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1 THE EFFECT OF EARLY VOTING AND THE LENGTH OF EARLY VOTING ON VOTER TURNOUT Simona Altshuler University of Florida Email: email@example.com Advisor: Dr. Lawrence Kenny Abstract This paper explore s the effects of the early voting program and the length of the early voting period on voter turnout in the past four general presidential elections. Early voting was instituted to increase voter turnout in various population groups, such as full time employees working on Election Day, minorities less likely to vote due to lower income and education, and women. Early voting and its augmented length are expected to increase turnout in voting populations This analysis improves on existing research on voter turnout determinants and their si gnificanc e, particularly on the early voting e ffects on turnout. T he paper fails to find that early voting in a state wil l increase voter turnout, however, the data supports that longer early voting periods increase turnout. While a vast array of factors determine voter turnout, the income in equality of a state, personal income per capita, education level, and age of a population have very significant impacts Additionally, the margin of victory with in a state affects turnout when voters believe their ballots will impact election result s. T hough early voting program s in state s wi ll not necessarily increase voter turnout, once instituted, improving the prog ram and increasing days allotted to early voting will increase voter turnout percentages. I. Introduction
2 The first use of early voting began in 1992, with early voting accounting for 7% of total votes cast. By 2000, the percentage of early votes of total votes more than doubled. The institution of early voting was created to increase voter turnout throughout t he United States primarily among minorities, women, and the older, disabled or other populations who have trouble reaching the polls One of the main contributing factors to voter turnout is the convenience of voting; with the institution of early voting and increased lengths of early voting, voting becomes more convenient for citizens to vote by creating more opportunity to do so, though other factors may subdue the effects of early voting. 32 of the 50 states have a form of early voting, along with the District of Columbia, and the lengths of early voting in these states vary. This paper examines the effects of early voting and the length of early voting on the percentage of voter turnout in four general presidential elections of 2000, 2004, 2008, and 2 012. In these four election years, early voting had already been instituted and was commonly known as a method to vote. II. Early Voting and the Length of Early Voting Early voting was created with the goal of increasing voter participation. It was propo sed as a way to expand the franchise by making voting more convenient, and to extend the franchise, by encouraging turnout among the sectors of the population unable or unwilling to vote by traditional methods (Gronke et al. 2008) By allowing voters to vote during a longer period of time, voter turnout may increase by removing some barriers to participation, such as time cost for full time and/or high paid employees. However, whether early voting itself mobilizes previous nonvoters or makes voting easier for those who would vote anyway remains unclear; Neeley et al. (2001) found little support for a mobilizations effect, and more evidence that early voting simply made voting more convenient for those who would have voted anyway. Those is favor or early vo ting reforms argue that maximizing voter turnout is a primary goal and reducing the barriers between voters and the polls is an important to achieve greater voter turnout, and though there have been mixed results, prominent study suggests that [early] associated with a 10% increase in (Gronke et al. 2007) According to Filer et al. (1980) turnout rises as the probability of altering the election outcome rises, and falls as the cost of voting rises; early voting decreases the cost of voting by increasing the convenience and
3 number of opportunities and methods to vote, thus leading to an increase in percentage of voter turnout. III. Data and Methodology This paper uses statewide data from the general elections in years 2000, 2004, 2 008, and 2012. Data from all 50 states and DC was used, for a comparison to the 32 states and DC that have early votin g The data will be collected primarily from the Federal Election Commission, U.S. Elections Project, U.S. Census Bureau, Bureau of Labor Statistics, and Bureau of Economic Analysis. The effect on voting eligible population and voting age population was explored to see the varying effects on the different measures of voter turnout and better understand the overall effects of early voting on voter tu rnout in the United States. Eight regressions were run to explore voter turnout; 1) consists of all 50 states and DC, and looks at the voting eligible population turnout rate; 1a) consists of the first regression with added regional dummy variables to account for extraneous data that may not be accounted for through the other independent variables; 2) consists of all 50 states and DC, and looks at the voting age population turnout rate; 2a) consists of the second regression with added regional dummy variables to account for extraneous data that may not be accounted for through the other independent variables; 3) consists of the 32 early voting states and DC, and looks at the voting