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Considering the Relationship between Business 
Applications and State Tax Rank:   

Effect of Tax Cuts and Jobs Act of 2017  
 

Benjamin B Boozer, Jr 
Jacksonville State University 

 
John Sneed 

Jacksonville State University 
 

Cindy Sneed 
Jacksonville State University 

 
 
 

This research measures the relationship between state-level taxes and per capita business applications.  
State-level taxes used in the analysis are based on the tax rank of each state from 1 to 50, with a lower 
number rank associated with lower tax burden for that category and a higher number rank with higher 
tax burden for that category. Tax categories used as independent variables in the model are the 
following: corporate tax rank, individual income tax rank, sales tax rank, property tax rank, and 
unemployment insurance tax rank. Five-time periods were measured from 2015-2016, 2016-2017, 
2017-2018, 2018-2019, and 2019-2020. The research question for the model considers how the 2017 
Tax Cuts and Jobs Act (TCJA) legislation affects business applications within each state as measured 
through state-level variables. Sales taxes are found to inversely affect business applications two years 
before TCJA passage and during the year of passage, but to have a positive relationship one year after 
passage. Income tax is positively correlated in that immediate year following passage, while corporate 
tax inversely impacts two years following passage.   
 
Keywords: Business development, Taxes 
 

Introduction 
 Prior research addresses the issue of cause-and-effect relationships between state tax policy 
and business activity within a state. The prior studies used a time-series analysis so data from multiple 
years were combined in the model. These prior studies found conflicting results in terms of the specific 
taxes that affected business activity as well as the relationship to business activity. Since factors beyond 
state taxes, including federal taxes paid, impact business activity these additional factors could impact 

http://journals.sfu.ca/abr ADVAN CE S IN B U SI N E SS R ESEAR CH   
2021, Volume 11, pages 1-10 

http://journals.sfu.ca/abr


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the relationship between state tax policy and business activity (Shuai & Chmura, 2013). These 
differences across time are likely responsible for the varying results in prior research. This paper will 
address the issue by using five annual models for the third quarter of 2015 to the second quarter of 
2020 to examine the relationship between state business activity and state tax policy. 

This paper uses total business applications per capita for each year to measure business activity. 
The establishment of new businesses relative to other states provides a good indication of the level of 
business activity in the state. One weakness of this measure is that a sole proprietorship and a one-
member LLC are not required to file for a taxpayer identification number so they will not be included 
in the analysis. For state tax policy five state taxes are ranked from most favorable (lower taxes) to 
least favorable (higher taxes) for all 50 states. The taxes included in the model are corporate income 
taxes, individual income taxes, sales taxes, property taxes, and unemployment insurance taxes. Using 
the ranking for each state by year will help to control for any changes in the state taxes across time as 
the changes will be incorporated into the state tax rank. Economic theory argues that states with lower 
taxes, decreasing the cost of operating a business, would lead to increased business activity. The 
majority of businesses generate business income that is taxed at the individual rates and business 
location decisions are frequently made based on this criterion (Russell, 2011). Specific sales taxes on 
equipment and the level of property taxes were found to have a negative relationship to new businesses 
started (Bartik, 1989). The enforcement of taxes on online sales is an emerging issue in the impact of 
taxes on entrepreneurial activity (Conroy, Cutler, & Weller, 2016).   

