APPENDIX B: RELATIONSHIP BETWEEN 

TIME ALLOTTED TO VERIFY PROVISIONAL BALLOTS 

AND THE LEVEL OF BALLOTS THAT ARE VERIFIED 

Dave Anderson 

Research Assistant 

Eagleton Institute of Politics 

June 28, 2006
Executive Summary 

 One possible variable that can impact the rate at which states accept the 

provisional ballots that are cast is the time frame that they allow election officials to 

review those ballots. In this analysis, the time frame variable is analyzed to see if it has 

an impact on the counting rate, and if so, what that impact is. The results show that there 

is an impact that is reflected in the data. The greater amount of time permitted to 

officials, the greater proportion of ballots are counted. This seems to be best reflected by 

the number of weeks that are permitted for the counting of ballots. While this trend is 

apparent, the impact of the time frame seems to not have as strong as an effect as other 

variables that have been looked at previously. 

 The distribution of states along the time frame measurement is nearly identical 

when examined on the weeks-permitted measurement. 14 states permitted less than one 

week to evaluate provisional ballots, 15 states permitted between one and two weeks, and 

14 states permitted greater than two weeks1. The percentage of those ballots that were 

counted proceeds in a clear linear trend as time increases. Those states that permitted less 

than one week counted an average of 35.4% of their ballots, while those states that 

permitted between one and two weeks counted 47.1%. The greatest proportion of 

counting was in the states that permitted greater than 3 weeks, counting 60.8% of the 

provisional ballots cast2. 

 The time permitted to count provisional ballots would seem to be related to the 

amount of ballots that were cast. When this hypothesis is tested, the results show that 

there the impact of time is felt most strongly in states where greater than 1% of their 

overall turnout was of provisional ballots. In these high-cast level states, those that 

permitted less than one week to evaluate ballots counted 58.6% while those that 

permitted one to two weeks counted 65.0% of ballots. Again, those states that permitted 

greater than three weeks verified the highest proportion of provisional ballots, at 73.8%. 

In states where provisional ballots were less than 1% of the total turnout, the findings are 

less clear. States allowing less than a week counted 31.6% of their provisional ballots, 

while those permitting between one and two weeks counted less than that, 26.7%. Those 

permitting greater than two weeks again counted the highest rate, at 47.8%. 

 When the impact of time requirements is inputted into a regression equation, with 

the dependant variable being the percent of cast provisional ballots that are counted, the 

results are unclear in regards to how influential the time requirement was. The addition 

of this variable does explain an additional proportion of the overall variation in national 

provisional ballot numbers, but it proves to not be as influential as the level of ballots that 

was cast. These results indicate that while time requirements are a factor, they are not 

one of the most significant factors in this model, and that other variables seem to play a 

more important role. 

 

1

 Many thanks are due to Ben Shepler, who assembled complete data on the time 

requirements states permitted for the counting of provisional ballots. 

2

 43 states are included in this analysis, including Washington D.C. The 7 election-day 

registration states are omitted, as is Mississippi, which never provided data on provisional 

ballots. North Carolina is also omitted from the regressions, as it does not have a 

statewide policy on how it verifies provisional ballots. 
 Overall, the impact that time requirements for the counting of provisional ballots 

seems to be positive, with greater time allowed leading to more ballots being included in 

the final vote. This is especially true in areas where high levels of provisional ballots are 

cast. While this is the case, it also appears to be true that time requirements are not as 

significant factors as other variables. The 3 major determinants of how many provisional 

ballots are counted are more likely to be: the level of ballots that are cast out of overall 

turnout (the higher the number, the more are counted), the verification requirements to 

count provisional ballots (the more rigorous the requirements, the fewer are counted) and 

whether ballots cast outside of the correct precinct are eligible (if they are not, fewer are 

counted). 

 
Relationship Between Time Allotted to Verify Provisional Ballots 

and the 

Level of Ballots that are Verified 

 One possible variable that can impact how many provisional ballots that have 

been cast will eventually be counted is the amount of time election officials have to 

process those ballots. If officials do not have an adequate amount of time, they may be 

forced to arbitrarily rule on the validity of provisional ballots that they simply do not 

have the time to assess. This could increase the level of ballots counted, or decrease the 

level, depending on whether election officials choose to accept or reject the ballots they 

were unable to evaluate. By examining the data for overall trends, some conclusions may 

be drawn out in regards to how provisional ballots were handled when little time was 

available to evaluate them. 

