Variances in State Sales Tax Rates:A Study of the Source of Sales Tax Differences in the States

Sean Connell

Will Monkowski

Brian Prewitt

Political Science 425

Dr. Zachary Baumann

April 13, 2012

What causes variances in state sales tax rates?

What seems like a few cents at the cash register can really add up. Sales tax plays a role

in the fiscal lives of most -- but not all -- Americans nearly every day of their lives. Whether it is

the 2.9% tax added on to transactions in Colorado or the 7.25% tax in California, the rate of

sales tax levied by the states across the United States varies greatly. There are even 5 states that

don’t have a sales tax at all. While some areas make up for smaller sales tax by instituting a

higher local sales tax, theses variances have implications in a number of areas. One of the most

prevalent aspects of state government that sales tax affects is the budgeting process. Some states

earn up to 60% of their revenue from sales tax, while others earn 0% (Book of States, 2010). In

fact, sales tax accounts for one-third of the entire total of state revenue in the U.S. (Fletcher &

Murray, 2006). Since so much of state revenue is derived from sales tax, the state’s budget will

inevitably be impacted in some manner by the sales tax rate. As the graph below (A) shows, the

most common sales tax rate in the nation is around 6%, with most other states within 1% of this

figure. Through our research, we aimed to find what can account for these differences in sales

tax rates and in the end, we found that we’ve only scratched the surface of sourcing these

variances, but we did find at least one factor that has an impact.

Graph A: State Sales Tax Rate Distribution

Overview of Literature

In order to fully understand what might cause a state to adopt a sales tax at a particular

rate, it is important to understand why a state would adopt a sales tax in the first place. A natural

first place to look within the literature was at the history of sales tax, specifically the first use of

it and the reasoning behind the implementation. Howe and Reeb surveyed the evolution of state

and local tax systems over the time period beginning with the colonial era and extending to the

present day tax system. They focused on how governments have found ways to access the

wallets of their tax base by implementing alternative taxes, one of which was the sales tax. They

pointed out that although various excise and sin taxes had existed since 1919, when the first

motor fuel tax was implemented, the first retail sales tax was created in 1932 in Mississippi at a

meager 2% rate. Howe and Reeb added that within 6 years, 20 states had implemented the tax

due to its success. Since this was during the Great Depression, local and state governments

weren’t able to raise the classical taxes -- politically speaking -- such as property and income

taxes. Sales taxes provided a way to generate tax revenue a little at a time from consumers, so

they wouldn’t be as outraged by the increase in taxation since they spike was less noticeable

(Howe & Reeb, 1997). Based off of this study, we discovered that sales tax seems to have a close

relationship with the classical taxes, as it was originally implemented as an alternative source of

income from those. They made a point to emphasize the close relationship sales tax shares with

income tax, which seemed to be a common point that emerged amongst the rest of the literature.

Numerous studies have shown that sales taxes have also been used as a means of enacting

various types of social and health changes. Peterson, Zeger, Remington, and Anderson found that

over the 33-year span preceding 1988, 249 increases in the tax rates on cigarettes were associated

with a decrease by 3.0 packs per capita in the amount of cigarettes consumed by Americans.

Over the same time period, when there were no tax increases implemented, cigarette

consumption actually rose by 0.6 packs per capita (Peterson, Zeger, Remington & Anderson,

1992). In recent years, studies have been conducted regarding potential associations in taxes on

soft drinks and the rates of obesity in the United States. In 2009, Fletcher, Frisvold, and Tefft

conducted a study measuring taxes on soft drinks and the national average of body mass index

(BMI). They actually found that a 1% increase in these taxes led to a .001 decrease in national

BMI. While these figures are relatively miniscule, this is a relatively new area of research since

the taxes themselves are relatively new (Fletcher, Frisvold & Tefft, 2009). Regardless, the

intention seems to be there in terms of enacting social change in the form of a sales tax. Mikesell

put it best when he called the sales tax a “tax on consumption” (Mikesell, 1997). All of these

studies point at the idea that taxation can have implications beyond just fiscal use, therefore

factors other than just monetary reasons could come into play in determining what a state’s sales

tax rate will be.

