Employment tax compliance across the states in 2018
5-1-2015 PHaxel ETI Final Paper PDF
1. Angel Investor Tax Credits in the United States: An Economic
Impact Evaluation
Philip Haxel
School of International Relations and Pacific Studies
University of California, San Diego
May 1, 2015
Abstract: Angel investor tax credits have become an increasingly popular policy that purports to
boost job growth and economic output for state governments, yet there has been little
empirical investigation, and no systematic comparison against non-implementing states, to
evaluate their effectiveness. This paper seeks to fill this void, using annual data from 43 states
from the period 1990-2013. Using fixed effects estimation returns a positive and significant
relationship between these tax credit programs and state-level GDP, as well as mixed evidence
for increased tax revenue growth in the wake of these programs, while failing to find a
statistically significant relationship between these programs and jobs created by firms less than
one year old.
2. Overview
Angel investors, defined as individual accredited investors who provide modest amounts of
‘seed capital’ to pre-revenue firms in return for a stake in the company’s future cash flows, play
a pivotal role in helping entrepreneurs with good ideas but no revenue stream to actualize their
visions.1 They not only provide start-up capital for innovative firms, but also mentor and guide
entrepreneurs towards successful strategies for monetizing their ideas. Typically, companies
that attract angel investors are located in high-growth and innovative sectors, such as software,
biotechnology, and information technology, and investment levels fall roughly between 10,000
and 2 million dollars.2 The success derived from angel investors in recent decades are notable.
Google, Apple, Facebook, Starbucks, and Amazon are just some of the many examples of
successful firms whose corporate genesis is owed to the assistance of angel investors.
More importantly, this success is not just a positive for the firm; in the process of
starting a new business, new jobs are created, income is earned, and tax revenue increases.
From the perspective of a state policymaker, the presence of angel investor activity in their
jurisdiction could potentially contain a great deal of upside—the San Francisco Bay Area, which
is the epicenter of angel and early stage investment, and has the highest level of per capita
median household income of any metropolitan area in the United States, serves as a prominent
example.
1
Hayter, Chris. “State Strategies to Promote Angel Investment for Economic Growth.” NGA Center for Best
Practices. February 14, 2008
http://www.angelcapitalassociation.org/data/Documents/Public%20Policy/State/NGA%20Issue%20Brief%20Angel
%20Investment.pdf
2
Ibid. 1
3. Yet, in spite of its purported successes, angel investing is a high-risk venture, as a 2007 study
found that 52% of angel investments lose some or all of the money invested.3 In recent
decades, state level policymakers in the United States have sought to lower the potential losses
from these investments through legislated tax credits, in order to incentivize wealthy
individuals to make more and larger investments to entrepreneurs. By lowering the risk profile
of these investments, it is argued that an increase in the funding of entrepreneurs will in the
aggregate create profitable businesses with quality jobs, raising income, lowering
unemployment, and in the final tally contributing more tax revenue than was initially foregone
through the credit itself. The perceived effectiveness of these policies has been growing
sharply, as the number of states offering these credits has increased from 9 in 2004 to 23 in
2014.
While the rationale behind angel investor tax credits is seemingly straightforward, empirical
evidence of their efficacy is lacking, perhaps as a result of the fact that the majorities of the
states implementing this policy have only done so in the past 10 years. The most rigorous
empirical study on the subject (Bell et. al) was completed in 2013, and utilized a t-test in the
difference of means for states implementing the policy in the periods before and after
implementing credits, finding a statistically significant difference in entrepreneurial activity,
measured by the ‘Kaufmann Entrepreneurial Activity Index’—an indicator which tracks “the
percentage of adult, non-business owners that start businesses each month”— in states that
3
Boeker, Warren, and Robert Wiltbank. “Returns to Angel Investors in Groups.” Kaufmann Foundation. November
2007. http://sites.kauffman.org/pdf/angel_groups_111207.pdf
4. applied angel investor tax credits between 1997-2011.4 Even though this paper was a worthy
step forward, its counterfactual, the state before it implemented the policy compared to after,
does not account for potential selection bias, not to mention observable and unobservable
confounding factors. Another study, conducted in 2013 by the state of Minnesota to evaluate
its angel tax credit program, utilized survey information to compare ‘qualified small businesses’
to actual recipients of the program, but did not rigorously account for the unobservable
differences that may be inherent in being a recipient of the program vs. a qualified small
business.5 The remainder of studies, such as the Angel Resource Institute’s 2008 white paper on
Angel Investor Tax Credits, use a case study approach which arrives at conclusions without a
rigorous empirical examination.6
This paper seeks to fill the void of empirical research on angel investor tax credits, using
available observational data and econometric techniques to test three separate hypotheses on
where or not these tax credit policies produce: 1) new firm job growth, 2) overall economic
output within the states, and 3) state tax revenue. Two coinciding treatment effects, the
annual cap on these tax credits within the state, and the percentage of the investment available
as a tax credit are examined. A fixed-effects estimating model is used to remove unobservable
differences between treatment and control groups, with several forms of ‘parallel trends’
4
Bell, Joseph, James Wilibanks, and John Hendon. “Examining the Effectiveness of State Funded Angel Investor Tax
Credits: Initial Empirical Analysis.” Small Business Institute Journal, Vol. 9, No. 2, 23-28.