Unemployment levels have traditionally been considered as a function of macroeconomic 
effects in measuring economic activity. Barron and Westley (1981) consider unemployment insurance 
taxes paid and the level of unemployment. To the extent that business activity may or may not be 
affected, Murray (2018) found that the unemployment tax burden does not have a significant impact 
on the formation of benefit corporations in the United States, suggesting no effect for this variable.  
However, our data runs through the second quarter of 2020 which will include two or three months 
impacted by the pandemic which could have a major impact on the significance of this variable. 
    Although this paper focuses on the impact of state taxes on business activity businesses must 
also pay federal taxes. Changes in the federal tax laws can impact the relationship between state taxes 
and business activity (Huffer, Iselin, Sammartino, & Weiner (2019). This is relevant to our study as 
the Tax Cuts and Job Creation Act (TCJA) was passed in 2017, implemented in 2018, and made 
substantial changes to the federal tax laws. One direct impact of this act related to state taxes was that 
it limited the deduction for state and local taxes paid (property, sales, or income) to $10,000 where 
before all of these taxes could be deducted on the federal tax return. This change would have a negative 
impact on high tax states as their citizens would see a decrease in their deductions on their federal tax 
Hines (2017). Since the limit applied to all state and local taxes and not just specific taxes states could 
not help their residents avoid the limit by shifting from one source of tax revenue to another. To 
address this issue New Jersey set up a fund where taxpayers could donate money to the fund and claim 
a charitable contribution. Then, any amount contributed to the fund could be taken as a credit to 
reduce their state taxes, allowing them to reduce their state taxes below the $10,000 and transfer the 
excess into a charitable contribution which can be deducted on the federal tax return. However, the 
federal government amended the law so that any contributions to these funds would then reduce the 
$10,000 limit on the federal return to prevent this practice in the states. 
 The other major impact of the TCJA was that it substantially reduced the taxes paid on 
business income (Tax Foundation, 2018). For corporations, the tax rate on business income was 
reduced from 35% to 21%, with the 14% reduction in the tax rate representing a 40% decrease in the 
federal tax on corporate income. The corporate income tax rates at the state level range from 2.5% in 
North Carolina to a maximum of 12% in Iowa; two states - South Dakota and Wyoming - have no 
corporate income tax. The decrease in the federal corporate tax rate is larger than even the highest 



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state corporate income tax rate which represents a substantial decrease in the taxes paid on corporate 
income. 
 The other types of businesses have their income taxed at the individual tax rates which range 
from 10% for low-income taxpayers to a high of 37% for high-income taxpayers. The TCJA added a 
20% qualified business income deduction for these firms to reduce the taxes paid on the business 
income. For taxpayers in the 37% bracket, the deduction reduces the rate on their business income 
from 37% to 29.6% (80% of income * 37% tax rate) which is a reduction of 7.4%. At the state level, 
individual income tax rates range from 2.9% in North Dakota to a high of 13.3% in California; nine 
states have no individual income tax. Across the 41 states with an individual income tax, 31 states have 
a tax rate of 7% or less, which is smaller than the savings at the federal level. The significant reduction 
in the federal taxes on income could result in states being more willing to open new operations in high 
tax states as their overall tax liability could still be lower than before the TCJA was passed. 
 For this paper, models are run for the second quarter of 2015 to the second quarter of 2016 
and the second quarter of 2016 to the second quarter of 2017 to examine the relationship between 
state tax policy and business activity for the two years before the passage and implementation of the 
TCJA. The model for the second quarter of 2017 to the second quarter of 2018 was used to examine 
the impact of the passage of the act and the initial application of the new rules. The last two models, 
quarter 2 2018 to quarter 2 2019 and quarter 2 2019 to quarter 2 2020 examine the relationship for 
two years after the passage of the TCJA. This methodology will illustrate the impact of the TCJA on 
the relationship between state tax policy and business activity. 
 What follows is a review of prior literature, research methodology, results, analysis of results, 
conclusion and future research, and bibliography.  
 

Review of Prior Literature 
There are two competing theories as to what drives economic growth in a city or a region. The 

first relates to the climate for business in terms of tax policies and regulation as locations with lower 
taxes and less regulation would lead to increased economic growth. The second theory is that 
economic growth is driven by factors tied to operating the business and attracting employees like 
locally available talent, affordable housing, and affordable commercial property. The local educational 
system, public transportation, and the quality of public services will also impact business decisions. 
The argument is tied to the idea that cities in the same state facing similar tax and regulatory 
environments have substantially different levels of economic growth. Nashville and Memphis, both 
in Tennessee, are two cities facing similar tax and regulatory environments where Nashville has had 
substantial economic growth as compared to Memphis (Renn, 2018). While both theories have been 
supported in the research, the large increase in employees working remotely could potentially increase 
the importance of local and regional tax policies on business location decisions.  