eligible population; 3a) consists of the third regression with added regional dummy variables to account for extraneous data that may not be accounted for through the other independent variables; 4) consists of the 32 early voting states and DC, and looks at the voting age population ; and 4a) consists of the fourth regressi on with added regional dummy variables to account for extraneous data that may not be accounted for through the other independent variables This will result in 2 04 observations in the first four regressions and 13 2 observations in the second four regressi ons. Independent Variables Early Voting (E arly V oting ) Early voting is a dummy variable used in regressions 1 and 2 to measure whether or not the state in question has early voting not; a value of 1 is given if a state has early voting and a The effect of early voting is expect ed to increase voter turnout
4 since early voting provides more opportunity for people to vote by allocating a period of time to vote before Election Day, making voting more convenient for the average voter. Length of Early Voting (LengthEV) The length of e arly voting measures the number of days a state has allotted for early voting, and is used in the third and fourth regressions The length is expected to have a positive impact on both voting eligible and voting age population turnout rates for similar rea sons as early voting. The more days allotted for early voting, voters have more choices and opportunity to go vote at a time most convenient for them. By having more days, even if in some states there are fewer polling centers open for early voting than on Election Day, there are more days in general to accommodate voters, and they could avoid the long waits that are normally encountered on Election Day. Gini Coefficient (Gini) The Gini coefficient is a measure of equality, by using income equality/distri bution of the population. A Gini coefficient closer to 0 means that a state is more equal, whereas a value closer to 1 means that there is great inequality. The effect of this variable on the percentage of voter turnout is ambiguous. The relative power the ory predicts that voter turnout will decrease with increased disparity between high income and low income voters because greater inequality increases the relative power of the wealthy over the poor to influence politics in their favor. However, conflict th eories predicts that higher income inequality will increase voter turnout because groups in society strive to maximize their share of the limited resources that exist. Since resources are limited, the strive for maximized shares leads to conflict and compe tition, which can lead to desires and attempts to change institutions, like through elections. Though most studies have found a negative of higher inequality on voter turnout (Solt 2008) which would lead to a prediction of a negative effect on the depende nt variable in this study, other studies have shown unclear ef fects of inequality on turnout (Geys 2006) while still others have shown positive effects (Oliver, 2001). Unemployment Rate (Unemployment ) High unemployment promotes political mobilization; wh en there is low unemployment, a greater number of people are satisfied with having a job, will have less time to vote because of the job, and will feel less inclined to vote. The unemployment rate within each state affects the
5 job market and economy. This variable is lagged by one year since seems more permanent and gives a clearer idea of the general economic situation as opposed to th e current rate which can fluctuate. Therefore, a high unemployment rate variable should have a positive effect on the percentage of voter turnout. Research done by Burden et al. (2012 ) employed and unemployed shrinks as state unemployment increases. In sum, it appears that a sour economic performance, at least in terms of unemployment statistics, invigorates rather than This study thus supports the prediction of this paper t hat the unemployment rate will have a positive effect on voter turnout. Personal Income Per Capita (PIPC) Personal income per capita measures the average real personal income per person within a state. A higher income individual is more likely to vote, an d thus a higher personal income per capita measure is expected to have a positive impact on voter turnout percentage. Though income is often reflective of employment, education, and amount of leisure time, the higher income voters are more involved in and informed about elections to care to go vote and the variable are not correlated thus the effect of the personal income per capita is truly an accurate representation. Some literature finds that as absolute real income rises, turnout falls, which reflects a n incre ase in the time cost of voting (Filer et al. 1993) However, the mean gains of voting outweigh the cost, since those whose income are higher are typically more educated and politically active, are involved with redistributive programs and thus care about voting. Therefore, voter turnout is still expected to rise. Education (E ducation ) Education is a main factor in determining voter turnout. The education variable in this a B r higher. The less educated are less likely to vote, thus this variable would have a positive impact on the percentage of voter turnout; the higher the education level, it is accepted that the more people understand the implications and reasons to vote, an d the importance of casting a ballot to affect the election outcome. The more educated citizens have a greater a self interest in voting; they understand
