Is There a Tradeoff Between Policy Responsiveness and Government Effectiveness? Evidence From the American States

Abstract

Citizens in a democracy expect elected officials will be responsive to their political opinions and govern in an effective way that improves their quality of life. However, a government that is too responsive to public sentiments may, in practice, be unable to govern effectively and promote societal well-being. This study is the first attempt to date to empirically evaluate this important potential tradeoff. Using newly developed measures of public opinion and public policy liberalism in the American states over time and a diverse battery of societal outcomes as well as multiple estimation strategies and timeframes, I find a weak and directionally inconsistent statistical relationship between policy responsiveness and government effectiveness. These findings have significant normative and theoretical implications because they suggest there is not a tradeoff between a government responding to its citizens’ opinions and it governing effectively by promoting citizens’ well-being.

Keywords

political representation government performance public opinion public policy

Two fundamental expectations citizens have about democratic government is that elected officials will be responsive to their political opinions and that they will govern in a way that improves their quality of life. Accordingly, there are large literatures in political science that investigate whether (and under what conditions) public policy reflects citizens’ opinions (Page & Shapiro, 1983; Erikson et al., 1993; Erikson et al., 2002; Lax & Phillips, 2012; Gilens & Page, 2014) and what institutional and cultural factors promote good and effective governance (La Porta et al., 1999; Knack, 2002; Helliwell & Huang, 2008; Rothstein, 2011; Fukuyama, 2013). Similarly, there are large literatures that evaluate the practice of democratic accountability by examining whether citizens punish policymakers at election time who support policies that are out of step with public opinion (Canes-Wrone et al., 2002; Hutchings, 2003; Ansolabehere & Jones, 2010; Rogers, 2017) or govern ineffectively and preside over poor outcomes (Fiorina, 1981; Huber et al., 2012; Healy & Malhotra, 2013; Achen & Bartels, 2016).

However, at least dating back to debates at the time of the American Founding and subsequent Constitutional Convention, there have been serious questions raised about whether there is an inherent tradeoff between policy responsiveness and government effectiveness.

Indeed, one of the primary arguments posited by the Federalists in support of indirect elections and longer terms in office is that a government that is too close to the people and too responsive to public sentiments may be unable to govern effectively and avoid chaos. Put another way, a government that is too responsive to public opinion may not be able to effectively promote societal well-being and the common good. Instead, as James Madison writes in Federalist 10, “it may well happen that the public voice, pronounced by the representatives of the people, will be more consonant to the public good than if pronounced by the people themselves.” This question of a potential tradeoff remains relevant from both a theoretical and empirical standpoint still today, as elected officials who seek to cater to public opinion may advocate for overspending or be unable to make difficult political choices that could ultimately promote a more effective government and better outcomes. Moreover, this potential tradeoff is of particular importance for new and emerging democracies as they make key decisions about election laws and institutional design.

The literature on comparative electoral systems (e.g., Lijphart, 1999; Powell, 2000; Rogowski & Kayser, 2002; Persson & Tabellini, 2004) has highlighted (albeit indirectly) the potential tradeoff between responsiveness and effectiveness by debating whether consensus democracies (with institutions of proportional representation) tend to create greater incentives for responsiveness than majoritarian systems (with plurality electoral systems) while simultaneously reducing the government’s effectiveness. For example, in the previous study that most closely relates to this question, Pickup and Hobolt (2015) suggest a tradeoff by demonstrating that minority governments in Canada are more responsive to the median voter but less legislatively effective¹ than majority governments. Although related, the success of a governing party in enacting its policy agenda is a qualitatively different metric than the ultimate societal outcomes it presides over while in office. To date, no empirical study has directly evaluated the possible linkage between the quality of political representation and societal well-being outcomes.

This study uses newly developed measures of public opinion and public policy liberalism in the U.S. states over time (Caughey & Warshaw, 2018) and a diverse battery of societal outcomes (Dynes & Holbein, 2020) to systematically evaluate the relationship between policy responsiveness and government effectiveness. Using multiple estimation strategies and timeframes, I find a weak and directionally inconsistent statistical relationship between responsiveness and effectiveness. These empirical findings have important normative and theoretical implications because they suggest that there is not a tradeoff between a government that is responsive to its citizens’ political opinions and a government that governs effectively by promoting citizens’ well-being.