With the origins of sales tax in mind, next it was important to take a look at the literature

regarding what explains the differences in sales tax rates across the states. One study by Jason

Fletcher and Matthew Murray that stood out among the rest since it used a very similar method

to the one being applied in this study. Fletcher and Murray were looking to uncover what causes

states to choose their sales tax bases, as well as researching the effect that competition among

neighboring bodies on a state and local sales tax rate. It’s worth noting that one line from their

paper revealed the difficulty in finding relevant research when it comes to state sales tax rates, as

they wrote, “There has been little research undertaken to date to explain these variations in the

structure of sales taxation across the states.” Fortunately, this study provides plenty of valuable

knowledge. They use empirical analysis to study 21 different independent variables including

demographic factors, income tax rates, and various sales tax exemptions among many others.

Ultimately, they discovered that income tax rates and demographic factors played a role in how a

state’s sales tax base was determined. This study uses both of these variables that they found to

be significant in our own research. They also found that geographic competition did not actually

cause competition in sales tax rates, but rather it tended to induce mimicking of sales tax levels

(Fletcher & Murray, 2006). Some of their data applied only to the local level, so that would only

prove partially valuable in this study, but their findings of those significant factors proved

invaluable. In this study, significant variables from a variety of studies were run in a single

model, and Fletcher and Murray provided a few that seemed likely to have an impact on the state

sales tax rate variations.

As is evident at this point, the idea of income tax playing a role in a state’s sales tax level

seemed to recur across the literature that was found. John Mikesell conducted a study that

surveyed states’ reliance on sales tax as a source of revenue since its implementation. He found

that by the 1930’s, sales tax had become the highest revenue producer among state taxes. By the

1990’s, however, income tax had equaled the status of sales tax. He ultimately concluded that

states will not be doing away with sales tax anytime soon, despite the emergence of income tax

as a strong source of revenue. In the paper, he writes that, “Disappointing sales tax

performance...bring major fiscal distress to the many states depending on its revenue -- and

absolute terror to the handful of states without a broad individual income tax” (Mikesell, 1992).

Ultimately, the relevance in this article is that sales tax and income tax are very closely linked

and the income tax rate in a state should have some bearing in the state’s sales tax rate. This is a

vital finding when it comes to this studies specific research.

Another study revealed that the partisanship of a state could also play a substantial role in

setting a sales tax rate. David Nice performed a study regarding whether party ideology actually

affects party policy once in office. While his ultimate goal wasn’t to discover what effects party

I.D. might have on a state’s sales tax rate, he use sales tax reliance as one of his variables to

discover whether parties affect policy. Using two different measures of party ideology --

Costain’s method and McGregor’s method -- Nice performed a cross-sectional analysis on a

number of independent variables, one of which was sales tax reliance. He added more controls

each time he ran the data and ultimately found that even when controlling for social and

economic conditions of a state, higher levels of Republicanism in a state was associated with

higher reliance on income tax and lower reliance on sales tax (Nice, 1985). Based on this

finding, it was crucial that state party I.D. be tested in this research.

Hypotheses

Based on the literature and the collective knowledge of the researchers, a set of hypotheses

were formulated as to what effects certain independent variables will have on the sales tax and

why that might be so.

1.) States with a higher income tax rate will have a lower sales tax rate.

Throughout the afore-mentioned literature, a common theme that emerged was a strong

relationship between income tax and sales tax. Mikesell’s quote regarding poor sales tax

performance being a “terror” to states without a broad income tax summed up the relationship

nicely (Mikesell, 1992). In fact, sales taxes were instituted in the first place to combat the

inability of income and property tax to generate adequate funds during the Great Depression

(Howe & Reeb, 1997). The rationale here is that the two taxes compensate for each other as the

main sources of revenue for a state. If a state has a high income tax, they wouldn’t need to have a

high sales tax since they were making sufficient revenue from the income tax.