The high corporate income tax rate in the United States has often been mentioned as a reason 
why many businesses choose to operate in foreign countries as opposed to the United States to lower 
their tax burden. Since it is easier to move a business across states as opposed to across countries state 
tax structures should also have a major impact on business location decisions (Russell 2011). A Small 
Business and Entrepreneurial Council study argues that personal income tax rates impact business 
location decisions as over 92% of businesses have business income taxed on individual tax returns at 
the individual tax rates. They also argue that high state unemployment tax rates increase the cost of 
labor so labor-intensive firms should avoid these states (Russell, 2011). Barron and Wesley (1981) 
found significant differences in the unemployment rates across states with the highest rates in the 
northeastern states and lowest rates in many southern states. The highest unemployment rate for taxes 
paid was in California at 2.1% of total wages paid while in Texas, the state with the lowest rate, it was 
only 0.3%. However, McClure Jr. (1981) argues that the objectives of state tax policy should focus on 



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allocation neutrality and not the redistribution of wealth so it should not impact business location 
decisions. He also argues that state corporate income taxes can impact business location decisions if 
they are different across the states.  

Prior research has specifically addressed the impact of state or local tax policies on business 
location decisions. Lower entrepreneurial rates have been found in states with higher individual tax 
rates, the existence of state-level estate and inheritance taxes beyond the federal tax, and a higher 
weight on the sales factor in the state corporate income tax apportionment formula. Higher 
entrepreneurial rates were found in states with more progressive personal income tax structures and 
having aggressive corporate income tax structures with a combined reporting requirement (Bruce & 
Deskins, 2012). They also found that the composition of the state tax portfolio was not a significant 
factor in entrepreneurial activity for states. Cline (2006) argued that the state and local tax burden in 
the United States includes much more than direct business taxes like income and franchise taxes, as 
firms also pay property and sales taxes.  

Higher state corporate income taxes and higher state minimum wage rates were found to result 
in a reduced rate of entrepreneurial activity (Garrett & Wall, 2006). Kreft and Sobel (2003) found that 
the existence of a state inheritance tax led to a lower level of growth in the number of sole 
proprietorships formed. Carlton (1979) found no evidence that local taxes influence the number of 
firm births. Bartik (1989) found that higher property taxes, corporate taxes, and sales taxes on 
equipment negatively impact the number of small business startups. However, he also found that 
personal income taxes and general sales taxes had no significant impact. Summarizing the impact of 
state tax policy on business location decisions finds that state tax policies can influence the decision 
but the results on the magnitude and direction of the impact have been inconclusive (Bruce & Deskins, 
2012).  

The Tax Cuts and Job Creation Act (TCJA) of 2017 will likely have a significant impact on 
state tax policy as it makes substantial changes in the federal income tax laws. Forty-one of the fifty 
states have state-level broad-based income taxes while two states only tax unearned income and seven 
states have no income tax. Of the forty-one states, thirty start their calculation of state taxable income 
with the adjusted gross income from the federal return. Five states start the calculation of state taxable 
income with federal taxable income. The last six states do not directly tie their taxable income 
calculation to a number from the federal tax return but use many of the same rules for measuring 
income and deductions. Since the majority of states tie their taxable income to numbers reported on 
the federal tax return any changes in the federal tax laws will also impact state income taxes (Huffer, 
et al, 2019). 

The major changes in the TCJA are summarized below. The basic standard deduction was 
nearly doubled for each filing status which reduces taxable income. Several states tie their standard 
deduction to the federal standard deduction which would have the same impact at the state level. The 
TJCA also eliminated the personal exemption, $4,150 per taxpayer on the return, as well as the 
deduction for dependency exemptions which increases taxable income. To offset the impact of the 
loss of the dependency deductions the child credit for qualifying children was increased from $1,000 
to $2,000 and a dependent credit of $500 was created for all other dependents. Since the corporate 
income tax rate was reduced from 35% to 21% in the TCJA they also adopted the Qualified Business 
Income (QBI) deduction of up to 20% of business income for firms not operating as a corporation, 
reducing taxable income.  

Another significant change was limiting the itemized deduction for taxes paid to $10,000 per 
year. While the increase in the standard deduction will significantly decrease the number of taxpayers 
claiming the standard deduction taxpayers in states with high property values, such as New York, New 
Jersey, and California, are home to many taxpayers whose combined state property and income taxes 
exceed the $10,000 and still itemize and will face an increase in taxable income. Huffer et al (2019) 



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examined the impact of the TCJA on state income taxes. They found that the two largest impacts were 
from the increase in the standard deduction and the elimination of the personal and dependency 
exemptions which offset each other with a slightly larger impact from the elimination of the personal 
and dependency exemptions. The changes in the federal tax laws will potentially result in states 
examining their tax policies to adapt to the new rules.  
 