Conceptualizing and Measuring Responsiveness and Effectiveness

The goal (and original contribution) of this study is to evaluate whether there is a negative relationship between policy responsiveness and government effectiveness. In a recent article, Caughey and Warshaw (2018) develop new annual measures of public opinion and government policy liberalism for economic (e.g., taxes, social welfare, labor regulation) and social issues (e.g., alcohol, abortion, gay rights, women’s rights, school prayer) in the U.S. states that are comparable over time for 1936-2014. I use these data on opinion and policy to create a measure of responsiveness (for economic issues and then, separately, for social issues) that has a value for each state-year. Specifically, I first regress state policy on opinion_t-1 (i.e. one year earlier) as in previous studies of policy responsiveness (Achen, 1978; Erikson et al., 1993; Caughey & Warshaw, 2018) across all state-years in a single model. The resulting regression coefficient is positive and statistically different from zero, indicating that (as expected) state-years with more liberal public opinion tend to have more liberal public policy outputs.² Then, relying on the same methodological foundation of previous studies that evaluate the extent to which legislators, through their roll call voting behavior, “shirk” (or are discordant with) public opinion in their district (Kau & Rubin, 1979; Carson & Oppenheimer, 1984; Kalt & Zupan, 1984), I use the resulting residuals (the difference between predicted and actual policy liberalism) from the regression as a measure of the magnitude of disjuncture between opinion and policy.³ To create a value of responsiveness for each state-year, I multiply the absolute value of the residual (thus treating policy that is more liberal than public opinion would predict similar to policy that is more conservative than public opinion would predict) by negative one so that larger values (i.e. approaching zero) indicate a smaller disjuncture and more or better responsiveness.⁴ For easier interpretation, I then standardize those responsiveness values across the entire range of state-years to a mean of zero and standard deviation of one with larger values meaning better responsiveness (i.e. a smaller disjuncture between opinion and policy). I complete this procedure for opinion and policy for economic issues and then, separately, for opinion and policy for social issues.

Methodologically, it is important to highlight that this is not an attempt to measure proximity or congruence between opinion and policy (Achen, 1978; Matsusaka, 2001; Griffin & Newman., 2008; Lax & Phillips, 2012). Indeed, Caughey and Warshaw (2018, 264) expressly caution against using their measures to do so, stating that “since our measures of mass and state policy liberalism are not on the same scale, we cannot directly evaluate whether state policies are congruent with mass preferences at any given moment.” Instead, the method described above of using the size of the residual from regressing state policy on opinion as a measure of disjuncture or discord rests on the logic that, across the entirety of the data points, state-years that are further away from the regression line exhibit more “slippage” between opinion and policy than those that are closer to the line. In other words, this measurement technique uses the linear pattern of the data whereby more liberal opinion generally predicts more liberal policy across all state-years to identify and quantify the extent to which state-years deviate from that pattern and, as such, exhibit less or worse opinion-policy responsiveness. This “slippage” technique is similar to Grumbach (2021, 7) who uses “the squared residuals from a bivariate regression of state policy liberalism on state opinion liberalism, which capture how ‘out of step’ a state’s policy is with its residents' policy attitudes” when quantifying the quality of democracy across the U.S. states.

Turning next to measurement of government effectiveness or performance, I draw on Dynes and Holbein’s (2020) recent study that examines whether party control of state government has a discernable effect on societal outcomes.⁵ Specifically, they use data from the Correlates of State Policy Project Database (CSPPD) to examine 28 separate outcomes that capture states’ economic, health/family, civic, criminal, educational, and environmental well-being. These outcome data are available for different timespans, with the longest going back to 1962 and the shortest to 1991. Collectively, they allow for assessment of the various different ways that government can be effective (or not) at promoting well-being among its citizens.

Similar to above, for easier interpretation I standardize each of the 28 outcome measures to a mean of zero and standard deviation of one across all state-years.

Empirical Strategy

To evaluate the relationship between policy responsiveness and government effectiveness⁶ in the U.S. states over time, I use the following regression equation with state-year as the unit of analysis:

{GE}_{st} = β_{0} + β_{1} {PR}_{st} + β_{2} {GE}_{st - 1} + α_{t} + γ_{s} + ε_{st}

where GE is government effectiveness (societal outcome) in state (s) at year (t), PR is the measure of policy responsiveness using the recovered residual method described above, GE_st-1 is a one-year lagged measure of government effectiveness (i.e. a lagged dependent variable), and α_t and γ_s are year and state fixed effects (respectively). For all regression results reported below, standard errors are clustered by state.

Following the logic articulated by Dynes and Holbein (2020) that some period of time likely needs to elapse before the possible impact of government policy decisions (or, in this case, policy responsiveness) on societal outcomes can be evaluated, I assess the relationship between responsiveness and effectiveness two and four years later. In other words, the regression coefficient β₁ estimates, for example, the possible linear relationship between opinion-policy responsiveness measured in 1990 on measures of government effectiveness (societal outcomes) in 1992 and, separately, in 1994.