Analysis by Days Allotted to Evaluate Cast Provisional Ballots

 A quick scatterplot reveals a potential relationship between the time States allow 

for the evaluation of provisional ballots and the percentage of those ballots that are finally 

counted as legitimate (Figure 1). Though the relationship is not perfect, there does seem 

to be a tendency for states that allow more days to evaluate provisional ballots to validate 

more of those ballots. Those states that allow fewer days to evaluate tend to occur in the 

lower left hand quadrant of the scatterplot, while those that allow more days tend to move 

towards the upper right hand quadrant. A computer-generated trend line helps to make 

this tendency more apparent.

Figure 1: Percent of Cast Ballots Counted, by Days Allowed 

Days a llowe d t o p roces s ballo ts

0 1 0 2 0 3 0 4 0

1 .0

. 8

. 6

. 4

. 2

0 .0

 Another method to analyze the impact that time has upon the verification of 

ballots is a means plot of the average level of ballots that were counted for each amount 
of days allotted. This is essentially condensing the previous scatterplot to reflect one 

average rate of counting for each point on the independent variable (time allotted). The 

results clearly indicate a positive trend line around which the points cluster. As more 

days are permitted to election officials, those officials tend to count a higher level of 

provisional ballots. 

Figure 2: Average Percent of Cast Ballots Counted, by Days Allowed 

Days a llowe d to p roces s ballo ts

2 3 4 6 7 1 0 1 1 1 2 1 3 1 4 1 5 1 9 2 1 2 3 2 8 3 0 3 5

1 .0

.8

.6

.4

.2

0 .0

Analysis by Weeks Permitted to Evaluate Ballots

 A weakness of the analysis conducted by the number of days permitted is that it 

spreads the limited number of cases available over a wide range of potential values. This 

weakens any ability to draw conclusions from the data, because it spreads the trend over 

too many points, using too little data. This can be refined somewhat by reducing the data 

to the amount of weeks allowed, rather than days. The days allotted measurement was 

reduced to: one week or less (2-7 days), between one and two weeks (8-14 days), and 

greater than two weeks (15 days or more). Similar, but clearer, results are obtained using 

this measurement. 

 The scatterplot (Figure 3) reflects this change in measurement and clusters all of 

the measurements within the three new values permitted. The trend line that occurs is 

similar to the original scatterplot, but the tendency of the actual data points becomes 

more apparent. Aside from the outliers at the very bottom of the chart, it becomes 

obvious that the lower data point in each value area is well above the average in the value 

that precedes it. The positive relationship is much more clearly revealed.

Figure 3: Percent of Cast Ballots Counted, by Level of Weeks Allowed
Weeks a llowe d to proce ss ba llots

- .5 0 .0 . 5 1 .0 1 .5 2 .0 2 .5

1 .0

. 8

. 6

. 4

. 2

0 .0

Replicating the means plot conducted earlier produces more striking results, 

providing a better clearer of the overall role played by time restrictions on the counting 

process. A steady positive trend becomes apparent between the weeks permitted and the 

amount of provisional ballots that are confirmed. There are large differences between the 

results for states that allowed “Less than one week” (35.4%) and “Between one and two 

weeks” (47.11%) for evaluating ballots. The results also indicate large differences 

between the “Between one and two weeks” and the “greater than two weeks” (60.8%) 

time frame. Each additional week permitted seem to add approximately an additional 

12% to the rate of counting ballots. 

Figure 4: Average Percent of Cast Ballots Counted, by Level of Weeks Allowed 

Weeks a llowe d to proce ss ballots

Less than 1 week Be twee n one and two T wo we eks or lon ge r

.7

.6

.5

.4

.3
 

Role of High and Low Cast Rates

 An additional question seems to need to be asked regarding whether the amount

of ballots cast impacts this role. Longer time lines would seem to be more necessary in 

areas where high levels of ballots are cast, whereas in areas where few ballots are cast, 

additional time would seem to be less necessary. The distinction between high and low 

levels was made by separating the states based upon whether provisional ballots counted 

for greater or less than 1% of the overall number of ballots cast in the election. 17 states 

fall into the high-cast level category, while 25 states are in the low-cast level category. 