6 how voting and elections directly affect them and desire to influence the results, hopefully in their favor. They realize that voting is the main way to achieve policies and actions that act in their favor, thus voters are self interested, more educated citizens understand the self interest involved, thus explaining why a greater percentage of voters are m ore highly educated. Gronke et al. (2008) suggest that early voters are older, better educated, and more cognitively engaged in the campaign and in politics, which supports the hypothesis for the positive effect of the education variable on voter turnout. The data on the perc ent of state population with a B unavailabl e for the year 2012, so the percentages were extrapolated based on the 2000 and 2008 percentages; the basic equation used to extrapolate the percentages was: %BA = a + b (Year), corresponding year either 2000 or 2008 to solve f degree or higher in Al abama in year 2012 would be: 22% = a + b (2008) 20.4% = a + b (2000) _______________ 1.6 = 8b After solving for b = 0.2 and a = 379.6, these values can be plugged into the equation %BA = a + b (2012), to result in the equation %BA = ( 379.6) + (0.2) (2012) = 22.8. was 22.8%. This process was done for every state and the District of Columbia to extrapolate the data for 2012. Median Age (A) The older portion of the population is more likely to vote than the younger. Although there has been a recent trend of younger generations participating more in elections, the older
7 generations are still more educate d have more leisure time, and more expe rience voting and thus make use of the vote. The median age variable measures the median age of a state in each election year, and is expected to have a positive impact on the percentage of voter turnout. In their article, Gronke et al. (2008) suggest that early voters are older, better educated, and more cognitively engaged in the campaign and in politics, which supports the hypothesis for the positive effect of the age variable on voter turnout. Gender Ratio (Male ) The male to female ratio o f a state measures the number of males per 100 females. Males are no longer more likely to vote t h an females, but the opposite now, so this variable is predicted to have a negative effect on the voter turnout percentage. Though m ale s are more accustomed to voting since th century and women have less of a habit of voting and have more out of work responsibilities than men trends have shown increa sed female voter participation There has been approximately equal turn out rates between males and females, but in the most recent 2012 election, women were more politically active, therefore supporting the prediction of a negative coefficient for this variable as the number of males to females in a state increases. The Cen ter for American Women and Politics n recent elections, voter turnout rates for women have equaled or exceeded voter turnout rates for men. Women, who constitute more than half the population, have cast between four and seven million more votes than men in recent elections. In every presidential election since 1980, the proportion [of] female adults who voted has exceeded the proportion of made adults who voted (Rutgers University 2013) which is reason to believe that as the gender ratio increases, percentage of voter turnout will decreases. Closeness (C) the election outcome. The closeness variable measures the margin within a state be tween the votes received by the two main candidates of an election: the Democ ratic and Republican candidates; this variable measures the closeness between candidates in an election at the state level. A smaller margin will incentivize people to vote becaus e their vote will have a greater impact than if the margin was larger or if there was a clear winner or forerunner from the
8 beginning of the race. Therefore, this closeness variable is predicted to have a negative impact; the smaller the percent, the close r the election margin is, and thus the higher voter turnout will be. Filer et al. 1980) so, in a competitive election with a smaller margin the voter has higher probability of altering outcome which would thus increase voter turnout. Region Region NE This variable is a dummy variable created to control for extraneous regional data that is not accounted for with the other independent variables in the regression. This regional variable is for the Northeast region, which includes Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. These states will receive a value of 1, while all other states will receive a value of 0. Region MW This variable is a dummy variable created to control for extraneous regional data that is not accounted for with the other independent variables in the regression. This regional variable is for the Midwest region, which includes Illinois, Indiana, Michigan, Ohio Wisconsin, Iowa, K ansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota These states will receive a value of 1, while all other states will receive a value of 0. Region S This v ariable is a dummy variable created to control for extraneous regional data that is not accounted for with the other independent variables in the regression. This regional variable is for the South region, which includes Delaware, District of Columbia, Flo rida, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky, Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma and Texas. These states will receive a value of 1, while all other states will receive a value of 0.