To ensure that the empirical results reported below are not contingent on this particular modeling strategy, I also report the findings of a series of supplementary models that include omitting the lagged dependent variable, omitting the state and year effects, as well as assessing the relationship between responsiveness and effectiveness in the same year and, separately, eight years later. In addition, because Caughey and Warshaw (2018) find different patterns of opinion-policy responsiveness based on region and timeframe, I also estimate models that exclude the southern states and include only years from 1972 onward.⁷As detailed below, these supplementary models reveal substantively similar results and suggest that the main findings are not driven by a particular model specification or timeframe.

Analysis

Using the equation described above, I conduct four sets of 28 regressions (a separate regression for each of the 28 outcome/well-being measures). The four sets of regressions are: (1) economic opinion-policy responsiveness and outcomes two years later, (2) economic opinion-policy responsiveness and outcomes four years later, (3) social opinion-policy responsiveness and outcomes two years later, and (4) social opinion-policy responsiveness and outcomes four years later. The goal of these estimations is to assess whether there is a statistically and substantively significant relationship between responsiveness and effectiveness (outcomes). To allow for straightforward inspection of the regression results, I present a series of Figures 1, 2, 3 and 4 that display the regression coefficient point estimate for policy responsiveness (which can be in interpreted as the predicted change in standard deviations of the outcome that corresponds to a one standard deviation increase in responsiveness) and 95% confidence interval for each of the 28 models with a different outcome as the dependent variable.⁸

Figure 1.

The figure display the regression coefficient point estimate and 95% confidence interval for policy responsiveness for 28 regression models with the dependent variable (societal outcome) for each listed on the left. Policy responsiveness and outcome variables are standardized.

Figure 2.

Figure 3.

Figure 4.

Looking first to Figure 1 (economic responsiveness and outcomes two years later), I find that the regression coefficient is statistically different from zero at conventional levels of statistical significance (p < .05) for only five of the 28 regressions. Moreover, the direction of the relationship for those five regressions is inconsistent and displays no discernable pattern.

Specifically, more responsiveness is associated with worse outcomes for two indicators (a higher car theft rate and lower inflation-adjusted gross state product) but with better outcomes for three indicators (a lower murder rate, a smaller share for the top one percent of income earners, and a lower divorce rate). Moreover, the substantive significance of the coefficients is quite small, with four of the five at around .01 (of a standard deviation) and the largest at .03. Along with the fact that fully 23 of the 28 models have a coefficient that is not statistically different from zero, these results do not suggest a negative relationship (or tradeoff) between policy responsiveness and government effectiveness.

Figure 2 (economic responsiveness and outcomes four years later) reveals similar results. Only six of the 28 regression coefficients are statistically different from zero, and they are again in different directions. For example, state-years with more policy responsiveness have worse outcomes for number of felons disenfranchised from voting, car theft rates, and gross state product, but better outcomes for murder rate and the percentage of citizens who have attained high school diplomas.⁹ Similar to above, the results in Figure 2 also suggest there is no tradeoff between responsiveness and effectiveness.

Turning to the possible link between opinion-policy responsiveness on social issues and outcomes, the relationship is even weaker. Figure 3 (outcomes two years later) and Figure 4 (outcomes four years later) report only three and four (respectively) coefficients out of 28 that are statistically different from zero. Again, the direction of the results do not have a discernable pattern, with better responsiveness associated with worse outcomes for divorce rate and better outcomes for property crimes and car theft. When considered in their totality, the empirical results reported in Figures 1, 2, 3 and 4 strongly suggest that there is not a consistent statistical relationship between opinion-policy responsiveness and societal outcomes and, importantly, that there is no tradeoff between responsiveness and effectiveness.

To further investigate the possible tradeoff and ensure that these empirical results are not contingent on a particular modeling strategy, I also report the results from a series of supplementary models. The cells of Table 1 report the number of regression coefficients for responsiveness (across 28 outcomes) that are statistically different from zero for each of the robustness checks.¹⁰ For the models that omit the one year lagged dependent variable and state and year effects, the number of coefficients that are statistically different from zero is slightly higher than in the main results reported above (perhaps as expected, given the predictive power of Y_t-1 and unit and time fixed effects on Y). For example, for social opinion-policy responsiveness and outcomes two (or four) years later, fully 10 of the 28 coefficients are statistically different from zero. However, the direction of the relationship again does not suggest a tradeoff between responsiveness and effectiveness. State-years with more responsiveness actually have better outcomes for crime (violent, robbery, car theft), unemployment, and high school graduation rate. In sum, regardless of the model specification or timeframe, the empirical results point to a consistent conclusion: there is no discernable tradeoff between policy responsiveness and government effectiveness.¹¹

Table 1.

Supplementary Models.

