers Center on Public Security. http://www.rutgerscps.org/uploads/2/7/3/7/27370595/countregressionmodels.pdf (accessed May 4, 2020).Google Scholar
Prochazka, Fabian, Weber, Patrick, and Schweiger, Wolfgang. 2018. “Effects of Civility and Reasoning in User Comments on Perceived Journalistic Quality.” Journalism Studies 19 (1): 6278. doi: 10.1080/1461670X.2016.1161497.CrossRefGoogle Scholar
Rogers, James R. 2005. “The Impact of Divided Government on Legislative Production.” Public Choice 123 (1): 217–33.CrossRefGoogle Scholar
Rom, Mark Carl, Peterson, Paul E., and Scheve, Kenneth F.. 1998. “Interstate Competition and Welfare Policy.” Publius: The Journal of Federalism 28 (3): 1737.CrossRefGoogle Scholar
Schraufnagel, Scot. 2005. “Testing the Implications of Incivility in the United States Congress 1977–2000: The Case of Judicial Confirmation Delay.” Journal of Legislative Studies 11 (2): 216–34.CrossRefGoogle Scholar
Schreckhise, William D., and Benjamin, Francis. 2021. “The Connection between Legislative Professionalism and Legislative Civility.” In Outside Looking in: Lobbyists’ Views on Civil Discourse in the U.S. State Legislatures, eds. Lovrich, Nicholas P., Benjamin, Francis A., Pierce, John C., and Schreckhise, William D., 3764. Pullman, WA: Washington State University Press.Google Scholar
Schreckhise, William D., and Chand, Daniel E., and Benjamin, Francis. 2021. “Term Limits: Presumed and Actual Impact on Civility.” In Outside Looking in: Lobbyists’ Views on Civil Discourse in the U.S. State Legislatures, eds. Lovrich, Nicholas P., Benjamin, Francis A., Pierce, John C., and Schreckhise, William D., pp. 89111. Pullman, WA: Washington State University Press.Google Scholar
Schreckhise, William D., Pierce, John C., Benjamin, Francis, Lovrich, Nicholas P., and Eric Button, D. 2023. “Replication Data for: Legislative Civility, Gridlock, Polarization, and Productivity,” https://doi.org/10.15139/S3/QOSJLL, UNC Dataverse, V1.CrossRefGoogle Scholar
Schuman, Sharon. 2016. Freedom and Dialogue in a Polarized World. Newark: University of Delaware Press.Google Scholar
Silver, Diana, and Macinko, James. 2013. “State Health Policy Research Dataset (SHEPRD): 1980–2010.” Ann Arbor, MI: Inter-University Consortium for Political and Social Research [distributor]. http://doi.org/10.3886/ICPSR_34789.v3.CrossRefGoogle Scholar
Skytte, Rasmus. 2021. “Dimensions of Elite Partisan Polarization: Disentangling the Effects of Incivility and Issue Polarization.” British Journal of Political Science 51 (4): 1457–75.CrossRefGoogle Scholar
Squire, Peverill. 1998. “Membership Turnover and the Efficient Processing of Legislation.” Legislative Studies Quarterly 23 (1): 2332.CrossRefGoogle Scholar
Squire, Peverill. 2007. “Measuring State Legislative Professionalism: The Squire Index Revisited.” State Politics & Policy Quarterly 7 (2): 211–27.CrossRefGoogle Scholar
Thrasher, James. 2016. “The Ethics of Legislative Vote Trading.” Political Studies 64 (3): 614–29. doi: 10.1111/1467-9248.12205.CrossRefGoogle Scholar
Thurber, James A. 2000. “The Senate Budget Committee: Bastion of Comity?” In Esteemed Colleagues: Civility and Deliberation in the U.S. Senate, ed. Loomis, Burdett A., 241–58. Washington, DC: Brookings Institution Press.Google Scholar
Tsebelis, George. 2002. Veto Players: How Political Institutions Work. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Uslaner, Eric M. 2000. “Is the Senate more Civil than the House?” In Esteemed Colleagues: Civility and Deliberation in the U.S. Senate, ed. Loomis, Burdett A., 3255. Washington, DC: Brookings Institution Press.Google Scholar
Valentino, Nicholas A., Brader, Ted, Groenendyk, Eric. W., Gregorowicz, Krysha, and Hutchings, Vincent L.. 2011. “Election Night’s Alright for Fighting: The Role of Emotions in Political Participation.Journal of Politics 73 (1):156170.CrossRefGoogle Scholar
Walker, Jack L. 1969. “The Diffusion of Innovations among the American States.” American Political Science Review 63 (3): 880–99.CrossRefGoogle Scholar
Warshaw, Christopher and Caughey, Devin. 2015, “Replication Data for: The Dynamics of State Policy Liberalism, 1936–2014.” https://doi.org/10.7910/DVN/ZXZMJB, Harvard Dataverse, V1, UNF:6:iT+06z1StLiNW/PM9TQpCA==[fileUNF].Google Scholar
Christopher, Weber. 2013. “Emotions, Campaigns, and Political Participation.” Political Research Quarterly 66 (2): 414428.Google Scholar
Yuan, Meng, and Schraufnagel, Scot. 2020. “Two-Dimensional Legislative Conflict: Unique Implications for the Effectiveness of Local Councils.” Local Government Studies 46 (5): 780–99.CrossRefGoogle Scholar
Figure 0

Table 1. Civility index survey items

Figure 1

Figure 1. Civility index scores by state.Note: Higher scores reflect higher rates of perceived civility among legislators.Source: Values from the Kettler, Fowler, and Witt (2021) and National Survey of State Legislative Lobbyists (2022).

Figure 2

Table 2. Total number of bills enacted in state legislatures, 2018–2019

Figure 3

Figure 2. The relationship between legislative civility and the predicted number of bills enacted, 2018–2019.Note: Shaded area reflects 95% confidence intervals. Estimates are obtained from Table 2.

Figure 4

Table 3. Percentage of bills enacted, 2018–2019

Figure 5

Table 4. Number of “important” bills passed, 2016–2017

Figure 6

Table 5. On-time budgets

Figure 7

Figure 3. The relationship between legislative civility, polarization, and the probability of the passage of a budget prior to the start of FY2020.Note: Shaded area reflects 95% confidence intervals. Estimates are obtained from Model 5.2 in Table 5.

Supplementary material: File

Schreckhise et al. supplementary material

Schreckhise et al. supplementary material
Download Schreckhise et al. supplementary material(File)
File 30 KB
Supplementary material: File

Schreckhise_et_al._Dataset

Dataset

Download Schreckhise_et_al._Dataset(File)
File