 Previous research has shown that States that had higher levels of ballots cast 

typically counted more of those ballots. States with more than 1% of their turnout 

consisting of cast provisional ballots typically counted an average of 67.9% of them, 

while those states with less than 1% counted almost half that amount, an average of 

34.6%. This analysis adds to that finding, and shows that States that had high-levels of 

ballots cast, and allowed greater time to evaluate those ballots, on average counted much 

higher levels of those ballots. Figures 5 and 6 show scatterplots and means plots of states 

that had high levels of provisional ballots cast. Figures 7 and 8 show the same 

information for states with low levels of provisional ballots cast. 

Figure 5: Scatterplot for High-Cast Level States Figure 6: Means plot for High-Cast Level States 

Weeks a llowe d t o proce ss ba llots

- .5 0 .0 . 5 1 .0 1 .5 2 .0 2 .5

1 .0

. 9

. 8

. 7

. 6

. 5

. 4

. 3

 Weeks a llowe d t o proce ss ballot s

Less than 1 week Be twee n one an d two Two we eks or longe r

. 8

. 7

. 6

. 5

 

Figure 7: Scatterplot for Low-Cast Level States Figure 8: Means Plot for Low-Cast Level States 
Weeks a llowe d t o proce ss ba llots

- .5 0 .0 . 5 1 .0 1 .5 2 .0 2 .5

1 .0

. 8

. 6

. 4

. 2

0 .0

 Weeks a llowe d t o proce ss ballot s

Less than 1 week Be twee n one an d two Two we eks or longe r

. 5

. 4

. 3

. 2

 

 In the high-cast level states, a straight positive trend is found from the “less than 

one week” on through the “Greater than two weeks” group. In the shortest time period, 

an average of 58.6% of the ballots were counted, rising to 65.0% in the “one to two 

week” time frame. In the “greater than two weeks” time frame, this level jumps up to 

73.8%. In the low-cast level States, the results were not as uniformly related to each 

other. The “less than one week” category counted 31.6% of its provisional ballots, while 

the “one to two week” category counted fewer ballots, 26.7%. The “greater than two 

weeks” time frame counted vastly more, around 47.9%. An interaction between the lowhigh cast rate, and the time period allowed to count ballots seems to exist. Specific 

Figures are shown for this in Table 1. 

Table 1: Interaction of Level of Ballots Cast and Time to Evaluate 

Level of Ballots Cast Time to Evaluate Percent Counted 

High Greater than 2 Weeks 73.8% 

High 1 – 2 Weeks 65.0% 

High Less than 1 Week 58.6% 

Low Greater than 2 Weeks 47.9% 

Low 1-2 Weeks 26.7% 

Low Less than 1 Week 31.6% 
Regression Analysis

 These results indicate that the time requirements states face when handling 

provisional ballots do impact how they count those ballots. There also appears to be an 

effect from the level of provisional ballots cast of overall turnout, as well as an 

interaction between those two factors. The role that these influences play within the 

overall counting process can be examined by entering them into an ordinary-least-squares 

regression, using other predictors previously examined in prior research3

 as a baseline for 

comparison. The results of that analysis are shown in Tables 2 and 3. 

Table 2: Predictive Utility of Regression Model 

Model R Square Std. Error of 

the Estimate

Sig. 

Original Model .415 .184 .000 

With Time, Cast 

Level and Interaction 

Variables 

.570 .158 .000 

a. The dependant variable in this case is the percent of cast provisional ballots that were finally counted in the vote 

total. The independent variables include whether the State: was new to provisional voting, had a registered voter 

database, counted out-of-precinct ballots, what type of ID they required at the polls and the method that they used to 

verify cast ballots. The R Square explains how much of the variance of in the dependant variable is explained by the 

independent variables. The Adjusted R Square accounts for how many variables are used in the model. The closer the 

R and adjusted R are to 1.0, the more predictive the model is.