9 IV. Results Eight regressions were run in this study to analyze the effects of early voting and the length of early voting on voter turnout. Throughout the regressions, there was no clear evidence for any correlation between the presence of early voting in a state and increased voter turnout. However, there was support of the hypothesis that increased length of early voting periods contingent on the state having early voting, would increase percentage of voter turnout, whether it is voting eligible population or voting age population. The Gini coefficient, Personal Income per Capita, Education, Age, and Closeness variables were all statistically significant in every regression and thus also affec t the voter turnout percentages, and the regression results are reported in Table 1. The standard error values are in parentheses. Significance at the 95% level.
10 Regression 1 Regression 1 tested the effect of the presence of e arly voting in all 50 states and DC on the percentage of voting eligible population in 2000, 2004, 2008, and 2012. The regression results are reported in Table 2 and the impacts are displayed in Table 3. Table 2 Table 3 This regression has the highest adjusted R square value of 0.42 of the regressions that did not include the regional fixed effect variables meaning that this regression is the best fit overall for the data collected. The impact of the early voting variable is equal to its coeffi cient because it is a dummy variable, thus the standard deviation is equal to 1, yet that variable is not significant. The Gini coefficient, Personal Income per Capita, Education, Age and Closeness variables were all
11 significant at the 95% level based on t heir p values. The Gini coefficient had the greatest impact of 2.969, followed by education with 2.337, then closeness, personal income per capita, early voting, and then age. The conclusion from these variables being significant is that they influence th e voting eligible population turnout rate; for the Gini coefficient, as income inequality rises, voter turnout falls. The other significant variables had the signs predicted. As real personal income per capita rises by 1, voter turnout will rise as well by 0.0002, and as the percentage of increase, by 0.418 for every 1% increase. For the closeness variable, as the margin of victory decreases meaning that the electi on becomes more competitive voter turnout will increase by 0.136 because people will feel that their votes will hold greater significance in a closer election in determining which candidate actually wins. The Gini coefficient in this regression produced a negative sign, dictating that as the Gini coefficient of a stat e increases and the state becomes more unequal, voter turnout decreases. The prediction for this variable was unclear because of the amount of conflicting research that shows a negative corre lation, no clear correlation, and positive correlation between voter turnout and equality. This study thus supports that there is a negative correlation between the Gini coefficient, and thus income inequality, and voter turnout. Solt (2008 ) has written o n income inequality and voter turnout, and that income can be easily converted into political resources, therefore making those with less equal incomes not political equals democra (Solt 2008) they consequently grow better able to define the alternatives that are considered within the political system and exclude matters o (Solt 2010) Hence poor will be less likely to cast a vote, as ineq uality goes up, since their expected benefit from voting (Horn 2011). In this regression, only the unemployment rate, the gender ratio, and early voting variables were not statistically significant, while the Gini coefficient variable had the gre atest impact on the dependent variable.