Research Methodology 
This research measures, as the dependent variable, per capita changes in the number of 

business applications submitted for each U.S. state. Five independent variables are employed in the 
model: corporate tax rank; individual income tax rank; sales tax rank; property tax rank; and 
unemployment insurance tax rank. The rank for each category is within a range of 1 to 50, inclusive, 
representing the rank within each of the 50 U.S. states. A higher rank (i.e. 50) denotes higher taxes for 
that category within that state relative to other states; a lower rank (i.e. 1) indicates less tax imposed 
within that category. Rank is not mutually exclusive between and among states; if a state does not tax 
income at the state level, then the rank for each of those states for that tax category would be 1.  
Population estimates are provided from the U.S. Census Bureau and the number of business 
applications and rank for each tax category from the 2020 State Business Tax climate index that is 
published by the Tax Foundation. 

The model examines five distinct periods in years from Q2 to Q2: 2015 to 2016; 2016 to 2017; 
2017 to 2018; 2018 to 2019; and 2019 to 2020. The TCJA was passed in 2017 and implemented in 
2018.  Effects are analyzed for two years before the legislation, 2015 to 2016 and 2016 to 2017, during 
the contemporaneous year of 2017 to 2018, and two years after enactment, 2018 to 2019 and 2019 to 
2020.   

Using ordinary least squares (OLS) regression analysis, changes in per capita business 
applications were analyzed. Five separate regressions were run to reflect how state tax activity within 
each tax category affects business applications within each of those five time periods. Data are 
standardized to reflect differences in size and scope of input variables. Business applications are 
measured as per capita change from year to year, while predictor variables are expressed by state rank 
from one to 50. To standardize, each predictor variable is multiplied by 1/100,000. Thus, the 
interpretability of output coefficient values is enhanced (Sweet & Grace-Martin, 2012).  

 
 

Results 
The model for this analysis is structured within two time periods: before the enactment of 

2017 TCJA, during the enactment of 2017 TCJA, and two years after the enactment of 2017 TCJA.  
Calculations are grouped and results are presented accordingly. Each table identified the variables, 
coefficient, t-statistic, p-values, and adjusted R square for each regression output.   

Table 1 presents results for before 2017 TCJA enactment for 2015 to 2016 and 2016 to 2017 
measured from Q3 to Q2 for each period. For this iteration of the model barely 5 percent of changes 
in business applications are explained by predictor variables. Constant or intercept variable is 
statistically significant, with sales tax showing an inverse relationship, although only at a level of p < 
.10. For the 2016 to 2017 time period, the constant continues to be statistically significant with a 
strong, positive association. Although higher taxes for each category are generally associated with 
lower levels of business application submission, the association is not statistically significant and is 
likely the result of chance.  
 

 



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Table 1 – Results before 2017 TCJA 

Q2 2015 to Q2 2016 
Variable Coefficient t-statistic p-value Adjusted R-

square 
Business Applications Change    0.05297 
Constant  42.6408 2.99736 .00446**  
Corporate Tax -0.3719 -1.33778 .18784  
Individual Income Tax -0.2931 -1.09168 .28092  
Sales Tax -0.5100 -1.87530 .06740  
Property Tax 0.2530 0.84655 .40183  
Unemployment Insurance Tax -0.3191 -1.08468 .28397  

 
Q2 2016 to Q2 2017 

Variable Coefficient t-statistic p-value Adjusted R-
square 

Business Applications Change    -0.01805 
Constant  49.1374 4.59654 .00004***  
Corporate Tax -0.1987 -0.98585 .32960  
Individual Income Tax -0.1875 -0.95414 .34523  
Sales Tax -0.1965 -0.98339 .33079  
Property Tax 0.0187 0.08754 .93064  
Unemployment Insurance Tax -0.2222 -1.04685 .30088  

p < .05*; p < .01**; and p < .001***  
 

Table 2 lists regression output for the model during the coeval period of 2017 to 2018 that 
TCJA was enacted. The model shows a much stronger fit with an adjusted R-square indicating more 
than 17 percent of the data fit the regression model.  Results find that sales tax shows a strong inverse 
relationship (t-statistic = -3.239) with changes in business application submissions for the period.  
While an inverse association existed from 2015 to 2016, during this time the association is stronger 
with a higher level of statistical significance (p-value = .002 compared to p-value = .067). 