2.) States with a higher GDP and higher budget will have a higher sales tax rate.

States with bigger economies and higher budget expenditures will need as much revenue as

they can get in order to pay for their complex fiscal system. One way to generate this revenue

could be through the use of a higher sales tax, as it would provide a stable source of money

throughout the fiscal year. Other taxes, like an income tax, are only collected in one huge chunk

once-a-year, so sales taxes provide a consistent flow of revenue for the state governments,

3.) States that generally lean Republican will have lower sales taxes.

The David Nice study regarding party ideology and party policy implementation found that

Republican state governments were more reliant on income taxes and less reliant on sales taxes

(Nice, 1985). He found that party policy is more apt to affect social issues, specifically welfare

spending, so we believe that since Republicans are less likely to spend more on welfare, they will

have less need of the sales tax to back that spending.

4.) States with a higher median age will have a higher sales tax.

Since states that have an older population will have more retired citizens, the states ability to

generate revenue through income tax will be greatly diminished. Similar to our hypothesis

regarding income tax, we believe that since the state will have a greater need for revenue from

non-income-tax sources, sales tax would be one of the places they would make up for the

difference.

5.) States with a larger amount of debt will have a higher sales tax.

Similar to the GDP/budget hypothesis, a state in need of more money will need to generate

revenue in as many ways as possible. Obviously, a state with a higher debt will be in need of

more money. Sales tax would allow the state to not only get more money from the tax-paying

citizens, but it would allow them to collect from tourists or any other non-citizens that would be

purchasing within the state.

6.) States with a higher non-white population will have a higher sales tax.

States with higher minority populations receive more grocery tax exemptions (Bahl &

Hawkins, 1998). In order to make up for the lack of ability to tax the sales on such a vital part of

the state economy, the state will need to increase the state’s overall sales tax rate in order to

make up for the lost revenue from groceries.

Research Design and Data Collection

When doing any type of research it is important to explain the methodology that is used

to analyze the data. Looking at the conclusions that were presented in the literature that was

previously analyzed, it is clear that outside of the Fletcher and Murray piece that most of the

findings presented showed only the significance of one variable in relationship to state sales tax

percentage (Fletcher & Murray, 2006). A handful of those variables and others from other

studies were taken and run through a multivariate analysis to see if the affects of the variables

discussed in the literature hold up when run in a model with other variables. This will help

further pinpoint what has an impact on state sales tax rates.

The data used in this study comes from many different sources. The first and most

important source of our data was the 2010 United States Census. Being that this is the most

recent census and holds the most accurate data possible, we found it necessary to gather our data

from the year of 2010 (if not the closest we could get) across all sources. Another important data

set we used came from the Book of States, which provides data from each of the 56 United

States and territories on a wide range of topics. Also used were data from the Bureau of

Economic Analysis, The National Association of State Budget officers, and

USGovernmentSpending.com. Though not main sources, these three still contributed important

numbers to the research.

Data Analysis and Interpretation

The variables were run in both a bivariate and multivariate model. A bivariate analysis

shows simply the relationships between one independent variable and the dependent variable,

and for the purposes of our study it is unwise to draw serious conclusions from these

relationships. That being said the bivariate analyses reveal very interesting results. Below are

coefficients, significances, graphs, and other statistics that will reveal a very interesting pattern

produced by the bivariate analysis.

