Expressive Voting and Government Redistribution *

Transcription

1 Expressive Voting and Government Redistribution * Russell S. Sobel Department of Economics P.O. Box 6025 West Virginia University Morgantown, WV sobel@be.wvu.edu Gary A. Wagner Department of Economics & Finance University of Arkansas at Little Rock 2801 South University Avenue Little Rock, AR gawagner@ualr.edu Abstract Models of expressive voting postulate that voters will consume ideological or moral stances on issues by voting for them, even when they are against the voter s own narrow self interest, if the probability of being a decisive voter is low. When a voter is unlikely to sway the outcome of the vote, the odds that a voter will incur any real personal cost (a higher tax burden, for example) from his or her own expressive vote is small. We test and find support for a straightforward empirical implication of this model, that public sector welfare (transfer) payments should be directly related to the probability of being the decisive voter. JEL classification codes: D72, H11, H53 Keywords: Expressive voting, Rational voter models, Government redistribution * The authors would like to thank William N. Trumbull for helpful comments. Any remaining errors are the sole responsibility of the authors.

2 Expressive Voting and Government Redistribution I. Introduction Traditional models of rational voting following the works of Downs (1957), Tullock (1967), and Riker and Ordeshook (1968, 1973) show how the voting calculus of individuals is affected by the costs and benefits of voting. In these models, a central factor influencing voter turnout is the probability that the voter s single vote will change the outcome of the election, (i.e., the probability that he or she is the decisive voter). 1 And while the average citizen might consider voter turnout rates in the U.S. quite low, the typical economist generally finds it puzzling as to why so many people still vote given the remote odds that they will influence the outcome of the election. The probability of being the decisive voter is so low for major U.S. national elections, say less than , that many writers have commented on how an individual is more likely to be in an auto accident on the way to vote than being the decisive voter in the election. The apparent inability of the rational voter model to explain why individuals vote has become known more generally as the paradox of voting. One of the most prominent lines along which the rational voter model has been extended to rectify the paradox of voting is to allow for expressive voting. First proposed by Buchanan (1954), and further developed by Tullock (1971) and Brennan and Lomasky (1993), the theory of expressive voting holds that when there is a relatively low probability of casting the decisive vote, individuals will chose to vote as an act of expressive behavior, often voting along ideological or moral lines for what might be considered public minded policies that are apparently against the voter s own narrow personal self interest. In essence, the notion of 2

3 expressive voting extended the traditional rational voter model by allowing voters to receive direct utility from the act of voting itself. At first glance, the motives of a typical voter seem to run counter to the theory of expressive voting. After all, why should an individual voter vote in favor of a public-minded policy if it is against their own personal self interest? An explanation for this apparent clash in motives was noted by Tullock (1971, p. 387) to be the individual s attempt to reduce internal dissonance. Tullock elaborates using an example in which an individual has the choice to give $100 to charity directly or vote on whether to be taxed the $100. According to Tullock, an individual that derives little satisfaction from charity will not voluntarily contribute, but would be willing to vote in favor of the tax to reduce internal dissonance because the probability that they will influence the outcome is so small. Eichenberger and Oberholzer-Gee (1998) also address the more psychological factors behind expressive voting that account for why voters apparently behave more in line with social norms for fairness in their voting behavior than in market behavior. In this paper we make no attempt to directly address the theoretical merit of the expressive voting hypothesis itself, but rather to test a straightforward implication of the model. We believe that if this model has power in explaining real-world voting behavior, there should be a clear negative relationship between the probability of being a decisive voter and the level of public sector welfare spending. In essence, the expressive voting model predicts that whenever the personal cost of voting for additional redistributional transfers is low, voters will consume more of this expressive behavior, despite it being against their own narrow self interest. We test our hypothesis using U.S. state level data from Our results provide very strong 3

4 evidence that the probability of casting the decisive vote in a state is an important determinant of welfare spending. Our fixed-effects, panel estimation technique allows us to confirm that not only do differences across states in this probability explain cross-state differences in welfare spending, but also that changes in the probability of casting the decisive vote in a given state through time is an important determinant of changes in that state s welfare spending through time. The paper continues by describing the data and estimation techniques used in our analysis. We then present the results of our analysis and conclude with a discussion of the policy implications of our results. We begin by first reviewing the previous studies of expressive voting and the probability of casting the decisive vote. II. Expressive Voting and The Probability of Being the Decisive Voter 2.1 Previous research The number of empirical studies investigating expressive voting has been rather small, but they have generally provided support for the hypothesis. A recent study by Copeland and Laband (1999) explored the relationship between actual voter turnout and expressive behavior. Using data from the National Election Survey, they find strong evidence correlating the act of voting with political expressiveness (i.e. wearing campaign buttons, posting political signs, and contributing to the Federal Election Commission via income tax returns). 2 Feigenbaum, Karoly, and Levy (1988) use county-level voting data from California on the 1982 Nuclear Freeze Referendum to test whether a model of expressive voting better explains the data than a model 4