http://www.sbij.ecu.edu/index.php/SBIJ/article/viewFile/178/125
5
“Evaluation of the Minnesota Angel Tax Credit Program: 2010-2012.” Economic Development Research Group.
January 31, 2014.
http://www.revenue.state.mn.us/research_stats/research_reports/2014/evaluation_of_the_mn_angel_tax_credit
_program.pdf
6
Williams, Jeffrey. “Tax Credits and Government Incentives for Angel Investing in Various States.” Angel Resource
Institute. July 2008. http://www.angelresourceinstitute.org/resource-
center/~/media/ARI/Files/Non%20Research/ARI%20BEST%20PRACTICES%20Tax%20Credits.pdf
5. analysis utilized to test the identifying assumption that pre-treatment trends in economic
output, job growth and tax revenue were similar between states who did and did not
implement the tax credit. The main findings are a statistically significant effect on tax revenues
and GDP growth, but an insignificant effect on new firm job growth.
The Data: Construction and Characteristics
The data for this project was collected through both formal databases as well as primary data
collection. In terms of formal databases, the first outcome variable for this project, the number
of jobs created by firms less than one year old, was acquired from the US Census Bureau’s
business dynamic statistics, through a specific dataset titled “Firm Age by State”, which
contained a wealth of descriptive aggregate statistics about firms categorized by state and age
of firm, from 1970 to 2012. The second outcome variable, total annual state tax revenue, was
acquired from the US Census Bureau’s Annual Survey of State Tax Collections database. The
third outcome variable, state level GDP, as well as annual state population estimates, were
gathered through the Federal Reserve Bank of St. Louis’ ‘FRED’ database. Variables for new firm
job creation, tax revenue, and state level GDP were all transformed from their level forms into
annual growth rates in order to make them comparable across states.
The data for the treatment variables, the percent level of the tax credit and the annual cap on
tax credits allotted, had not previously been organized into a single source and required
collection via primary sources such as legal documents, press releases, and prior research from
organizations such as the Angel Capital Association. Collection of this data involved over 60
sources, all of which are cited in the appendix. Predictably, collection of this data involved case-
by-case judgment calls, as states such as Maryland had policies which were not labelled an
6. angel investor tax credit, but were nonetheless geared towards small investments of seed
capital in high tech industries which had similar motivations and intended outcomes. In these
borderline cases, explicit mention of the intention to increase investment in seed capital and
high tech industries in legal documents sufficed to categorize these tax credits as a treatment
effect.
Unfortunately, several states which had angel investor tax credit policies—Utah, Hawaii, West
Virginia, New York, and Oklahoma—were omitted from the analysis, as they either had only
one or none of the policy levers of interest explicitly mentioned in legislative documents. In
several states, such as Ohio and Arizona, the dollar amount of the cap on tax credits was not
annual but instead spread over several years. In these cases, the cap was averaged over the
years it was available. While the percent level of the tax credit was not transformed, the cap
was divided over the state population to make it comparable across states. (See Section 1 for
charts covering descriptive statistics of various tax credit policies)
Lastly, it would also be prudent to mention that each dependent variable covers a slightly
different timeframe. State-level GDP runs from the years 1997-2013, tax revenue data runs
from 1990-2013, while job growth data runs from 1990-2012. Accordingly, the years analyzed
will be slightly different for each dependent variable.
Methodology and Pre-Analysis Endogeneity Tests
The method of causal identification used for this analysis will be a two-way fixed effects model,
which examines the variation within states over time to test the hypothesis that the ‘within’
variation of new firm job growth experiences a statistically significant change when the tax
7. credits are applied. Three separate outcome variables will be estimated for the tax credit
treatment.
Estimating equation: Yit= αi + λt + δTit + εit
Where: Yit = a) new firm job creation
b) gdp growth
c) tax revenue growth
Tit= Treatment Variables:
a) binary (policy/no policy)
b) % level of tax credit
c) Annual cap on $ quantity of tax credits
αi = cross-sectional variation
λt= time-specific fixed effects
The key benefit of this model is that it eliminates the concern of endogeneity based on time-
invariant unobservable traits that are correlated with the treatment effect. In examining the
differential effects of applying or not applying the tax credit, one can imagine the number of
time-constant traits that could potentially be correlated with entrepreneurial activity and new
firm job growth. For example the rugged geography of West Virginia and Kentucky, make it a
logistical nightmare for commerce, and the low population densities lead to lower demand and
smaller markets. It also seems reasonable that factors such as educational levels are also
effectively time invariant, at least on the time scale of this analysis. Fortunately, this potential
bias is nullified by the within effect produced by the fixed effects estimator.