12 Regression 1a Regression 1a tested the effect of the presence of early voting in all 50 states and DC on the percentage of voting eligible population in 2000, 2004, 2008, and 2012, along with fixed effect variables to take into account extraneous effects of the regions on voter turnout. The regression results are reported in Table 4 and the impacts are displayed in Table 5. Table 4 Table 5 This regression, an altered version of regression 1, produced a highe r adjusted R square value (0.47) and a greater number of variables that are significant. The Gini coefficient is again
13 the variable with the greatest impact on voter turnout with an impact value of 2.791. The early voting variable is significant in this r egression, while Personal Income per Capita is only marginally significant and the new Region MW variable is also significant at the 95% level The early voting variable in this regression is significant at the 95% level, however the sign is opposite that expected; the sign for this variable is negative meaning that if a state has implemented the early voting program, they will result in lower voter turnout percentages. A possible explanation for this results is that as early voting is instituted, people b elieve that many more people will participate in voting, thus diminishing the effect that their vote has on the outcome, acting as a disincentive to cast a ballot. All of the other variables that were significant produced the signs expected, while only the Region MW variable was significant, and positive, meaning that it was accounting for factors not enumerated in this paper. Regression 2 Regress i on 2 tested the effect of the presence of early voting in all 50 states and DC on the percentage of voting age population in 2000, 2004, 2008, and 2012. The regression results are reported in Table 4 and the impacts are displayed in Table 5. Table 6
14 Table 7 Similarly to the results of Regression 1, the unemployment r ate variable had the opposite predicted coefficient sign, and was also not significant The causes for the negative signs of t he unemployment rate variable is likely the same as specified above. Again, the Gini coefficient is negative, significant, and has the greatest impact, further supporting the reasoning that income inequality depressed voter turnout. The Gini coefficient, Education, Age, and Closeness variables were the only significant variables by their p values, and the Gini coefficient had the greatest impact with a value of 3.875. Additionally, these significant had the signs predicted for them, thus supporting the hypotheses that they influence voter turnout. However, the early voting dummy was again not significant, and had a negative sign. In contrast to Regressions 1 and 1a, the personal income per capita variable is not significant in this regression based on its p value. Regression 2a Regression 2 a tested the effect of the presence of early voting in all 50 states and DC on t he percentage of voting age population in 2000, 2004, 2008, and 2012, along with fixed effect variables to take into account extraneous effects of the regions on voter turnout. The regression results are reported in Table 8 and the impacts are displayed in Table 9
15 Table 8 Table 9 This regression, an altered version of regression 2, produced a higher adjusted R square value (0.48) and a greater number of variables that are significant. The Gini coefficient is again the variable with the greatest impact on voter turnout with an imp act value of 3.737. The early voting variable is significant in this regression, along with the Educatio n, Age, and Region MW variables, while the Personal Income per Capita and Closeness variables were not significant in these regression but were in prev ious regressions. The Education and Age variables had the positive signs expected of them, meaning that as the percentage of the state population
16 The Gini coef ficient was again negative, supporting the Schattschneider hypothesis and the relative power theory. And the early voting dummy variable had an unexpected negative sign, which would mean that if a state had early voting, then voter turnout would decrease, which does not support the hypothesis in this paper. The early voting variable was not significant in Regressions 1 and 2, but was significant in Regressions 1a and 2a that took into account regional fixed effects. H owever the sign was negative, meaning t hat if a state had the early voting program, the voter turnout rate would actually decrease, which does not support the hypothesis for early voting increasing voter turnout. The reason could be that as early voting is instituted, people believe that more people will vote which would decrease the impact that their vote would have on election results. Or, if there is early voting, people may plan to go vote but postpone when to go and eventually not go because they either forget, decide against voting, do not want to wait in the line, or not go during early voting and cannot go on Election Day. Regression 3 Regression 3 tested the effect of the length of early voting in the 32 states and DC that have early voting on the percentage of voting eligible population in 2000, 2004, 2008, and 2012. The regression results are reported in Table 10 and the impacts are displayed in Table 11