5 containing only standard economic-consequences variables. Their results strongly support the idea that votes on this issue can be better explained by a model of expressive voting. 3 In addition to linking expressive behavior and voter turnout, several studies have investigated how being the decisive voter affects individuals choices. Sobel (1992), using data on state legislatures, finds that the probability of being the decisive voter is a significant determinant of legislative shirking. Sobel finds that legislators are more likely to shirk (i.e. vote against their constituents interests) when the probability of being the decisive voter is higher (i.e. in smaller legislative bodies). Moreover, several experimental studies, conducted by Carter and Guerette (1992) and Fischer (1996), claim to provide evidence that individuals vote expressively. In these studies, individuals are given the choice to vote on earmarking funds for charity or for themselves. The authors find that individuals are indeed more likely to earmark the funds for charity as the probability of influencing the outcome declines, providing support for expressive voting. 2.2 The probability of being the decisive voter The probability that an individual voter s vote will actually change the final outcome has been the subject of several technical papers. 4 Essentially, in say a two-candidate election, this probability may be thought of as the probability that exactly half of the other voters will vote for Candidate 1, while the other half vote for Candidate 2. Mueller (1989, p. 350) summarizes this mathematical work and presents a unified formula for calculating the probability that any one voter will be decisive (P): 5

6 P = 3 2 2π ( N 1) (1) Where N is the number of voters participating in the vote. 5 For our empirical work, we calculate this probability for each state, for each year from 1972 to Using data from the Statistical Abstract of the United States, we employ the actual ex post voter turnout for N. We used turnout by state in presidential elections during presidential election years, followed by turnout for the governor s race, Senate race, or House race in non-presidential years, in descending order of preference. 6 Our sample period begins in 1972 in order to avoid including periods where substantial legal changes were made in the way of poll taxes, literacy tests, and legal voting age restrictions. While these institutional changes significantly altered the size of the voting franchise, and thus altered the probability of being a decisive voter dramatically, such institutional changes also had the effect of moving the pivotal median voter down the income distribution, which should also increase the demand for state welfare payments. This is precisely the hypothesis presented and confirmed in Husted and Kenny (1997). They strongly conclude that these expansions in the voting franchise, resulting from institutional change, led to a sharp rise in state welfare spending because of the systematic effect of moving the median voter down the income distribution. 7 The periods of institutional change exploited by Husted and Kenny (1997) is thus problematic here because the expressive voting model also predicts that a larger voting franchise would result in more welfare spending (but here because it lowers the probability that any one voter is decisive). It is likely that both effects were present during the pre-1972 period, but they 6

7 would be very difficult to separate. We believe that by restricting our sample to the post-1972 period, so that the variation in the voting franchise is due more to population growth, immigration, and mobility than to institutional changes, and by including variables to capture changes in the preferences and characteristics of the median voter, we increase the certainty that the correlation we find between welfare spending and the probability of being the decisive voter is not alternatively explained by institutionally-driven changes in the median voter. Thus, our empirical model closely follows the Husted and Kenny model, but we use a more recent sample period and incorporate an additional independent variable into the model to capture the effect of changes in the probability of being a decisive voter in the state. III. Empirical Model and Results The dependent variable in our model is state welfare spending and we estimate the model measuring welfare spending both as a share of general expenditures and as a real per capita dollar amount. Following Husted and Kenny (1997) we present results for both the linear and double log specifications. The double log specifications are advantageous because the coefficients can be directly interpreted as elasticities. In addition, the double log models tended to explain more of the variation in the dependent variable. We present only the results of the double log models in the text to conserve space. However, the results of the linear specifications are included as Appendix Tables 1 and 2 of the paper. As independent variables in the model we include the probability of being a decisive voter in the state in addition to the general set of independent variables used by Husted and Kenny (1997). These independent variables for each state include the percent black, the percent 7