 
Table 2 – Results for the model during 2017 - 2018 
 

2017 TCJA Enacted 
Q2 2017 to Q2 2018 

Variable Coefficient t-statistic p-value Adjusted R-
square 

Business Applications Change    0.17077 
Constant  85.9425 5.14356 .00001***  
Corporate Tax -0.2490 -0.78020 .43945  
Individual Income Tax -0.4780 -1.56463 .12483  
Sales Tax -1.0174 -3.23918 .00228**  
Property Tax 0.1636 0.48877 .62743  
Unemployment Insurance Tax -0.3801 -1.15199 .25555  

p < .05*; p < .01**; and p < .001***  



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Table 3 presents regression output for two years after the enactment of the 2017 TCJA. For 
the 2018 to 2019 time period the model explains over 17 percent of changes in business application 
submissions. Two variables, individual income tax, and sales tax are statistically significant in addition 
to the intercept. What is interesting is that sales tax is strongly positively associated in this iteration, 
unlike strong inverse associations during two prior periods in this analysis.   

With an adjusted R-square of 0.132, the fit of the model in 2019 to 2020 is not as robust as 
the immediately prior year. However, corporate tax is strongly statistically significant (p-value = .009) 
in this regression output, unlike the output for any of the prior four years, with an inverse correlation 
(t-statistic = - 2.719). Property tax and unemployment insurance tax would be predictors in the model 
at a level of p < .10 but are otherwise subject to chance. The directional impact of property tax is 
positive, while unemployment insurance tax is negative or inverse.   
 
Table 3 – Results for the model after the 2017 TCJA 
 

After 2017 TCJA 
Q2 2018 to Q2 2019 

Variable Coefficient t-statistic p-value Adjusted R-
square 

Business Applications Change    0.17460 
Constant  -69.2490 -3.99191 .00025***  
Corporate Tax 0.2536 0.73226 .46789  
Individual Income Tax 0.6422 1.93271 .05972*  
Sales Tax 0.9345 2.84319 .00675**  
Property Tax -0.0690 -0.19824 .84377  
Unemployment Insurance Tax 0.1516 0.44001 .66208  

 
Q2 2019 to Q2 2020 

Variable Coefficient t-statistic p-value Adjusted R-
square 

Business Applications Change    0.13242 
Constant  38.0140 1.76409 .08466  
Corporate Tax -1.1319 -2.71856 .00935**  
Individual Income Tax -0.2356 -0.59020 .55808  
Sales Tax 0.0706 0.17515 .86176  
Property Tax 0.7973 1.91242 .06235  
Unemployment Insurance Tax -0.6816 -1.64343 .10742  

p < .05*; p < .01**; and p < .001***  
 

Analysis of Results 
Unlike the majority of prior research which used time-series data this analysis examining the 

impact of a state’s tax policies on economic activity is conducted yearly, analyzing the impact on 
business applications from July 1 to June 30 for each year. For the 2015-2016 year the ranking for 
corporate income taxes, individual income taxes, sales taxes, and unemployment taxes are negatively 
related to business applications while property taxes are positively related. However, the only 
significant relationship is for sales taxes with a p-value of .067. For the 2016-2017 year the direction 
of the relationships remains the same with no significant relationships. These are the two years right 
before the Tax Cuts and Job Creation Act of 2017 took effect making significant changes in the federal 



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tax laws. Since most states tie their tax systems to the federal laws it also had a significant impact on 
state taxes as well.  One key difference between the two years is that a Republican administration was 
elected in November 2016 and took office in January 2017. For the 2016-2017 year almost all of the 
states had significant increases in business applications making it more difficult to find significant 
relationships. This could have resulted from having an administration focused on reducing regulations 
to encourage business activity. The 2017-2018 year was the transition year as the new tax law was in 
place for half of the year covered during this period. The results for this year mirror the results for 
2015-2016 as only sales taxes had a significant (p-value = 0.002) negative relationship with business 
applications. The results from the first three years, before the new tax law took full effect, indicate 
that all taxes except property taxes have a negative relationship with business applications with sales 
taxes representing the only significant relationship. 