Table 3: Individual Variable Coefficients of New Variables

 Standardized 

Coefficients

 

Model Beta Std. Error T Sig. 

(Constant) .340 .234 1.455 .155 

New Weeks to Evaluate -.004 .108 -.033 .974 

New Level of Ballots Cast .235 .128 1.837 .075* 

New Interaction (Time * Cast 

Level) 

.019 .077 .251 .804 

Old States that used 

Provisional Ballots for 

the First Time in 2004 

-.080 .070 -1.141 .262 

Old Does State have a 

Statewide Voter 

Registration Database 

-.063 .055 -1.157 .256 

Old Does State Count PB's 

Cast Outside of Correct 

Precinct 

.092 .057 1.628 .113 

Old Voter Identification 

Required In Order To 

Cast a Normal Ballot 

-.010 .025 -.411 .683 

Old What Criteria was used 

to Count or Discard a 

PV 

-.059 .030 -1.941 .061* 

a Dependent Variable: Percent of Provisional Ballots Cast that were Counted in 2004 

* - Indicates statistical significance at the .10 level. 

 

3

 See Andersen “Statistical Analysis of Provisional Voting”. 
 Of note in this chart is that the only two variables that are statistically significant, 

and that there is thus full confidence in, are the level of ballots cast in the state and the 

verification requirements used to count ballots. Whether a state permitted out-of-precinct 

ballots to be counted is also nearly significant. The rest of the variables have a greater 

degree of uncertainty to them. The standardized coefficients indicate the impact that 

these variables have upon the final value of the dependant variable. The larger the 

coefficients are, the greater degree of impact the variable had in the counting process. 

The role that the time allotted to review ballots plays appears to be weak, in comparison 

to the other variables. This is not to argue that the role played by time allotments is 

insignificant, simply that it does not have as much of an impact as do other variables. 

Conclusion

 

 This analysis offers support for the assertion that allowing more time to evaluate 

provisional ballots increases the amount of those ballots that are finally counted within 

the final vote total. It seems difficult to generate a plausible reason why a short time 

frame would provide more accurate evaluations of ballots, so this may provide an 

indication that a majority of provisional ballots that are cast are in fact legitimate. Longer 

time frames suggest that more thorough evaluations can be conducted, leading to more 

accurate results in the counting process. If longer time frames can be expected to result 

in more accurate assessments, these findings can provide a validation of the provisional 

balloting process. Longer time frames led to higher counting rates, indicating that greater 

accuracy in the evaluation process consistently validated more of the ballots they 

encountered. This also suggests that legitimate voters who otherwise could have been 

disenfranchised by the elections process often cast provisional ballots. 

 The disparity in the counting of ballots between the high and low cast-level states 

may have arisen from the disparity in the importance of provisional ballots within the 

final vote tally. States that had low levels of provisional ballots cast may not have felt an 

incentive to evaluate them accurately, because they would not impact the final results. 

The role that provisional ballots played in these states may have been viewed as minimal 

to election officials, decreasing their incentive to evaluate them thoroughly. Short time 

restrictions would have compounded this indifference, and left many valid ballots 

uncounted. States with higher rates of provisional ballots cast may have viewed the 

number of provisional ballots as potentially influential in the final outcome. This would 

have provided a greater incentive to include them accurately within the final vote tally. 

In either case, it seems likely that greater amounts of time available to assess those ballots 

would provide greater accuracy in their evaluation. In a close election, such accuracy 

may be crucial in creating perceptions of legitimacy in an election. 
Attachment A: Data Used In This Analysis

State

Total 

Provisional 

Ballots Cast

% of Turnout 

of Cast 

Provisional 

Ballots

% of Cast 

Provisional 

Ballots that 

were Counted

Total 

Provisional 

Ballots 

Counted

% of Turnout 

of Counted 

Provisional 

Ballots

What is the 

time line for 

counting PV 

ballots?