8 over age 65, the poverty rate, real median income, a variable reflecting the income spread in the state, per capita real federal aid, and indicator variables for whether the states governor and both houses of the legislature were all republican or all democrat. 8 Our set of regressors differs from that of Husted and Kenny in that we use the state s poverty rate in place of the percent of residents residing in a metropolitan area. The complete list of dependent and independent variables, as well as their descriptive statistics, can be found in Table 1. [Table 1 about here] Our sample is biennial data on 49 states over the period from , resulting in 637 observations. Nebraska could not be included in the sample because the state s legislators are elected without party designation, making it impossible to determine the controlling political party. We first estimate each model by pooled OLS, then we extend each model to include fixed time effects, fixed state effects, and finally fixed time and state effects. The models allowing for fixed state effects essentially dummy out each state so that the model is forced to find time-series correlations within each state. This is a much more stringent test of the hypothesis than a single cross section could provide. The results of our estimations are provided in Tables 2 and 3. [Tables 2 and 3 about here] Tables 2 and 3 differ only in the way the dependent variable is measured. In Table 2 the dependent variable is state welfare spending as a share of the state budget, while in Table 3 the dependent variable is real per capita state welfare spending. Each table presents the coefficient estimates obtained from the different specifications of the model. The first column shows the results obtained on the pooled data set with no variables included for either time or state fixed 8

9 effects. The second and third columns of coefficient estimates show how these results change as either time fixed effects, or state fixed effects, are included individually. The final column shows the results from the two-way fixed effects model. This is the most stringent model, and provides the most possible correction for any factors specific to any state or to any given year. The results are similar to those obtained in Husted and Kenny for the other control variables. Most important, our new variable of interest, the probability of casting the decisive vote, is significant and negative at the 1 percent level in every specification as expected. Not only is this true across the four specifications in Table 2, but also for all specifications when welfare spending is measured in real per capita terms (Table 3). It is also worth mentioning that the strength of this result does not substantively differ between the double log models and the linear models in the appendix. In fact, in every single specification we tried (even including subsamples) the probability of casing the decisive vote was significant in the expected direction at the 1 percent level. We include the linear models in the appendix, however, to illustrate the robustness of our main result. In terms of the magnitude of the coefficient on the probability of being the decisive voter variable (which in the double log specifications is an elasticity), it is very consistent across specifications falling somewhere between and The most stringent models, the ones presented in the final columns that control for both fixed state and time effects, are almost identical for both models, despite the very different specification of the dependent variable. It appears that it is generally the case that a 10 percent reduction in the probability of casting the decisive vote would cause just more than a 6 percent increase in state welfare spending, 9

10 regardless of whether it is measured as a percent of the state budget or as a real per capita amount. IV. Policy Implications The results presented here seem to confirm a strong and statistically significant relationship between the probability of being a decisive voter and the level of welfare spending in states. Because the main determinant of this probability is the population (or perhaps more precisely the number of registered voters, or true voter turnout), it is clearly the case that expansions in the number of voters can be a driving force in causing growth in public sector welfare spending. Given the rather rapid growth of U.S. Federal transfer expenditures during the past century, a growth that did not occur in the early and mid 1800s, one must wonder if the reductions in this probability that resulted from expansions in the U.S. population through immigration and birth, and also the institutional changes in voting restrictions during this time, were not responsible for some of this growth in transfer spending. The lower the probability that any single voter will be decisive, the less incentive there is for voters to turnout, and the less of an incentive there is for voters to become informed. There are some potential ways to restructure the political system to intentionally increase this probability. One such method that has been suggested by authors such as Page (1992) is specialized elections in which only a relatively small randomly drawn subset of voters would be eligible to vote on each election or item on the ballot. There would be many of these randomly drawn small subsets and each would, in a sense, specialize on a single issue to decide it. These groups would be small enough that voters would have a higher incentive to become informed, 10

11 and their burden would only be to become informed on that single issue for which they are eligible to vote. While this reform may or may not be good for many different goals, the point here is that we could expect, in addition, that any policy that reduced the voting group (and thus increased the probability of being decisive) would also result in smaller government transfer spending. V. Conclusion Models of expressive voting postulate that voters will consume ideological or moral stances on issues by voting for them, even when they are against the voter s own narrow self interest, if the probability of being a decisive voter is low. When a voter is unlikely to sway the outcome of the vote, the odds that a voter will incur any real personal cost (a higher tax burden, for example) from his or her own expressive vote is small. We test and find support for a straightforward empirical implication of this model, that public sector welfare (transfer) payments should be directly related to the probability of being a decisive voter. Using a biennial panel data of states over the period from 1972 to 1996, we find that a ten percent reduction in the probability of casting the decisive vote generally leads to approximately a 6 percent increase in public welfare spending produced by the political process. This finding begs further inquiry into the role that changes in this probability played in the transition of the federal government to a large welfare state during the 20 th century. 11