17 Table 10 Table 11 Regression 3 measures the effects of the independent variables on voting eligible population turnout, and shows that the length of early voting in contrast to simply the presence of early voting does have a significant and positive effect on the percen tage of voter turnout. Again, and for the same reasons, the unemployment rate is negative and insignificant. The Length of Early Voting, Gini coefficient Education, Gender, and Closeness variables are all significant based on their p values, while the Per sonal Income per Capita is only marginally significant. The Gini coefficient again has the greatest impact on the reg ression results with a value of 3.173 All of the significant variables had the signs predicted for them, except for the Gini coefficient because the prediction for the coefficient was unclear. As the length of early voting
18 turnout percentages will also increase. Whereas, when the gender ratio and th e closeness variables increases, voter turnout will decrease; as more males than females vote, total voter turnout percentages will decline since females are more likely to vote than males and as the election margin becomes greater and thus the election b ecomes less competitive, voter turnout will again decline, as people are more likely to vote when elections are close to have their votes make more of an impact on outcomes. Regression 3a Regression 3a tested the effect of the length of early voting in the 32 states and DC that have early voting on the percentage of voting eligible population in 2000, 2004, 2008, and 2012, along with fixed effect variables to take into account extraneous ef fects of the regions on voter turnout. The regression results are reported in Table 12 and the i mpacts are displayed in Table 13 Table 12 Table 13
19 This regression, an altered version of regression 3, produced a higher adjusted R square value (0 .42) The Length of Early Voting variable in this regression is not significant, and the Gini coefficient, Education, Closeness, and Region MW variables are all significant at the 95% significance level The Gini coefficient is again the variable with the greatest impact on voter turnou t with an impact value of 3.453 The Education variable had the predicted positive signs, meaning that as education percentages and median age rose, so did voter turnout, while the Closeness variable had the predicted negat ive sign, and the Gini coefficient was again negative, meaning that the relative power theory is a stronger effect than the conflict theory in terms of income inequality. Personal Income per Capita and Age were not significant like they were in previous re gressions. The Length of Early Voting had an unexpected negative sign, and was significant in this regression. The length early voting could be negative in this regression because of the fixed effect variables included in this and not the previous regressi on, or that as the time to vote early increases, people take their time and postpone voting immediately and eventually forget or decide against voting, as they think about voting for longer. Regression 4 Regression 4 tested the effect of the length of ea rly voting in the 32 states and DC that have early voting on the percentage of voting age population in 2000, 2004, 2008, and 2012. The regression results are reported in Table 14 and the impacts are displayed in Table 15 T able 14
20 Table 15 This regression only has the unemployment rate being statistically insignificant. The adjusted R squared value of 0.41 is the second highest of all the regressions without the regional fixed effect variables and the highest between the two regressions measurin g the effect of the length of early voting in the 32 states and DC that have early voting instituted, and this regression has the greatest number of variables being statistically significant only the unemployment rate variable is insignificant. Like all of the previous regressions, the unemployment rate variable ha s a negative sign when a positive sign was predicted, but it was not significant, while all of the other variables were significant at the 95% level. The Gini coefficient had the greatest impact on this regression with an impact value of 4.143, followed by Gender with a value of 2.423.