Model Specification	Economic Opinion-Policy Responsiveness & Outcomes…		Social Opinion-Policy Responsiveness & Outcomes…
Model Specification	2 years Later	4 years Later	2 years Later	4 years Later
No lagged dependent variable	7	7	9	10
No lagged dependent variable nor state & year fixed effects	4	4	10	10
Only non-southern states	4	2	2	4
Only 1972 onward	5	4	3	3
Outcomes in same year	6		3
Outcomes 8 years later	4		2

Note: Cells report the number of regression coefficients for policy responsiveness (out of 28 models with societal outcomes as the dependent variable) that are statistically different from zero (p < .05, two-tailed).

Finally, the rich institutional and political variation across the states and over time allows for an additional examination into possible factors that might condition the relationship between responsiveness and effectiveness. Accordingly, I investigate the potential conditioning effect of three factors that are staples in the American state politics literature (Gray, Hanson, and Kousser 2017): (1) whether a state has the ballot initiative process (direct democracy) or not, (2) whether a state imposes legislative term limits or not, and (3) whether a state has unified or divided government.¹² To assess any potential conditioning effects, I use the same modeling strategy as above (with the 28 outcomes two years later as the dependent variable) and include an interaction term between the state factor under consideration and the economic or social policy responsiveness variable. This interaction analysis allows, for example, an assessment of whether the presence of the ballot initiative process conditions the relationship between responsiveness and effectiveness for state crime rates or economic performance. I run separate interaction models for each of the three factors (ballot initiative, term limits, divided government) for economic and, separately, social policy responsiveness for the 28 outcomes (six sets of 28 estimations and a total of 168 regressions).

The findings from these interaction models are very similar to the main analysis reported above in that they reveal a weak and directionally inconsistent conditioning effect for each of the three factors. Specifically, the factor x responsiveness interaction term is statistically different from zero for anywhere from zero to only five outcomes (out of 28) for the six sets of estimations and, importantly, is about equally likely to condition the relationship between responsiveness towards better outcomes (i.e. lower crime rates) as towards worse outcomes (i.e. greater income inequality). In short, the presence of the ballot initiative process, legislative term limits, or divided state government does not seem to have any consistent conditioning effect on the relationship between policy responsiveness and government effectiveness.

Implications and Future Research

A government that responds to its citizens’ political opinions and governs effectively by advancing well-being are two of the essential features of a functioning democracy.

Consequently, political scientists have devoted considerable attention to evaluating the extent to which government attains each of these goals. However, given debates about institutional design dating back at least to the American Founding, there may be an inherent tension between the two goals. That is, a government that is too responsive to public opinion and sentiments may be less likely to govern effectively and preside over positive societal outcomes. This study is the first attempt to date to empirically evaluate the possible tradeoff between policy responsiveness and government effectiveness.

Using newly developed measures of public opinion and public policy liberalism in the U.S. states over time (Caughey & Warshaw, 2018) and 28 different societal outcomes (Dynes & Holbein, 2020), I find little evidence of a tradeoff between responsiveness and effectiveness. When there is a statistical relationship between the two, it is inconsistent and equally likely to be positive rather than negative – more responsiveness is linked to better outcomes for some areas and worse for others and there appears to be no discernable pattern. These findings have important implications for our understanding of American democracy because they suggest that elected officials can seek to incorporate public opinion into policy decisions without sacrificing government effectiveness. In other words, there is little evidence to suggest that heeding the public leads to less desirable societal outcomes as measured by metrics like crime rates, economic performance, and educational attainment.

Future research should build on this initial examination and further evaluate the relationship between these two fundamental objectives of democratic government. For example, although I find little evidence of a conditioning effect for direct democracy, term limits, or unified vs. divided government, it is possible that other differences in institutional design (i.e. legislative professionalism, constituent to legislator ratio) and political conditions (i.e. electoral competitiveness, polarization) across the states may have a conditioning effect on the relationship between responsiveness and effectiveness. In addition, although this study investigates fully 28 different well-being outcomes, it is possible that other measures of government effectiveness not included here could indicate a different relationship. Moreover, the increasing sophistication and precision of measurement of public opinion and government policy over time may allow for an extension of this analysis that is able to situate opinion and policy on the same scale and assess congruence (as opposed to responsiveness). Finally, the analysis here focuses on the U.S. states, but the question of a possible tradeoff between responsiveness and effectiveness is equally applicable to different levels of government (federal and local) as well as to other countries with differing political institutions and electoral systems.

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Supplemental Material - Is There a Tradeoff Between Policy Responsiveness and Government Effectiveness? Evidence From the American States

Supplemental Material for Is There a Tradeoff Between Policy Responsiveness and Government Effectiveness? Evidence From the American States by Patrick Flavin inAmerican Politics Research

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Patrick Flavin

Supplemental Material

Supplemental material for this article is available online.

Notes

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