12 Endnotes 1. See Mueller (1989, p. 350) for a discussion of this probability and the related literature. Also, a recent summary of the factors influencing voter turnout may be found in Aldrich (1993). 2. The expressive voting framework is used as an assumption underlying other broader public choice models. For example, Clark and Lee (1999) employ the notion of expressive voting in a model of the optimal trust in government. Their model derives a Laffer-curve type relationship between the trust citizens have in government and government performance. Thus, increased trust in government reduces government performance over some ranges and increases it over others. 3. For additional evidence in favor of the expressive voting model see Faith and Tollison (1990). 4. See, for example, Riker and Ordeshook (1968, 1973), Tollison, Crain and Pautler (1975), Barzel and Silberberg (1973), and Owen and Grofman (1984). 5. Here we have simplified the equation by assuming a diffuse prior with respect to the voter s ex ante knowledge of other voters preferences across the two outcomes. In Mueller s original formula, this is obtained by setting p= Husted and Kenny (1997) follow the same methodology of using turnout figures from presidential races down to House races, albeit at the county level, to construct the ratio of median family income weighted by voter turnout to median family income weighted by voting age population. In addition, we estimated our empirical model both with only presidential year data and only non-presidential year data to ensure that variation in voter turnout between presidential and non-presidential years was not driving the results. We found no qualitative differences between the magnitude or significance of any of our results. 7. The impact of moving the median voter down the income distribution on government expenditures on services is less clear than the impact it has on welfare spending. Kenny (1978) explores this issue, and it is again tested in Husted and Kenny (1997). Essentially they find no increase in other areas of spending. This is consistent with studies that find the estimated income elasticity for government services generally exceeding the elasticity of substitution between government services and private goods (the opposite condition would have to be true for expenditures on government services to rise as the median voter moves down the income distribution). 8. Following Husted and Kenny (1997), intercensus year data was interpolated. Percent black was interpolated for 1972, 1974, 1976, and Real median income was interpolated for 1972, 1974, 1976, 1978, and Poverty rate by state was interpolated for 1972, 1974, 1976, and 1978; data for all other years are from Census figures or estimates. The first- and thirdquartile incomes for each state, used to compute income spread, were interpolated using Census figures for the U.S. income quartiles. 12

13 Table 1: Descriptive Statistics for Data ( ) Mean Std. Deviation Minimum Maximum Real public welfare spending as a share of general expenditures [share*100] Real per capita public welfare spending [in dollars] %Black [*100] %Over65 [*100] Poverty rate [*100] Real median income [in dollars] Income spread Per capita real federal aid [in dollars] Democratic control Republican control Probability of being decisive voter Notes: Sample excludes Nebraska. Income spread was computed following Husted and Kenny (1997). It is the difference between the first- and third-quartile family income divided by the median income. Data on actual voter turnout are from the various issues of the Statistical Abstract of the United States. Data on states governorship and legislature were obtained from various issues of The Book of States. All remaining data are from the U.S. Census Bureau. State welfare spending was deflated using the CPI (1998=100). Real median income was deflated using the CPI for urban consumers (CPI-U, 1998=100). Real federal grants to states was deflated using the GDP Deflator (1992=100). 13

14 Table 2: Log of Real Public Welfare Spending as a Share of General Expenditures Pooled OLS Fixed Time Effects Fixed State Effects Fixed Time & State Effects Constant ** (2.3172) (6.8621) * (3.1866) (7.7947) log (%Black) ** (0.0139) *** (0.0148) (0.0461) (0.0393) log (%Over65) ** (0.0641) * (0.0639) (0.0295) (0.0298) log (Poverty rate) *** (0.0821) ** (0.0771) (0.0997) (0.0970) log (Real median income) *** (0.1510) *** (0.1327) *** (0.2809) ** (0.3054) log (Income spread) *** (0.8239) ( ) *** (0.8069) ( ) log (Per capita real federal aid) *** (0.0762) (0.1172) *** (0.0825) * (0.1000) log (Probability of being decisive voter) *** (0.0505) *** (0.0584) *** (0.1203) *** (0.1418) Democratic control *** (0.0371) *** (0.0382) (0.0392) (0.0397) Republican Control (0.0501) (0.0484) (0.4679) (0.0560) F-Test of model *** 8.71 *** 8.89 *** 7.99 *** R-squared Notes: Significance levels are as follows: *** denotes the 1% level, ** denotes the 5% level, and * denotes the 10% level. Estimated coefficients of the state and time effects are not reported. Standard errors corrected for heteroskedasticity. All models exclude Nebraska. 14