The 2018-2109 year is the first year after the new tax law took effect, which had a significant 
impact on the results. For the 2018-2019 year the relationship between the different taxes and business 
applications completely reversed from the prior three years as corporate income taxes, individual 
income taxes, sales taxes, and unemployment taxes were positively related to business applications, 
and property taxes were negatively related. Also, individual income taxes (p-value = .060) and sales 
taxes (p-value = .007) had a significant positive impact on business applications. The new tax law 
significantly reduced the income taxes paid on business income which would encourage business 
activity and reduce the negative impact of higher tax rates across states as less income was subject to 
tax.  The most surprising result is having sales taxes go from a significant negative relationship for two 
of the prior three years to having a significant positive relationship. One potential explanation is if the 
decrease in the income taxes paid reduced the negative impact of paying higher sales taxes. The 2019-
2020 year is the second full year under the new tax law but also includes a few months that were 
impacted by the coronavirus pandemic which shut down substantial segments of the economy. For 
the 2019-2020 year corporate income taxes, individual income taxes, and unemployment taxes were 
negatively related to business applications while sales taxes and property taxes had a positive 
relationship.  Thus, all taxes except sales taxes reverted to the same relationship as the three years 
before the 2018-2019 year. However, the significant relationships are now corporate income taxes (p-
value = .009) with a significant negative relationship and property taxes (p-value = .062) with a 
significant positive relationship. The increased risk in the economy may have encouraged economic 
activity in states who collect substantial amounts in property taxes as it represents a more stable source 
of tax revenue. One other significant finding for 2019-2020 is that unemployment taxes (p-value = 
.107) are approaching being significant as the coronavirus substantially increased the number of people 
on unemployment. Since the year only included one quarter that was impacted by the virus, it is very 
possible that its significance will increase until the virus is controlled. 

Prior research has found that the tax climates of the states did impact business activity, but 
the magnitude and direction of the impact varied across different studies. One factor frequently 
mentioned to explain the differences is that different measures were used to measure economic activity 
and different measures were used to measure the business and tax climate of the different states. This 
paper addresses this issue as the same measures were used to examine five separate years. The results 
again indicate that the magnitude and the direction of the impact of the different taxes vary across the 
years even when using the same measure for economic activity and the business climate. While the tax 
structure of the states can have a significant impact on economic activity in the state, the actual impact 
is tied to other events happening in the economy. For our analysis, the main factors were the new tax 
law and the coronavirus which had major impacts on the economy. 
   
 
 



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Conclusion and Future Research 
The analysis indicates that federal tax legislation creates disruptions in state-level taxation 

through intricate relationships between each variable. That higher taxes generally thwart 
entrepreneurial activity as a result of higher costs is to be expected. With this analysis using the 2017 
TCJA as the focal point around which incentives and disincentives are created by extension at the 
state level, results indicate that effects are measurable but positive directional impacts were unexpected 
in 2018 to 2019, for example.  

With the implementation of the TCJA in early 2018, additional observational time periods may 
further explain each phenomenon, or otherwise find changes to be an aberration. Further, future 
research in this area is expected to be very robust as a result of COVID-19 pandemic conditions. To 
what extent has the pandemic shifted tax incentives at the corporate level or individual level in 
explaining an area of economic development? Remote working is a research area opportunity as 
physical location domiciles are replaced by a more fluid movement of workers away from traditional 
settings. How will tax legislation and incentives approach these changes in supporting economic 
development?  

Including a measure of a state’s minimum wage to the federal minimum wage is also a variable 
to consider (Garrett & Wall, 2006).   Do higher state minimum wage levels continue to adversely 
impact entrepreneurial activity after an overall reduction in the tax burden in 2017? Linking those 
interactions with higher percentages of remote work could identify shifts in the flow of capital.   

Finally, while this study considered business applications to reflect any business structure (sole 
proprietorship, partnership, Limited Liability Company, and corporate structures), exploring if these 
relationships hold when considering corporate forms, for example, could add another component to 
the research for analysis. Fewer observations, however, may reduce data validity in explaining those 
relationships.   
 

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