21 All of the significant variables had their predicted signs, except for the Gini coefficient because the prediction for the variable was ambiguous. As the Length of Early Voting, Personal Income per Capita, Education, and Age within a state rise, voter turnout percentages will follow. While as the Gender Ratio and the Closeness variables of a state rise, turnout will fall similar to the effect of the Gini coeffic ient. Regression 4a Regression 4a tested the effect of the length of early voting in the 32 states and DC that have early voting on the percentage of voting age population in 2000, 2004, 2008, and 2012, along with fixed effect variables to take into acco unt extraneous effects of the regions on voter turnout. The regression results are reported in Table 16 and the impacts are displayed in Table 17. Table 16 Table 17
22 This regression, an altered version of regression 4, produced a higher adjusted R square value. The Length of Ear ly Voting, Gini co efficient, Education, Region MW, and Region S variables are all significant and the Age (p = 0.064) and Closeness (p = 0.05) variables are marginally significant The Gini coefficient is again the variabl e with the greatest impact on voter turnout with an impact value of 4.475. While Education, Age, and Closeness had their expected signs, and the Gini coefficient was negative, the Length of Early Voting was negative when it was predicted to be positive and significant. The length early voting could be negative in this regression because of the fixed effect variables included in this and not the previous regression, or that as the time to vote early increases, people take their time and postpone voting immediately and eventually forget or decide against voting, as they think about voting for longer. Eight tests were run to measure the correlation between all of the variables to check for multicollinearity within each regressions, which r esults are reported in Tables 18 thr ough 25
24 None of the variables have a correlation above 0.8, but in Regressions 1, 1a, 2, and 2a the correlation between the Gender Ratio and the Gini coefficient is 0.67, and the correlation between Education and Personal Income per Capita is 0.74. In Regressions 3, 3a, 4, and 4a, the
25 correlation between the Gender Rati o and the Gini coefficient is 0.7 and the correlation between Education and Personal Income per Capita is 0.69. Additionally in Regressions 3a and 4a, the correlation between Region S and the Length of Early Voting is 0.6, the correlation between Regio n S and the Gini coefficient is 0.65, and the correlation between Region S and the Gender Ratio is 0.57. These values are above 0.5, so there can be multicollinearity, but the values are not 0.8 or above so there is no confirmatio n of correlation betw een the independent variables. The unemployment rate variable was not statistically significant in any of the regressions, thus the unemployment rate within a state the year prior to an election year does not have an effect on the percentage of the voter esidential election. Of the eight regressions run, there is no clear evidence to determine whether the voting eligible population turnout rate, which was a better fit between Regression 1 and 2 measuring the presence of early voting in all 50 states and DC, or the voting age population turnout rate, which was a bette r fit for between the other regressions measuring the length of early voting in the 32 states and DC that have early voting. V. Conclusion Early voting was implemented to increase voter turnout throughout the population, and especially to increase voter turnout percentages among minorities, women, and employees. According to the results of this study, the presence of early voting in a state versus the absenc e of it is not statistically significant in increasing voter turnout percentages by either the voting eligible population measure or the voting age population measure until regional fixed effects are included in the regressions, where the early voting dum my variable becomes significant, but has a negative sign, meaning that if a state as early voting, that will result in lower voter turnout However, once the early voting program has already been implemented, the length of time allotted for early voting do es have a positive and significant effect on both voting eligible population turnout an d voting age population turnout, though there is evidence that once regional fixed effects are taken into consideration, the length of early voting also does not have a positive effect on voter turnout and is significant.
26 The variables that are consistently significant in affecting voter turnout, regardless of whether the dummy variable of early voting was used or the len gth of early voting variable was used, are the Gini coeffic ient and Education variables as well as Region MW in the regressions that the fixed effect was included Consequently, the factors most influential of voter turnout are rela ted to income and distribution and what percentage of the population is co llege educated. When analyzing the effect of the length of early voting on voter turnout, t he Lengt h of Early Voting variable was also consist ently statistically significant, however in half of the regressions using the Length of Early Voting variable the sign was negative when it was predicted to be positive. This supports the hypothesis that early voting increases the percentage of voter turnout when the number of days allotted for the early voting program increases but does not provide clear and undoubt ed evidence of this Although arguments are made that early voting is not effective because fewer polling locations are open, the presence and convenience that early voting provides especially are the number of days increases thus increasing the number o f opportunities to vote overcomes the fewer polling stations specifically because of the This study provides no evidence to support a claim that the presence o f early voting will increase voter turnout percentages, which echoes previous research done reaching this conclusion, possibly because the people who are likely to vote will already make time to vote and this will act more as a program for convenience than for increased participation. However, once the program is already impleme nted in a state, there is some evidence showing that to increase voter turnout across the population, the length of early voting period matter; the longer the period, the higher the voter turnout. In comparing the voting eligible population turnout rate and the voting age population turnout rate, this study provides no determination for which is a better measure Yet, VEPT seems to be a better measure, since VAPT rates include persons within the population that have had the right to vote taken, i.e. felons, non citizens, and citizens who have chosen not to register.
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