Alabama 6,560 0.35% 27.99% 1,836 0.10% 7 days 

Alaska 23,275 7.45% 96.66% 22,498 7.20% 15 days 

Arizona 101,536 5.04% 72.54% 73,658 3.66% 10 days 

Arkansas 7,675 0.73% 47.92% 3,678 0.35% 15 days 

California 668,408 5.38% 73.57% 491,765 3.96% 28 days 

Colorado 51,477 2.42% 76.08% 39,163 1.84% 12 days 

Connecticut 1,573 0.10% 31.66% 498 0.03% 6 days 

Delaware 384 0.10% 6.25% 24 0.01% 35 days 

D.C. 11,212 4.93% 71.15% 7,977 3.51% 7 days 

Florida 27,742 0.36% 36.07% 10,007 0.13% 11 days 

Georgia 12,893 0.39% 29.78% 3,839 0.12% 7 days 

Hawaii 348 0.08% 7.18% 25 0.01% 6 days 

Idaho N/A N/A N/A N/A N/A N/A 

Illinois 43,464 0.82% 51.00% 22,167 0.42% 14 days 

Indiana 5,707 0.23% 15.95% 910 0.04% 13 days 

Iowa 15,454 1.03% 52.08% 8,048 0.53% 2 days 

Kansas 45,563 3.84% 69.80% 31,805 2.68% 11 days 

Kentucky 1,494 0.08% 14.79% 221 0.01% 3 days 

Louisiana 5,880 0.30% 39.32% 2,312 0.12% 4 days 

Maine N/A N/A N/A N/A N/A N/A 

Maryland 48,963 2.05% 65.07% 31,860 1.33% 4 days 

Massachusetts 10,060 0.35% 23.05% 2,319 0.08% 4 days 

Michigan 5,610 0.12% 58.41% 3,277 0.07% 14 days 

Minnesota N/A N/A N/A N/A N/A 14 days 

Mississippi UK UK UK UK UK UK 

Missouri 8,183 0.30% 40.23% 3,292 0.12% 14 days 

Montana 653 0.14% 54.67% 357 0.08% 6 days 

Nebraska 17,003 2.18% 78.21% 13,298 1.71% 7 days 

Nevada 6,154 0.74% 39.76% 2,447 0.29% 7 days 

New Hamp. N/A N/A N/A N/A N/A N/A 

New Jersey 64,226 1.78% 55.26% 35,493 0.98% 28 days 

New Mexico 15,360 2.03% 57.08% 8,767 1.16% 10 days 

New York 243,450 3.29% 40.26% 98,003 1.33% 10 days 

N. Carolina 77,469 2.21% 54.66% 42,348 1.21% 7 days 

North Dakota N/A N/A N/A N/A N/A N/A 

Ohio 158,642 2.82% 77.88% 123,548 2.20% 23 days 

Oklahoma 2,615 0.18% 7.69% 201 0.01% 3 days 

Oregon 8,298 0.45% 85.29% 7,077 0.39% 19 days 

Pennsylvania 53,698 0.93% 48.59% 26,092 0.45% 3 days 

Rhode Island 2,147 0.49% 45.83% 984 0.23% 30 days 

South Carolina 4,930 0.30% 65.05% 3,207 0.20% 4 days 

South Dakota 533 0.14% 12.38% 66 0.02% 3 days 

Tennessee 8,778 0.36% 37.57% 3,298 0.14% 2 days 

Texas 36,193 0.49% 21.47% 7,770 0.10% 7 days 

Utah 26,389 2.84% 70.39% 18,575 2.00% 14 days 

Vermont 101 0.03% 36.63% 37 0.01% 2 days 

Virginia 4,609 0.14% 15.80% 728 0.02% 7 days 
Washington 86,239 3.01% 80.33% 69,273 2.42% * 

West Virginia 13,367 1.77% 62.68% 8,378 1.11% 30 days 

Wisconsin N/A N/A N/A N/A N/A * 

Wyoming N/A N/A N/A N/A N/A * 

* No data for how long is permitted for the evaluation of provisional ballots was found. Sources searched 

include state legislation and elections websites 

N/A – These states used Election Day registration, and were exempt from HAVA mandated provisional 

ballots 

UK – No data on provisional balloting has been obtained from this state 

Bold – States in bold experienced greater than 1% of their overall turnout as cast provisional ballots.