Table 1: Bivariate Analysis

Coefficient Std. Error P > T Significance State GDP 0.00000152 0.000000802 0.065 *Partisanship 3.171 2.4631 0.204Budget 0.00001 0.00000803 0.097 *Age -0.0743 0.126 0.558Income -0.2129 0.1356 0.123Debt -0.0873 0.0738 0.243Minority Population 0.0202 0.0153 0.192GDP Per Capita -60.5832 30.511 0.053 *Budget Per Capita -369.8486 118.5193 0.003 ***

Graph B.) Bivariate Analysis of State Sales Tax Rates vs. State GDP (in millions)

Graph C.) Bivariate Analysis of State Sales Tax Rates vs State Partisanship

Graph D.) Bivariate Analysis of State Sales Tax Rates vs State Budget (in millions)

Graph E.)Bivariate Analysis of State Sales Tax Rates vs. Median Age

Graph F.) Bivariate Analysis of State Sales Tax Rates vs % of Personal Revenue from

Income Taxes

Graph G.) Bivariate Analysis of State Sales Tax Rates vs State Debt (% of GDP)

Graph G.) Bivariate Analysis of State Sales Tax Rates vs Minority Population

Graph H). Bivariate Analysis of State Sales Tax Rates vs GDP Per Capita

Graph I.) Bivariate Analysis of State Sales Tax Rates vs Budget Per Capita

When looking at the bivariate data and graphs, it is quite easy to see a theme: there is a very

intense, very dense grouping of states, containing a few outliers. Many of these graphs (most

severely B, D, F, and H) show a “lollipop” effect, where states are grouped very heavily towards

one end of the graph, with a few outliers scattered throughout. This occurs because when looked

at more intensely and when one thinks about it, the variances in state sales tax rates are actually

not very high; at least not as high as one might imagine. It is again important to note that while

some variables are statistically significant (Table 1) there are no conclusions about the relevancy

of our hypothesis being drawn. The bivariate analyses are being used simply to determine what

relationships may have an impact on the multivariate analysis.

Next is the important analysis: the multivariate analysis. A multivariate analysis

essentially tests the impact an independent variable has on a dependent variable in the presence

of other independent variables. This is the test that we can use to show just how much of an

impact the variables the literature put forth have in a more advanced situation where they must

interact with other variables, opposed to a more naïve bivariate model. Looking at the table of

coefficients and significance is eye opening as an interesting result is revealed.

Table 2: Multivariate Analysis

Coef. Std. Err. P>t Significance

GDP Per Capita -39.80247 36.06547 0.276Budget Per Capita -357.6437 183.7427 0.058 *Partisanship 3.476236 2.810184 0.223Age -0.109702 0.1576638 0.49Income Tax 0.1589727 0.1989525 0.429Debt -.0116314 0.0834845 0.89Minority Populatipon 0.0093503 0.0158096 0.557Constant 4.222406 6.172629 0.498

Significance: *=0.10; **=0.05; ***=0.01

The statistical analysis (Table 2) shows that all of the variables except for one had no statistically

significant impact. The only one that had a statistically significant impact was the budget per

capita variable which was significant at the .10 level (94.2%). As far as significance goes, this is

not a high significance at all. The correlation says that sales tax rates actually go up as budget per

capita goes down ( for every .01 unit increase in budget per capita there is a 3.576 unit decrease

in sales tax rates). This is a shocking result and undoubtedly the most important, as none of the

variables from the previous literature seem to have an impact on state sales tax rates when run

through and multivariate model.

Another interesting statistic to look at is the R-squared value (Table 3). The R-squared

value is a calculation of how much of the variance is explained by the variables used in the

study. The R-squared value was .2438, meaning that only 24 percent of the variance was

explained by the variables that were included in the analysis. This is a very low number because

it says that 75 percent of the variance in states sales tax rates was not explained in our model.

This may be because of other variables not used in the study that may have had an impact.

So far this study has shown that a.) the multivariate analysis shows that the variables in

the previous literature do not hold up in the presence of other variables b.) only budget per capita

is significant when it comes to state sales tax variance and c.) our model only explained 24% of

the variance. At this point it is extremely important to discuss why the results might happen.

They were very unexpected and are very odd when one looks at the previous literature. Next it

will be important to rationalize why the findings came out the way they did, and look deeper into

what may actually be causing variances in state sales tax rates.