Yet, all potential forms of endogeneity have not yet been eliminated. Thus, for this model the
key identifying assumption is that in the absence of treatment rates of change between control
and treatment groups are approximately identical. If confirmed, then the implementation of
8. the tax credits can be treated in an ‘as-if random’ manner, and the observational data can be
used to create non-biased estimators via the fixed-effects model.
This assumption can be tested graphically and statistically through a ‘parallel trends’ analysis.
Even more, these graphic and statistical tests can evaluate two different forms of parallel
trends—treatment vs. control, and year of entry. Both tests take advantage of the fact that the
majority of these tax credit policies were not introduced until after 2004, as can be seen in the
chart below.
As a result, the treatment vs. control tests compare the average annual rates of change for
states who never implemented the policy with the rates of change for those that introduced
the policy after 2005, using pre-treatment data from 1990 to 2004. The specific assumption this
test seeks to falsify is that the outcome of interest was changing in a similar way to the
treatment and control group. The second test analyses the average pre-treatment rates of
change for those states who introduced a tax credit policy after 2004, by the chronological
9. order in which they entered. This test is also important, since if the order of entry is correlated
with the outcome of interest this will bias estimates of the treatment effect.
In addition to the graphical analysis, both of these tests utilize ordinary least squares
regression. To test pre-entry parallel growth rates between treatment and control groups, the
outcome variables were second differenced from their level forms, and subsequently collapsed
into a cross-section with the averaged second difference of each respective outcome as the
dependent variable, individual states as the unit of analysis, and the independent variable as a
dummy variable delineating the treatment and control observations. For the year of entry tests,
rates of growth (first differences of the level form) of the outcome variable were also averaged
by collapsing on the state, with these averages used as the dependent variable and the first
year of implementation serving as the independent variable.
The outcomes of these tests fail to falsify the assumption of parallel trends for any of the three
outcome variables, as the coefficients of the statistical analysis were not significant at a 1%
level, while the graphs provide strong evidence to support the parallel trends assumption, in
terms of both treatment vs. control and order of entry (See Section 2). As a result, we can use
the fixed-effects model with some confidence that states who did not implement the policy
10. were relatively similar in nature, on average, to those states that do enact the policy before
such policy was implemented, and furthermore that those states who enacted it earlier did not
look that much different from those who enacted it at a later point in time.
Results and Interpretation
The most basic test to measure the effectiveness of angel investor tax credit policies would be
to use a binary variable that switches on when such a policy is in place, ignoring the
heterogeneous nature of the policy in terms of the size of the percent tax credit and annual
cap. Such a specification yields statistically insignificant results for all dependent variables,
meaning that simply having such a policy in place has not implied any sort of positive returns.
Adding lagged terms to pick up delayed effects, which is certainly plausible given that it may
take several years for a start-up firm to become fully operational after receiving a seed
investment, does not change these results.
The next specification takes advantage of the heterogeneous levels of treatment across states
to test whether higher levels of either the percent tax credit or total annual cap lead to
11. significant differences. Both of these treatments have a plausible rationale for being positive
and significant. A higher percent tax credit would lower the up-front cost of making an
investment, while the higher annual cap, measured on a per capita basis, raises the total
amount of investment funds eligible for these tax credits, increasing the total potential effect
size in relation to the size of the state economy.
When regressed upon separately, these two treatment effects yield different results. Including
the percent level of the tax credit returns statistically insignificant estimates for all three
dependent variables. On the other hand, the estimate of the effect of annual per capita tax
credit caps on GDP growth is positive and highly significant. The interpretation of the coefficient
(.007) is that a 1 dollar increase in the per capita tax credit cap leads to a .7% increase in GDP.
Given that the average level of per capita tax credit caps for those states with such a policy is
$1.23, the average predicted effect would be an increase of approximately .9% in GDP. The
estimate for the marginal effect of increasing the cap on growth in tax revenues is also positive
and significant, while for new firm job growth it is insignificant.