15 Table 3: Log of Real Per Capita Public Welfare Spending Pooled OLS Fixed Time Effects Fixed State Effects Fixed Time & State Effects Constant (2.2153) (7.1609) ** (3.2983) (7.9742) log (%Black) (0.0146) (0.0156) (0.0441) * (0.0373) log (%Over65) (0.0449) (0.0534) (0.0298) (0.0335) log (Poverty rate) ** (0.0841) (0.0816) ** (0.1013) (0.0982) log (Real median income) (0.1428) (0.1346) *** (0.2869) ** (0.3115) log (Income spread) *** (0.8384) ( ) *** (0.8323) ( ) log (Per capita real federal aid) *** (0.0776) *** (0.1188) *** (0.0841) *** (0.1166) log (Probability of being decisive voter) *** (0.0514) *** (0.0598) *** (0.1220) *** (0.1415) Democratic control *** (0.0379) *** (0.0392) (0.0392) (0.0396) Republican Control (0.0525) (0.0514) (0.0509) (0.0598) F-Test of model *** *** *** *** R-squared Notes: Significance levels are as follows: *** denotes the 1% level, ** denotes the 5% level, and * denotes the 10% level. Estimated coefficients of the state and time effects are not reported. Standard errors corrected for heteroskedasticity. All models exclude Nebraska. 15

16 Appendix Table 1: Real Public Welfare Spending as a Share of General Expenditures Pooled OLS Fixed Time Effects Fixed State Effects Fixed Time & State Effects Constant ** ( ) (454.66) *** (15.422) (517.19) %Black (0.0757) (0.0800) ** (0.2362) * (0.2116) %Over (0.1985) (0.2077) (0.1695) (0.1754) Poverty rate (0.2141) (0.2312) ** (0.2045) * (.2012) Real median income ** (0.0001) * (0.0001) ** (0.0002) ** (0.0002) Income spread *** (32.103) (620.29) *** (32.904) (705.46) Per capita real federal aid ** (0.0022) ** (0.0023) * (0.0033) ** (0.0035) Probability of being decisive voter *** (1111.4) *** (1134.5) *** (2715.6) *** (3396.7) Democratic control (0.9463) (0.9345) (1.3325) (1.3055) Republican Control (1.2568) (1.2649) (1.1144) (1.1417) F-Test of model 8.59 *** 4.27 *** 5.70 *** 4.93 *** R-squared Notes: Significance levels are as follows: *** denotes the 1% level, ** denotes the 5% level, and * denotes the 10% level. Estimated coefficients of the state and time effects are not reported. Standard errors corrected for heteroskedasticity. All models exclude Nebraska. 16

17 Appendix Table 2: Real Per Capita Public Welfare Spending Pooled OLS Fixed Time Effects Fixed State Effects Fixed Time & State Effects Constant *** (377.91) ( ) *** (351.71) ( ) %Black (1.6956) (1.7759) (6.4117) (5.7750) %Over (4.5492) (4.9909) (3.9274) (3.8384) Poverty rate (5.1982) (5.5126) ** (4.7350) (4.6819) Real median income (0.0023) (0.0025) * (0.0052) * (0.0063) Income spread *** (668.22) ( ) *** (762.63) ( ) Per capita real federal aid *** (0.0639) *** (0.0717) *** (0.0824) *** (0.0952) Probability of being decisive voter *** ( ) *** ( ) *** ( ) *** ( ) Democratic control ( ) ( ) ( ) ( ) Republican Control ( ) ( ) ( ) ( ) F-Test of model *** *** 9.00 *** 7.83 *** R-squared Notes: Significance levels are as follows: *** denotes the 1% level, ** denotes the 5% level, and * denotes the 10% level. Estimated coefficients of the state and time effects are not reported. Standard errors corrected for heteroskedasticity. All models exclude Nebraska. 17

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