Conclusions

The study’s findings revealed only one statistically significant multivariate variable. This

finding that budget per capita was a negatively correlated relationship was actually the opposite

of what was hypothesized. This combined with the other variables being insignificant lends to

the discovery that variations in sales tax rates are much more complex than the original theories

suggested. It is now easy to conclude that there are many factors that contribute to the variation

of sales tax rates among states.

While discovering the lack of significance of the variables, a few reasons developed as to

why this study’s results differ from the results observed in the literature and what was initially

hypothesized. In examining the data one of the things that sticks out is that the data are not as

diverse as initially observed. Since nearly one third of states have a sales tax about equal to 6%

the data tends to get grouped together around that number. This grouping is one the main reasons

that the variables did not have the predicted effect.

The lack of significance of the variables was especially surprising given the previous

literature suggesting that many of the variables that were used would have some degree of

significance. Upon further analysis of the data and that of the studies cited in the literature

review, it is logical to draw the conclusion that these results differ because many of the studies

cited only examined the significance of one variable’s effect on the sales tax rate of a state. Our

study used a multivariate analysis of many variables at once; when performed in this research

these other theories fell apart.

The lack of significance of the variables in the study led to a search for other explanations

for the variation that is seen in sales tax rates among states. A possible explanation is that the

right variables were not chosen for this study. When looking at the states with high sales tax rates

it seems that many of these states are states which raise a large portion of their revenue from

tourism. In a future study it would be ensured that percentage of revenue raised from tourism is

one of the variables because given what has been observed this variable seems like it would have

some significance. It is also likely that since this tax affects everyone there are many more

factors at play than few that what was chosen to study.

Another factor that was not researched was the effect that local sales tax have on the

statewide rate. Some states allow their localities to tax the sale of goods separately from the

statewide rate; this could have an effect on rates that lawmakers choose to charge across the

entire state. In future studies the affect of local sales taxes on the statewide rate would certainly

be examined.

The main take away from this study is that given majority of the states have sales tax

rates between 5-7%, and that although no clear evidence was found in the research about what

causes this variance, there must be some sort of significance of these numbers. It is likely that

there is some political or historical reason that most states have settled on a rate in this range. A

number like 6% seems to be a happy medium that politicians select that will raise enough capital

for the tax to be effective, while not being so high that citizens are upset by the amount they are

being charged on their purchases. Fletcher and Murray’s paper states that counties are more

likely to mimic each other’s tax policies rather than compete with each other (Fletcher &

Murray, 2006). This seems to be supported by our data given that many states have decided to

adopt similar tax rates rather than undercutting their neighbors in order to attract consumers. This

mimicking is the best explanation that that could be formulated to explain not only why many

states have similar tax rates, but also why there is a lack of variation between most states.

Though this study only provided one significant variable, but upon further analysis and

conclusions showed that there are many more things influencing the sales tax rates other than the

short list of variables that were tested. Most notably of these are the effects of a political or

historical significance of having a rate in the 5-7% range. Future studies will examine the

significance of tourism and local sales tax rates also. Though the research may have not been

able to confirm our original hypotheses or the previous literature, the study did provide valuable

insight into the variation of sales tax rates and why these variables were not as significant as

originally hypothesized.

Works Cited

Bahl, Roy & Hawkins, Richard. (1998). A Georgia Sales Tax for the 21st Century.

Atlanta, GA: Georgia State University.

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weight?.Contemporary Economic Policy, 28(1), 23-35. doi: 10.1111/j.1465-7287.2009.00182.x

Fletcher, J. M., & Murray, M. N. (2006). Competition over the tax base in the state sales

tax. Public Finance Review, 34(3), 258-280. doi: 10.1177/1091142105285571

Howe, E. T., & Reeb, D. J. (1997). The historical evolution of state and local tax systems.

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Peterson, D. E., Zeger, S. L., Remington, P. L., & Anderson, H. A. (1992). The effect of

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