Percent Tax Credit .000 -- .010*** .000 -- .006 .000 -- .018***
(.000) -- (.003) (.000) -- (.009) (.000) -- (.009)
Annual Cap -- .007*** -.000** -- .004 -.000 -- .014*** -.000
-- (.002) (.000) -- (.007) (.000) -- (.004) (.000)
Constant .042*** .070*** .049*** -.001 -.001 -.000 .077*** .083*** .040***
.004 (.004) (.005) (.017) (.017) (.015) (.013) (.012) (.008)
R^2 .372 .390 .431 .337 .338 .340 .213 .220 .229
Observations 688 687 687 946 945 945 989 988 988
Standard Errors in Parentheses
*** p<0.01, ** p<0.05, * p<0.1
Percent Tax Credit and Per Capita Annual Cap Specifications
GDPGrowth JobGrowth TaxGrowth
12. Of course, in reality both of these policy variables occur in tandem, so estimating each
separately opens up the possibility of omitted variable bias. As a result, it makes sense to
include both treatment effects in a single model. No statistically significant effects were found
for the new firm job growth, while for GDP growth, significant coefficients at the .05 level were
found for both treatment variables. On the other hand, a statistically significant effect on tax
revenue growth was found for percent tax credits but not for the annual caps.
The negative coefficient on the percent tax credit is not immediately intuitive, and as a result a
quadratic specification was attempted. The logic behind a possible quadratic fit is that at low
levels, economic indicators such as GDP and employment are positively affected as worthy
investment increase, but as the tax credits reach a threshold investment returns diminish and
turn negative as moral hazard increasingly comes into play and resources are allocated to poor
investments. The results of this re-specified model did not lead to a plausible interpretation
(See Section 3), and were consequently dismissed.
Robustness Checks
A possible scenario that could be biasing the estimators upwards is an Ashenfelter’s dip type
phenomenon, whereby states whose economic output or job creation was dropping cyclically,
states implemented the tax credit policy at the bottom of the business cycle, later recovered,
and would have grown in either case, but the fixed effects estimator attributes these changes
to the introduction of the tax policy. The staggered entry style introduction allows us to
construct lead lag tables to check for the occurrence of mean reversion bias. In looking at the
charts for all three outcome variables, there is no visible reversion to the mean occurring (See
Section 4).
13. Another plausible form of bias could occur through autocorrelation, where residuals are
correlated within each unit in different periods. In addition to the statistical trouble this may
induce, there is possibly an worthwhile narrative behind the autocorrelation, as the benefits of
the tax credit may take longer than a year to pay off, as for example, a business funded in one
year may create jobs and boost statewide output in future years. To test this, standard errors
were clustered (See Section 5). Standard errors did indeed increase, but this loss in efficiency
had little to no effect on statistical significance.
Finally, as an aside it is worthwhile to take a look at possible spatial determinants of the policy
effects. More specifically, with state level economic policies such as tax credits it is generally a
concern that these policies are a beggar-thy-neighbor policy, where the gains one state
achieves from such a policy is merely business taken from a neighboring state. This is a
plausible scenario for angel investing, as in a 2008 survey, nearly 20% of angel investors said
they were comfortable investing anywhere within their geographic region (Northeast, Midwest,
etc.), while another 18% were comfortable investing in a business within a 4 hour-drive. To test
this zero-sum hypothesis, a dummy variable was created to indicate whether or not a state
bordered another state with an active angel investor tax credit policy. This model produced no
significant effects (See Section 6).
Discussion and Conclusion
The results derived from the main fixed effects model have considerable policy implications. In
particular, the estimated increase in states’ GDP in the wake of implementation is significant
and of a sizeable magnitude. The results for tax revenues are noteworthy as well. On the other
hand, employment from new firms did not increase. This is intriguing, as the causal chain—
14. where tax credits incentivized additional investments, leading to new firms who would need
employees to start up their business—appeared to skip a step, increasing output and tax
revenues without increasing employment in the process.
A possible cause for this lack of significance may be a misspecification of the employment
variable. Although a state level count of new jobs created by firms less than one year old would
seem to be a precise target, it may be the case that the firms boosted by angel investment hire
quality, in terms of high-skill, over quantity. A more precise variable that includes new jobs over
a certain income threshold could turn out to be significant.
Another factor is the timing of the introduction of tax credit policies and the recession of 2008.
Eight out of eighteen states implemented their policy directly preceding the recession. Given
that employment rates typically lag behind economic growth, it is plausible that GDP growth
gained steam while unemployment recovered more slowly. This is detectable in the means of
states who implemented the tax credit, as their mean job growth rates when implementing are
actually negative, leading to a Cohen’s D effect size of -.0315 (with a confidence interval
hovering around zero).
This analysis has practical policy implications nonetheless, as the robust estimates of positive
economic growth serve as empirical evidence of the efficacy of these policies. Like all sound
science, these results should be replicable. Seeing that the rise of these tax credits has occurred
in recent years, in subsequent years there will be an even greater wealth of data occurring
across both ends of the business cycle, providing the potential for an even more robust analysis
in the near future.
